A47 Wansford to Sutton

A47 WANSFORD TO SUTTON

PCF STAGE 3 | | 11/09/20 Notice

This document has been prepared on behalf of Galliford Try by Sweco UK Ltd for Highways England's Delivery Integration Partners (DiP) Framework. It is issued for the party which commissioned it and for specific purposes connected with the above-captioned project only. It should not be relied upon by any other party or used for any other purpose. Sweco UK Ltd accepts no responsibility for the consequences of this document being relied upon by any other party, or being used for any other purpose, or containing any error or omission which is due to an error or omission in data supplied to us by other parties.

This document contains confidential information and proprietary intellectual property. It should not be shown to other parties without consent from Galliford Try. A47 WANSFORD TO SUTTON

Highways England Programme Leader:

Highways England Project Manager:

Galliford Try Sweco Delivery Integration Partner, Project Manager:

PCF STAGE 3 Supplier:

Document control

Client GALLIFORD TRY

Project A47 WANSFORD TO SUTTON

Document title

Document reference

Revision history

Revision Purpose description Originator Checked Approved Authorised Date

Prepared for: Prepared by:

Galliford Try Sweco UK Ltd Cowley Business Park Grove House Cowley Mansion Gate Dr Uxbridge Leeds Middlesex LS7 4DN UB8 2AL A47 WANSFORD TO SUTTON

Table of contents

1. Introduction 1 1.1 Context 1 1.2 Existing scheme section 2 1.3 Current traffic issues 3 1.4 Scheme objectives 4 1.5 Current stage of the project 6 1.6 The scheme 6 1.7 Purpose of the report 7 1.8 Report structure 7 2. Summary of previous work 9 2.1 Overview 9 2.2 Model area and network 10 2.3 Modelled time periods 12 2.4 User class segmentation 12 2.5 Software used 12 2.6 Assignment procedure and generalised cost parameters 13 2.7 Model calibration and validation 14 2.8 Variable demand modelling 17 2.9 Previous PCF Stage 0-2 traffic forecasting report 17 3. Forecasting approach 20 4. Uncertainty log and forecast years 22 4.1 Introduction 22 4.2 Forecasting at PCF Stage 0-2 22 4.3 Forecasting at PCF Stage 3 22 4.4 Forecast years 22 4.5 Uncertainty log (local area plan data and transport supply data) 23 4.6 Modelled scenarios 25 5. Reference forecast demand 26 5.1 Overview 26 5.2 NTEM growth of car trips 27 5.3 NTEM growth of goods vehicles 28 5.4 Trip generation and distribution for modelled developments 28 5.5 Dependent development 30 5.6 Constraining to district level 31 5.7 Combined background and development trip matrices 31 5.8 Alternative scenarios 32 6. Forecast supply 34 6.1 Introduction 34 6.2 Do Minimum scenario 34 6.3 Do Something scenarios 35 6.4 Forecast network calibration 37 7. Equilibrium demand forecasts 38 7.1 Overview of variable demand response 38 A47 WANSFORD TO SUTTON

7.2 Demand modelling zone aggregation 39 7.3 Types of VDM response 39 7.4 Calibration of the DIADEM model 42 7.5 Journey time elasticity 45 7.6 ‘Pivot point’ method 46 7.7 Generalised cost 47 7.8 Convergence in DIADEM 48 7.9 Outputs from DIADEM 48 8. Assignment results for economic assessment 54 8.1 Introduction 54 8.2 Assignment model convergence statistics 54 8.3 Do Minimum variable demand forecast results 56 8.4 Do Something variable demand forecast results 57 8.5 Key statistics for the core scenarios 61 8.6 Sensitivity testing 62 8.7 Traffic flows 64 8.8 Journey times 64 8.9 Model constraints 66 9. Assignment results for environmental assessment 67 9.1 Required outputs 67 9.2 Speed banding 67 9.3 Use of WebTRIS data 68 9.4 Derivation of average annual traffic flows 69 9.5 Other required outputs 71 9.6 Speed pivoting 71 10. Assignment results for operational performance assessment 72 11. Conclusions 73 Selected validation summaries 75 Sectored demand analysis 79 Trip length distribution changes 103 Uncertainty log for the development zones 109 Stick diagrams 113 Link actual flow differences 117 NTEM v7.2 trip rates – car 130 Demand matrix growth summary table 133 Low and high growth outputs 134

Figures

Figure 1-1: Package Location Plan 1 Figure 1-2: Location of Wansford to Sutton dualling scheme (outlined by the blue region) 2 Figure 1-3: Existing A47 / A1 interchange at Wansford 3 Figure 2-1: Wansford PCF 2-3 study areas 10 Figure 2-2: Wansford traffic model and the buffer area within the area of impact 11 A47 WANSFORD TO SUTTON

Figure 2-3: Top line summary statistics 16 Figure 2-4: Extent of the cordoned Wansford model for Stage 1 18 Figure 2-5: Wansford Paramics model (network defined in red) 19 Figure 3-1: Flowchart of forecasting process 20 Figure 4-1: Major development sites identified in Peterborough local plan 24 Figure 5-1: A47 Wansford Stage 3 development locations in Peterborough 27 Figure 6-1: Wansford model Do Minimum coding change locations 34 Figure 6-2: A47 Wansford to Sutton dualling scheme with further improvements at A1 / A47 Western Roundabout 36 Figure 6-3: Highway network changes in Do Something compared to Do Minimum 37 Figure 7-1: Sectors 50 Figure 7-2: 2025 car trip length distribution – AM peak 53 Figure 7-3: 2040 car trip length distribution – AM peak 53 Figure 8-1: Actual flow difference – DM40 final vs DS40– AM (bandwidth 250PCU/mm) 58 Figure 8-2: Actual flow difference – DM40 final vs DS40 (pivoting to base) – AM (bandwidth 250PCU/mm) 58 Figure 8-3: Actual flow difference – DM40 final vs DS40– IP (bandwidth 250PCU/mm) 59 Figure 8-4: Actual flow difference – DM40 final vs DS40 (pivoting to base) - IP (bandwidth 250PCU/mm) 59 Figure 8-5: Actual flow difference – DM40 final vs DS40– PM (bandwidth 250PCU/mm) 60 Figure 8-6: Actual flow difference – DM40 final vs DS40 (pivoting to base) – PM (bandwidth 250PCU/mm) 60 Figure 8-7: Journey time routes 65 Figure 9-1: Count sites used to create AADT conversion factors 69

Tables

Table 1-1: RIS performance specification and KPIs 4 Table 2-1: Value of time assumptions, pence per minute (PPM, 2010 prices, 2015 values) 13 Table 2-2: Vehicle operating cost assumptions, pence per kilometre (PPK, 2010 prices, 2015 values) 14 Table 2-3: Summary of model calibration and validation 15 Table 2-4: Wansford Paramics model base year specifications for Stage 2 19 Table 4-1: Classification of future impacts 23 Table 5-1: RTF18 goods vehicle growth rates from 2018 28 Table 5-2: Jobs per 100 sqm of GFA 29 Table 5-3: Average TRICs trip rates - A1 29 Table 5-4: Average TRICs trip rates - B1 29 Table 5-5: Average TRICs trip rates – B2 30 Table 5-6: Average TRICs trip rates - B8 30 Table 5-7: Balancing Area Descriptions 31 Table 5-8: Highway Reference Demand at OD Level – Core Scenario AM Peak 31 A47 WANSFORD TO SUTTON

Table 5-9: Highway Reference Demand at OD Level – Core Scenario Inter Peak 32 Table 5-10: Highway Reference Demand at OD Level – Core Scenario PM Peak 32 Table 5-11: Core, low and high scenario definitions 33 Table 6-1: DM network assumptions 35 Table 7-1: VDM parameters / model response and hierarchy 40 Table 7-2: DIADEM logit parameters 43 Table 7-3: DIADEM distribution Lambda (λ) parameter values 43 Table 7-4: Outturn fuel cost elasticity by time period and purpose 44 Table 7-5: Outturn journey time elasticity by time period and purpose 45 Table 7-6: Generalised cost parameters (2010 prices) 48 Table 7-7: DIADEM demand-supply convergence statistics 2025 and 2040 48 Table 7-8: Sector system for VDM analysis 49 Table 7-9: 2040 highway demand (car only) % change between DM and DS - AM 51 Table 7-10: 2040 highway demand (car only) % change between DM and DS - IP 51 Table 7-11: 2040 highway demand (car only) % change between DM and DS – PM 51 Table 8-1: Post VDM assignment convergence statistics 54 Table 8-2: Primary model convergence criteria: final assignment 55 Table 8-3: A47 traffic growth in 2-way AADT 57 Table 8-4: Percentage splits on A47 traffic growth in 2-way AADT 57 Table 8-5: SATURN simulation network overall average speed (km/h) 61 Table 8-6: SATURN simulation network overall total travel distance (PCU.km/h) 61 Table 8-7: SATURN simulation network overall total travel time (PCU.hrs) 61 Table 8-8: Average speed (km/h) low growth, core scenario and high growth opening year 2025 62 Table 8-9: Average speed ((km/h) low growth, core scenario and high growth designing year 2040 63 Table 8-10: Total travel distance (PCU.km/h) low growth, core scenario and high growth opening year 2025 63 Table 8-11: Total travel distance (PCU.km/h) low growth, core scenario and high growth designing year 2040 63 Table 8-12: Modelled journey time results (unit: second) 65 Table 9-1: Motorway speed bands 67 Table 9-2: Non-motorway speed bands 68 Table 9-3: Motorway speed flow curves 68 Table 9-4: Peak hour to period conversion factors 70 Table 9-5: Global conversion factors 70 A47 WANSFORD TO SUTTON

1. Introduction

1.1 Context

1.1.1. As part of Highways England’s Regional Delivery Partnership’s (DIP), Galliford Try has commissioned Sweco as a lead consultant to undertake the PCF stage 3 - Preliminary Design assessment of six improvement schemes within the wider A47 /A12 Corridor Feasibility Study. Six schemes are proposed as part of the A47 corridor improvement programme, including the following:

· A47 Wansford to Sutton · A47/A141 Guyhirn junction · A47 North Tuddenham to Easton · A47 Blofield to North Burlingham · A47/A11 Thickthorn Junction Improvement · A47 Great Yarmouth Junction Improvements

1.1.2. These schemes are part of the Road Investment Strategy (RIS) for the 2015 to 2020 period. Construction is programmed to commence between 2021/22 and 2024/25, depending on the scheme. The requirement of this Package Contract is to progress the schemes through PCF Stage 3 Preliminary Design, and PCF Stage 4 (Statutory Procedures and Powers).

Figure 1-1: Package Location Plan

Source: AECOM & Amey. This Map is based upon Ordnance Survey material with the permission of Ordnance Survey on behalf of the Controller of Her Majesty's Stationery Office © Crown copyright. Unauthorised reproduction infringes Crown copyright and may lead to prosecution or civil proceedings. Highways England 100030649 2016.

1.1.3. All six schemes were delivered through PCF Stage 0 to 2 by AECOM and Amey. These stages identified a good strategic case for investment as well as a preferred route.

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1.1.4. In PCF Stage 3 Mott MacDonald Sweco Joint Venture (MMSJV) previously undertook the works required to develop a preliminary design.

1.1.5. As part of Highways England’s Regional Delivery Partnership’s (DIP), this work is now overtaken by Galliford Try who has commissioned Sweco as a lead consultant to undertake the PCF Stage 3 to Stage 5 of five progressing schemes, excluding Great Yarmouth Junction. This report details the transport forecasting package available for the PCF stage 3 study which relates to the A47 Wansford to Sutton Improvement Scheme.

1.2 Existing scheme section

1.2.1 The A47 Wansford to Sutton dualling scheme is located between the A1 junction at Wansford and Nene Way Roundabout in Sutton to the west of Peterborough. Figure 1-2 shows the location of the scheme with the major links shown by blue lines.

Figure 1-2: Location of Wansford to Sutton dualling scheme (outlined by the blue region)

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1.2.2 Figure 1-3 shows an aerial image (from Highways England WebTRIS map view) of the A47 / A1 Interchange at Wansford.

Figure 1-3: Existing A47 / A1 interchange at Wansford

Source: Highways England (WebTRIS Map view)

1.3 Current traffic issues

1.3.1 As described in section 1.1, the A47 corridor varies considerably over its length with a combination of single and dual-carriageway sections and grade-separated and at-grade junctions. The A47 Wansford to Sutton dualling scheme is located between the A1 junction at Wansford and Nene Way Roundabout in Sutton to the west of Peterborough. The 2.4 kilometre stretch of road is currently single- carriageway.

1.3.2 The single-carriageway section of the A47 acts as a bottleneck, resulting in congestion and leading to longer and unreliable journey times. In particular, peak hour congestion is experienced in the eastbound direction in the AM with queuing occurring on the A1 and A47 roundabout approaches. The poor performance of the A47 Wansford to Sutton section has also contributed to a poor safety record. In addition to this, there are a number of known growth hotspots around Wansford as well as along the corridor, and it is considered that this proposed growth will exacerbate the current transport issues experienced along the A47 Wansford to Sutton section. Furthermore, this increase in demand from development growth could worsen any associated economic, environmental and social impacts.

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1.4 Scheme objectives

1.4.1 The genetic objectives established for the A47 Wansford to Sutton dualling scheme identified in the corridor feasibility study are: support economic growth, improve capacity, resilience and safety, and address environmental concerns. These objectives align with the wider RIS Performance Specification which identifies Key Performance Indicators (KPIs) for the network as a whole, described in Table 1-1. The objectives for the scheme have been selected to align with those at the national level.

Table 1-1: RIS performance specification and KPIs

Performance Key performance indicator Additional clarification within RIS specification

Although KSIs have been selected as the key The number of people killed indicator, Highways England should Making the network and seriously injured aim to reduce all incidents. The safer (KSI) on the strategic causation of all incidents should be road network (SRN). investigated.

Number of noise important Ambitious schemes which significantly areas mitigated. improve the environment should become the norm. Further Delivering better performance indicators should be environmental Delivery of improved developed for air quality and CO2 outcomes biodiversity, as set out emissions. Schemes should in the company's demonstrate the aspirational goals of Biodiversity Action the Natural Environment White Plan. Paper.

A narrow KPI has been chosen as there are currently few established metrics for Helping cyclists, assessing accessibility for vulnerable walkers and users. However, it is expected that The number of new or other Highways England will consult with upgraded crossings. vulnerable relevant non-governmental users organisations in order to develop schemes that will improve accessibility.

Average delay is a simplistic measure of the benefits of the network to economic growth. Broader consideration Encouraging should be given to how the SRN can Average delay (time lost per economic support the flow of goods and freight, vehicle per mile). growth improve productivity and competitiveness, as well as helping to unlock key housing and economic development sites.

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Performance Key performance indicator Additional clarification within RIS specification

Currently, the only available metric is the The percentage of pavement National Pavement Condition Keeping the network asset that does not indicator. However, the performance in good require further specification covers all asset classes condition investigation for within the network: pavement, possible maintenance. structures, technology, drainage and geotechnical works.

Network availability: the percentage of the SRN These 2 metrics are chosen to improve the Supporting the available to traffic. management of planned works smooth flow Incident management: (network availability) and improve of traffic percentage of response to incidents that cannot be motorway incidents predicted (incident management). cleared within 1 hour.

Cost savings: savings on capital expenditure.

Delivery plan progress: progress of work, These indicators concern the change in Achieving real relative to forecast set management structure with the efficiency out in the delivery plan, creation of Highways England. and annual updates to that plan, and expectations at the start of Road Period 1.

The percentage of National Road Users' While user satisfaction as measured by Improving user Satisfaction Survey NRUSS has remained high, there satisfaction (NRUSS) respondents has been a downward trend in recent who are very or fairly years. satisfied.

1.4.2 The specific objectives for the A47 Wansford to Sutton Dualling scheme as outlined in Highways England's A47 Corridor Improvements Analytical Requirements Report (May 2018) include the following:

· Supporting Economic Growth - Contributing to sustainable economic growth by supporting employment and residential development opportunities. The scheme aims to reduce congestion-related delay, improve journey time reliability and increase the overall capacity of the A47.

· A Safe and Serviceable Network - Improving road safety for all road users through being designed to modern highway standards appropriate for a strategic road.

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· A More Free-Flowing Network - Increasing the resilience of the road in coping with incidents such as collisions, breakdowns, maintenance and extreme weather. The improved route between Wansford to Sutton will be more reliable, reducing journey times and providing capacity for future traffic growth.

· Improved Environment - Protecting the environment by minimising adverse impacts and where possible deliver enhancements by improving the environmental impact of transport on those living along the existing A47 and by minimising the impact of new infrastructure on the natural and built environment.

· An Accessible and Integrated Network - Ensuring the proposals take into account local communities and access to the road network, providing a safer route between communities for cyclists, walkers, equestrians and other non-motorist groups.

· Value for Money - Ensuring that the scheme is affordable and delivers good value for money.

1.5 Current stage of the project

1.5.1 The A47 RIS schemes were delivered through PCF Stage 0 to 2 by AECOM and Amey. These stages identified a good strategic case for investment, as well as a preferred design. In PCF stage 3, following the previous work undertaken by Mott MacDonald SWECO Joint Venture (MMSJV) in 2017/2018, SWECO have been now fully undertaking the remaining works required to develop a preliminary design since November 2019.

1.6 The scheme

1.6.1 The A47 Wansford to Sutton dualling scheme is located between the A1 junction at Wansford and Nene Way Roundabout in Sutton to the west of Peterborough. The 2.4 kilometre stretch of road is currently single-carriageway. This section of the A47 currently acts as a bottleneck, resulting in congestion and leading to longer journey times and a poor safety record.

1.6.2 Three options were taken to public consultation, including dualling the existing A47 on its current alignment (Option 1), building a new dual-carriageway partly to the north and partly to the south of the existing alignment (Option 2) and building a new dual-carriageway entirely to the north of the existing A47 alignment (Option 3). Option 2 was originally chosen as the preferred option and presented at the statutory consultation undertaken in 2018.

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1.6.3 Since statutory consultation 2018 and ongoing design reviews, some features of the design are unchanged and some aspects have been subject to ongoing development. As a result, Option 3 is now selected as the preferred route instead and so the latest scheme proposal is to provide a new dual-carriageway to the north of the existing A47 alignment.

1.6.4 The latest scheme proposal also includes further improvements at the A1 / A47 Western Roundabout as follows:

· Improved entry from the A1 northbound diverge slip road; · Improved exit to the A47 eastbound; · New Segregated Left Turn Lane (SLTL) between A1 northbound slip road and A47 eastbound; and · New cycle crossing of the A47 west of the roundabout, removing cycle traffic from the A1 overbridge.

1.6.5 An illustration of the full scheme is shown in Figure 6-2.

1.7 Purpose of the report

1.7.1 This report describes the traffic forecasting for only the Wansford to Sutton Dualling scheme and sets out the assumptions on which these forecasts have been based. The outputs of the forecasting work provide:

· The future year design traffic flows · Traffic flows for operational appraisal of the scheme junctions · Traffic impacts across the network and on the A47 corridor · Inputs to the environmental appraisal · Inputs to the economic appraisal

1.8 Report structure

1.8.1 This report is structured as follows:

· Section 2: Summary of previous work – summary of previous PCF Stage work as well as the base year model’s development, calibration and validation · Section 3: Forecasting approach – a brief summary of the overlay forecasting methodology · Section 4: Uncertainty log and forecast years – the scope and main elements of the uncertainty log, explicitly modelled developments and the forecast years

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· Section 5: Reference forecast demand – details of the use of road traffic forecasts (RTF) and national trip end model (NTEM) to create core, low and high reference case scenarios · Section 6: Forecast supply – summary of the assumptions contained in the Do Minimum (DM) and Do Something (DS) models · Section 7: Equilibrium demand forecasts – application of variable demand modelling (VDM) and the results of the Dynamic Integrated Assignment and Demand Modelling (DIADEM) modelling · Section 8: Assignment results for economic assessment – results and analysis of the DS and DM Simulation and Assignment of Traffic to Urban Road Networks (SATURN) assignments · Section 9: Assignment results for environmental assessment – derivation of the environmental assessment information · Section 10: Assignment Results for operational performance – derivation of the operational assessment information · Section 11: Conclusions

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2. Summary of previous work

2.1 Overview

2.1.1 This chapter details the development and calibration of the PCF Stage 3 Wansford Traffic Model. As documented in the Appraisal Specification Report (ASR) (HE551494-MMSJV-VTR-000-RP-TR-00008.PDF), analysis of the Wansford PCF Stage 2 base year west side of the Peterborough Traffic Model (WPTM), indicated that the model had not been calibrated or validated. In addition, the scope of the Wansford model was limited and as such not suitable for predicting re-routing of traffic and the interaction between local junctions and the major traffic attractor (Peterborough). Lastly, the size of the Stage 2 WPTM model did not allow any scope for any realistic Variable Demand Model (VDM) assessment for trip re-distribution or mode shift over a wider area.

2.1.2 Based on those assessments, the existing PCF Stage 2 WPTM model was enhanced to meet the PCF Stage 3 modelling objectives as shown in Figure 2-1. To achieve this, the SATURN simulation network was extended to provide a greater coverage to the south and west of the scheme. In addition, the south-east region traffic model (SERTM) network and demand was utilised to provide a fuller representation of strategic travel and a more realistic representation of longer distance trips. As outlined in section 2.2, to achieve a combined network the desired WPTM network was spliced into the SERTM buffer link structure.

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Figure 2-1: Wansford PCF 2-3 study areas

Source: Mott MacDonald Sweco Joint Venture. This map is based upon Ordnance Survey material with the permission of Ordnance Survey on behalf of the Controller of Her Majesty's Stationery Office © Crown copyright. Unauthorised reproduction infringes Crown copyright and may lead to prosecution or civil proceedings. Highways England 100030649 2016.

2.1.3 In addition to this, the SERTM demand was utilised instead of the existing PCF Stage 2 WPTM matrices to provide an up-to-date and robust demand starting point with suitable purpose disaggregation for VDM assessment. Details of how the SERTM model data was used in the PCF Stage 3 study, as well as the development of the VDM, are outlined in this chapter.

2.2 Model area and network

2.2.1 The base year for the Wansford traffic model is 2015. The network structure was enhanced to represent 2 distinct spatial areas as follows:

· Simulation area: this covers all areas where the schemes are likely to have an impact. Modelling within this area is characterised by representation of all trip movements, small zones and detailed network representation with junction modelling (including flow metering and blocking back). This area extends to where the schemes are likely to have the greatest impact. · Buffer area: this area is characterised by representation of all trip movements but from somewhat larger zones, less network detail with fixed speeds and

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no junction simulation. The buffer area covers most of England and is based on the SERTM network. To achieve the desired area of impact, the SATURN simulation network was expanded to provide greater coverage to the south and west of the A1, covering the A605 and A43 near to Corby and Duddington. This was necessary as the previous WPTM simulation network extent was limited around the scheme in those directions.

2.2.2 Additional network enhancement was provided to assist with the update of the base model. This included a review of all speed flow curves. As a result, some new speed flow curves were added to reflect the extended network within the simulation area. Zone centroid connectors were also revised; SATURN spigot links were used in the simulation area to connect the centroid connector onto stub links.

2.2.3 The use of the spliced SERTM network provided a fuller representation of strategic travel (in the buffer area) and a more realistic representation of longer distance trips. The study area showing the simulation area together with the buffer area within the area of impact is shown in Figure 2-2.

Figure 2-2: Wansford traffic model and the buffer area within the area of impact

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copyright. Unauthorised reproduction infringes Crown copyright and may lead to prosecution or civil proceedings. Highways England 100030649 2016.

2.3 Modelled time periods

2.3.1 Three SATURN models are used to model representative weekday single hours which cover the most important periods of traffic flow. For all data sources, the following time periods have been used for the Wansford traffic model:

· AM peak hour: 07:30 – 08:30 · Inter-peak (IP) hour: 13:00 – 14:00 · PM peak hour: 16:30 – 17:30

2.4 User class segmentation

2.4.1 The Wansford traffic model uses 5 user classes that are consistent with the SERTM user classes:

· Car – employer’s business · Car – home based work · Car - other · Light goods vehicles (LGV) · Heavy goods vehicles (HGV)

2.5 Software used

2.5.1 The following software and their respective versions (in brackets) used for the A47 Wansford to Sutton modelling are as follows:

· SATURN (11.3.12W – Level N4 – MULTI-CORE) · DIADEM (version 5.0.9 64-bit)

2.5.2 SATURN is a suite of flexible network analysis programmes. SATURN is most widely used as a highway assignment modelling software package for the robust modelling of congested road networks due to its accurate representation of junction behaviour and the resulting delay.

2.5.3 DIADEM is a software tool. The purpose of DIADEM is to enable users to easily set-up variable demand models in accordance with the advice provided in TAG unit M2 variable demand modelling. The main demand model used in DIADEM is an incremental hierarchical logit model with the option of the following demand responses:

· Trip frequency · Time period choice

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· Mode choice · Destination choice / distribution

2.5.4 Travel times and other costs are provided by a traffic assignment model. DIADEM is principally used in conjunction with SATURN but the CONTRAM package may also be used.

2.6 Assignment procedure and generalised cost parameters

2.6.1 The route choice during a highway assignment is determined by the generalised travel cost incurred on each route. Generalised cost for a route between an Origin(O) and Destination (D) is a function of the travel time for a route and the distance travelled on the route plus any fares/tolls for the route, as shown in the equation below.

2.6.2 Generalised Cost = VOT*Time + VOC*Distance + Tolls, where:

VOT = values of time (pence per minute; PPM)

VOC = vehicle operating cost (pence per km; PPK)

2.6.3 The assignment utilises the Wardrop Equilibrium assignment algorithm which seeks to arrange traffic on congested networks such that the cost of travel on all routes used between each O-D pair is equal to the minimum cost of travel and all unused routes have equal or greater cost.

2.6.4 The generalised cost parameters (value of time and vehicle operating cost) used in the Stage 3 base model were derived from TAG data book, July 2017 release. The derived values are shown in Table 2-1 and Table 2-2 which are calculated in 2010 prices.

2.6.5 The Stage 3 base model and some of the initial forecasting work were commenced before December 2017 and therefore were developed based on TAG data book July 2017. However, it was agreed with Highways England that the latest version of May 2019 (at the time when this work is undertaking) should be used for the new forecast models.

Table 2-1: Value of time assumptions, pence per minute (PPM, 2010 prices, 2015 values)

Pence Per Minute (PPM) User class AM peak Inter-peak PM peak Car - employer’s business 29.81 30.54 30.24 Car - commuting 19.99 20.31 20.06 Car - other 13.79 14.69 15.58 LGV 21.07 21.07 21.07

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Pence Per Minute (PPM) User class AM peak Inter-peak PM peak HGV 49.19 49.19 49.19

Table 2-2: Vehicle operating cost assumptions, pence per kilometre (PPK, 2010 prices, 2015 values)

Pence per kilometre (PPK) User class AM peak Inter-peak PM peak Car - employer’s business 12.62 12.62 12.62 Car - commuting 6.25 6.25 6.25 Car - other 6.25 6.25 6.25 LGV 13.71 13.71 13.71 HGV 45.17 45.17 45.17

2.7 Model calibration and validation

2.7.1 Figure 2-3 below shows a high-level summary of the top line statistics for each modelled time period. These are displayed as a “spider” graph, where the area of the graph represents the total level of calibration / validation of the model. Analysis of these graphs confirms the above analysis, in that a high level of model calibration and validation performance has been achieved. Based on this assessment, it is considered that the model is fit for the purpose of assessing the A47 Wansford to Sutton dualling scheme. In summary, Table 2-3 shows that the following results have been achieved:

· Link calibration AM = 97%, IP = 96%, PM = 96% · Link validation AM = 100%, IP = 100%, PM = 90% · Turn calibration AM = 89%, IP = 90%, PM = 86% · Screenline calibration: AM = 83%, IP = 83%, PM = 100% · Screenline validation: AM = 100%, IP = 100%, PM = 100% · Journey time validation: AM = 83%, IP = 100%, PM = 83%

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Table 2-3: Summary of model calibration and validation

Criteria AM peak Inter-peak PM peak Link calibration (< GEH1 5) 97% 96% 96% Link validation (< GEH 5) 100% 100% 90% Turn calibration (< GEH 5) 89% 90% 86% Screenline calibration (< 5%) 83% 83% 100% Screenline validation (< 5%) 100% 100% 100% Journey time validation 83% 100% 83%

2.7.2 Overall, the A47 Wansford model has achieved a very high level of calibration across its count calibration and validation data. A similarly high level of calibration has been achieved with respect to the models screenline and journey time results. In addition to these metrics it should be noted that analysis of the Wansford A47 / A1 junction model shows that the calibration of all turns at this junction are within TAG guidance.

2.7.3 Despite mostly satisfying TAG criteria, in some cases in the AM or PM peak model results, it can be seen that the model is slightly below a pass rate of 85%. Further analysis of these results indicates that although the model may not pass the TAG criteria for all screenlines and journey times, the model passes if a slightly wider threshold is adopted, suggesting that the failure may be a result of the low (rural) screenline volumes making the TAG criteria harder to achieve.

1 The GEH (Geoffrey E. Havers) statistic based on a comparison of observed and modelled flow and is used 2 as an indicator of “goodness of fit”. The formula for the GEH statistic is ( ) ( . . ) √

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Figure 2-3: Top line summary statistics Top Line Summary Statstics - A47 Wansford to Sutton

AM Peak

No of Criteria % AM Calibration\ Validation Counts 97% Link GEH Cal Link Cal- Counts With GEH <5 238 97% 83% 99% JT Val Link DMRB Cal Link Cal- DMRB Flow Criteria 238 99% Link Val- Counts with GEH <5 10 100% 100% 100% Link Val- DMRB Flow Criteria 10 100% SL Val Link GEH Val Turn Cal- Counts With GEH <5 401 89% Turn Cal- DMRB Flow Criteria 401 96% 100% 83% Screen Line Cal- Flow Difference <5% 12 83% SL Cal Link DMRB Val Screen Line Val- Flow Difference <5% 4 100% 96% JT Routes- Time difference <15% or 1 min if greater12 83% Turn DMRB Cal Turn GEH Cal 89%

IP Peak

No of IP Calibration\ Validation Criteria % 96% Counts 100% Link GEH Cal 97% Link Cal- Counts With GEH <5 238 96% JT Val Link DMRB Cal Link Cal- DMRB Flow Criteria 238 97% Link Val- Counts with GEH <5 10 100% 100% 100% Link Val- DMRB Flow Criteria 10 100% SL Val Link GEH Val Turn Cal- Counts With GEH <5 401 90% Turn Cal- DMRB Flow Criteria 401 98% 83% 100% Screen Line Cal- Flow Difference <5% 12 83% SL Cal Link DMRB Val Screen Line Val- Flow Difference <5% 4 100% JT Routes- Time difference <15% or 1 min if greater12 100% 98% Turn DMRB Cal Turn GEH Cal 90%

PM Peak

No of PM Calibration\ Validation Criteria % 96% Counts Link GEH Cal Link Cal- Counts With GEH <5 238 96% 83% 96% JT Val Link DMRB Cal Link Cal- DMRB Flow Criteria 238 96% Link Val- Counts with GEH <5 10 90% 100% 90% Link Val- DMRB Flow Criteria 10 90% SL Val Link GEH Val Turn Cal- Counts With GEH <5 401 86% Turn Cal- DMRB Flow Criteria 401 93% 100% 90% Screen Line Cal- Flow Difference <5% 12 100% SL Cal Link DMRB Val Screen Line Val- Flow Difference <5% 4 100% JT Routes- Time difference <15% or 1 min if greater12 83% Turn DMRB Cal Turn GEH Cal 93% 86%

* Third column in the tables above shows % of links, turns, screenlines or journey time routes passing each criterion Source: SWECO

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2.8 Variable demand modelling

2.8.1 As the Stage 2 A47 Wansford modelling was not completed, no VDM was available at the inception of the Stage 3 study. Therefore, the Stage 3 VDM has been developed in its entirety during the Stage 3 study. Due to time constraints a proportionate approach was undertaken.

2.8.2 The VDM has been developed using DIADEM v6.3 software with the inclusion of applying fitting on factors (FOF) as part of SATNET before the main SATURN assignment procedure is called. The purpose of applying these FOFs is to adjust the DIADEM origin destination (OD) matrices split from production attraction (PA) input matrices to fit the calibrated base year matrices.

2.8.3 To support Road Investment Strategy (RIS) schemes such as these throughout the development process and especially for economic appraisal, it is required to use VDM in accordance with TAG unit M2. For PCF Stage 2, production attraction (PA) modelling was not specified in the VDM set up, which is not TAG compliant. For PCF Stage 3, home based PA and non-home based OD VDM will be developed with a DIADEM demand model with the following attributes:

· Segmentation by purpose: employers’ business / commute / other · Vehicle types: car / LGV / HGV · Home based (PA) / non-home based (OD) · Fixed no VDM: external to external home based work (HBW) movements / freight movements · Cost dampening for trips greater than 30 kilometres · Hierarchical incremental model · Model responses to frequency and distribution · Use of illustrative parameter values and adjustment using realism testing · Monitoring of demand model convergence to achieve TAG criteria

2.8.4 The cost damping curve was implemented during the realism testing. We retained the TAG / DIADEM guidance of capping the minimum trip length on which the cost damping takes effect to 30 kilometres. In order to calibrate the fuel cost elasticities, we looked at the sector-sector breakdown of the fuel cost elasticities based on internal (broadly the area of scheme impact) and external sectors (elsewhere). As the fuel cost elasticity in the external-external, and internal-external regions was significantly higher than the TAG recommendations, we incrementally changed the cost damping function until the elasticity was a similar order of magnitude to the internal-internal region.

2.9 Previous PCF Stage 0-2 traffic forecasting report

Wansford base year model

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2.9.1 For Stage 1, the existing Peterborough Traffic Model (PTM) developed by Peterborough City Council (PCC) has been cordoned around the west side of the PTM model (now called WPTM) to include the proposed scheme as shown in Figure 2-4.

Figure 2-4: Extent of the cordoned Wansford model for Stage 1

2.9.2 The original PTM was based on 2006 traffic data including roadside interview surveys (RSIs) collected in 2006 but later rebased to 2015. The validation of the 2015 rebased WPTM was never completed during Stage 2 due to the tight timescale of Stage 2 and as such, a Paramics model was developed during Stage 2 for forecasting and appraisal of the scheme for Stage 2.

Wansford Paramics model

2.9.3 As stated above, the WPTM was not fully completed on time due to the tight timescale of Stage 2, instead a Paramics model was used for the forecasting and the appraisal of the final stage 2 submission. The extent of the Paramics model is shown in

2.9.4 Figure 2-5 below.

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2.9.5 A summary of the Paramics model specification is shown in Table 2-4.

Figure 2-5: Wansford Paramics model (network defined in red)

Source: SWECO. This map is based upon Ordnance Survey material with the permission of Ordnance Survey on behalf of the Controller of Her Majesty's Stationery Office © Crown copyright. Unauthorised reproduction infringes Crown copyright and may lead to prosecution or civil proceedings. Highways England 100030649 2016. Table 2-4: Wansford Paramics model base year specifications for Stage 2 Category Comment Base year 2015 Time period AM / IP / PM peak hour

OD data year 2015 counts - OD estimated

Count data year Some data collection in 2015 Two User Classes: User classes · Car / LGVs · HGVs Consists of the A47 from west of Wansford to its bypass of Castor, Coverage and each of the roads connecting it the A1, Old North Road, and accesses from other intermediate junctions along the link. Convergence Not known

Variable Demand Model (VDM) No VDM was undertaken using the Paramics model.

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3. Forecasting approach

3.1.1 An overview of the approach taken to the forecasting of the scheme can be seen in Figure 3-1.

Figure 3-1: Flowchart of forecasting process

Source: SWECO

3.1.2 As discussed in the transport modelling package report (TMPR, HE551494- MMSJV-VTR-000-RP-TR-00016), dated April 2018, the base model for Stage 3 was an enhancement of the Stage 2 base highway assignment model. The opening year for the scheme in the forecast model is now 2025 and the scheme design year is 2040.

3.1.3 The traffic forecasts account for future proposals for residential and employment developments in the local area as well as corresponding transport network changes. The forecast scenarios comprise of the following:

· A set of transport network changes

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· Assumptions about changes in values of time and vehicle operating costs over time · A specific set of development assumptions · Application of the National Trip End Model (NTEM) growth factors extracted from TEMPRO 7.2 as a constraint on trip growth for cars and public transport (PT) · Application of growth of freight traffic from the Department for Transport (DfT) road traffic forecasts (RTF2018) · Variable demand modelling (VDM) was undertaken using dynamic integrated assignment and demand modelling (DIADEM Version 6.3). Demand model parameters were derived from realism tests on the refined Stage 3 base model. Details of the VDM process are reported in section 7 of this report.

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4. Uncertainty log and forecast years

4.1 Introduction

4.1.1 In line with TAG unit M4, forecasting and uncertainty, an uncertainty log has been developed. The purpose of the uncertainty log is to record the central forecasting assumptions that underpin the core scenario and record the degree of uncertainty around these central assumptions. These assumptions will be the basis for developing a set of alternative scenarios.

4.1.2 The uncertainty log deals with local uncertainty about future land use (demand side uncertainty) and transport schemes (supply side uncertainty), which will affect the transport network. The uncertainty relates to the likelihood of a given scheme or development taking place, as well as the nature and size of the development.

4.2 Forecasting at PCF Stage 0-2

4.2.1 It was confirmed that for Stage 2 that no uncertainty log was available for Wansford. However, Skanska, the transport consultant for Peterborough City Council (PCC), provided a spreadsheet detailing the developments assumptions included in the Peterborough Transport Model (PTM), although no classification on the certainty of these developments is included in the assessment.

4.3 Forecasting at PCF Stage 3

4.3.1 For Stage 3, following discussions with Skanska and Peterborough City Council, clarification was provided on which committed and allocated sites should be included in the “core” scenario assumptions. In the Skanska spreadsheet, the non-committed developments are factored down from the 2036 assumptions to create intermediate years between 2015 and 2036. It was agreed with PCC that no further development should be assumed for the period 2036-2037. Following discussion with Highways England, it has been assumed that this proportioning approach will be suitable for the PCF Stage 3 modelling. The development proportion approach factors derived from the Skanska modelling are shown in Appendix D.

4.4 Forecast years

4.4.1 After consultation with Highways England, it is envisaged that the estimated of completion of construction for the A47 Wansford scheme will be between 2023 and 2025 according to Highways England’s latest delivery plan (2019-2020). Therefore, it has been agreed with Highways England to change the model forecast years from 2022 and 2037 to 2025 (opening year) and 2040 (design

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year) and the new forecast year demand matrices will be derived by applying TEMPro background growth factors (2022 to 2025 and 2037 to 2040) to original forecast year reference demand matrices. This assumption is fully explained in the technical note – A47 RIS Scheme Opening Years – Update (HE551490-GTY- VTR-000-RP-TR-30002).

4.5 Uncertainty log (local area plan data and transport supply data)

4.5.1 The uncertainty log contains the local authority network schemes and Highways England schemes in regions nearby and significant to the model and forecasts for the scheme.

4.5.2 As per TAG, the schemes included in the Do Minimum (DM) scenarios have a likelihood of at least ‘near certain’ or ‘more than likely’. Table 4-1 provides the TAG definitions of the uncertainty log classifications.

Table 4-1: Classification of future impacts

Probability of the Local authority / development scheme Highways England input Near certain: The · Intent announced by proponent to Stage 4 completed, scheme outcome will happen regulatory agencies entering or in Stage 5 (such as or there is a high · Approved development proposals scheme consented). probability that it will · Projects under construction happen

More than likely: · Submission of planning or consent Stage 2 completed, scheme The outcome is application imminent entering or in Stage 3 (such as likely to happen but · Development application within the consent preferred route announced). there is some process uncertainty · Projects under construction Reasonably · Identified within a development plan Scheme in Stage 1 or 2 (such foreseeable: The · Not directly associated with the transport as option selection. outcome may strategy / scheme, but may occur if the happen, but there is strategy / scheme is implemented significant · Development conditional upon the transport uncertainty strategy / scheme proceeding · A committed policy goal, subject to tests (for example, of deliverability) whose outcomes are subject to significant uncertainty Hypothetical: There · Conjecture based upon currently available Scheme in Stage 0 (such as is considerable information major road project initiated). uncertainty whether · Discussed on a conceptual basis the outcome will · One of a number of possible inputs in an ever happen initial consultation process · Or a policy aspiration

4.5.3 It was confirmed by Amey that no uncertainty log was available for the west Peterborough Traffic Model (WPTM) Stage 2 works. As a substitute, a

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spreadsheet was provided by Skanska that detailed the developments assumed in the PTM model. However, no classification on the certainty of these developments is included in the assessment. However, as these developments have been adopted in the PTM forecasting they have been assumed to be “core” scenario assumptions

4.5.4 The non-committed developments reported by Skanska were factored down from the 2036 assumptions to create interim years between 2015 and 2037. This interpolating approach has been adopted for the PCF Stage 3 modelling.

4.5.5 The area covered by the Skanska uncertainty log covers the entire PCC area and has informed by their local plan for the period 2016 to 2036. Within the plan is a commitment to deliver housing and other mixed-use development over the 20- year period and PCC has identified 6 large-scale development sites on their urban fringe to support this objective as shown in Figure 4-1.

4.5.6 Following further consultation with PCC, they confirmed that no major changes were made to the planning data and hence it was still appropriate to use the uncertainty log as prepared during the MMSJV work in 2018.

Figure 4-1: Major development sites identified in Peterborough local plan

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4.5.7 The major development sites shown above are summarised as follows:

· Great Haddon: an area bounded by the A1(M) and A15 to the south-west of the city · Hampton: an area adjoining the existing suburb of Hampton Hargate to the south of the city · Stanground South: an area near the A605 road to the south-east of the city · Norwood: an area bounded by the A47 and A16 to the north-east of the city · Paston Reserve: an area adjoining the Norwood development to the north-east of the city · Land north of Ailsworth and Castor: an area on the A47 to the west of the city

4.5.8 Hampton, Paston Reserve and Stanground South are fully committed in the local plan. The remaining 3 developments have land set aside but are not at this time fully committed.

4.5.9 In addition to developments, other nearby changes to the transport network such as highway improvements or new public transport infrastructure may result in changes to local traffic demand. The primary highway infrastructure project in the vicinity of Peterborough is a road investment strategy (RIS) junction improvement scheme at Guyhirn approximately 20 kilometres east of the city centre. The Guyhirn scheme is being progressed through the same framework as this one, but is unlikely to transmit significant benefits across to the west of the city.

4.6 Modelled scenarios

4.6.1 For forecasting purposes, transport networks representing the supply and cost of transport in future years were required as a basis to assess the impact of the proposed scheme. Future year transport supply and costs relate to changes in the transport networks, for example new transport infrastructure.

4.6.2 Highway networks have been produced for 2 forecast scenarios:

· Do Minimum (base + committed schemes) · Do Something (base + committed schemes + Wansford option 2)

4.6.3 These have all been created for 2 forecasting years:

· 2025 – opening year · 2040 – design year

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5. Reference forecast demand

5.1 Overview

5.1.1 This chapter summarises the approach adopted to produce reference forecast demand for use in the original future year forecasts of 2025 and 2040. Traffic generated by planned specific developments has been included in the forecast demand, which has been constrained to forecast National Trip End Model (NTEM) levels of growth at balancing area level.

5.1.2 This section describes how the developments that are planned in the future years of 2025 and 2040 have been taken into account and how the predicted generations / attractions from / to the developments have been included into the future traffic models. The data required to undertake this task can be summarised as follows:

· Uncertainty log information from Skanska (agreed with Peterborough City Council) · Trip rates derived from NTEM v7.2 for cars · Trip rates derived from TRICs for LGVs and HGVs

5.1.3 All developments, provided by Skanska, within the west Peterborough traffic model (WPTM) study area are included in the uncertainty log as shown in Figure 5-1. It should be noted, due to the network extension some of the WPTM network is outside of the catchment area for the PTM model (such as the Peterborough region). It is assumed for this area TEMPRO growth factors will be applied.

5.1.4 The uncertainty log entries which correspond to Figure 5-1 are shown in Appendix D. All the developments which were based on TAG likelihood classification of at least ‘near certain’ or ‘more than likely’, designated as “core” scenario by Skanska have been included in the uncertainty log.

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Figure 5-1: A47 Wansford Stage 3 development locations in Peterborough

5.2 NTEM growth of car trips

5.2.1 Forecast trip ends from version 7.2 of the NTEM were used to derive trip end growth factors at model zone level, via an NTEM to model zone correspondence list.

5.2.2 The growth factors are derived as origin and destination factors (or production and attraction factors for home based trips) for each of the demand segments required for input into the variable demand model.

5.2.3 Growth factors have been derived for car vehicle trips for the 2015 base year and the 2 forecast years (2025 and 2040). Growth factors have been derived for car trips between the 2015 base year and the 2 forecast years (2025 and 2040).

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5.3 NTEM growth of goods vehicles

5.3.1 Freight growth factors have been extracted from road traffic forecasts (RTF) 2018 scenario 1 as shown in Table 5-1.

Table 5-1: RTF18 goods vehicle growth rates from 2018

LGV HGV

Region 2025 2040 2025 2040

East Midlands 1.164 1.399 0.989 1.019 Eastern England 1.149 1.378 1.031 1.117 Rest of UK (GB) 1.160 1.393 1.002 1.050

5.4 Trip generation and distribution for modelled developments

5.4.1 Car trips ends were generated from the identified development schemes based on trip rates derived from NTEM v7.2 using the ‘alternative forecasting assumptions’ available within the TEMPRO software. The procedures allow, for each of the areas identified, the manual introduction of a number of households, jobs (for instance 1,000) and the calculation of a trip rate per house / job by dividing the expected NTEM v7.2 output number of trips by 1,000. This approach has been adopted using the 2015 year for:

· Each home-based and non-home trip purpose modelled · An average 24 hours weekday (production / attraction home based trip purposes, that is, employers’ business, commute, other) · Each peak period for the origin destination trip purposes (specifically AM, Inter-peak (IP), PM and Off-peak (OP) for non-home based employer’s business and non-home based other)

5.4.2 Appendix G shows the car NTEM v7.2 trip rates for the year of 2015, for each purpose, for each period and for each area where the planned developments are located. These trip rates were then applied to the quantum (housing, number of jobs) in each development for each forecast year.

5.4.3 For the majority of the employment developments listed in the uncertainty log the amount of floorspace available was provided (rather than the number of jobs as per units of the NTEM v7.2 tip rate). Therefore, a conversion between square metres of floorspace to number of jobs was applied for each of the land uses of A1 (retail), B1b (R&D Space), B2 (industrial and manufacturing), B8 (storage and distribution) and mixed B class. Table 5-2 shows the number of jobs per 100sqm of (gross floor area) GFA for the different floorspace usage. The methodology has applied the guidance outlined in the home and community’s agency employment density guide (2015).

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Table 5-2: Jobs per 100 sqm of GFA

Jobs per 100 sqm of GFA (NIA – Net User Class Internal Area) A1 (Retail) 6.66 B1 (General offices) 10.07

B2 (Industrial and manufacturing) 3.03 B8 (Storage and distribution) 1.45

5.4.4 The proposed employment sites were also expected to generate LGV and HGV trips. For the purpose of calculating trip ends generated by these, NTEM v7.2 trip rates are shown in Appendix G could not be used as they refer to car only. Therefore, TRICs trip rates were used instead.

5.4.5 Average TRICs trip rate per employee were extracted from the software for each of the modelled time periods for the A1, B1, B2 & B8 categories taking into account only those sub-categories likely to be relevant for the developments considered. Table 5-3 to Table 5-6 show the average TRICs trip rates per employee by vehicle type applied to the number of jobs specified or calculated for each of the development employment sites.

Table 5-3: Average TRICs trip rates - A1

LGV HGV Trip rate / employee Arrivals Departures Arrivals Departures AM 07:00-10:00 0.211 0.205 0.011 0.007 IP 10:00-16:00 0.181 0.168 0.009 0.009 PM 16:00-19:00 0.152 0.173 0.005 0.009 OP 19:00-07:00 0.024 0.027 0.001 0.001

Table 5-4: Average TRICs trip rates - B1

LGV HGV Trip rate / employee Arrivals Departures Arrivals Departures AM 07:00-10:00 0.009 0.012 0.001 0.001 IP 10:00-16:00 0.009 0.009 0.001 0.001

PM 16:00-19:00 0.007 0.005 0.001 0.001 OP 19:00-07:00 0 0 0 0

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Table 5-5: Average TRICs trip rates – B2

LGV HGV Trip rate / employee Arrivals Departures Arrivals Departures AM 07:00-10:00 0.044 0.052 0.013 0.013

IP 10:00-16:00 0.043 0.043 0.012 0.013 PM 16:00-19:00 0.032 0022 0.010 0.007

Table 5-6: Average TRICs trip rates - B8

LGV HGV Trip rate / employee Arrivals Departures Arrivals Departures AM 07:00-10:00 0.007 0.005 0.092 0.093 IP 10:00-16:00 0.008 0.007 0.071 0.083 PM 16:00-19:00 0.004 0.006 0.086 0.088 OP 19:00-07:00 0.001 0.001 0.018 0.014

5.4.6 The calculated trip ends obtained by applying the NTEM v7.2 and the TRICs trip rates were then distributed using the base demand trip distribution through a SATURN furness process to output a set of development matrices for both 2022 and 2037. This was done for each modelled period (24 hours for the home based trips) and average period (AM, IP, PM and OP) for the non-home based trips as well as for each mode (Cars / LGVs / HGVs).

5.4.7 The methodology for distributing future development trip ends using the base demand matrices required the base demand at zone level, where the development has been allocated to be populated with some trips in the base year. For the vast majority of the developments the existing model zones had associated trips which could be used. In rare occurrences were the base zone was empty, a zone with a similar trip distribution was chosen to distribute the development trips. The same approach has been adopted when development trips for a time period were missing in the base year matrices, and in that case a distribution taken from a nearby similar zone was used.

5.5 Dependent development

5.5.1 At the time of writing this report, no dependent developments have been identified. The forecast modelling assessment does however examine the impact of the explicitly modelled developments identified in the uncertainty log, to identify any potential traffic impacts in both the Do Minimum (DM) and Do Something (DS) scenarios.

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5.6 Constraining to district level

5.6.1 The derivation of the 2025 and 2040 reference demand matrices was carried out by using TEMPRO trip end calculations and SATURN’s MX Furness procedure with a spreadsheet interface. This process allows for the forecast demand to be constrained at “balancing areas”, which are user-defined collections of NTEM zones, potentially representing counties, regions or districts. The balancing areas defined in this analysis are listed in Table 5-7.

5.6.2 A Furness process was then carried out to constrain the growth to NTEM, which in general consists of the following steps:

· Apply alternative assumptions facility within TEMPRO to exclude explicitly modelled development growth · Apply growth factors to base demand to create target trip end · Furness base demand to forecast target trip ends · Add in development demand Table 5-7: Balancing Area Descriptions

Balancing Area Description 1 Peterborough 2 East England 3 East Midlands 4 Great Britain

5.7 Combined background and development trip matrices

5.7.1 Using TEMPro alternative development assumptions feature target trip ends were reduced, according to the forecasted household and job growth for each development. Explicitly modelled development demand is then added in addition to the balanced NTEM growth. This ensured that the balancing area target growth for NTEM zones are maintained. Overall growth between the base year and the future year reference demand are reported in Table 5-8and Table 5-10 below.

Table 5-8: Highway Reference Demand at OD Level – Core Scenario AM Peak

2025 2040 Purpose Base Ref Growth% Base Ref Growth% EB 477,588 515,526 7.9% 477,588 559,757 17.2% Commute 2,113,219 2,248,033 6.4% 2,113,219 2,431,067 15.0% Other 2,023,900 2,245,896 11.0% 2,023,900 2,523,148 24.7% LGV 533,801 618,581 15.9% 533,801 743,094 39.2%

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2025 2040 Purpose Base Ref Growth% Base Ref Growth% HGV 217,914 218,777 0.4% 217,914 229,891 5.5% Total 5,366,423 5,846,814 9.0% 5,366,423 6,486,957 20.9%

Table 5-9: Highway Reference Demand at OD Level – Core Scenario Inter Peak

2025 2040 Purpose Base Ref Growth% Base Ref Growth% EB 412,213 440,972 7.0% 412,213 478,236 16.0% Commute 728,865 769,440 5.6% 728,865 826,838 13.4% Other 2,863,263 3,151,454 10.1% 2,863,263 3,537,068 23.5% LGV 513,250 594,843 15.9% 513,250 714,601 39.2% HGV 209,357 210,149 0.4% 209,357 220,780 5.5% Total 4,726,948 5,166,858 9.3% 4,726,948 5,777,522 22.2%

Table 5-10: Highway Reference Demand at OD Level – Core Scenario PM Peak

2025 2040 Purpose Base Ref Growth% Base Ref Growth% EB 499,349 542,862 8.7% 499,349 589,507 18.1% Commute 1,983,961 2,098,762 5.8% 1,983,961 2,258,020 13.8% Other 2,913,228 3,215,664 10.4% 2,913,228 3,607,647 23.8% LGV 426,016 493,629 15.9% 426,016 592,970 39.2% HGV 138,216 138,772 0.4% 138,216 145,832 5.5% Total 5,960,769 6,489,689 8.9% 5,960,769 7,193,976 20.7%

5.8 Alternative scenarios

5.8.1 An optimistic alternative scenario test, which includes additional highway and land use developments which have not been included in the core scenario, will not be conducted. Additional sensitivity tests will be undertaken for the high and low scenarios to inform the economic assessment. Table 5-11 below details the assumptions applied to the core, high and low scenarios.

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Table 5-11: Core, low and high scenario definitions

National Trip End Model Scenario Supply Demand (NTEM) constraint Near certain and more than likely Near certain and more than Core Standard NTEM schemes likely developments Near certain, more than likely Near certain, more than High growth High growth NTEM schemes likely developments Near certain and more than likely Near certain and more than Low growth Low growth NTEM schemes likely developments

5.8.2 Based on TAG guidance, high and low growth scenarios will be developed in which the core demand will be amended by a proportion of the base year demand using the formula:

· u is the uncertainty, the proportion ∙of base− year demand to be added to (in the high growth scenario) or subtracted from (in the low growth scenario) the core forecast demand · p is a factor representing the uncertainty in macroeconomic variables influencing travel demand, defined in TAG unit M4 “forecasting and uncertainty” as 2.5% for national highway traffic · f is the forecast year being modelled (up to a maximum of 36 years after the base model) · b is the model base year

5.8.3 The derivation of u for the forecast models is therefore:

= 2.5% × 2025 2015 = 7.91% =2.5%× 2040 2015 = 12.50%

√ − √ −

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6. Forecast supply

6.1 Introduction

6.1.1 This section details the assumptions applied in the development of the Do Minimum (DM) and Do Something (DS) networks. In addition to details of the preliminary forecast networks robustness assessments can be found in this section.

6.2 Do Minimum scenario

6.2.1 The schemes that have been included in the DM forecast models can be seen in Figure 6-1 (highlighted in green and orange – shown as the difference between base and DM models in SATURN P1X) and Table 6-1. Schemes have been excluded if they are too far away, would not have an impact on the study area network, if no opening date for the scheme was available or if they are only hypothetical or reasonably foreseeable.

Figure 6-1: Wansford model Do Minimum coding change locations

Source: SWECO

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Table 6-1: DM network assumptions

Scheme

A(1) M Junction 17 A605 / Fletton Parkway junction 1 Great North Road to Fletton Parkway link Great Kyme development access A47 / A1 interchange east roundabout signalisation (AM peak only) Wansford Village - speed limit order Wansford 20MPH 2018 The effect of this order is to introduce a 20mph speed limit on the following roads: 1. Old Leicester Road from a point 190m east of its junction with Robins Field to its junction with Yarwell Road 2. Yarwell Road from a point 100m west of its junction with Old Leicester Road to its junction with Old Leicester Road 3. Bridge End from a point 248m south of its junction with Peterborough Road to its junction with Peterborough Road 4. Peterborough Road from a point 15m west of its junction with the A1 to its junction with Old North Road 5. Old North road from a point 12m south of its junction with Swanhill and its junction with Peterborough Road 6. Nene Close, Riverside Spinney & Wansford Road for their entire length

6.3 Do Something scenarios

6.3.1 The Do Something (base + committed schemes + the proposed Wansford to Sutton dualling scheme) option has been modelled, which includes the proposed scheme with further improvements at the A1 / A47 Western Roundabout as shown in Figure 6-2 below. The SATURN changes in the network are shown in Figure 6-3 (highlighted in dark red and green).

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Figure 6-2: A47 Wansford to Sutton dualling scheme with further improvements at A1 / A47 Western Roundabout

Source: SWECO

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Figure 6-3: Highway network changes in Do Something compared to Do Minimum

Source: SWECO

6.4 Forecast network calibration

6.4.1 On completion of the preliminary forecast done previously, a significant review process was undertaken on the network structure to:

· Review the completeness of the network around the scheme area to ensure that the modelled and designed representations were appropriate · Reviewed the directionality and connectivity of the proposed network changes around the scheme area

6.4.2 A TUBA run was completed, and the serious warnings were looked at. Issues identified were rectified by making appropriate changes to the networks.

6.4.3 To check the robustness of the network, assignment checks were carried out, including delay differences at node level between the Do Minimum and Do Something networks. This analysis took into account of both increases or decreases in delay and the impact on the affected traffic flow. The results are displayed in Appendix F, where the pink circles indicate a reduction in model delay in the Do Something compared to the Do Minimum and the green circles indicate an increase in model delay. A review of magnitude of these delay and flow differences gave assurance as the DM and DS model results are in-line with the expected benefits of the scheme.

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7. Equilibrium demand forecasts

7.1 Overview of variable demand response

7.1.1 Variable demand modelling is undertaken by using Department for Transport (DfT’s) DIADEM software (version 6.0.3). A summary of the Variable Demand Modelling (VDM) methodology is provided in section 2.8, full details can be found in the Transport Modelling Package Report (TMPR) chapter 6.

7.1.2 DIADEM is a TAG compliant incremental demand model with respect to model form, most notably model hierarchy and incremental nature of the model. The approach makes use of cost changes from incremental differences between base and test scenarios operated using a pivot point approach, whereby the model calculates the forecast demand using the cost changes between the forecast scenario and a reference scenario.

7.1.3 The Do Minimum (DM) scenario pivots from the 2015 base assignments while the Do Something (DS) in turn pivots from the DM. The generalised cost (value of time and vehicle operating cost) used in DIADEM and SATURN were derived from TAG data book, May 2019 release. The derived values are shown in

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7.1.5 Table 7-6, which are calculated in 2010 prices.

7.2 Demand modelling zone aggregation

7.2.1 The base model is built upon the south-east region traffic model (SERTM) 2391- zone network and matrices with full coverage of Great Britain. As the SATURN assignments, and in particular the DIADEM modelling, was slow with such a large number of zones (taking 3 days to complete a DM run), the decision was made to aggregate the zoning for the DIADEM demand model runs. The aggregation process was initially considered so that the realism testing could be undertaken in a much quicker timeframe.

7.2.2 In the initial Stages an aggregated zone system was devised that condensed the number of zones from 2,391 to around 300. Where there were significant flow differences near the area of detailed modelling, the aggregated zones were reverted back to one-one until the differences were minimal. After this iterative process was undertaken, where the aggregated AM / IP / PM assignments were compared against their full zone equivalents the resulting aggregated zone model had 504 zones. With this model the assignments were virtually identical to their 2,391 zone equivalents, but with DIADEM run times a fraction of the original, taking only a couple of hours. This time reduction allowed the realism testing to be fine-tuned and for stress testing to be undertaken.

7.3 Types of VDM response

7.3.1 Based on the proportionate approach adopted for the development of the A47 Wansford VDM, the following demand responses are included in the VDM:

· Trip generation / frequency · Trip distribution

7.3.2 Mode choice has not been included in the models demand responses. The A47 Wansford to Sutton dualling scheme is located within a rural area with a low modal share in the simulation area. Within the simulation study area there is only 1 rail station, Nene Valley railway, which operates primarily as a tourist operation. Therefore, the public transport (PT) travel within the Wansford study area is predominantly by bus, which will not realistically compete with the car mode. From SERTM model data, it is estimated that the car modal share in the Wansford study area is approximately 99%. In addition, the Wansford dualling scheme will not have any impact on PT costs. This is considered a proportionate methodology with respect to the scope of the Wansford study area defined at the inception of the PCF Stage 3 works.

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7.3.3 Table 7-2 contains VDM parameters / model response and hierarchy. Each Do Something (DS) VDM run has been carried out by pivoting off a Do Minimum (DM). In turn the DM has been derived from a VDM run pivoting off the base model

Table 7-1: VDM parameters / model response and hierarchy

Parameter / Data source Notes setting

Segmentation

AM, IP, PM travel costs derived from peak hour (AM and PM) and average peak hour (IP) calibrated assignments. Modelled AM 07:00-10:00, IP 10:00-16:00, PM 16:00-19:00 hours, time periods OP 19:00-07:00 Off-peak (OP) travel costs derived from uncalibrated assignment of derived OP matrix to IP network to represent free- flow conditions.

From assignment models: Car employers’ business Assigned Car commute user classes Car other Light goods vehicles Heavy goods vehicles

Car Segment available

Home based employers’ business (HBEB) 1

Home based commute (HBC) 2

Home based other (HBO) 3

Non-home based employer’s business 4 Fixed elements relate to (NHBEB) VDM movements that are not subject segments to VDM response. Non-home based other (NHBO) 5

Fixed – employers’ business 6

Fixed – commute 7

Fixed - other 8

Light goods vehicles 9

Heavy goods vehicles 10

Sectors All model zones are grouped together into a single sector

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Parameter / Data source Notes setting

Model parameters

Incremental Home based PA

Model type Incremental Non-home based OD

Goods Fixed

Distribution is singly constrained Model for employer’s business and responses Frequency other, doubly constrained for and Distribution commute. hierarchy Frequency is for other purpose only.

Logit parameters: Confirmed through realism testing Within ranges recommended by TAG lambda, (see section 6.2 of TMPR). theta

Cost Highways England VOT and VOC spreadsheet (release coefficients 040817 v2) based on May 2019 TAG databook (VOTs etc.)

Occupancy TAG factors

Demand matrices

Home based NTEM growth factors to calibrated base (24hr assignment matrices PA)

Non- home Highway NTEM growth factors to calibrated base based assignment matrices (hourly OD)

Goods RTF growth factors to calibrated base (hourly assignment matrices OD)

Cost matrices

Reference Extracted from SATURN road SATURN assignment. UFS files

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Parameter / Data source Notes setting

PA data

Outbound & return Standard tour proportions from NTS data as specified in proportions the DIADEM manual

Default values provided in DIADEM from National Travel Tour Survey data, which are then furnessed within DIADEM proportions application to match defined Outbound and Return proportions (see above).

DIADEM parameters

Algorithm Fixed step length (0.5, as per base model calibration)

Target relative GAP of 0.025% for DM and 0.0025% for Convergence DS

Highway assignment convergence criteria

Delta and less than 0.1% or at least stable with convergence fully %GAP documented and all other criteria met.

7.4 Calibration of the DIADEM model

7.4.1 Realism testing was undertaken on the base-year demand model in accordance with TAG M2 (6.4). TAG unit M2 sets out the variable demand model realism testing procedures that should be used. These procedures are designed to ensure that the variable demand behaves realistically, by changing the various components of travel costs and times and checking that the overall response is in accordance with expected levels of response. If the responses are outside of expected ranges, then the parameter values are adjusted until an acceptable response is achieved. TAG unit M2 notes that there is more scope for adjusting imported or illustrative parameter values than those that have been calibrated from local data.

7.4.2 The acceptability of the model’s responses is determined by the demand elasticities it predicts. These are measured by changing a cost or time component by a small proportional amount and calculating the proportionate change in trips made. The elasticity formulation recommended is the arc elasticity formulation:

e = (log(T1)-log(T0))/(log(C1)-log(C0))

Where 0 and 1 indicate values before and after the change in cost, respectively.

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7.4.3 TAG unit M2 states that the analysis should check the elasticity of demand with respect to the demand drivers given below and provides recommended indicative elasticity values

· 10% increase in car fuel cost · 10% increase car journey time · 10% increase public transport fare

7.4.4 Of these, the public transport test has not been undertaken, as the public transport demand impacted by the scheme is small and therefore will have minimal variable demand response.

7.4.5 TAG unit M2 states that, wherever possible, “…each variable demand response should be calibrated on local data, to reflect the local strengths of the choice mechanisms”. Alternatively, they may be derived from existing locally calibrated models of the area. If these options are not available, TAG unit M2 provides a set of illustrative parameter values, obtained from a review of a number of UK transport models, which “…provide an acceptable approach to including variable demand modelling in transport appraisals where it is deemed too difficult to establish local values.”

7.4.6 The variable demand model parameters used in DIADEM are shown in Table 7-2 and Table 7-3. As local parameters data was unavailable for the A47 Wansford to Sutton dualling scheme study, TAG illustrative parameters were used as a starting point and were adjusted as part of realism testing. As shown in Table 7-2, although the distribution parameters are at the high end of the range, they are within the range suggested in TAG 6.4.17 and are therefore considered to provide reasonable variable demand response to changes in travel costs.

Table 7-2: DIADEM logit parameters

Distribution Purpose Trip frequency (Highway) Home based employer’s business -0.106 - Home based work -0.113 - Home based other -0.108 0.1 Non-home based employer’s business -0.107 - Non-home based other -0.092 0.1

Table 7-3: DIADEM distribution Lambda (λ) parameter values

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Logit Parameters – λ Value

Mode - Purpose Value TAG Minimum TAG Median TAG Maximum Used

Car home based employer’s -0.038 -0.067 -0.106 -0.106 business Car home based work -0.054 -0.065 -0.113 -0.113 Car home based other -0.074 -0.09 -0.16 -0.108 Car non-home based employer’s -0.069 -0.081 -0.107 -0.107 business Car non-home based other -0.073 -0.077 -0.105 -0.092

Fuel cost elasticity

7.4.7 Two new sets of generalised cost coefficient files were created containing the generalised cost with a rise in fuel prices. The demand model was run with 10% fuel cost increase. This increase was reflected in the model by revised assignment generalised costs in the SATURN network.

7.4.8 The fuel-cost elasticity tests began with TAG median parameters and 2 further tests were run until the final parameters gave TAG accepted results. The final outturn elasticities are shown in Table 7-4.

Table 7-4: Outturn fuel cost elasticity by time period and purpose

Employers Time period Commuting Other Total business AM -0.13 -0.03 -0.40 -0.24 Inter-Peak (IP) -0.13 -0.05 -0.39 -0.29 PM -0.12 -0.04 -0.37 -0.24 Off-Peak (OP) -0.12 0.00 -0.35 -0.26 Total (24 hr) -0.12 -0.03 -0.38 -0.26

7.4.9 Table 7-4 above indicates that the total elasticity is within the TAG M2 guidance acceptable range of -0.25 to -0.35 for IP and OP modelled time periods, with the AM and PM peaks showing slightly less responsiveness to fuel cost. Overall, the 24 hour time period, calculated from factoring up the individual time periods is also within the TAG M2 guidance acceptable range, albeit at the lower end. Analysis of the individual user class results show that commuting is the least responsive to changes in fuel cost, with ‘other’ showing the largest response in line with expectation.

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7.5 Journey time elasticity

7.5.1 Within DIADEM guidance an approximate approach is defined to calculate the journey time elasticity. The advantage of this approach is that it does not require an additional realism test and the results from the fuel cost test can be used. As the results of this test indicated the journey time elasticity was well within the TAG limit, it was not considered necessary to refine the methodology to a more sophisticated approach to verify the validity.

7.5.2 Table 7-5 shows the journey time elasticities calculated from the fuel cost realism test for the simulation network, as defined in the guidance from TAG Unit M2. TAG unit M2 advises that journey time elasticities will vary more than fuel cost elasticities, and the results should be checked to ensure that the model does not produce output elasticities stronger than -2.0. As shown in Table 7-5 below, none of the journey time elasticities from the realism testing are outside the TAG guidance limit of -2.0.

Table 7-5: Outturn journey time elasticity by time period and purpose

Employers Time period Commuting Other Total business AM -0.21 -0.49 -1.51 -0.35 IP -0.33 -0.66 -1.29 -0.49 PM -0.21 -0.47 -1.39 -0.34 OP -0.49 -0.55 -1.22 -0.49 Total (24 hr) -0.31 -0.54 -1.33 -0.42

Realism test summary

7.5.3 The calculated overall fuel cost elasticity (-0.26) is at the lower end of the TAG indicated range (-0.25 to -0.35) while the overall journey time (JT) elasticity (- 0.42) is less than the TAG guidance limit (of -2.0).

7.5.4 The variation of elasticity by time period fits with TAG expectations, such as the peak periods are generally less elastic. The other fuel cost elasticity, at -0.37 is slightly high compared with TAG expectations. Employers business’s fuel cost elasticity is lower than TAG expectations (-0.25 to -0.35), but this is partly a consequence of the recent reduction in values of time for employment business (EB).

7.5.5 The commute fuel cost elasticity is lower than TAG expectations. However, as some of the demand model parameters are already at the TAG limit of median values plus 25%, it would not be possible to increase the elasticity exceeding this limit. Given the fact that the overall model elasticity is acceptable, we do not think this would add any value to the conclusion of this test.

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7.6 ‘Pivot point’ method

7.6.1 The logit modelling process can be applied either as an ‘absolute’ process to predict the absolute numbers of trips made between each origin and destination, by each mode in each traveller category and time period, or as an ‘incremental’ process to predict the relative changes from a pivot point. TAG unit M2 states that:

7.6.2 “The department’s recommendation for scheme appraisal is to use an incremental form of model, whether pivot-point or based on incremental application of absolute estimates, unless there are strong reasons for not doing so. Such reasons could include situations where there are large changes in land use between the base and forecast years, which will significantly change the distributions of origins and destinations”.

7.6.3 An incremental logit model has been adopted for the Wansford VDM. This has been applied within the Wansford VDM by using the following pivot points:

· DM is generated by pivoting from the base · DS is generated by pivoting from the DM

Consistency between DM and DS

7.6.4 After the post-DIADEM TUBA runs produced counterintuitive results, a full review of the forecasting process was undertaken to check whether there were any anomalies. An initial comparison of the post-DIADEM DM and DS assignments identified significant assignment differences that were far away from the scheme and seemingly unrelated to it (such as in central London and the midlands). This concern resulted in checks of the SATURN assignment convergence, however, when the same matrix was assigned to both DM and DS, the flow differences were localised to the scheme with no suggestion of flow and time changes that could cause the noise observed in the TUBA outputs and the DIADEM outputs.

7.6.5 The next step in the process reviewed whether the noise was being caused by DIADEM. To check this the post-DIADEM DM assignment was compared with the DIADEM "loop 0" DS assignment. These different plots revealed some of the same effects as seen in the post-DIADEM DM vs post-DIADEM DS comparisons. This suggested that there was an inconsistency with the post-DIADEM DM assignment and the demand within “loop 0” of the DS DIADEM run. As the demand should be the same, this was likely to be a source of the counterintuitive outputs from TUBA.

7.6.6 The final step in the process was to understand the cause of this inconsistency. The source of the post-DIADEM DM SATURN.UFS files was the final iteration of

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the DM DIADEM run. It was clear however, that what was being input into the DS DIADEM run was inconsistent with this. To understand the difference further, the DS run was modified where the DS networks were replaced with the DM networks. The DIADEM run was stopped after “loop 0” which is before the demand model redistributes the demand. This revealed that the demand was different between them.

7.6.7 The conclusion from this analysis is that the cost skims and demand input to the DS DIADEM runs are inconsistent. This is because the demand is produced from a subsequent iteration of DIADEM. Although the DM run is highly converged (as shown in Table 7-7) there is still relatively significant demand fluctuations between iterations. Although this is not an issue in the DM assignment (where there are much more significant changes between the base year and the DM assignments including significant demand growth and changes to the values of time) this is a significant issue in the DS where there is only a very small scheme.

7.6.8 When the TUBA runs were rerun using the “loop 0” assignments from the modified DS DIADEM (with the DM networks) as a replacement for the DM assignments the outputs and flow difference comparisons were intuitive. With this observation, the modified “loop 0” DM assignments were used as a replacement to the DM runs from the final iteration of the DM DIADEM assignment. This was implemented to overcome the relatively small loop-to-loop noise from the DM assignment; although this was relatively small for the DM given the significant cost changes, this was inappropriate for input to the DS model.

7.7 Generalised cost

7.7.1 Cost changes have been calculated for each forecast year and are applicable to both the Do Minimum and Do Something scenarios.

7.7.2 The highway trip costs are made up of time, distance and charge impacts. The value of time (VOT) and vehicle operating cost (VOC) vary by journey purpose and also vary by forecast year to represent changes in fuel costs and income. Changes in fuel costs, vehicle efficiency and values of time have been taken from the TAG data book May 2019. These have been used to calculate the forecast year values of time and operating costs.

7.7.3 details the highway generalised cost coefficients used for 2025 and 2040 in pence per minute (PPM) and pence per kilometre (PPK). As outlined in the transport modelling package, PT demand responses have been excluded.

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Table 7-6: Generalised cost parameters (2010 prices)

AM IP PM Year Purpose PPM PPK PPM PPK PPM PPK

Car - business 29.81 12.62 30.54 12.62 30.24 12.62 Car - commuting 19.99 6.25 20.31 6.25 20.06 6.25 2015 Car - other 13.79 6.25 14.69 6.25 15.58 6.25 LGV 21.07 13.71 21.07 13.71 21.07 13.71 HGV 49.19 45.17 49.19 45.17 49.19 45.17 Car - business 33.19 11.96 34.01 11.96 33.66 11.96 Car – commuting 22.25 5.40 22.62 5.40 22.33 5.40 2025 Car - other 15.35 5.40 16.36 5.40 16.08 5.40 LGV 23.46 13.85 23.46 13.85 23.46 13.85 HGV 54.77 41.66 54.77 41.66 54.77 41.66 Car - business 43.8 11.89 44.9 11.89 44.4 11.89 Car - commuting 29.4 5.20 29.8 5.20 29.5 5.20 2040 Car - other 20.3 5.20 21.6 5.20 21.2 5.20 LGV 30.9 13.91 30.9 13.91 30.9 13.91 HGV 72.2 44.49 72.2 44.49 72.2 44.49

7.8 Convergence in DIADEM

7.8.1 The VDM convergence statistics for each forecast year DIADEM run are shown in Table 7-7 where all scenarios achieve a full model GAP lower than 0.021% for DM and 0.0026% for DS.

Table 7-7: DIADEM demand-supply convergence statistics 2025 and 2040

Full model Number of Year Scenario GAP Cost (%<5%) Flow (%<5%) loops % DM 0.0185% 100.0% 100.0% 9 2025 DS 0.0019% 100.0% 100.0% 4 DM 0.0205% 100.0% 100.0% 10 2040 DS 0.0025% 100.0% 100.0% 6

7.9 Outputs from DIADEM

7.9.1 The impacts of the variable demand model are analysed using the sector system outlined in Table 7-8 and Figure 7-1 below. Only changes on highway demand for car are reported here because LGVs and HGVs are modelled as fixed demand. Because none of the 6 A47 schemes’ PCF Stage 3 modelling assessments will include any evaluation of the public transport system, and the

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Wansford model excluded the responses, no analysis of VDM on PT trips was carried out.

Table 7-8: Sector system for VDM analysis

Sector Description 1 Simulation 2 North-east Simulation 3 South-east Simulation 4 South-west Simulation 5 North-west Simulation 6 East of England 7 South-west Midlands 8 London 9 Wales 10 East Midlands 11 Scotland

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Figure 7-1: Sectors

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Highway demand changes

7.9.2 The 12x12 sector demand changes can be seen in Appendix B. The outputs indicate very small changes as a result of DIADEM between DM and DS which reflects the overall size and relatively local impacts of the scheme with negligible impacts outside sectors 1-5.

7.9.3 From this sector analysis it can be seen that the redistribution response from the DS scenario, including the external-external movements, is logical and reasonable. To illustrate this, Table 7-9 to Table 7-11 summarise the percentage demand changes between the 2040 DM and DS scenarios for internal and external movements. The absolute changes between DM and DS for both 2025 and 2040 are reported in Appendix B.

Table 7-9: 2040 highway demand (car only) % change between DM and DS - AM Sim Inner Ext Total Sim 0.3% 0.1% -0.6% 0.1% inner -0.7% 0.1% -0.1% -0.1% Ext -0.3% -0.1% 0.0% 0.0% Total -0.5% 0.0% 0.0% 0.0% Table 7-10: 2040 highway demand (car only) % change between DM and DS - IP Sim Inner Ext Total Sim 0.2% 0.1% 0.1% 0.1% inner 0.1% 0.0% 0.0% 0.0% Ext -0.2% 0.0% 0.0% 0.0% Total 0.1% 0.0% 0.0% 0.0% Table 7-11: 2040 highway demand (car only) % change between DM and DS – PM Sim Inner Ext Total Sim 0.2% -0.2% -0.3% -0.1% inner -0.1% 0.0% -0.2% -0.1% Ext -0.9% -0.4% 0.0% 0.0% Total -0.1% -0.1% 0.0% 0.0%

Trip length distribution changes

7.9.4 Figure 7-2 and Figure 7-3 show the highway car trip length distribution for 2025 AM peak and 2040 AM peak respectively. Between the DM “loop 0” (reference case) and the DM and DS scenarios, there is a reduction in the number of trips in the shorter distance bands and an increase in the number of trips in the longer distance bands.

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7.9.5 Again, this pattern is repeated for the other peak periods and each forecast year with minor variations. The highway car trip length distribution for the other peak periods and forecast years can be seen in Appendix C.

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Figure 7-2: 2025 car trip length distribution – AM peak

Source: SWECO

Figure 7-3: 2040 car trip length distribution – AM peak

Source: SWECO

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8. Assignment results for economic assessment

8.1 Introduction

8.1.1 The traffic forecasts and demand modelling form the basis of economic appraisal. More specifically Transport User Benefit Appraisal (TUBA) requires as input a set of matrices giving for each origin-destination (OD) pair including: vehicle trips, distance (km), time (hour) and monetary charges (pence). These matrices are further disaggregated by user class, time period, trip purposes. Based on these trip and cost matrices from the transport models (both Do Minimum (DM) and Do Something (DS)), TUBA calculates user benefits discounted to the present value year and produces results for various degrees of disaggregation and summarises the outputs.

8.2 Assignment model convergence statistics

8.2.1 Table 8-1 contains convergence statistics for both the base and forecast year scenarios. For all time period models, forecasting years and scenarios, the assignment model convergence ‘gap’ is below the recommended TAG value of 0.1% by a substantial margin (values lower than this target mean that the model has better convergence). The measurements of flow and cost changes also exceed the 98% target in all cases (in these cases values higher than the target show that the model has better converged). The ‘gap’ measures the proximity to an equilibrium solution for the iterative assignment process and the flow and cost changes measure the stability of the solution from one iteration to another.

Table 8-1: Post VDM assignment convergence statistics

AM IP PM

Delta % % Delta % % Delta % % Iteration %Gap %Gap %Gap (δ) Flow Delay (δ) Flow Delay (δ) Flow Delay

2025 DM final 4 successive convergence statistics

N-3 0.0001 99.9 99.7 0.00027 0.0002 100.0 99.6 0.00012 0.0004 100 99.7 0.00053

N-2 0.0001 99.9 99.7 0.00029 0.0002 99.9 99.6 0.00010 0.0004 99.9 99.8 0.00045

N-1 0.0002 99.9 99.7 0.00012 0.0002 100.0 99.9 0.00010 0.0004 99.9 99.7 0.00047

Final (N) 0.0001 99.9 99.6 0.00020 0.0001 100.0 99.8 0.00016 0.0004 100 99.6 0.00042

2025 DS final 4 successive convergence statistics

N-3 0.0001 99.9 99.6 0.00019 0.0001 100.0 99.8 0.00013 0.0004 99.8 98.6 0.00048

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AM IP PM

Delta % % Delta % % Delta % % Iteration %Gap %Gap %Gap (δ) Flow Delay (δ) Flow Delay (δ) Flow Delay

N-2 0.0001 99.9 99.6 0.00019 0.0002 100.0 99.9 0.00011 0.0004 100.0 99.2 0.00045

N-1 0.0001 99.9 99.6 0.00019 0.0002 100.0 99.9 0.00010 0.0004 99.9 99.1 0.00043

Final (N) 0.0002 99.9 99.8 0.00009 0.0002 100.0 99.9 0.00010 0.0004 99.9 99.2 0.00046

2040 DM final 4 successive convergence statistics

N-3 0.0004 99.4 97.5 0.00110 0.0004 99.9 99.5 0.00027 0.0007 99.8 98.4 0.00073

N-2 0.0004 99.6 98.0 0.00081 0.0002 99.9 99.5 0.00045 0.0007 99.8 98.5 0.00072

N-1 0.0004 99.8 98.2 0.00100 0.0004 100.0 99.3 0.00024 0.0005 99.8 97.9 0.00073

Final (N) 0.0004 99.7 97.7 0.00110 0.0002 100.0 99.5 0.00040 0.0004 99.8 98 0.00058

2040 DS final 4 successive convergence statistics

N-3 0.0004 99.7 98.3 0.00160 0.0003 99.9 99.6 0.0005 0.0006 99.9 98.7 0.00065

N-2 0.0007 99.2 97.0 0.00130 0.0005 100.0 99.6 0.00024 0.0005 99.9 98.8 0.00068

N-1 0.0007 99.3 97.3 0.00120 0.0002 100.0 99.7 0.0003 0.0006 99.9 99 0.00064

Final (N) 0.0004 99.4 97.8 0.00100 0.0004 100.0 99.7 0.00021 0.0004 99.8 98.6 0.0006

8.2.2 The parameters controlling the stopping criteria for the final assignment runs of the Wansford traffic model are shown in Table 8-2 with the proximity (%gap) target set by the STPGAP parameter. The model convergence was judged directly against meeting the %gap and RSTOP criterion on 4 (NISTOP) successive iterations, consistent with the choice of KONSTP equal to 5 in the SATURN parameters.

Table 8-2: Primary model convergence criteria: final assignment

SATURN Value Description parameter STPGAP 0.025 Critical %gap value to stop assignment loops UNCRTS 0.015 Wardrop assignment parameter monitoring epsilon NISTOP 4 The number of successive loops which must satisfy RSTOP RSTOP 99 Stopping criteria for assignment / simulation loops PCNEAR 1 Percentage change in flows in successive assignments KONSTP 5 KONtrol of StoPping Criteria - STPGAP AND RSTOP

8.2.3 The RSTOP test for convergence of the assignment / simulation loops stops the assignment automatically if RSTOP (%Flows) of the link flows change by less

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than “PCNEAR” percent (default 1%) from 1 assignment to the next. In addition to these values, a minimum number of 25 assignment-simulation loops were defined by setting the MASL_M parameter to 25. The models “STPGAP” (stopping criteria) for assignment convergence was set to a more stringent value of 0.025 than the suggested TAG guidance of 0.05 to achieve a high level of convergence and reduce any possible model noise.

8.2.4 From SATURN Manual section 9.2 it is noted that: “% DELAYS” is similar to “% FLOWS” but is based on the fraction of simulation turns whose delays change by less than 1% (such as PCNEAR%) from those calculated by the previous assignment. Note that the simulation turns are a sub-set of the assignment links; hence the latter measure is based on a different - and larger - “sample” than the former. % DELAYS has no effect on stopping the loops. Therefore, in highly congested networks, where delays are a very sensitive function of flows, it is quite possible for the flows to settle down (high %FLOWS) but for the delays to fluctuate wildly (low %DELAYS). Therefore, if only 1 of these measures is high it probably implies that your overall convergence is acceptable even though either flow or delay is uncertain.

8.2.5 It is noted that the delay% for the 2040 AM is 97.7% in the DM and 97.8% in the DS which is lower than other scenarios, such as the DS 2040 PM which is 98.0% in the DM and 98.6% in the DS. However, as the model has successfully converged on %GAP and %FLOWS it is considered that the reduction of approximately 1-2% delay% is not significant and does not affect the robustness of the model assessment.

8.3 Do Minimum variable demand forecast results

Select link analysis

8.3.1 Select link analysis was carried out to determine whether the growth on the A47 west of Sutton Heath Road is mainly due to through trips or local traffic. This select link analysis was carried out for both the 2040 DM and base year AM, IP and PM models in both the eastbound and westbound directions separately.

8.3.2 The select link matrices where sectored to differentiate between the local and through trips. Table 8-3 indicates that there is an overall growth of 32% along the A47 in the DM 2040 compared to the base. Based on the sectoring analysis the local traffic has increased by 35% while through trips has increased by 29%.

8.3.3 Table 8-4 shows the total percentage split between the base through traffic and local traffic in both the base year (BY) and DM scenarios. Analysis of this table indicates there is no significant change in the split between local and through traffic between the BY and DM scenarios.

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8.3.4 This suggests that the A47 is capacity constrained restricting both growth and through traffic.

Table 8-3: A47 traffic growth in 2-way AADT

2-Way AADT Base Year Do Minimum Difference % Difference Local 11578 15640 4062 35% Through 11775 15147 3371 29% Total 23353 30786 7433 32% Table 8-4: Percentage splits on A47 traffic growth in 2-way AADT

2-Way AADT Base Year Do Minimum % Difference Local 49.6% 50.8% 1.2% Through 50.4% 49.2% -1.2% Total 100% 100%

8.4 Do Something variable demand forecast results

Pivoting consistency checks

8.4.1 As part of the Wansford forecasting assessment the DM pivot point used to forecast the DS model is derived from a nominal “loop 0” assignment generated as a separate single loop post DIADEM procedure. To assess if this process is suitably accurate for generating the DS pivot assignment, an additional VDM DS 2040 run pivoting from the base year as opposed to the DM was conducted. Figure 8-1 to Figure 8-6 below illustrates the comparison between the DS “loop 0” 2040 and DM final 2040 for AM, IP and PM for both the sensitivity test and the full DS assessment.

8.4.2 Analysis of the comparison of the DM to the DS sensitivity test shows the same pattern as the comparison of the DM to the DS “loop 0” results. This indicates that the DS, pivoting from BY, sensitivity model run is replicating the results of the customisation methodology with a suitable degree of accuracy.

8.4.3 Figure 8-1 to Figure 8-6 show an increase of traffic flow (green bandwidths) on both the A1 and A47 due to the presence of the scheme. The northern alignment scheme has attracted more traffic to use A47 corridor from all directions particularly in AM and PM period, with reassigned traffic coming from the areas of Huntingdon from the south, Leicester from the East, Grantham from the North and Peterborough from the West.

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Figure 8-1: Actual flow difference – DM40 final vs DS40– AM (bandwidth 250PCU/mm)

Source: SWECO

Figure 8-2: Actual flow difference – DM40 final vs DS40 (pivoting to base) – AM (bandwidth 250PCU/mm)

Source: SWECO

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Figure 8-3: Actual flow difference – DM40 final vs DS40– IP (bandwidth 250PCU/mm)

Source: SWECO

Figure 8-4: Actual flow difference – DM40 final vs DS40 (pivoting to base) - IP (bandwidth 250PCU/mm)

Source: SWECO

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Figure 8-5: Actual flow difference – DM40 final vs DS40– PM (bandwidth 250PCU/mm)

Source: SWECO

Figure 8-6: Actual flow difference – DM40 final vs DS40 (pivoting to base) – PM (bandwidth 250PCU/mm)

Source: SWECO

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8.5 Key statistics for the core scenarios

8.5.1 The overall average speeds extracted from SATURN are displayed in Table 8-5. Analysis of these results indicate that in 2040 there will be an improvement in network speeds in the DS scenario of around 2km/h or about 3-5%.

Table 8-5: SATURN simulation network overall average speed (km/h) Scenario AM IP PM 2015 base 66.75 74.93 64.24 2025 DM 62.73 72.66 62.73 2025 DS 64.63 73.14 63.20 2040 DM 56.23 68.02 54.12 2040 DS 58.78 70.32 56.54

8.5.2 The total travel distance across the whole simulation network extracted from SATURN are displayed in Table 8-6. In all cases, there is an increase in total travel distance from the DM scenario to the DS scenario and it is much more prominent in the second modelling year as expected.

Table 8-6: SATURN simulation network overall total travel distance (PCU.km/h) Scenario AM IP PM 2015 base 311,960 225,769 310,684 2025 DM 351,692 262,026 352,264 2025 DS 359,078 263,880 354,424 2040 DM 402,605 307,920 399,322 2040 DS 412,201 311,808 404,988

8.5.3 As with the average speeds and total travel distances, the total travel times across the simulation network are displayed in Table 8-7. Analysis of these results indicate that in 2040 there will be an improvement in aggregate simulation network travel times in the DS scenario of around 90-220 PCU hours or about 2- 3%.

Table 8-7: SATURN simulation network overall total travel time (PCU.hrs) Scenario AM IP PM 2015 base 4,674 3,013 4,836 2025 DM 5,519 3,620 5,702 2025 DS 5,454 3,557 5,592 2040 DM 7,326 4,628 7,362 2040 DS 7,149 4,505 7,184

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8.6 Sensitivity testing

High and low traffic growth

8.6.1 Appendix I shows the assignment convergence statistics for both low and high growth scenario. For all times period, forecasting years and scenarios, the assignment model convergence ‘gap’ is well below the recommended value of 0.1%. The measurement of flow changes for the final 4 iterations also exceed the 98% target in all modelled scenarios. Forecasted Annual Average Daily Traffic (AADTs) for the A47 scheme section in the low, core and high 2040 DS scenarios are also shown in Appendix I. From Appendix I, it can be seen that the high scenario results in a 2-way AADT increase of approximately 5% along the A47 between Wansford and Sutton, whereas the low scenario results in a 5% reduction in 2-way AADT.

8.6.2 Table 8-8 to

8.6.3 Table 8-11 show the network average simulation speeds and vehicle distance results from the low, core and high 2025 and 2040 DM and DS scenarios. From these tables the following trends can be seen:

· 2025 o Average speed is increasing / decreasing by + / - (1% - 5%) in the low and high DM scenario and similar magnitude by + / - (1% - 4%) in the low and high DS compared to core scenario o Total vehicle distance is increasing / decreasing by + / -5% in the high and low scenarios for DM and DS compared to the core scenario · 2040 o Average speed is increasing / decreasing by + / - (0% - 11%) in the low and high DM scenario and similar magnitude by + / - (2% - 15%) in the low and high DS compared to core scenario o Total vehicle distance is increasing / decreasing by -6-15% in low scenario and +3-5% in the high scenario for DM and DS compared to the core scenario Table 8-8: Average speed (km/h) low growth, core scenario and high growth opening year 2025 Scenario AM IP PM 2015 Base 66.75 74.93 64.24 2025 DM Low 65.57 73.69 64.96 2025 DM Core 62.73 72.66 62.73 2025 DM High 60.22 71.21 60.09 2025 DS Low 67.10 74.15 65.36 2025 DS Core 64.63 73.14 63.20 2025 DS High 61.98 71.96 60.53

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Table 8-9: Average speed ((km/h) low growth, core scenario and high growth designing year 2040 Scenario AM IP PM 2015 Base 66.75 74.93 64.24 2040 DM Low 60.33 71.48 60.14 2040 DM Core 56.23 68.02 54.12 2040 DM High 51.98 67.61 50.61 2040 DS Low 62.16 72.05 60.60 2040 DS Core 58.78 70.32 56.54 2040 DS High 54.28 68.16 51.77

Table 8-10: Total travel distance (PCU.km/h) low growth, core scenario and high growth opening year 2025 Scenario AM IP PM 2015 Base 311,960 225,769 310,684 2025 DM Low 332,186 246,631 333,269 2025 DM Core 351,692 262,026 352,264 2025 DM High 369,937 277,440 369,178 2025 DS Low 338,136 248,208 336,011 2025 DS Core 359,078 263,880 354,424 2025 DS High 376,507 279,391 371,261

Table 8-11: Total travel distance (PCU.km/h) low growth, core scenario and high growth designing year 2040 Scenario AM IP PM 2015 Base 311,960 225,769 310,684 2040 DM Low 373,858 283,629 375,542 2040 DM Core 402,605 307,920 399,322 2040 DM High 426,254 331,088 422,302 2040 DS Low 381,362 285,839 377,499 2040 DS Core 412,201 311,808 404,988 2040 DS High 432,649 333,602 422,064

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8.7 Traffic flows

8.7.1 The 12hr Annual Average Weekday Traffic (AAWT) and 24hr AADT stick diagrams localised around the scheme for 2015 base and 2040 DS are shown in Appendix E. Overall, these diagrams illustrate the 50% increase on the A47 main corridor as a result of the demand growth and scheme impact. The post-DIADEM matrix demand summary table, as shown in Appendix H, illustrates that the scheme growth is not purely the result of demand growth (approx. 22%-23%), but also reassignment and variable demand effects.

8.8 Journey times

8.8.1 To assess the journey time savings across the Wansford model, a number of journey time routes are selected from the base year validation, as shown in Figure 8-7. Table 8-12 shows the modelled journey time results across these routes for both the DM and DS scenarios. Analysis of this table indicates that journey time savings are achieved on most of the routes in the DS scenario, except for the A1 NB route where minor increases in journey time are observed. This minor increase in A1 journey time is caused by a slight increase of traffic along A1 as a result of the scheme.

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Figure 8-7: Journey time routes

Table 8-12: Modelled journey time results (unit: second) 2025 2040 Description Route Direction Scenario AM IP PM AM IP PM A47 Base 397 247 259 397 247 259 Wansford DM 462 252 274 617 267 348 WB rbt to R1 EB DS 291 206 234 507 215 299 Upton / DS-DM AIlsworth -170 -45 -40 -109 -52 -50 Upton / Base 261 243 302 261 243 302 Ailsworth to DM 271 250 318 293 260 403 A47 R2 WB DS 223 223 292 229 225 395 Wansford WB rbt DS-DM -48 -27 -25 -64 -35 -7 Base 589 599 722 589 599 722 A1 NB (A1 DM 613 630 814 662 699 936 jct 17 to R3 NB DS 624 635 818 674 705 940 Wothorpe) DS-DM 11 5 4 12 6 4

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2025 2040 Description Route Direction Scenario AM IP PM AM IP PM Base 659 575 595 659 575 595 A1 SB DM 748 601 638 842 649 711 (Wothorpe R4 SB DS 747 600 639 843 648 706 to A1 jct 17) DS-DM -1 0 1 1 -1 -5 Base 1012 893 969 1012 893 969 A1 jct 17 to DM 1084 913 1028 1227 957 1147 R5 EB A47 jct 18 DS 947 864 975 974 902 1087 DS-DM -138 -48 -53 -253 -55 -60 Base 943 889 917 943 889 917 A47 jct 18 DM 1094 908 971 1164 941 1058 R6 WB to A1 jct 17 DS 951 885 919 989 909 1047 DS-DM -143 -23 -53 -175 -32 -10 Base 925 742 759 925 742 759 A1 DM 927 761 784 985 797 835 Wothorpe to R7 EB DS 830 715 733 901 740 770 A47 jct 18 DS-DM -97 -47 -52 -84 -58 -64 Base 821 801 917 821 801 917 A47 jct 18 DM 847 825 975 902 873 1083 to A1 R8 WB DS 794 794 944 835 836 1068 Wothorpe DS-DM -53 -31 -31 -67 -37 -15

8.9 Model constraints

8.9.1 It should be noted that there is some limitation with the base year modelling. In particular, the AM journey time validation is 83% with 10 routes passing out of a total of 12 routes. However, the modelled journey time on the A1 southbound (route 7) is outside of the acceptable 15% threshold (model is faster by 21%). Furthermore, as the base year model represents the year 2015, this is before the implementation of the partial signalisation of the A1 / A47 junction.

8.9.2 Although overall the model validation is generally very good for a strategic model of this size, with IP and PM peak models passing the journey time TAG criteria, it should be recognised that the AM journey time validation is not as good as the IP and PM peak models. Therefore, a VISSIM microsimulation model has also been used to inform the design process.

8.9.3 From an economic perspective it should be recognised that no benefits will be assumed to accrue during the off-peak period or at weekends, when traffic volumes are presently lower. As these periods have not been explicitly modelled it is inappropriate to extrapolate any benefits for such; this is in line with current best practice and will result in a conservative estimate of benefits.

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9. Assignment results for environmental assessment

9.1 Required outputs

9.1.1 For air quality assessments, the following average annual output intervals are required:

· AM peak period (07:00-10:00) · Inter-peak period (10:00-16:00) · PM peak period (16:00-19:00) · Off-peak period (19:00-07:00) · Annual average weekly traffic (AAWT) (00:00-24:00) · Annual average daily traffic (AADT) (00:00-24:00)

9.1.2 For noise assessments, the following average annual output intervals are required:

· AAWT 18-hours (06:00-24:00) · AAWT 12-hours (07:00-19:00) · AAWT 4-hours (19:00-23:00) · AAWT 8-hours (23:00-07:00)

9.2 Speed banding

9.2.1 Data presented for environmental assessments include all specified links from the model. In order to classify these by the required motorway or non-motorway categories required for speed banding, the modelled speed flow curves were used to identify motorway links. Motorway links were assumed to always have a speed flow curve applied. For links where no speed flow curve was present it was assumed that they would be non-motorway links, which was set as the default.

9.2.2 Table 9-1 and Table 9-2 show the speed banding guidance used for motorway and non-motorway respectively. Table 9-3 shows the SATURN speed flow curves that have been assumed to represent motorway links, with their corresponding free flow speed and capacity.

Table 9-1: Motorway speed bands Speed range (km/h) Speed band Upper limit <30 20 30 30-80 55 80 >80 97 9999

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Table 9-2: Non-motorway speed bands Speed range (km/h) Speed band Upper limit <20 20 20 20-45 33 45 45-80 63 80 >80 97 9999

Table 9-3: Motorway speed flow curves SATURN speed flow curve Free-flow speed Capacity number 1 113 11650 62 108 4000 72 116 6990

9.3 Use of WebTRIS data

9.3.1 WebTRIS count data was downloaded for several representative locations for the Wansford to Sutton dualling scheme as shown in Figure 9-1. Annual and daily outputs were downloaded for 2015 and used to calculate the necessary AAWT / AADT factors required to produce the set of outputs detailed in section 8.7.

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Figure 9-1: Count sites used to create AADT conversion factors

9.4 Derivation of average annual traffic flows

9.4.1 The SATURN models produce traffic outputs mainly for an average November weekday in 2015. For the purposes of scheme appraisal, for example for air quality and noise, traffic forecasts are required for a number of different average annual time intervals as detailed in section 9.1. In order to create the average annual totals, seasonality adjustment factors were applied to the November totals to remove the seasonal bias. To achieve this, the WebTRIS data was used to derive factors that converted from a November day to an average day. These factors were combined with the WebTRIS-derived factors for peak hour to peak period. The factors calculated are shown in Table 9-4. As the average annual periods were the smallest building block for the other required outputs, this was the only place where the conversion from November day to average day was required.

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Table 9-4: Peak hour to period conversion factors Peakk hour to peak Period Period AM (3 hours) 2.505 IP (6 hours) 5.994 PM (3 hours) 2.663 OP (12 hours) 12.465

9.4.2 Using the (November 2015) model peak hour flows and multiplying by the peak hour to period conversion factors in Table 9-4, the average annual periods are derived as required for the air quality assessment:

· A: AM average weekday period (07:00-10:00) · B: IP average weekday period (10:00-16:00) · C: PM average weekday period (16:00-19:00) · D: OP average weekday period (19:00-07:00)

9.4.3 From these period totals (A-D), the average annual weekday traffic flow can be calculated by adding A, B, C and D together. In order to calculate the remainder of the outputs required in section 9.1 the factors shown in Table 9-5 were calculated.

Table 9-5: Global conversion factors Global factors Factor ID AAWT 12 hr to AADT 12 hr 0.922 E AADT 12 hr OP to AADT 12 hr OP 0.924 F OP 1 hour to 4 hours 6.501 G OP 1 hour to 8 hours 5.964 H OP 1 hour to 6 hours 9.868 I OP 1 hour to 6 hours 12.465 J

9.4.4 Using the factors in Table 9-5 (E-I) and the average weekday period totals (A-D), the following calculations define the calculations used to derive the remaining outputs:

· AAWT (00:00-24:00) = A + B + C + D · AADT (00:00-24:00) = [ (A + B + C) * E] + [ D * F ] · AAWT 18-hours (06:00-24:00) = A + B + C + [ D * (I / J) ] · AAWT 12-hours (07:00-19:00) = A + B + C · AAWT 4-hours (19:00-23:00) = D * (G / J) · AAWT 8-hours (23:00-07:00) = D* (H / J)

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9.5 Other required outputs

9.5.1 In addition to flow volumes, speeds, speed band and the percentage of heavy goods vehicles (%HGV) are required for each of the outputs specified in section 9.1. Where the output required aggregation such as for a 12hr or 18hr average. This has been done using the corresponding flow where appropriate for consistency with the flow volumes.

9.6 Speed pivoting

9.6.1 Pivoted traffic speeds and speed band assignment are also required for environmental appraisals as per the interim advice note 185 / 15. Speed pivoting is a process that calculates the relative difference between the base year speed and observed speed for each link and then factors up the forecast speed by the same amount. Observed speed data has been provided from TrafficMaster data and this has been used in the environmental output template for pivoting.

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10. Assignment results for operational performance assessment

10.1.1 The results of the model forecasts are fed into the operational assessments in the same manner as the environmental assessment. Annual average daily traffic (AADT) and annual average weekly traffic (AAWT) are calculated as per section 9. In addition to this, peak hour traffic flows are provided for the operational assessment, which are extracted directly from the SATURN model for the time periods stated in section 2.3.

10.1.2 The network coverage of the operational model in VISSIM extends from the Sutton Roundabout to the A47/A1 Wansford Interchange with local roads around Wansford Village also being included. The methodology and full results of the operational modelling are provided in A47 Wansford to Sutton Microsimulation Model Development Technical Note (HE551494-GTY-VTR-000-RP-TR-30007).

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11. Conclusions

11.1.1 This report describes how the Stage 3 base transport model has been developed to produce the forecast of travel demand for the proposed A47 Wansford dualling road investment strategy (RIS) scheme. The base year and forecast years for the Wansford scheme assessment are listed below:

· 2015 Base; · 2025 Opening Year; and · 2040 Design Year (15 years after opening).

11.1.2 As documented in the Appraisal Specification Report (ASR) (HE551494-MMSJV- VTR-000-RP-TR-00008.PDF), analysis of the Wansford PCF Stage 2 base year west side of the Peterborough Traffic Model (WPTM), indicated that the model had not been calibrated or validated. In addition, the scope of the Wansford model was limited and as such not suitable for predicting re-routing of traffic and the interaction between local junctions and the major traffic attractor (Peterborough). Lastly, the size of the Stage 2 WPTM model did not allow any scope for any realistic Variable Demand Model (VDM) assessment for trip re-distribution or mode shift over a wider area. Based on those assessments, the existing PCF Stage 2 WPTM model was updated by enhancing the network, undertaking model calibration and by developing a VDM.

11.1.3 The forecasting has been carried out according to current TAG guidance and using data for forecasting from the TRICS database, national trip end model (NTEM) and road traffic forecasts. The forecast networks were developed on the basis of TAG uncertainty log principles.

11.1.4 For the forecasting matrices, future car growth was calculated by spatially allocating development trips from the uncertainty log using trip rates derived from TEMPRO 7.2 and LGV and HGV growth was derived using road traffic forecasts (RTF) growth and trip rates derived from TRICS. A furness process was then carried out to constrain the growth to TEMPRO.

11.1.5 Model convergence for both the demand model and the assignment model is achieved within the set criteria for all model runs across all scenarios and future years. Between the DM scenario and the DS scenario, there is a minor reduction in the number of trips in the shorter distance bands and an increase in the number of trips in the longer distance bands. Such a shift from shorter to longer distance trips, due to reduced travel time brought by the scheme, is a key component of the variable demand response.

11.1.6 The modelling results show the proposed dualling section of the A47 attracts approximately 30,000 2-way annual average daily traffic (AADT) flows in the

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design year 2040 Do Minimum (DM) scenario and about 35,000 2-way AADT flow in the 2040 Do Something (DS) scenario. This represents an increase of about 5,000 2-way AADT. This scheme growth is not purely the result of demand growth (approx. 22%-23%) but also reassignment and variable demand effects.

11.1.7 Analysis of the journey time results shows that dependent upon time period, journey time benefits in the region of 1 to 4 minutes across the scheme section in 2040. Furthermore, from the analysis of the network wide statistics it can be seen that improvements in vehicle speeds are achieved by the scheme across the whole network of about 3-5%.

11.1.8 In terms of network performance for the core scenario, the overall average network speeds indicate that there will be an improvement in the DS, relative to the DM, of around 1-3% in 2025 and 3-5% in 2040. In addition to this, the core scenario total travel distance results show an increase of approximately 1-2% in both 2025 and 2040. Thus, indicating that the Wansford scheme is successful in improving the capacity constraints and overall operational performance of the network.

11.1.9 Additional high and low growth sensitivity test scenarios have also been conducted to understand the impact that different traffic growth levels will have on the operation of the Wansford scheme. Analysis of these scenarios shows a logical pattern of results with average speeds changing, relative to the core scenario, by approximately 1%-5% in 2025 and 0%-15% in 2040.

11.1.10 In summary, the modelling above was carried out in accordance with TAG and based on a validated base model. The forecasts described above appear to show reasonable and plausible results that are in line with expectations about how this scheme should perform.

11.1.11 As such, the results from the core scenario were deemed suitable for input to the transport economics and to the environmental and operational assessment.

74 A47 WANSFORD TO SUTTON Selected validation summaries

A.1 AM peak screenline calibration

Flow GEH < Pass Ref Name Direction SL-Def Observed Modelled Diff % diff GEH diff 4 PASS <5% GEH 1 Purple Eastbound Purple-Eastbound 6094 6061 33 -0.5% 0 1 1 ü ü 1 Purple Westbound Purple-Westbound 6338 6249 89 -1.4% 1.1 1 1 ü ü 3 Light Blue Eastbound Light Blue-Eastbound 5385 5296 89 -1.6% 1.2 1 1 ü ü 3 Light Blue Westbound Light Blue-Westbound 3901 4009 -109 2.8% 1.7 1 1 ü ü 4 Red Eastbound Red-Eastbound 2176 2354 -178 8.2% 3.7 0 1 ü û 4 Red Westbound Red-Westbound 1464 1476 -12 0.8% 0.3 1 1 ü ü 6 Green Eastbound Green-Eastbound 3832 3861 -29 0.8% 0.5 1 1 ü ü 6 Green Westbound Green-Westbound 2481 2472 10 -0.4% 0.2 1 1 ü ü 7 Yellow Southbound Yellow-Southbound 3324 3340 -16 0.5% 0.3 1 1 ü ü 7 Yellow Northbound Yellow-Northbound 1838 2000 -162 8.8% 3.7 0 1 ü û 8 Brown Southbound Brown-Southbound 4485 4433 52 -1.2% 0.8 1 1 ü ü 8 Brown Northbound Brown-Northbound 5361 5356 5 -0.1% 0.1 1 1 ü ü Total 46678 46906 -229 0.5% 1.1 1 1 ü ü

No of counts 12 %Pass DMRB 83% %Pass GEH 100%

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A.2 Inter-peak screenline calibration

Flow GEH < Pass Ref Name Direction SL-Def Observed Modelled Diff % diff GEH diff 4 PASS <5% GEH 1 Purple Eastbound Purple-Eastbound 3921 3907 14 -0.4% 0 1 1 ü ü 1 Purple Westbound Purple-Westbound 4087 3899 188 -4.6% 3 1 1 ü ü 3 Light Blue Eastbound Light Blue-Eastbound 2818 2747 71 -2.5% 1 1 1 ü ü 3 Light Blue Westbound Light Blue-Westbound 2824 2780 44 -1.6% 1 1 1 ü ü 4 Red Eastbound Red-Eastbound 1512 1399 113 -7.5% 3 0 1 ü û 4 Red Westbound Red-Westbound 1563 1574 -11 0.7% 0 1 1 ü ü 6 Green Eastbound Green-Eastbound 2175 2144 31 -1.4% 1 1 1 ü ü 6 Green Westbound Green-Westbound 2475 2422 53 -2.1% 1 1 1 ü ü 7 Yellow Southbound Yellow-Southbound 1787 1721 66 -3.7% 2 1 1 ü ü 7 Yellow Northbound Yellow-Northbound 1941 2044 -103 5.3% 2 0 1 ü û 8 Brown Southbound Brown-Southbound 3535 3474 61 -1.7% 1 1 1 ü ü 8 Brown Northbound Brown-Northbound 3774 3909 -135 3.6% 2 1 1 ü ü Total 32412 32019 393 -1.2% 2 1 1 ü ü

No of counts 12 %Pass DMRB 83% %Pass GEH 100%

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A.3 PM peak screenline calibration

Flow GEH < Pass Ref Name Direction SL-Def Observed Modelled Diff % diff GEH diff 4 PASS <5% GEH 1 Purple Eastbound Purple-Eastbound 6415 6116 299 -4.7% 4 1 1 ü ü 1 Purple Westbound Purple-Westbound 5220 5158 62 -1.2% 1 1 1 ü ü 3 Light Blue Eastbound Light Blue-Eastbound 4140 3996 143 -3.5% 2 1 1 ü ü 3 Light Blue Westbound Light Blue-Westbound 4922 4847 74 -1.5% 1 1 1 ü ü 4 Red Eastbound Red-Eastbound 1742 1690 52 -3.0% 1 1 1 ü ü 4 Red Westbound Red-Westbound 2177 2228 -50 2.3% 1 1 1 ü ü 6 Green Eastbound Green-Eastbound 2830 2759 72 -2.5% 1 1 1 ü ü 6 Green Westbound Green-Westbound 3551 3550 1 0.0% 0 1 1 ü ü 7 Yellow Southbound Yellow-Southbound 2188 2177 11 -0.5% 0 1 1 ü ü 7 Yellow Northbound Yellow-Northbound 3193 3222 -30 0.9% 1 1 1 ü ü 8 Brown Southbound Brown-Southbound 5043 4838 205 -4.1% 3 1 1 ü ü 8 Brown Northbound Brown-Northbound 4924 4959 -35 0.7% 1 1 1 ü ü Total 46344 45540 804 -1.7% 4 1 1 ü ü

No of counts 12 %Pass DMRB 100% %Pass GEH 100%

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A.4 Journey time validation

AM IP PM Route No Description Dir. Observed Modelled Diff % Diff DMRB JT Observed Modelled Diff % Diff DMRB JT Observed Modelled Diff % Diff DMRB JT R1 A1139 Fletton Parkway EB 228 234 6 2.6% ü 211 218 7 3.3% ü 254 247 -7 -2.9% ü R2 A1139 Fletton Parkway WB 325 296 -29 -8.9% ü 220 249 28 12.9% ü 287 267 -20 -7.1% ü R3 A605 Oundle Road EB 479 437 -42 -8.8% ü 317 328 11 3.3% ü 461 394 -67 -14.5% ü R4 A605 Oundle Road WB 398 357 -41 -10.3% ü 312 317 6 1.8% ü 330 341 10 3.2% ü R7 A1 SB 765 606 -159 -20.8% û 573 535 -38 -6.6% ü 535 555 20 3.7% ü R8 A1 NB 533 587 55 10.3% ü 535 596 61 11.3% ü 609 701 91 15.0% ü R9 A1260 Nene Parkway NB 266 180 -86 -32.4% û 157 152 -4 -2.8% ü 230 161 -69 -30.1% û R10 A1260 Nene Parkway SB 198 180 -18 -9.1% ü 170 163 -7 -4.4% ü 304 225 -79 -26.0% û R11 A47 East EB 550 632 82 15.0% ü 508 533 24 4.8% ü 500 541 42 8.3% ü R12 A47 East WB 561 561 1 0.1% ü 527 537 10 1.9% ü 629 569 -60 -9.6% ü R13 A47 West to Alton SB 884 873 -10 -1.2% ü 850 842 -9 -1.0% ü 851 864 13 1.5% ü R14 A47 West to Alton NB 853 863 10 1.1% ü 856 829 -28 -3.2% ü 836 847 11 1.3% ü Total 6039 5807 -232 -3.8% 5237 5298 61 1.2% 5827 5711 -116 -2.0%

78 A47 WANSFORD TO SUTTON Sectored demand analysis

B.1 2025 AM DM loop 0 highway demand (car only)

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B.2 2025 AM Highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.3 2025 AM highway demand (car only) change between DM loop 0 and DM Final (DM Final minus DM loop 0)

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B.4 2025 AM highway demand (car only) change between DM Final and DS (DS minus DM)

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B.5 2025 IP DM loop 0 highway demand (car only)

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B.6 2025 IP Highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.7 2025 IP highway demand (car only) change between DM loop 0 and DM final (DM final minus DM loop 0)

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B.8 2025 IP highway demand (car only) change between DM final and DS (DS minus DM)

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B.9 2025 PM DM loop 0 highway demand (car only)

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B.10 2025 PM highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.11 2025 PM highway demand (car only) change between DM loop 0 and DM final (DM Final minus DM loop 0)

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B.12 2025 PM highway demand (car only) change between DM final and DS (DS minus DM)

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B.13 2040 AM DM loop 0 highway demand (car only)

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B.14 2040 AM highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.15 2040 AM highway demand (car only) change between DM loop 0 and DM final (DM final minus DM loop 0)

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B.16 2040 AM highway demand (car only) change between DM final and DS (DS minus DM)

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B.17 2040 IP DM loop 0 highway demand (car only)

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B.18 2040 IP highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.19 2040 IP highway demand (car only) change between DM loop 0 and DM final (DM final minus DM loop 0)

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B.20 2040 IP highway demand (car only) change between DM final and DS (DS minus DM)

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B.21 2040 PM DM loop 0 highway demand (car only)

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B.22 2040 PM Highway demand (car only) change between DM loop 0 and base (DM loop 0 minus base)

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B.23 2040 PM highway demand (car only) change between DM loop 0 and DM final (DM final minus DM loop 0)

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B.24 2040 PM highway demand (car only) change between DM final and DS (DS minus DM)

102 A47 WANSFORD TO SUTTON Trip length distribution changes

C.1 2025 Car trip length distribution - AM peak

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C.2 2025 Car trip length distribution – inter-peak

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C.3 2025 Car trip length distribution - PM peak

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C.4 2040 Car trip length distribution - AM peak

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C.5 2040 Car trip length distribution – inter-peak

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C.6 2040 Car trip length distribution – PM peak

108 A47 WANSFORD TO SUTTON Uncertainty log for the development zones

Quantum Site Model Site name TEMPRO zone Certainty Type reference zone No jobs No of households Peterborough 0 1 1 8 High Street 223 Near certain C3 Dwelling 018 Peterborough 0 68 2 Bretton Woods 204 Near certain C3 Dwelling 011 Peterborough 0 24 3 Bushfield House 216 Near certain C3 Dwelling 018 Peterborough 0 2 4 Dring Cottage, First Drift 200 Near certain C3 Dwelling 004 Peterborough 0 400 5 East of England Showground 214 Near certain C3 Dwelling 018 Peterborough 0 20 6 East of England Showground 214 Near certain C3 Dwelling 018 Peterborough 0 3 7 Elms Farm Great North Road 238 Near certain C3 Dwelling 004 Peterborough 0 13 8 Guthrie House 204 Near certain C3 Dwelling 011 Peterborough 0 20 9 Haywood House 204 Near certain C3 Dwelling 011 Peterborough 0 3 10 Hempsted Opportunity Area 242 Near certain C3 Dwelling 022 Peterborough 0 10 11 Hempsted Parcel NC5 242 Near certain C3 Dwelling 022 Peterborough 0 10 12 Hempsted Parcel NNC2 242 Near certain C3 Dwelling 022 Peterborough 0 65 13 Hempsted Parcels NC1, NC3, NC4 242 Near certain C3 Dwelling 022

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Quantum Site Model Site name TEMPRO zone Certainty Type reference zone No jobs No of households Peterborough 0 60 15 Land off Broadwheel Road, Helpston 243 Near certain C3 Dwelling 004 Peterborough 0 190 16 Land of Lawrence Road Wittering 238 Near certain C3 Dwelling 004 Peterborough 0 95 17 Land off London Road 242 Near certain C3 Dwelling 022 Peterborough 0 130 18 Land to the south of Oundle Road 214 Near certain C3 Dwelling 018 Peterborough 0 34 19 Land west of Woodland Lea 243 Near certain C3 Dwelling 004 Peterborough 0 14 20 NNC1 London Road 242 Near certain C3 Dwelling 022 Peterborough 0 54 21 North of Matley 216 Near certain C3 Dwelling 018 Peterborough 0 14 22 NT1C Hempsted 242 Near certain C3 Dwelling 022 Peterborough 0 40 23 NT2 (part) and NT8 including part NG12 242 Near certain C3 Dwelling 022 Peterborough 0 100 24 The Gloucester Centre 242 Near certain C3 Dwelling 022 Peterborough 0 56 25 Tranche NC2 Hempsted 242 Near certain C3 Dwelling 022 Peterborough 180 0 26 Orton Centre 216 Near certain A1 Retail 018 Peterborough 655 0 27 Lynchwood (south) 213 Near certain B1 General office 018 Peterborough 866 0 28 Lynchwood (north) 213 Near certain B1 General office 018

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Quantum Site Model Site name TEMPRO zone Certainty Type reference zone No jobs No of households Peterborough 1114 0 29 Hampton (Cygnet Park) 242 Near certain B1 General office 022 Peterborough 334 0 30 Orton Southgate 230 Near certain B1 General office 022 Peterborough Industrial & 636 0 31 Hampton (Cygnet Park) 242 Near certain B2 022 manufacturing

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D.1 Uncertainty log – development phasing / trajectory summary

Year Committed Allocated 2016 0% 0% 2017 13% 0% 2018 26% 0% 2019 39% 3% 2020 49% 7% 2021 56% 13% 2022 63% 21% 2023 68% 28% 2024 72% 37% 2025 77% 45% 2026 81% 53% 2027 84% 62% 2028 87% 70% 2029 89% 76% 2030 92% 81% 2031 94% 87% 2032 96% 90% 2033 98% 93% 2034 99% 96% 2035 100% 98% 2036 100% 100%

112 A47 WANSFORD TO SUTTON Stick diagrams

E.1 Base year – 24 hr AADT link flows (vehicles)

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E.2 Base year – 12 hr AAWT link flows (vehicles)

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E.3 DS2040 – 24 hr AADT link flows (vehicles)

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E.4 DS2040 – 12 hr AAWT link flows (vehicles)

116 A47 WANSFORD TO SUTTON Link actual flow differences

To check the robustness of the network, assignment checks were carried out, including delay differences at node level between the Do Minimum and Do Something networks. This analysis took into account both increases or decreases in delay and the impact on the affected traffic flow. The results are displayed in Appendix F, where the pink circles indicate a reduction in model delay in the Do Something compared to the Do Minimum and the green circles indicate an increase in model delay. A review of magnitude of these delay and flow differences gave assurance as the DM and DS model results are in-line with the expected benefits of the scheme.

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F.1 2025 AM peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.2 2025 Inter-peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.3 2025 PM peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.4 2040 AM peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.5 2040 Inter-peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.6 2040 PM peak – DS minus DM (bandwidth units = 200/mm, green indicates an increase and blue indicates a decrease)

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F.7 Node delay PCU hrs 2025 AM peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

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F.8 Node delay PCU hrs 2025 IP peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

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F.9 Node delay PCU hrs 2025 PM peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

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F.10 Node delay PCU hrs 2040 AM peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

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F.11 Node delay PCU hrs 2040 IP peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

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F.12 Node delay PCU hrs 2040 PM peak – DS minus DM (bandwidth units = 40/mm, blue indicates an increase and pink indicates a decrease)

129 A47 WANSFORD TO SUTTON NTEM v7.2 trip rates – car

Car trip rates [trip / job] – AM, IP, PM and OP AM peak hour Inter-peak average hour PM peak hour Off-peak average hour Area description NHBEB NHBO NHBEB NHBO NHBEB NHBO NHBEB NHBO

TEMPRO Zone O D O D O D O D O D O D O D O D

GB 0.004 0.004 0.006 0.006 0.006 0.006 0.015 0.015 0.004 0.004 0.014 0.014 0.000 0.000 0.001 0.001 EAST 0.005 0.004 0.007 0.007 0.007 0.006 0.016 0.017 0.005 0.004 0.015 0.016 0.000 0.000 0.001 0.001 Cambridgeshire 0.005 0.005 0.007 0.007 0.007 0.007 0.016 0.016 0.005 0.005 0.015 0.015 0.000 0.000 0.001 0.001 Huntingdonshire 0.005 0.005 0.007 0.007 0.007 0.008 0.017 0.017 0.005 0.005 0.016 0.016 0.000 0.000 0.001 0.001 Peterborough 0.005 0.005 0.007 0.008 0.007 0.007 0.016 0.018 0.005 0.004 0.015 0.017 0.000 0.000 0.001 0.001 Peterborough 004 0.004 0.004 0.005 0.005 0.006 0.005 0.012 0.012 0.004 0.004 0.012 0.014 0.000 0.000 0.001 0.001 Peterborough 011 0.005 0.004 0.008 0.010 0.006 0.005 0.019 0.026 0.005 0.004 0.018 0.024 0.000 0.000 0.001 0.002 Peterborough 018 0.005 0.004 0.005 0.004 0.007 0.006 0.012 0.009 0.005 0.004 0.011 0.009 0.000 0.000 0.001 0.001 Peterborough 021 0.005 0.005 0.008 0.009 0.007 0.007 0.017 0.021 0.005 0.005 0.016 0.023 0.000 0.000 0.001 0.002 Peterborough 022 0.005 0.006 0.007 0.007 0.007 0.008 0.016 0.017 0.005 0.005 0.015 0.015 0.000 0.000 0.001 0.001 East 0.005 0.005 0.007 0.008 0.007 0.008 0.017 0.017 0.005 0.005 0.016 0.017 0.000 0.000 0.001 0.001 Northamptonshire East Northamptonshire 0.006 0.006 0.009 0.012 0.009 0.008 0.024 0.032 0.005 0.005 0.023 0.040 0.000 0.000 0.002 0.004 001 East Northamptonshire 0.005 0.005 0.008 0.009 0.008 0.008 0.019 0.016 0.005 0.005 0.018 0.015 0.000 0.000 0.001 0.001 002

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Car trip rates [trip / household] – 24hr Home based-employer business Area description Home based work (HBW) Home based other (HBO) (HBEB) TEMPRO zone P A P A P A

GB 0.055 0.000 0.373 0.000 0.537 0.091

EAST 0.063 0.000 0.421 0.000 0.614 0.089

Cambridgeshire 0.067 0.000 0.446 0.000 0.620 0.100

Huntingdonshire 0.075 0.000 0.474 0.000 0.673 0.103

Peterborough 0.057 0.000 0.418 0.000 0.565 0.098

Peterborough 004 0.100 0.000 0.593 0.000 0.741 0.100

Peterborough 011 0.047 0.000 0.374 0.000 0.512 0.097

Peterborough 018 0.065 0.000 0.489 0.000 0.657 0.101

Peterborough 021 0.042 0.000 0.338 0.000 0.475 0.097

Peterborough 022 0.062 0.000 0.479 0.000 0.598 0.100

East Northamptonshire 0.072 0.000 0.469 0.000 0.663 0.104

East Northamptonshire 001 0.079 0.000 0.442 0.000 0.752 0.105

East Northamptonshire 002 0.075 0.000 0.435 0.000 0.727 0.099

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Car trip rates [trip / job] – 24hr

Area description HBEB HBW HBO

TEMPRO zone P A P A P A GB 0.000 0.046 0.000 0.312 0.000 0.375

EAST 0.000 0.055 0.000 0.360 0.000 0.386

Cambridgeshire 0.000 0.052 0.000 0.348 0.000 0.394

Huntingdonshire 0.000 0.058 0.000 0.379 0.000 0.437

Peterborough 0.000 0.053 0.000 0.355 0.000 0.438 Peterborough 004 0.000 0.045 0.000 0.339 0.000 0.283

Peterborough 011 0.000 0.044 0.000 0.331 0.000 0.615

Peterborough 018 0.000 0.055 0.000 0.404 0.000 0.224

Peterborough 021 0.000 0.054 0.000 0.355 0.000 0.469

Peterborough 022 0.000 0.060 0.000 0.372 0.000 0.404 East Northamptonshire 0.000 0.055 0.000 0.358 0.000 0.432

East Northamptonshire 001 0.000 0.063 0.000 0.412 0.000 0.868

East Northamptonshire 002 0.000 0.057 0.000 0.364 0.000 0.334

132 A47 WANSFORD TO SUTTON Demand matrix growth summary table

DM DS % DM Growth vs 2015 % DS Gro wth Vs 2015 Base 2015 2025 2040 2025 2040 2025 2040 2025 2040

O D O D O D O D O D O D O D O D O D Simulation 9,136 11,807 10,074 13,035 11,329 14,661 10,082 13,042 11,335 14,604 10% 10% 24% 24% 10% 10% 24% 24% Simualtion+ Dev 9,136 11,807 10,207 13,260 11,676 15,151 10,215 13,267 11,683 15,090 12% 12% 28% 28% 12% 12% 28% 28% AM Outside 5,357,287 5,354,616 5,857,682 5,854,631 6,523,270 6,519,797 5,857,678 5,854,626 6,523,219 6,519,812 9% 9% 22% 22% 9% 9% 22% 22% Total 5,366,423 5,366,423 5,867,888 5,867,891 6,534,947 6,534,947 5,867,893 5,867,893 6,534,902 6,534,903 9% 9% 22% 22% 9% 9% 22% 22% Simulation 6,907 8,001 7,790 8,952 8,925 10,199 7,791 8,955 8,932 10,206 13% 12% 29% 27% 13% 12% 29% 28% Simualtion+ Dev 6,907 8,001 7,925 9,084 9,237 10,503 7,927 9,087 9,246 10,510 15% 14% 34% 31% 15% 14% 34% 31% IP Outside 4,720,040 4,718,946 5,183,816 5,182,658 5,822,415 5,821,149 5,183,819 5,182,659 5,822,405 5,821,139 10% 10% 23% 23% 10% 10% 23% 23% Total 4,726,948 4,726,948 5,191,742 5,191,743 5,831,653 5,831,652 5,191,745 5,191,745 5,831,650 5,831,650 10% 10% 23% 23% 10% 10% 23% 23% Simulation 10,795 10,492 12,015 11,601 13,593 13,049 12,054 11,597 13,581 13,041 11% 11% 26% 24% 12% 11% 26% 24% Simualtion+ Dev 10,795 10,492 12,220 11,737 14,060 13,403 12,258 11,733 14,042 13,394 13% 12% 30% 28% 14% 12% 30% 28% PM Outside 5,949,975 5,950,278 6,503,580 6,504,063 7,236,676 7,237,333 6,503,512 6,504,038 7,236,570 7,237,217 9% 9% 22% 22% 9% 9% 22% 22% Total 5,960,770 5,960,770 6,515,800 6,515,800 7,250,735 7,250,736 6,515,769 6,515,770 7,250,612 7,250,611 9% 9% 22% 22% 9% 9% 22% 22%

133 A47 WANSFORD TO SUTTON Low and high growth outputs

I.1 DS2040 – AADT link flows (vehicles) - low growth, core scenario and high growth

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I.2 Post VDM assignment convergence statistics low growth

AM IP PM % % % Iteration Delta (δ) % Flow %Gap Delta (δ) % Flow %Gap Delta (δ) % Flow %Gap Delay Delay Delay 2025 DM final 4 successive convergence statistics N-3 0.0002 100.0 100 0.00007 0.0001 100.0 99.9 0.00008 0.0004 99.9 99.8 0.00029

N-2 0.0001 100.0 100 0.00013 0.0001 100.0 99.9 0.00013 0.0003 99.9 99.8 0.00031

N-1 0.0001 100.0 100 0.00007 0.0001 100.0 99.9 0.00007 0.0003 99.9 99.7 0.00035

Final (N) 0.0002 100.0 100 0.00006 0.0001 99.9 99.9 0.00016 0.0004 99.9 99.9 0.00031 2025 DS final 4 successive convergence statistics N-3 0.0002 99.9 100 0.00007 0.0001 99.9 99.8 0.00031 0.0003 99.9 99.8 0.00038

N-2 0.0002 99.9 100 0.00007 0.0002 99.9 99.9 0.00011 0.0003 99.9 99.7 0.00034

N-1 0.0001 99.9 100 0.00012 0.0003 99.9 99.9 0.00011 0.0003 99.9 99.9 0.0003

Final (N) 0.0001 99.9 100 0.00017 0.0001 99.9 99.9 0.00024 0.0004 100 100 0.00026 2040 DM final 4 successive convergence statistics N-3 0.0002 99.9 99.5 0.00014 0.0008 99.8 99.5 0.00032 0.0004 99.9 99.3 0.00049

N-2 0.0003 99.9 99.6 0.00013 0.0003 99.8 99.6 0.00043 0.0005 100 99.5 0.00046

N-1 0.0003 100.0 99.6 0.00012 0.0004 99.8 99.6 0.00055 0.0005 99.9 99.2 0.00042

Final (N) 0.0003 100.0 99.9 0.00012 0.0003 99.8 99.6 0.00037 0.0004 99.9 99.3 0.00042 2040 DS final 4 successive convergence statistics N-3 0.0001 99.9 99.4 0.00027 0.0001 99.9 99.6 0.00033 0.0004 99.9 98.7 0.00062

N-2 0.0003 99.9 99.6 0.00013 0.0001 99.9 99.6 0.00017 0.0005 99.8 98.4 0.00053

N-1 0.0002 100.0 99.7 0.00013 0.0003 100.0 99.9 0.00013 0.0005 99.9 99.5 0.00048

Final (N) 0.0002 100.0 99.7 0.00012 0.0002 100.0 99.8 0.00013 0.0004 99.9 98.8 0.00051

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I.3 Post VDM assignment convergence statistics high growth

AM IP PM % % % Iteration Delta (δ) % Flow %Gap Delta (δ) % Flow %Gap Delta (δ) % Flow %Gap Delay Delay Delay 2025 DM final 4 successive convergence statistics N-3 0.0001 99.9 99.5 0.00021 0.0001 100.0 99.6 0.00032 0.0004 99.9 98.7 0.00049

N-2 0.0002 99.9 99.7 0.00012 0.0003 99.9 99.5 0.00014 0.0004 99.9 99.3 0.00047

N-1 0.0002 99.9 99.5 0.00012 0.0002 99.9 99.7 0.00014 0.0004 99.9 99.5 0.00045

Final (N) 0.0002 99.9 99.7 0.00012 0.0001 100.0 99.6 0.00033 0.0004 100 99.6 0.00047 2025 DS final 4 successive convergence statistics N-3 0.0002 99.9 99.7 0.00034 0.0002 99.8 99.6 0.00032 0.0005 99.9 99 0.0006

N-2 0.0003 99.9 99.5 0.00019 0.0001 99.9 99.7 0.00034 0.0005 99.7 97.6 0.00078

N-1 0.0001 99.9 99.7 0.00018 0.0003 100.0 99.7 0.00013 0.0008 99.8 98.4 0.00052

Final (N) 0.0002 99.9 99.6 0.00023 0.0001 99.9 99.8 0.00028 0.0005 99.9 99.5 0.00044 2040 DM final 4 successive convergence statistics N-3 0.0009 99.4 95.5 0.00110 0.0002 99.9 99.3 0.00032 0.0018 99.1 94.4 0.0023

N-2 0.0007 99.6 95.2 0.00200 0.0003 100.0 99.5 0.00023 0.0014 99.3 95.9 0.0018

N-1 0.0006 99.3 93.6 0.00120 0.0002 99.9 99.3 0.00039 0.0011 99.5 97.3 0.0017

Final (N) 0.0011 99.3 94.5 0.00130 0.0003 99.9 99.4 0.00019 0.001 99.7 97.8 0.0012 2040 DS final 4 successive convergence statistics N-3 0.0006 99.6 96.2 0.00140 0.0003 100.0 99.8 0.00021 0.0011 99.6 97.8 0.0011

N-2 0.0005 99.6 95.8 0.00094 0.0003 100.0 99.8 0.00019 0.0009 99.8 98.1 0.00088

N-1 0.0006 99.7 96.4 0.00150 0.0003 100.0 99.8 0.00018 0.001 99.9 99 0.00085

Final (N) 0.0006 99.6 95.6 0.00099 0.0002 100.0 99.8 0.00020 0.0008 99.9 98.2 0.00068

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