Baseline and Year 1 Outcomes Monitoring Report

October 2013

Table of Contents

1 Introduction ...... 2

2 Network Wide Baseline Data ...... 8

3 Scheme Specific Baseline Data – Smarter Choices ...... 44

4 Scheme Specific Baseline Data – Infrastructure Improvements ...... 77

5 Scheme Specific Baseline Data – Technology Showcase ...... 86

Introduction

1 Introduction

1.1 Introduction

The Department for Transport awarded Centro £33 million from the Local Sustainable Transport Fund (LSTF) towards the Smart Network, Smarter Choices (SNSC) bid. The SNSC programme involves carrying out a wide range of sustainable travel schemes along ten key corridors in the West Midlands to help underpin economic growth, job creation and meet tough carbon reduction targets. In line with DfT guidance, this report establishes a baseline for the purpose of monitoring the effect of the SNSC on travel behaviour along these corridors. It is noted that this report largely focuses on corridor and scheme specific baseline data as there is limited outcome information available at this time. The report does establish the outcome data that will be reported in future annual reports. The pteg 1 Monitoring and Evaluation Guidance identified that the assessment of transportation investment can provide a range of informative evidence to support: • Future policy making and strategic planning; • The identification of efficient and effective delivery procedures; • The building of institutional skills and resource capacity; • The demonstration of local accountability; and • The generation of learning and understanding of what works.

In line with the DfT Framework 2, and indeed industry good practice, the SNSC Management Team has indicated a desire to embed the assessment of local accountability within the monitoring programme, thereby enabling the assessment of: • The change in travel behaviour, such as mode of travel to SNSC businesses and within the individual project corridors; • Whether the level of observed behaviour change was in line with expectations; • The level of change that occurred in peak period traffic/congestion on each of the corridors; • The observed changes in walking and cycling activity, particularly for commuting purposes; and • The changes in the barriers to employment and the role of SNSC.

1.2 Programme Background and Context

As the largest conurbation in Britain outside of London, transport connectivity is vital to the economic life of the West Midlands, and so therefore it is imperative to improve mobility, as this in turn increases access to employment, skills training and leisure/retail opportunities, further supporting economic growth. Within the West Midlands Metropolitan Area (WMMA), there are high levels of overall deprivation experienced in some areas including Birmingham, Sandwell and Wolverhampton. Those from deprived communities are highly dependent on public transport, and cost and reliability constraints can present numerous difficulties. High levels of deprivation also lead to a number of

1 LSTF Monitoring and Evaluation Guidance – Final Report, January 2013, AECOM 2 Local Sustainable Transport Fund Monitoring and Evaluation Framework, December 2012, DfT

people facing poor health, education, housing and employment opportunities. There are also significantly lower levels of life expectancy in deprived communities. At the same time, there are rising levels of obesity within the WMMA, which has lower levels of physical activity than any other metropolitan area. It is therefore imperative to increase mobility through sustainable travel choices, to reduce social exclusion and access to opportunities, and for health benefits. It is imperative that transport provision is aligned to local travel needs, and so therefore the corridor approach of the SNSC project allows sustainable travel modes to be fully aligned with the travel needs of local residents/businesses, thus reducing social exclusion and the need for short distance car journeys, which in turn has health benefits and positive impacts on the environment. The corridor approach adopted for the SNSC project identifies and targets two main priority groups: those who would benefit most from initiatives to improve access to employment and regeneration, and those who are more likely to be receptive to ‘green’ initiatives encouraging them to switch from car to sustainable modes for carbon reduction. By targeting specific corridors and priority groups through different approaches, people will be able to make better informed sustainable travel choices for their journeys, especially those under 5km, which will lead to improved network efficiency, supporting economic growth and access to employment opportunities whilst also reducing carbon impacts.

The SNSC programme fully aligns itself with the five key objectives of the West Midlands Local Transport Plan: • Economy – underpin private sector led growth and economic regeneration in the Metropolitan Area, including support for housing development and population growth, increased employment and low carbon technologies; • Climate Change – to contribute towards tackling climate change through achieving a reduction in greenhouse gas emissions and to ensure the resilience of the transport system to any changes to the Metropolitan Area’s climate; and • Health, Safety and Security – to improve the health, personal security and safety of people travelling in the Metropolitan Area; • Equality of Opportunity - to tackle deprivation and worklessness, so enhancing equality of opportunity and social inclusion, by improved access to services and other desired destinations within and adjacent to the Metropolitan Area; and • Quality of Life and Local Environment - to enhance wellbeing and the quality of life for people in the Metropolitan area and the quality of the local environment.

SNSC uses 10 selected corridors throughout the West Midlands Metropolitan Area to tailor specific packages to enable people to make better informed sustainable travel choices for their journeys. Figure 1.1 shows the location of the ten selected corridors. Coventry City Council secured LSTF funding for 'Cycle Coventry' and due to the synergies between this bid and the 10 SNSC regional corridors, it was descided to extend part of Centro's Monitoring Plan to include this corridor .

Figure 1.1: The WMMA and the 10 Smart Network, Smarter Choices Corridors

Produced by the Strategic Planning Group of Centro 2013. 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. Centro. 100019543 2013. Legend Corridor Description 1 Black Country West Corridor A4123/A456 2 Corridor 4 - Walsall and Merry Hill 3 Wolverhampton to West Bromwich Growth Corridor A41 4 Connecting Dudley with Birmingham A457 Dudley Road 5 Walsall Road Corridor A34 Walsall Road 6 Warwick Road Corridor A41S 7 North Solihull Regeneration Corridor A452 Chester Road 8 Airport and NEC Corridor A45 Coventry Road 9 South Birmingham Technology Corridor Bristol Road and Pershore Road 10 North Coventry Corridor

1.3 Programme Objectives

A number of SNSC programme level objectives were derived from the LTP3 objectives for monitoring purposes, as summarised in Table 1.1. These programme level objectives are presented againt the three SNSC packages – Smarter Choices, Infrastructure Improvements and Technology Showcase, indicating the contribution and combinations of these packages in order to successfully achieve the overall LSTF objectives.

1.4 Report Structure

For the purposes of monitoring the SNSC programme Centro will be combining a number of existing and bespoke datasets. This includes the existing Local Transport Plan monitoring programme to provide network level and contextual monitoring data. To supplement this, and to provide outcome data more closely aligned to SNSC investment, data derived directly from SNSC schemes will be reported. This Baseline Report therefore contains the following chapters and contents: • Chapter Two : Network Wide LTP Baseline Data; • Chapter Three : Scheme Specific Baseline Data - Smarter Choices; • Chapter Four : Scheme Specific Baseline Data – Infrastructure Improvements; and • Chapter Five : Scheme Specific Baseline Data – Technology Showcase

Table 1.1: Smart Network, Smarter Choices – Specific Objectives

Smart Network, Smarter Choices Package Ref LSTF Objective Smarter Technology Infrastructure Choices Showcase Improvements 1. Facilitate greater network efficiency within the LSTF corridors

2. Reduce local congestion at locations targeted for infrastructure improvements 3. Improve journey times and reliability on bus routes within the LSTF corridors Improve the urban realm and local environment along all transport corridors, 4. including reductions in both C02 and N02 emissions, so as to support the regeneration of local centres Achieve a continued year on year reduction in bus crime and contribute 5. towards improving the perception of safety for bus users through the Safer Travel Police Partnership 6. Enhance infrastructure at bus stops within LSTF corridors Increase the number of people using E-purse Smartcard technology for bus 7. travel in the LSTF corridors 8. Increase patronage levels for public transport within the LSTF corridors Increase the numbers of people successfully finding employment through 9. WorkWise initiatives and support, and maintaining sustainable travel to work Increase walking and cycling for short trips made by residents within LSTF 10. corridors Increase the levels of active travel for secondary schools and further education 11. establishments within the 10 corridors 12. Increase the levels of active travel for workplaces within the 10 corridors 13. Reduce the accident rate for vulnerable road users within all LSTF corridors

Network Wide Baseline Data

2 Network Wide Baseline Data

2.1 Introduction

This Chapter presents a summary of the network wide Local Transport Plan 3 (LTP3) data. In order to assess whether the SNSC programme has an impact on network wide travel patterns, it is necessary to establish a baseline for comparison throughout the LSTF programme period. It is important to note that as this data is network-wide, the three SNSC packages of Smarter Choices, Infrastructure Improvements and Technology Showcase will all contribute to changes against the baseline data. The following network-wide data is presented within this chapter:

• Unemployment Data; • Number of WorkWise Passes Issued; • Public Transport Patronage Data (Bus, Train and Metro); • Bus Reliability and Punctuality Data; • Public Transport Customer Satisfaction Data; • Safer Travel Police Partnership Crime Monitoring Data; • Accident Data; • Air Quality Data; • Traffic Counts (1500 Point Survey); • Pedestrian and Cyclist Counts; • Journey Time and Delay; • Modal Share – Cordon Surveys;

In order to align with the SNSC programme, the network-wide baseline has been defined as the 2012/13 financial year; this is the standard reporting period for LTP related monitoring data. The use of this baseline period aligns with the SNSC programme where the majority of delivery will occur in the 2013/14 and 2014/15 finacial years. Section 2.2 presents the network-wide baseline data.

2.2 Network-Wide Baseline Data

It is important to note that although each dataset within this section contains network-wide baseline data, there is a variation in the time periods in which each dataset has been collected due to when each dataset was commissioned. The geographical coverage of each dataset also varies, with some datasets covering the whole of the West Midlands region and other datasets covering the SNSC specific corridors. Therefore, Section 2.2.1 contains the regional datasets and Section 2.2.2 contains the SNSC corridor specific datasets.

2.2.1 Regional Baseline Data This section presents the following data, reported at the WMMA level: • Unemployment; • Workwise passes; • Public transport patronage; • Public transport reliability; • Public transport satisfaction; and • Public transport crime.

Unemployment Data: The data in Table 2.1 and Figure 2.1 shows the West Midlands unemployment data from 1998/99 to 2012/13, and shows that since 2006/07 unemployment has increased within the West Midlands. The baseline unemployment figure for the West Midlands for 2012/13 is 150,000.

Table 2.1: West Midlands Unemployment Data, 1998/99 – 2012/13 Year Number of People Unemployed in the West Midlands 1998/99 102,000 1999/2000 96,000 2000/01 92,000 2001/02 85,000 2002/03 91,000 2003/04 84,000 2004/05 85,600 2005/06 81,300 2006/07 85,500 2007/08 100,600 2008/09 124,600 2009/10 152,500 2010/11 137,400 2011/12 139,900 2012/13 150,000

Source: ONS Local Area Labour Force Survey: 2010/11

Figure 2.1: West Midlands Unemployment Trend Data, 1998/99 – 2012/13

Number of WorkWise Passes Issued:

Table 2.2 and Figure 2.2 show the number of WorkWise passes issued on an annual basis since 2003/04, and the number of people supported by WorkWise to travel to a new job on an annual basis since 2003/04. The baseline WorkWise figures for 2012/13 are 519 interview/day passes issued and 1440 people supported to travel to a new job.

Table 2.2: Number of WorkWise Passes Issued, 2003/04 – 2012/13 Year Number of Interview/Day Passes Number of People Helped to Issued Travel to Employment 2003/04 726 223 2004/05 1250 385 2005/06 1034 354 2006/07 833 710 2007/08 1441 2104 2008/09 861 1183 2009/10 917 1937 2010/11 1300 4093 2011/12 418 1089 2012/13 519 1440 Source: Centro Passenger Services – Sustainable Travel

Figure 2.2: WorkWise Pass Trend Data, 2003/04 – 2012/13

Public Transport Patronage Data:

The data in Table 2.3 and Figure 2.3 shows bus patronage from 2006/07 to 2012/13, and indicates that the general trend is that bus patronage is decreasing year on year. The baseline bus patronage figure for 2012/13 is 276.3 million passengers.

Table 2.3 : Bus Patronage Data, 2006/07 – 2012/13 Year Annual Bus Patronage (Millions) 2006/07 325.6 2007/08 325.4 2008/09 326.7 2009/10 319.5 2010/11 300.2 2011/12 286.1 2012/13 276.3 Source: Centro, Finance – Concessions and Payments

Figure 2.3: Bus Patronage Trends, 2006/07 – 2012/13

The data in Table 2.4 shows that there is a general trend of increasing rail patronage year on year since 2006/07 to 2012/13. The baseline rail patronage figure for 2012/13 is 46.5 million passengers.

Table 2.4: Rail Patronage Data, 2006/07 – 2012/13 Year An nual Rail Patronage (Millions) 2006/07 32.8 2007/08 35.5 2008/09 37.6 2009/10 40.0 2010/11 41.8 2011/12 44.2 2012/13 46.5

Source: Centro, Strategy and Commissioning – Research and Intelligence

The data in Table 2. 5 shows that Metro Patronage has been fairly static since 2006/07, remaining at 5.0 million annual passengers since 2007/08. Therefore, the baseline Metro patronage figure for 2012/13 is 5.0 million passengers.

Table 2.5: Metro Patronage Data, 2006/07 – 2012/13 Year Annual Metr o Patronage (Millions) 2006/07 4.9 2007/08 5.0 2008/09 5.0 2009/10 5.0 2010/11 5.0 2011/12 5.0 2012/13 5.0

Source: Centro, Strategy and Commissioning – Research and Intelligence

Bus Reliability and Punctuality Data: The data in Table 2.6 shows that there has been a general increase in the percentage of buses running on time and the percentage of buses operated since 2006/07, and a general decrease in the excess wait time. Figure 2.4 shows the change in the percentage of buses running on time since 2006/07, and Figure 2.5 shows the change in the percentage of buses operated since 2006/07. The baseline figures for 2012/13 in relation to bus reliability and punctuality data are: 74.4% of buses running on time, 96.4% of buses operating and 1.2115 minutes of excess wait time.

Table 2.6: Bus Reliability and Punctuality Data, 2006/07 – 2012/13 Year % of Buses Year % of Buses Year Excess Wait “On Time” Operated Time (Mins) 2006/07 72.9 2006/07 94.4 2006/07 1.5375 2007/08 73.6 2007/08 95.2 2007/08 1.5768 2008/09 73.3 2008/09 95.2 2008/09 1.0577 2009/10 75.2 2009/10 96.5 2009/10 1.2849 2010/11 75.8 2010/11 95.3 2010/11 1.2099 2011/12 74.9 2011/12 96.4 2011/12 1.1676 2012/13 74.4 2012/13 96.4 2012/13 1.2115 Source: Centro, Passenger Services – Transport Operations

Figure 2.4: “On-Time” Bus Trends, 2006/07 – 2012/13

Figure 2.5: Operated Bus Trends, 2006/07 – 2012/13

Public Transport Customer Satisfaction Data:

Table 2.7 contains the baseline figures for public transport customer satisfaction for 2012/13: 84% overall bus satisfaction, 81% overall train satisfaction and 98% overall Metro satisfaction.

Table 2.7: Public Transport Customer Satisfaction Baseline Data, 2012/13 Public Transport Mode Overall Customer Satisfaction Baseline Bus 79%3 Train 81% 4 Metro 90%5

Safer Travel Police Crime Monitoring Data: The data in Table 2.8 and Figure 2.6 shows bus crime monitoring data from 2007/08 to 2012/13, and indicates that the general trend is a reduction of total reported crimes year on year. The baseline total reported bus crime figure for 2012/13 is 2398 reported crimes.

Table 2.8: Bus Crime Monitoring Data, 2007/08 – 2012/13 Year Total Reported Crimes 2007/08 4696 2008/09 4104 2009/10 3340 2010/11 2875 2011/12 2530 2012/13 2398 Source: Safer Travel Police Team

3 Source: Passenger Focus Centro level report, overall satisfaction for all passengers, Autumn 2012 data. 4 Source: Passenger Focus Rail National Passenger Survey, overall satisfaction by route; London Midland: West Midlands, Spring 2013 data. 5 Source: Passenger Focus Tram Trial, Spring 2013 data.

Figure 2.6: Total Reported Bus Crimes, 2007/08 – 2012/13

The data in Table 2.9 shows train crime monitoring data from 2010/11 to 2012/13, and shows that both total reported crimes and public order/antisocial behaviour crimes have decreased year on year. The baseline total reported train crime figure for 2012/13 is 2775 reported crimes, and the baseline public order/antisocial behaviour crime figure for 2012/13 is 497 crimes.

Table 2.9: Train Crime Monitoring Data, 2010/11 – 2012/13 Year Total Reported Crimes Public Order/ASB Reported Crimes 2010/11 2922 541 2011/12 2586 288 2012/13 2775 497 Source: Safer Travel Police Team

The data in Table 2.10 shows Metro crime monitoring date from 2010/11 to 2012/13, and shows that both total reported crimes and public order/antisocial behaviour crimes have generally decreases. The baseline total reported Metro crime figure for 2012/13 is 134 reported crimes, and the baseline public order/antisocial behaviour crime figure for 2012/13 is 28 crimes.

Table 2.10: Midland Metro Crime Monitoring Data, 2010/11 – 2012/13 Year Total Reported Crimes Public Order/ASB Reported Crimes 2010/11 185 33 2011/12 178 42 2012/13 134 28 Source: Safer Travel Police Team

2.2.2 SNSC Corridor Specific Datasets

This section presents the following data, reported for the SNSC Corridors; • Accident data; • Air quality; • Traffic, pedestrian and cycling counts; • Journey time and delay; and • Modal share.

Accident Data: Accident data is collated from from the Police STATS19 forms that are completed for all accidents that involve injury, and Table 2.11 shows the combined number of accidents along the 10 LSTF corridors for 2012/13, indicating that there have been 178 accidents involving pedestrians, 103 accidents involving cyclists and 519 accidents involving vehicle passengers or drivers. The baseline accident figure for 2012/13 is 800 accidents across the 10 LSTF corridors. Table 2.11: Number of Accidents along the 10 LSTF Corridors, 2012/13 Slight Serious Fatal All Pedestrian 113 56 9 178 Cyclist 87 15 1 103 Vehicle Passenger or Driver 466 51 2 519 All Accidents 666 122 12 800

Source: LTP3 Monitoring

Accidents for each corridor have been extracted for the period April 2012 to March 3012, and Figure 2.7 shows the location of all these accidents in relation to the 10 SNSC corridors for 2012/13, whilst Figure 2.8 shows the location of the killed or seriously injured accidents in relation to the 10 SNSC corridors. Corridor 9 has had the greatest total number of accidents (155 accidents) and the greatest number of fatal accidents (3 accidents). The three fatal accidents included one pedestrian, one cyclist and one vehicle driver/passenger. Two of the fatal accidents occurred in the vicinity of Edgbaston, one to the north and one to the south. The other fatal accident occurred north of Hopwood, towards the south of the Corridor.

Corridors 1, 5 and 8 have all incurred two fatal accidents. On Corridor 1, one fatal accident was pedestrian and the other vehicle passenger/driver. On Corridor 5, both fatal accidents were pedestrian; one accident occurred south of Hamstead and the other south of Aston. Both accidents on Corridor 8 were also pedestrian; the two fatal pedestrian accidents occurred within close proximity of one another, to the south of Yardley.

Corridors 3, 4 and 6 have all incurred one fatal pedestrian accident. Both fatal accidents on Corridor 3 and 4 occurred in the darkness, on Corridor 3 west of West Bromwich and on Corridor 4 north-east of Oldbury. The fatal accident on Corridor 6 occurred in the day, east of Highgate and north of Sparkbrook.

Corridors 2, 7 and 10 have not experienced any fatal accidents but have incurred serious accidents. Most accidents on Corridor 7 occur near Castle Bromwich.

Figure 2.7: All Accidents in Relation to SNSC Corridors, 2012/13

Figure 2.8: KSI Accidents in Relation to SNSC Corridors, 2012/13

Air Quality Data: Initially it was assumed that the PRISM model would be used to provide baseline flows for each corridor and an estimate of air quality could be determined by undertaking a separate calculation. The PRISM model is currently undergoing an update to develop a 2011 base model, resulting in difficulties extracting flows in its current form. Given the age of the 2006 PRISM model it is felt that this would be unsuitable to extract baseline flows, and so therefore it is not possible to present the air quality baseline data at the current time. However, it is expected that the updated 2011 PRISM base model will be available to extract the required information in the near future.

Traffic Counts (1500 Point Survey): Traffic counts for the period 01 April 2012 to 31 March 2013 were extracted from SPECTRUM. Only traffic counts with co-ordinates within 100m of each corridor were included. At a later stage, any traffic counts for parallel roads or side streets were removed. Only counts with flows aligned with the corridor were retained. No junction turning counts were available in the 2012/13 period but if they are available in the future, the average of the two relevant junction arms would be used. Traffic counts can be from any time within the period 01 April 2012 to 31 March 2013. The distribution of counts within this period was not investigated but the majority of counts were based on one week’s worth of data. Differences between counts along a corridor only provide a general overview of traffic flows on a corridor. The difference between two neighbouring counts may be partially accountable to the different times of year the surveys

were undertaken. In next year’s study, any counts within the period 01 April 2013 to 31 March 2014 will be considered representative in the same manner. The following series of maps all show traffic counts for both directions summed together. Figures are for the average weekday and the maximum duration of count is shown. For example; if the count included a 24-hour hourly profile, the 24-hour two-way traffic flow is provided. If only the hours between 0700 and 1900 or between 0800 and 1600 were available, these ranges were provided instead.

Figure 2.9 shows the location of the traffic counts in relation to the 10 SNSC corridors by flow band, whilst Tables 2.12-2.21 show the traffic count locations and 24 hour two-way traffic flows for each traffic count location by SNSC corridor.

Figure 2.9: Location and Flow Band of Traffic Counts in Relation to the SNSC Corridors

Table 2.12 shows the traffic flows along corridor 1. Available counts indicate there was an average two-way flow of 21,000 vehicles per day on the corridor. Flows on the A459 section of the corridor are consistently lower than flows on the A4123. Flows on the A4123 between Wolverhampton and the Hagley Road are relatively consistent. Flows are lowest around Dudley on the A459 portion of the corridor and highest towards Wolverhampton.

Table 2.12: Traffic Flow Data, Corridor 1

Traffic Count Location 24 Hour 2-Way Traffic Flow A459 THE BROADWAY SOUTH OF LIMEPIT LANE 7547 A459 THE BROADWAY WEST OF NITH PLACE 5798 A459 WOLVERHAMPTON ROAD SOUTH OF THE VISTA 10282 A4123(T) BIRMINGHAM NEW ROAD NORTH OF MASON STREET 28662 A4123(T) WOLVERHAMPTON ROAD SOUTH OF ST MICHAELS C 29421 A4123(T) WOLVERHAMPTON RD WEST OF GATLELEY ROAD 25338 A4123(T) WOLVERHAMPTON ROAD SOUTH OF FLOENCE ROAD 32671 A4123 WOLVERHAMPTON ROAD SOUTH OF POUND ROAD 28350 A4123(T) NEW BIRMINGHAM ROAD EAST OF WALFORD STREE 34619 A459 WOLVERHAMPTON ROAD E SOUTH OF DOVEDALE ROAD 15679 A459 DUDLEY ROAD NORTH OF BYRNE ROAD 16514 A4123 BIRMINGHAM ROAD NORTH OF DERRY STREET 20741 A459 DUDLEY ROAD NORTH OF DRAYTON STREET 17654 Source: LTP3 Monitoring

Traffic flows on Corridor 2 vary significantly along the corridor, particularly around West Bromwich and Oldbury, as shown in Table 2.13 . In Walsall 24 hour two-way traffic flows average at around 12,500 vehicles. Between Blackheath and Brierley Hill they average at around 17,000. However, two-way traffic flows vary between 8,481 vehicles and 20,139 vehicles around West Bromwich and Oldbury.

Table 2.13: Traffic Flow Data, Corridor 2

Traffic Count Location 24 Hour 2-Way Traffic Flow A4100 HIGH STREET EAST OF SHEFFIELD STREET 17451 A4100 POWKE LANE NORTH OF YEWTREE LANE 17485 A4100 FORGE LANE WEST OF NEW POOL ROAD 16506 A4034 BROMFORD ROAD NORTH OF CREDENDA ROAD 20139 A4031 WEST BROMWICH ROAD NORTH OF FULLBROOK ROAD 13356 WEST BROMWICH STREET NORTH OF ARUNDEL STREET 11679 CRONEHILLS LINKWAY SOUTH OF EXPRESSWAY 8481 BROMFORD LANE NO RTH OF KELVIN WAY 10174 Source: LTP3 Monitoring

Table 2.14 shows the traffic flows along corridor 3. Two-way traffic flows are highest east of the A41 / A4444 junction where they vary between 23,593 vehicles and 26,800 vehicles over a 24 hour period. The A41 is predominantly single-carriageway west of this point compared to dual carriageway towards West Bromwich.

Table 2.14: Traffic Flow Data, Corridor 3

Traffic Count Location 24 Hour 2-Way Traffic Flow A41 THE EXPRESSWAY EAST OF CARTERS GREEN 25618 A41 BLACK COUNTRY NEW ROAD EAST OF DANGERFIELD LAN 26800 A41 BLACK COUNTRY NEW ROAD SOUTH OF GOLDS HILL WAY 23593 A41 OXFORD STREET EAST OF LOXDALE STREET 21942 A41 BILSTON ROAD NORTH OF JENNER STREET 18473 Source: LTP3 Monitoring

Table 2.15 shows the traffic flows along corridor 4. Two-way 24-hour traffic flows peak at 38,096 vehicles on the A457 Tollhouse Way between Smethwick Galton Bridge Railway Station and Smethwick Rolfe Street Railway Station. Flows are lowest on the A4092 Cape Hill. Traffic flows at the extremities of the corridor are similar to one another.

Table 2.15: Traffic Flow Data, Corridor 4 Traffic Count Location 24 Hour 2-Way Traffic Flow A457 DUDLEY ROAD WEST OF WESTERN ROAD 30348 A457 SAND PITS EAST OF CLEMENT STREET 22471 A457 TOLLHOUSE WAY WEST OF HIGH STREET 38096 A4092 CAPE HILL EAST OF WINDMILL LANE 10894 A457 DUDLEY ROAD EAST EAST OF LOWER CITY ROAD 24585 A457 DUDLEY ROAD EAST OF ALBION STREET 19932 Source: LTP3 Monitoring

Table 2.16 shows the traffic flows along corridor 5. Traffic counts were slightly higher on the corridor south of the M6 junction 7 on the A34 between the M6 and Perry Barr than between the M6 and Walsall. No traffic counts were available south of Perry Barr.

Table 2.16: Traffic Flow Data, Corridor 5 Traffic Count Location 24 Hour 2-Way Traffic Flow A34 WALSALL ROAD NORTH OF FAIRVIEW AVENUE 28271 BIRMINGHAM ROAD North Of Sundial Lane 32899 BIRMINGHAM ROAD East Of Broadway 22369 BIRMINGHAM ROAD South Of Beacon Road 26492 Source: LTP3 Monitoring

Table 2.17 shows the traffic flows along corridor 6. Two-way traffic flows in Acock’s Green are 15,835 vehicles in a 24 hour period. Elsewhere the average two-way 24-hour traffic flow was around 23,400 vehicles.

Table 2.17: Traffic Flow Data, Corridor 6

Traffic Count Location 24 Hour 2-Way Traffic Flow A41 WARWICK ROAD EAST OF REDDING LANE 22558 A41 WARWICK ROAD NORTH OF FLINT GREEN ROAD 15835 A41 WARWICK ROAD SOUTH OF OLD WARWICK ROAD 23158 A41 WARWICK ROAD WEST OF BURYFIELD ROAD 24505 Source: LTP3 Monitoring

Table 2.18 shows the traffic flows along corridor 7. Only one traffic flow count was available showing a two-way, 24 hour flow of 17,155 vehicles on the B4114 Chester Road portion of the corridor.

Table 2.18: Traffic Flow Data, Corridor 7

Traffic Count Location 24 Hour 2-Way Traffic Flow B4114 CHESTER ROAD EAST OF HAZELHURST ROAD 17155 Source: LTP3 Monitoring

Table 2.19 shows the traffic flows along corridor 8. There are a number of 24 hour traffic counts on the corridor. These indicate that two-way traffic flows on the corridor vary between 33,733 vehicles and 55,653 vehicles, with the peak occuring near Yardley.

Table 2.19: Traffic Flows, Corridor 8

Traffic Count Location 24 Hour 2-Way Traffic Flow A45 COVENTRY ROAD EAST OF PRESTON ROAD 55653 A45 SMALL HEATH HIGHWAY WEST OF GOLDEN HILLOCK RD 40082 A45 COVENTRY ROAD WEST OF DAMSON PARKWAY 39579 COVENTRY ROAD West Of Goodway Road 33733 Source: LTP3 Monitoring

Table 2.20 shows the traffic flows along corridor 9. No traffic flow count locations were available for the A38 portion of the corridor within the period April 2012 to March 2013. However, a number of counts were undertaken in February and March 2012. These indicate two-way flows of between 28,491 vehicles and 37,890 vehicles between Longbridge and Birmingham. Flows beyond Longbridge past Rubery are lower. Two-way, 24 hour flows on the A441 portion of the corridor varied from 16,214 vehicles south of Kings Norton to 30,276 vehicles towards Birmingham.

Table 2.20: Traffic Flows, Corridor 9

Traffic Count Location 24 Hour 2-Way Traffic Flow PERSHORE ROAD VEHICULAR TRAFFIC 30276 A441 REDDITCH ROAD SOUTH OF FOYLE ROAD 16214 A4040 WATFORD ROAD NORTH OF PERSHORE ROAD 17224 A441 PERSHORE ROAD SOUTH OF FRANCES ROAD 23701 A441 PERSHORE ROAD NORTH OF KENSINGTON ROAD 20827 A441 PERSHORE ROAD NORTH OF WYATT CLOSE 29425 Source: LTP3 Monitoring

Table 2.21 shows the traffic flows along corridor 10. Two traffic flow counts on the B4119 section of the corridor, both north and south of the A444, showed a relatively consistent two-way, 24 hour flow of between 17,031 and 17,630 vehicles. A single count on the B4109 section of the corridor indicates a slightly lower flow of 14,525 vehicles.

Table 2.21: Traffic Flows, Corridor 10

Traffic Count Location 24 Hour 2-Way Traffic Flow B4109 STONEY STANTON ROAD SOUTH OF LEICESTER CAUSE 14525 B4113 FOLESHILL ROAD NORTH OF CHURCHILL AVENUE 17630 B4113 LONGFORD ROAD SOUTH OF LONGFORD SQUARE 17031 Source: LTP3 Monitoring

Pedestrian Counts

Counts of pedestrians were also downloaded from SPECTRUM for the same period as traffic flows (01 April 2012 to 31 March 2013). All counts within 100m of the corridor were included, regardless of directionality. The maps which provide figures for each corridor individually show counts aligned with the corridor separately from flows of pedestrians counted crossing the corridor. In the case of corridor seven, nine and ten, the search area was expanded to include the area within different branches of the corridor and other key pedestrian routes such as the Rea Valley Route in corridor nine and the canal towpaths in corridors nine and ten. In the majority of cases an average weekday flow was taken from a week long count or a single day count. Any count within the period 01 April 2012 and 31 March 2013 was included. Therefore some pedestrian flows may be higher or lower than others due to the different times of year the surveys were undertaken. Several surveys on corridor nine were continuous surveys and had data available for most of the year. In the case of these surveys, the figures shown on the following maps were based on the average of two weeks, namely 14-18 May 2012 and 01-05 October 2012. These weeks were chosen from neutral months and to avoid bank holidays. In some cases only data for the October week was available so this was used in the same manner as any other survey for the period 01 April 2012 to 31 March 2013. Walking and pedestrian counts are shown as two-way flows in Figure 2.10 . Counts are occasionally the sum of flows at different crossing points (such as at a junction, 50m north of a junction and 50m south of a junction. Cross- corridor flows are separated from flows aligned with the corridor. However, counts elsewhere (not on the corridor itself) are shown as the sum of all flows.

Figure 2.10: Pedestrian Count Locations, 0700-1900 Counts

Table 2.22 shows the pedestrian flows for corridor 1. No pedestrian flows are available to the A4123 section of the corridor. Pedestrian flows are highest at the location towards Dudley in comparison to the location north of Sedgley. Further pedestrian count locations would be required to provide a more accurate picture.

Table 2.22: Pedestrian Flows, Corridor 1

Pedestrian Count Location 0700-1900 2-Way Flow A COMBINATION OF 'JEWS LANE 0 -50M SOUTH OF KENT STREET' AND 'EVE LANE 0-50M NORTH OF BURTON ROAD' 487 A COMBINATION OF 'BURTON ROAD 0 -50M EAST OF EVE LANE' AND 'KENT STREET 0-50M WEST OF JEWS LANE' 100 Source: LTP3 Monitoring

Table 2.23 shows the pedestrian flows for corridor 3. There was only one site available on the corridor that included a pedestrian count, and 127 pedestrians were counted at the location in the period 0800 to 1600.

Table 2.23: Pedestrian Flows, Corridor 3 Pedestrian Count Location 0800-1600 2-Way Flow OXFORD STREET AT GOZZARD STREET SCP G149 127

Source: LTP3 Monitoring

Table 2.24 shows the pedestrian flows for corridor 6. Only one pedestrian count location was available on the corridor, where a total of 344 pedestrians were counted travelling in all directions at the Warwick Road/Severn Star Road junction between 0700 and 1900.

Table 2.24: Pedestrian Flows, Corridor 6

Pedestrian Count Location 0700-1900 2-Way Flow A COMBINATION OF 'WARWICK ROAD JUST SOUTH OF SEVEN STAR ROAD', 'WARWICK ROAD JUST NORTH OF SEVEN STAR ROAD', 'WARWICK ROAD SLIP ROAD TO SEVEN STAR ROAD' AND 'SEVEN STAR ROAD JUST EAST OF WARWICK ROAD'. 344 Source: LTP3 Monitoring

Table 2.25 shows the pedestrian flows for corridor 7. All available pedestrian counts were located off the corridor, however two were located on Windward Way and Auckland Drive which run parallel (for some distance) with the B4114 Chester Road and the A452 respectively. All locations showed pedestrian flows of greater than 400 people within the period 0700 to 1900.

Table 2.25: Pedestrian Flows, Corridor 7

Pedestrian Count Location 0700-1900 2-Way Flow A COMBINATION OF 'AUCKLAND DRIVE 50 -100M SOUTH OF SANDRA CROFT' AND 'AUCKLAND DRIVE 0-50M SOUTH OF SANDRA CROFT' 405 COMBINATION OF THE FOLLOWING: 'WINDWARD WAY 0 -50M SOUTH OF SHETLAND WALK EASTBOUND', 'WINDWARD WAY 0-50M SOUTH OF SHETLAND WALK WESTBOUND', 'WINDWARD WAY 0-50M NORTH OF SHETLAND WALK EASTBOUND', 'WINDWARD WAY 0-50M NORTH OF SHETLAND WALK WESTBOUND', 'WINDWARD WAY AT SHETLAND WALK EASTBOUND', 'WINDWARD WAY AT SHETLAND WALK WESTBOUND'. 577 COMBINATION OF THE FOLLOWING: 'GREEN LANE 100 -150M WEST OF WINDLEAVES ROAD', 'GREEN LANE 50-100M WEST OF WINDLEAVES ROAD', 'GREEN LANE 0-50M WEST OF WINDLEAVES ROAD' 700 Source: LTP3 Monitoring

Table 2.26 shows the pedestrian flows for corridor 9. Only one pedestrian count location was available showing pedestrian flows running in-line with the corridor which showed 550 pedestrians walking in both directions between 0700 and 1900 on the Bristol Road near Priory Road. The number of pedestrians crossing the corridor was highest near the University of Birmingham’s Selly Oak Campus. However, it should be noted that no pedestrian counts were available near the main campus .

Table 2.26: Pedestrian Flows, Corridor 9

Pedestrian Count Location 0700-1900 2-Way Flow A COMBINATION OF 'PRIORY ROAD EAST OF BRISTOL ROAD' AND 'PRIORY ROAD WEST OF BRISTOL ROAD' 550 A COMBINATION OF 'PELICAN CROSSING SOUTH OF PRIORY ROAD' , 'BRISTOL ROAD NORTH OF PRIORY ROAD' AND 'PRIORY ROAD WEST OF BRISTOL ROAD'. 730 BRISTOL ROAD SOUTH NEAR WITHERFORD WAY 104 BRISTOL ROAD SOUTH UP TO 50M NORTH AND 75M SOUTH OF QUARRY LANE (A COMBINATION OF TWO SITES) 252 A COMBINATION OF 'BRISTOL ROAD SOUTH NEAR GRIFFINS BROOK LANE' AND 'BRISTOL ROAD SOUTH PELICAN CROSSING NEAR GRIFFINS BROOK LANE' 1221 A COMBINATION OF 'BRISTOL ROAD SOUTH - NORTH OF WEOLEY PARK ROAD', 'BRISTOL ROAD SOUTH - SOUTH OF WEOLEY PARK ROAD' AND 'BRISTOL ROAD SOUTH NEAR WEOLEY PARK ROAD CROSSING BETWEEN PELICAN CROSSINGS' 673 Source: LTP3 Monitoring

Table 2.27 shows the pedestrian flows for corridor 10. The number of pedestrians crossing the corridor was highest at Hales Street and lowest at Queens Road, with 2904 and 103 pedestrians walking in both directions between 0700 and 1230 respectively. Table 2.27: Pedestrian Flows, Corridor 10 Pedestrian Count Location 0700-1230 2-Way Flow WARWICK ROAD 931 STONEY ROAD 297 QUINTON ROAD 679 MILE LANE 653 PUMA WAY ROUNDA BOUT 245 WHITEFRIARS STREET 735 GOSFORD STREET 2872 LOWER FORD STREET CAR PARK 2738 LOWER FORD STREET/COX ST 1127 HALES STREET 2904 BISHOP STREET 1018 RADFORD ROAD 503 UPPER HILL STREET 1174 SPON STREET SUBWAY 1385 BUTTS ROAD 1476 QUEENS ROAD 10 3 CENTRAL SIX FOORTBRIDGE 512

Source: LTP3 Monitoring

Cycling Counts

Counts of cyclists were also downloaded from SPECTRUM for the same period as traffic flows (01 April 2012 to 31 March 2013). All counts within 100m of the corridor were included, regardless of directionality. The maps which provide figures for each corridor individually show counts aligned with the corridor separately from flows of cyclists counted crossing the corridor. In the case of corridor seven, nine and ten, the search area was expanded to include the area within different branches of the corridor and other key cycle routes such as the Rea Valley Route in corridor nine and the canal towpaths in corridors nine and ten. In the majority of cases an average weekday flow was taken from a week long count or a single day count. Any count within the period 01 April 2012 and 31 March 2013 was included. Therefore some cycling flows may be higher or lower than others due to the different times of year the surveys were undertaken. Several surveys on corridor nine were continuous surveys and had data available for most of the year. In the case of these surveys, the figures shown on the following maps were based on the average of two weeks, namely 14-18 May 2012 and 01-05 October 2012. These weeks were chosen from neutral months and to avoid bank holidays. In some cases only data for the October week was available so this was used in the same manner as any other survey for the period 01 April 2012 to 31 March 2013. Counts are occasionally the sum of flows at different crossing points (such as at a junction, 50m north of a junction and 50m south of a junction. Cross-corridor flows are separated from flows aligned with the corridor. However, counts elsewhere (not on the corridor itself) are shown as the sum of all flows. Figure 2.11 shows the location of the cycle counts in relation to the corridors. Figure 2.11: Cycle Count Locations, 0700-1900 Counts

Table 2.28 shows the cycle flows for corridor 1. Cycle flows are relatively low compared to other corridors, and as there is only one cycle count on this corridor further cycle count locations would be required to provide a more accurate picture.

Table 2.28: Cycling Flows, Corridor 1

Cycling Count Location 0700-1900 2-Way Flow DUDLEY STREET AT HIGH HOLBORN/TIPTON STREET 19 Source: LTP3 Monitoring

Table 2.29 shows the cycle flows for corridor 2. 75 cyclists were counted in both directions between 0700 and 1900 north of Sandwell and Dudley Railway Station. No further cycle counts were available. Table 2.29: Cycling Flows, Corridor 2

Cycling Count Location 0700-1900 2-Way Flow BROMFORD LANE NORTH OF KELVIN WAY 75 Source: LTP3 Monitoring

Table 2.30 shows the cycle flows for corridor 3. There was only one site available on the corridor that included a cycle count, and 65 cyclists were counted at the location in the period 0800 to 1600.

Table 2.30: Cycling Flows, Corridor 3 Cycling Count Location 0800-1600 2-Way Flow OXFORD STREET AT GOZZARD STREET SCP G149 65

Source: LTP3 Monitoring Table 2.31 shows the cycle flows for corridor 6. Only one cycle count location was available on the corridor, where a total of 11 cyclists were counted travelling in all directions at the Warwick Road/Severn Star Road junction between 0700 and 1900.

Table 2.31: Cycling Flows, Corridor 6

Cycling Count Location 0700-1900 2-Way Flow A COMBINATION OF 'WARWICK ROAD JUST SOUTH OF SEVEN STAR ROAD', 'WARWICK ROAD JUST NORTH OF SEVEN STAR ROAD', 'WARWICK ROAD SLIP ROAD TO SEVEN STAR ROAD' AND 'SEVEN STAR ROAD JUST EAST OF WARWICK ROAD'. 11 Source: LTP3 Monitoring

Table 2.32 shows the cycle flows for corridor 7. A number of cycle counts were available within the vicinity of the B4114 Chester Road / Cooks Lane / B4114 Birmingham Road junction. The highest count was on Chester Road near the junction with Windward Way where 125 cyclists were counted in both directions between 0700 and 1900 hours. Table 2.32: Cycling Flows, Corridor 7

Cycling Count Location 0700-1900 2-Way Flow WINDWARD WAY ADJACENT NO 44 24 OUTSIDE 624 CHESTER ROAD 125 CHESTER ROAD OUTSIDE NUMBER 779 48 COMBINATION OF THE FOLLOWING: 'WINDWARD WAY 0 -50M SOUTH OF SHETLAND WALK EASTBOUND', 'WINDWARD WAY 0-50M SOUTH OF SHETLAND WALK WESTBOUND', 'WINDWARD WAY 0-50M NORTH OF SHETLAND WALK EASTBOUND', 'WINDWARD WAY 0-50M NORTH OF SHETLAND WALK WESTBOUND', 'WINDWARD WAY AT SHETLAND WALK EASTBOUND', 'WINDWARD WAY AT SHETLAND WALK WESTBOUND'. 16 COMBINATION OF THE FOLLOWING: 'GREEN LANE 100 -150M WEST OF WINDLEAVES ROAD', 'GREEN LANE 50-100M WEST OF WINDLEAVES ROAD', 'GREEN LANE 0-50M WEST OF WINDLEAVES ROAD' 11 Source: LTP3 Monitoring

Table 2.33 shows the cycle flows for corridor 9. As some of these sites provide weekly reports on an annual basis, two weeks avoiding school holidays and bank holidays were chosen to calculate average cycle flows. Namely 14 May to 18 May and 1 October to 5 October. For some count locations only the October data was available. The highest number of cyclists counted was 665 crossing the Bristol Road portion of the corridor near Cob Lane within the hours 0700 to 1900. However, this included cyclists using the footbridge and two other locations.

Table 2.33: Cycling Flows, Corridor 9

Cycling Count Location 0700-1900 2-Way Flow A COMBINATION OF 'BRISTOL ROAD SOUTH - SOUTH OF COB LANE', 'BRISTOL ROAD SOUTH NEAR COB LANE AND BOURNVILLE LANE' AND 'BRISTOL ROAD SOUTH NEAR BOURNVILLE LANE USING FOOTBRIDGE' 665 BRISTOL ROAD NORTH OF EASTERN ROAD 240 BRISTOL ROAD NEAR COB LANE 162 BRISTOL ROAD NORTH NEAR RYDE PARK ROAD AND CLIFF ROCK ROAD 57 BRISTOL ROAD SOUTH NEAR WEST PARK AVENUE 95 BRISTOL ROAD SOUTH OF SPEEDWELL ROAD 168 A38 FRANKLEY BEECHES ROAD 31 Source: LTP3 Monitoring

Table 2.34 shows the cycle flows for corridor 10. The number of cyclists was highest at Gosford Street and lowest at Queens Road, with 166 and 7 cyclists counted in both directions between 0700 and 1230 respectively.

Table 2.34: Cycling Flows, Corridor 10 Cycling Count Location 0700-1230 2-Way Flow WARWICK ROAD 50 STONEY ROAD 59 QUINTON ROAD 36 MILE LANE 41 PUM A WAY ROUNDABOUT 53 WHITEFRIARS STREET 57 GOSFORD STREET 166 LOWER FORD STREET CAR PARK 74 LOWER FORD STREET/COX ST 112 HALES STREET 120 BISHOP STREET 47 RADFORD ROAD 28 UPPER HILL STREET 82 SPON STREET SUBWAY 117 BUTTS ROAD 95 QUEENS ROAD 7 CE NTRAL SIX FOORTBRIDGE 73

Source: LTP3 Monitoring

Journey Time and Delay: Journey time and delay uses Traffic Master data for the period September 2012 to March 2013, AM peak only. Table 2.35 shows the average speed and delay for each SNSC corridor, and highlights that corridor 6 has the highest average delay and lowest average speed (62.8 secs/km and 18.4 mph respectively).

Table 2.35: Average Speed and Average Delay per SNSC Corridor Corridor Average Speed (mph) Average Delay (seconds per km) 1 21.5 48.7 2 19.8 47.4 3 25.5 33.8 4 19.9 51.7 5 23.9 45.2 6 18.4 62.8 7 38.0 12.9 8 31.7 26.3 9 26.5 39.2 10 19.4 42.3 Source: Traffic Master, September 2012 – March 2013

Below is a summary of the location of delays along each corridor. Maps detailing these locational delays can be found in Appendix A Figures A.1 to A.10 .

• Corridor 1: On Corridor 1 the greatest delays of more than 50 seconds per kilometre (km) occur at Birchley Island, near Junction 2 of the M5. There are also delays greater than 50 seconds per km to the south of Sedgley on Corridor 1 near the junction with A463, to the west of Coseley on the A4123 and on approach to Wolverhampton in the area surrounding Blakenhall .

• Corridor 2: On Corridor 2 the greatest delays of more than 50 seconds per km occur to the west of West Bromwich and to the north of Blackheath. Again there are also delays greater than 50 seconds at Birchley Island, near Junction 2 of the M5 and on approach to Quarry Bank and Brierley Hill.

• Corridor 3: On Corridor 3 the greatest delays of more than 50 seconds per km occur to the west of Bilston and Darlaston in the vicinity of roundabouts in these areas. Generally it appears to be the case that delays are greater in the north and shorter in the south.

• Corridor 4: On Corridor 4 the greatest delays of more than 50 seconds per km occur to the northwest of Oldbury and in the area surrounding Smethwick.

• Corridor 5: On Corridor 5 the greatest delays of more than 50 seconds per km occur to the west of Pheasey and in the vicinity of Great Barr and Hamstead. Generally delays are shorter in the vicinity of Aston, where they are less than 20 seconds per km.

• Corridor 6: There are delays of more than 50 seconds per km along the majority of Corridor 6, with the exception of Solihull By-Pass to the east of the Corridor, where delays are shorter than 10 seconds per km.

• Corridor 7: The majority of Corridor 7 experiences delays of between 1 – 10 seconds per km. The greatest delays occur to the west of Castle Bromwich on approach to the M6 where some areas experience delays of more than 50 seconds per km.

• Corridor 8: On Corridor 8 the greatest delays of more than 50 seconds per km occur east of Sheldon in the vicinity of Birmingham International Airport, as well as Small Heath Highway and Bordesley Circus.

• Corridor 9: On Corridor 9 the greatest delays of more than 50 seconds per km occur to the west of Kings Heath, Kings Norton and Northfield. The area with the fewest delays is to the south west of Rubery and Lickey, where delays are shorter than 10 seconds per km.

• Corridor 10: On Corridor 10 the greatest delays of more than 50 seconds per km occur on Corridor 10B and where Corridors 10A and 10B meet. There are areas of heavy delays to the west of Foleshill on Corridor 10C.

Modal Share – Cordon Survey: Modal share data has been obtained using the data collected for the biennial cordon surveys, as part of the LTP monitoring programme. There are a number of corridors which are not near the cordon sites and therefore no modal split data is available. The modal share baseline data was collected 2012/13.

Figures 2.12-2.14 show the modal share at the three different sites along/near corridor 2 – Bromford Lane North of Kelvin Way, The Boulevard Brierley Hill and Weston Street South of Tame Street. The most common mode of transport at all three sites is car/taxi, accounting for between 79.9% and 89.7% of trips. The next most common modes of transport for all three sites are bus/coach and good vehicles (less than 1.5T). Pedestrians and cyclists account for between 0.2% and 1.0% of journeys.

Figure 2.12: Modal Share, Corridor 2: Bromford Lane North of Kelvin Way

Figure 2.13: Modal Share, Corridor 2: The Boulevard Brierley Hill

Figure 2.14: Modal Share, Corridor 2: Weston Street South of Tame Street

Figure 2.15 shows the modal share at the cordon site, Parade East of Charlotte Street, along corridor/near 4. 100% of modes used are motorised modes of transport, with 92.3% cars and light goods vehicles, 6.4% HGVs and 1.3% buses/coaches.

Figure 2.15: Modal Share, Corridor 4: Parade East of Charlotte Street

Figures 2.16 – 2.18 show the modal share at three different sites along/near corridor 5 – Brimingham Road South of Jesson Close, Birmingham Road North of Sundial Lane and Birmingham Road East of Broadway. The most common form of transport at the Birmingham Road South of Jesson close site is car/taxi, accounting for 86.5% of transport modes, whilst pedestrians and cyclists account for 0.3%. Both the Birmingham Road North of Sundial Lane and Birmingham Road East of Broadway sites have modal shares that consist of 100% motorised modes of transport, the most common being car and LGVs, followed by HGVs and bus/coach.

Figure 2.16: Modal Share, Corridor 5: Birmingham Road South of Jesson Close

Figure 2.17: Modal Share, Corridor 5: Birmingham Road North of Sundial Lane

Figure 2.18: Modal Share, Corridor 5: Birmingham Road East of Broadway

Figure 2.19 shows the modal share at the cordon site, Lode Lane South of Kelvedon Grove at Hospital, along/near corridor 6. The most common mode of transport is car/taxi (87.2%), followed by goods vehicles less than 1.5T (6.0%) and bus/coach (3.6%). 0.7% of modal share is pedestrians and cyclists.

Figure 2.19: Modal Share, Corridor 6: Lode Lane South of Kelvedon Grove at Hospital

Figure 2.20 shows the modal share at the cordon site, Coventry Road West of Goodway Road, along/near corridor 8. The modal share consists of 100% motorised transport, with car and LGVs being the most common mode of transport (87.9%).

Figure 2.20: Modal Share, Corridor 8: Coventry Road West of Goodway Road

Figure 2.21 shows the modal share at the cordon site, Stoney Station just South of Red Lane CC02, along/near corridor 10. The most common mode of transport is car/taxi (82.2%) followed by good vehicles less than 1.5T (9.1%). Pedestrians and cyclists account for 2.2% of the modal share.

Figure 2.21: Modal Share, Corridor 10: Stoney Station Road just South of Red Lane CC02

Scheme Specific Baseline Data – Smarter Choices

3 Scheme Specific Baseline Data – Smarter Choices

3.1 Introduction

This Chapter presents Smarter Choices scheme specific data which has been collected through SNSC initiatives. The aim of this chapter is to establish a baseline for Smarter Choices data for future comparison of travel patterns relating to Smarter Choices interventions. Data from each scheme will be mapped onto the the causal chains presented in the Smarter Choices package logic maps to facilitate the assessment in future year reports of how the SNSC programme is progressing towards the programme objectives. The SNSC scheme specific datasets that have been used to inform the baseline for the Smarter Choices package are shown in Table 3.1 .

Table 3.1: SNSC Scheme Specific Datasets, Smarter Choices Package Baseline Dataset Baseline Data Collection Period Next Period of Data Collection Resident Panel Survey February - Mid July 2013 (Coventry Interim Survey June 2014 South Corridor baseline collection September 2013) Stakeholder Panel Survey June - September 2013 Interim Panel Refreshment Activities September – December 2014 Workplace Establishment Annual Baseline to close March 2014 (data April 2014 – March 2015 Monitoring Surveys presented within this report was collected to August 2013) Educational Establishment Baseline to close March 2014 (data April 2014 – March 2015 Annual Monitoring Surveys presented within this report was collected to August 2013) Personalised Travel Planning March – May 2013 and; 1 October – November 2013 11 th August – 8th November 2013 Follow up customer satisfaction 1 st October – 13 th December 2013

To account for the distinct elements of the smarter choices package, Section 3.2 will focus on the business and employment workstream data, Section 3.3 will focus on the education workstream data, Section 3.4 will focus on the marketing and communications workstream data and Section 3.5 will focus on the community and residential workstream data.

3.2 Business and Employment Workstream Data

Figure 3.1 shows the logic map for the business and employment Smarter Choices workstream and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 3.2 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is working towards these overall objectives. Following the logic map approach, these outcomes are separated into first order, second order and third order outcomes. Each order is presented in turn herein.

Table 3.2: Business and Employment Monitoring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Increased public transport use for trips to work Increased awareness of Reduced peak period congestion, sustainable transport modes Increased walking and cycling to journey times and delays (see work Section 2.2.2 )

Change in road traffic accident Reduced unemployment among the Increased awareness of public rates/severity and associated costs local population (see Table 2.1, transport services/routes (see Table 2.11 & Figures 2.1-2.2, Section 2.2.1 ) Section 2.2.2 ) Change in modal share for Increased skills and confidence Reduced car-based mileage commuter trips with cycling Improved air quality (see Section Increased physical activity and 2.2.2 ) improved general health Perceptions of improved accessibility to skills/employment/services

To demonstrate the anticipated impacts of the SNSC programme and to highlight how each package contributes to individual objectives, a number of logic maps were produced by package and smarter choices element. These maps will be used to track progress through the causal pathways for each intervention and package. Six logic maps were developed as part of the Monitoring Plan; one each for the Infrastructure and Technology packages and four logic maps for the Smarter Choices package, to account for the distinct elements of which it comprises. Figure 3.1 , shows the first of the logic maps, prepared for the Smarter Choices, Business and Employment workstream.

Figure 3.1: Business and Employment Engagement

3.2.1 First Order Outcomes

Increased awareness of sustainable transport modes/ Increased awareness of public transport services/routes: In order to assess if the SNSC programme is increasing the awareness of sustainable transport modes within the local population, it is necessary to present current levels of awareness. Awareness data has been collected by the SNSC resident’s panel and is presented in Figures 3.2 – 3.5 below. Figure 3.2 shows respondents’ awareness of bus facilities within their local area. It can be seen that people are most aware of bus routes for getting around their area (59.0% 6), followed by where to obtain information about travelling by bus (56.4% 7). Respondents were least aware of the range of tickets and passes available (51.6% 8).

Figure 3.2: Please rate your awareness of bus facilities in this area

Source: Resident’s Panel Baseline Data

6 N=6440 7 N=6441 8 N=6440

The data in Figure 3.3 shows respondents’ awareness of train facilities within their local area. It can be seen that 86.5% 9 of respondents are aware of how to access their nearest rail station on foot and bicycle, and that 74.5% 10 are aware of where to obtain information about travelling by train. Respondents were least aware of train times from their local stations (62.0% 11 ).

Figure 3.3: How familiar are you with the following train facilities where you live?

Source: Resident’s Panel Baseline Data

9 N=1811 10 N=1814 11 N=1813

The data in Figure 3.4 shows that in relation to awareness of Metro facilities, respondents were most aware of how to access their nearest Metro stop on foot or bicycle (75.3% 12 ) and where to obtain information about travelling by Metro (64.1%), but least aware of the ease of interchange between Metro and bus (48.7%).

Figure 3.4: How familiar are you with the following Metro facilities where you live?

Source: Resident’s Panel Baseline Data

12 N=611

The data in Figure 3.5 shows that respondents’ awareness of cycling facilities is currently poor. Respondents are most aware of off-road cycle routes (36.2% 13 ), followed by on-road cycle routes (31.2% 14 ), but only one-fifth (21.1% 15 ) of respondents are aware of where to park bicycles securely.

Figure 3.5: Please rate your awareness of the cycle facilities in this area

Source: Resident’s Panel Baseline Data

It is also possible to assess whether the SNSC programme leads to an increase in awareness of sustainable transport modes, through the use of the stakeholder panel survey data. The stakeholder panel survey collected data on the awareness of top trip generators/major employers of sustainable transport options in their area, and will therefore enable a year on year analysis of whether stakeholders’ awareness of sustainable transport in their area increases during the SNSC programme period. It is anticipated that this baseline data will be available for inclusion in the final Baseline report.

13 N=929 14 N=925 15 N=925

Increased skills and confidence with cycling: In order to assess if there has been an increase in skills and confidence of the local population with relation to cycling, it is necessary to measure levels of skills and confidence prior to a person receiving cycling training and support, and then again after training with a follow up survey. This method of assessing if cycle training does improve a person’s skills and confidence is already been implemented by BikeRight!, the SNSC cycle training provider. However, this data is not currently available but will be available for future annual reports.

3.2.2 Second Order Outcomes

Increased public transport use for trips to work / Increased walking and cycling to work: In order to assess if the SNSC programme results in increased public transport and walking and cycling trips to work, it is necessary to establish a baseline modal split for commuting. Workplace monitoring data provides this split, and Table 3.3 shows that 73% 16 of respondents surveyed travel to work by car, either as a driver or a passenger, and 64% of respondents drive to work alone. The most popular sustainable more of transport used for the commute is bus, with 17% of respondents travelling to work by bus. Table 3.3: Modal Split to Work Mode of Transport Respondents % Car driver (lone driver) 802 64 Car driver (with passenger) 61 5 Car passenger (driver at same 15 1 location) Car passenger (dropped off) 36 3 Bus 209 17 Rail 36 3 Cycle 26 2 Walk 55 4 Metro 9 1 Park and ride 0 0 Cycle and ride 0 0 Taxi 4 0 Other 9 1 Base 1262 101*

*Due to rounding. Source: Workplace Monitoring Baseline Data

16 N=1262

The stakeholder panel survey can also be used to assess whether the SNSC programme leads to an increase in public transport, walking and cycling trips to work as data has been collected related to the use of sustainable transport in stakeholders’ local areas. It is anticipated that this baseline data will be available for inclusion in the final Baseline report .

Reduced car-based mileage: In order to assess if car-based mileage is reduced through the SNSC programme it is necessary to establish a current baseline. Car-based mileage could be reduced through a change in work patterns as a result of more home working, and through a change in the mode of transport used to travel to work. Respondents who took part in the resident’s panel survey were asked to estimate the total number of miles they had driven in the last 12 months, which includes commuting trips (but excludes business trips), and Figure 3.6 shows that the most frequent distance driven by respondents in a year was between 5,000 miles and 9.999 miles, driven by 48.1% 17 of respondents surveyed. By analysing the percentage split between the mileage categories year on year it will be possible to deduce if the SNSC programme results in respondents’ reducing their estimated annual driven mileage.

Figure 3.6: Total miles driven in the last 12 months

Source: Resident’s Panel Baseline Data

17 N=3258

Perceptions of improved accessibility to skills/employment/services: Perceptions of accessibility to skills, employment and services have been surveyed through the resident’s panel surveys, and so therefore the baseline data can be used to assess whether the SNSC programme has been successful in changing the perceptions of the local population in relation to accessing skills, employment and services. Respondents in areas targeted for economic regeneration were asked to rate how they felt about employment opportunities in their areas. Figure 3.7 shows that just under half (48.1% 18 ) of respondents felt that employment opportunities in their area were either poor or very poor, whilst only 10.1% of respondents felt that opportunities were either good or very good.

Figure 3.7: Please indicate how you feel about employment opportunities in this area

Source: Resident’s Panel Baseline Data

18 N=4137

Respondents in areas targeted for economic regeneration were also asked to rate how they felt about training opportunities in their local area. Figure 3.8 shows that 39.6% 19 of respondents felt that training opportunities in their local area were either poor or very poor, and only 9.5% felt that training opportunitites were either good or very good.

Figure 3.8: Please indicate how good you feel training opportunities are in this area

Source: Resident’s Panel Baseline Data

19 N=4134

Respondents in areas targeted for economic regeneration were also asked if they were aware of any barriers preventing people from accessing employment or training in their local area. It can be seen in Table 3.4 that 25.6% of respondents felt that there were no barriers to accessing employment or training and 44.5% 20 didn’t know. 2.6% of respondents identified the cost of transport as a barrier to accessing employment or training, and 0.8% identified transport for people to get to jobs as a barrier.

Table 3.4: Are you aware of any barriers which prevent people from accessing employment or training in this area? Barriers to Employment and Training Percentage of Respondents* No barriers 25.6 Lack of jobs 20.6 Lack of skills/experience/qualifications/education 2.8 Cost of transport 2.6 Age restrictions 1.6 Companies are not willing to train people 1.5 No help for childcare/cost of childcare/fitting job around family 1.3 Poor attitude of people in the area/people don’t want to work 0.9 Transport for people to get to jobs 0.8 Language barrier 0.8 Lack of information/awareness 0.7 Jobs are too far away 0.7 Lack of funding 0.6 Don’t know 44.5 Total 108.5 N 4307

*Responses > 0.5%. Source: Resident’s Panel Baseline Data. Data collected as part of the stakeholder panel baseline survey can also be used to assess whether the SNSC programme leads to perceptions of improved accessibility to employment, as this survey collected data on the perceptions of transport as a barrier to economic growth. It is anticipated that this baseline data will be available for inclusion in the final Baseline report.

20 N=4307

Respondents who identified themselves as being unemployed and those who had experienced unemployment in the last 12 months were asked if they has not applied for a job or turned down a job because of difficulty travelling to or from it. Figure 3.9 shows that 6.6% 21 of respondents have not applied for a job and 4.4% have turned down jobs because of travel difficulties.

Figure 3.9: Have you ever not applied for or had to turn down a job in the West Midlands because of difficulty travelling to/from it?

Source: Resident’s Panel Baseline Data

21 N=950

3.2.3 Third Order Outcomes

As the majority of third order outcomes are long-term, network-wide outcomes, these will be monitored through the network wide baseline datasets that are discussed in Section 2 . However, scheme specific third order baseline data relating to the business and employment workstream is presented within this section.

Change in modal share for commuter trips: In order to assess if the SNSC programme results in increased public transport, walking and cycling trips to work, it is necessary to establish a baseline modal split for commuting. Workplace monitoring data provides this split, and the data in Table 3.3 within Section 3.2.2 shows the modal share used by respondents to travel to work, which is the baseline for future annual monitoring. By analysing year-on-year modal split, it will be possible to deduce if the SNSC programme has had an effect on legacy modal change for commuter trips. The stakeholder panel survey can also be used to assess whether the SNSC programme leads to a change in modal share for commuter trips as data has been collected related to the use of sustainable transport in stakeholders’ local areas. It is anticipated that this baseline data will be available for inclusion in the final Baseline report.

Increased physical activity and improved general health: This outcome indicator is directly linked to an increase in cycling, and in order to establish a baseline it is necessary to measure perceived health levels of a person prior to the person receiving cycling training and support, and then again after training with a follow up survey. This method of assessing if a respondent’s perception of their general health is already in place by BikeRight!, the SNSC cycle training provider. However, this data is not currently available but will be available for future annual reports. In order to assess if there is an increase in physical activity due to SNSC initiatives and incentives, the resident’s panel survey asked respondents to state how long they spent walking and cycling in a typical week during both Winter and Summer. The data in Figure 3.10 shows that the majority of respondents (52.8% 22 in Winter and 42.2% in Summer) walk up to five hours per week, whilst 7.9% and 6.7% of respondents do not walk at all during Winter and Summer respectively.

Figure 3.10: Time spent walking in a typical week, Resident’s Panel Baseline Data

22 N=6392

Source: Resident’s Panel Baseline Data

The data in Figure 3.11 shows that the majority of respondents (93.4% 23 in Winter and 88.9% in Summer) do not cycle at all during a typical week. 4.6% and 7.1% of respondents cycle up to five hours a week in Winter and Summer respectively. Figure 3.11: Time spent cycling in a typical week, Resident’s Panel Baseline Data

Source: Resident’s Panel Baseline Data

23 N=6392

3.3 Education Workstream Data

Figure 3.12 shows the logic map for the education smarter choices workstream and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 3.5 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is working towards these overall objectives. Following the logic map approach, these outcomes are separated into first order, second order and third order outcomes.

Table 3.5: Education Monitioring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Continued use of sustainable travel modes to educational Increased awareness of establishments sustainable transport modes (see

Figures 3.2-3.5, Section 3.2.1 ) Perceptions of improved accessibility Increased public transport use to to education and training (see Increased awareness of public educational establishments Figure 3.8 & Table 3.4 , Section transport services/routes (see 3.2.2 ) Figures 3.2-3.5, Section 3.2.1 ) Change in road traffic accident Increase in cycle ownership and Increased share of walking and rates/severity and associated costs confidence in cycling cycling to educational (see Section 2.2.2 ) establishments Increased safety and security on Improved air quality (see Section buses (see Table 2.8, Section 2.2.2 ) 2.2.1 ) Reduced peak period congestion, journey times and delays (see Section 2.2.2 )

Figure 3.12: Education Engagement

3.3.1 First Order Outcomes

Increase in cycle ownership and confidence in cycling: In order to monitor if the SNSC programme has led to an increase in cycle ownership, it is necessary to establish the percentage of those travelling to educational establishments that currently own a bicycle, which has been surveyed as part of the educational establishment monitoring survey data. Education monitoring data has been collected for three different categories; 11-16 year old pupils, 16+ students, and staff at educational establishments.Table 3.6 shows that 64% of 11-16 year old pupils own a bicycle but don’t use it to travel to school, and 3% or 11-16 year old pupils own a bicycle that they use to travel to school.

Table 3.6: Bicycle Ownership, 11-16 Pupils Do you own a bicycle? Responses % Yes – use it to cycle to school 152 3 Yes – but don’t use it to school 3038 64 No 1535 32 Base 4725 99*

*Due to rounding. Source: Educational Establishment Monitoring Baseline Data

Table 3.7 shows that no college student owns a bicycle and uses it to travel to college, whilst 26% of students own a bicycle but don’t use it to travel to college.

Table 3.7: Bicycle Ownership, 16+ Students Do you own a bicycle? Responses % Yes – use it to cycle to college 0 0 Yes – but don’t use it to college 19 26 No 53 74 Base 72 100

Source: Educational Establishment Monitoring Baseline Data

Table 3.8 shows that 6% of staff own a bicycle and use it to travel to school, whilst 37% own a bicycle but don’t use it to travel to school.

Table 3.8: Bicycle Ownership, Staff Do you own a bicycle? Responses % Yes – use it to cycle to school 115 6 Yes – but don’t use it to school 688 37 No 1033 56 Base 1836 99*

*Due to rounding. Source: Educational Establishment Monitoring Baseline Data

In order to assess if there has been an increase in confidence of those travelling to educational establishments in relation to cycling, it is necessary to measure levels of confidence prior to a person receiving cycling training and support, and then again after training with a follow up survey. This method of assessing if cycle training does improve a person’s cycle confidence is already in place by BikeRight!, the SNSC cycle training provider. However, this data is not currently available but will be available for future annual reports.

3.3.2 Second Order Outcomes

Increased public transport use to educational establishments/ Increased share of walking and cycling to educational establishments: In order to assess if the SNSC programme results in increased public transport and walking and cycling trips to educational establishments, it is necessary to establish a baseline modal split for educational trips. Educational establishment monitoring data provides this split, and the data in Table 3.9 shows that walking is the most popular mode of transport for educational trips used by 36.6% 24 of respondents, closely followed by car travel (36.3% of respondents travel by car, largely as passengers). Public bus is the most popular public transport mode used for trips to educational establishments, accounting for 20.5% of journeys made my respondents.

Table 3.9: Modal Split to Educational Establishments Mode of Transport Respondents % Car driver 1420 18.5 Car passenger (driver at same location) 20 0.3 Car passenger (dropped off) 1344 17.5 Park and walk (park nearby and walk 5 min+) 100 1.3 Park and ride 0 0.0 Cycle and ride 1 0.0 Walk 2812 36.6 Cycle 124 1.6 Public Bus 1574 20.5 School/private bus 46 0.6 Train 97 1.3 Metro 29 0.4 Other 107 1.4 Base 7674 100

Source: Educational Establishment Monitoring Baseline Data

24 N=7674

3.3.3 Third Order Outcomes

As the majority of third order outcomes are long-term, network-wide outcomes, these are monitored through the network wide baseline datasets that are discussed in Section 2 . However, scheme specific third order outcome baseline data relating to the education workstream is presented within this section.

Continued use of sustainable travel modes to educational establishments: Educational establishment monitoring data provides a baseline modal split for educational trips, and Table 3.9 within Section 3.3.2 shows the modal share used by respondents to travel to educational establishments, which is the baseline for future annual monitoring. By analysing year-on-year modal split, it will be possible to deduce if the SNSC programme has had an effect on legacy modal change for trips to educational establishments.

3.4 Marketing and Communications Workstream Data

Figure 3.13 shows the logic map for the marketing and communications smarter choices workstream and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 3.10 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is working towards these overall objectives. Following the logic map approach, these outcomes are separated into first order, second order and third order outcomes

Table 3.10: Marketing and Communications Monitoring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Increased awareness of Reduced car-based mileage (see sustainable travel modes (see Figure 3.6, Section 3.2.2 ) Reduced unemployment among Figures 3.2-3.5, Section 3.2.1 ) Increased public transport/walking local population (see Table 2.1, Increased skills and confidence and cycling for trips to work (see Section 2.2.1 ) with walking and cycling Table 3.3, Section 3.2.2 ) Reduced accident rates for Perceptions of improved Increased public transport/walking vulnerable road users (see Table accessibility to and cycling for trips to educational 2.11 & Figures 2.1-2.2, Section skills/employment/services (see establishments (see Table 3.9, 2.2.2 ) Figures 3.7-3.9, Table 3.4, Section 3.3.2 ) Reduced congestion, journey times Section 3.2.2 ) Increased share of public and delays (see Section 2.2.2 ) Increased awareness of WorkWise transport/walking and cycling trips Improved air quality (see Section initiatives and support for short/local journeys 2.2.2 )

Figure 3.13: Marketing and Communications Engagement

3.4.1 First Order Outcomes

Increased awareness of sustainable transport modes: Although this data has been reported in Section 3.2.1 (as indicated in Table 3.10 ), further data collected as part of Personalised Travel Planning baseline survey will also be able to be used to assess whether the SNSC programme leads to an increase in awareness of sustainable transport modes, as this survey is collecting data on respondents’ awareness of walking, cycling and public transport within their local area. This data is still in the process of being collected, and so is not currently available to report, although this data will be available to report in future annual monitoring reports.

Increased skills and confidence with cycling: In order to assess if there has been an increase in skills and confidence of the local population in relation to cycling, it is necessary to measure levels of skills and confidence prior to a person receiving cycling training and support, and then again after training with a follow up survey. This method of assessing if cycle training does improve a person’s skills and confidence is already in place by BikeRight!, the SNSC cycle training provider. However, this data is not currently available but will be available for future annual reports.

Increased awareness of WorkWise initiatives and support: The resident’s panel survey collected data relating to respondents’ awareness of WorkWise which provides the baseline for monitoring if the SNSC programme increases awareness of WorkWise initiatives and support. Figure 3.14 shows that of those respondents who took part in the resident’s panel and were unemployed or had experienced unemployment in the last 12 months, 89.8% 25 had not heard of WorkWise before the day of the survey, whilst only 2.8% had heard of WorkWise and used the WorkWise service.

Figure 3.14: Before today, had you ever heard of or used WorkWise?

Source: Resident’s Panel Baseline Data

25 N=1112

Unemployed respondents and those that had been unemployed in the last 12 months were also shown a copy of the WorkWise poster and asked whether or not they had seen it before. Table 3.11 shows that only 4.5% 26 of respondents recognised the poster.

Table 3.11: Have you seen one of these leaflets/posters?

Awareness of leaflets/posters Percentage Respondents Yes 4.5 No 95.5 Total 100 N 1102

Source: Resident’s Panel Baseline Data

In order to assess whether the SNSC programme increases the awareness of WorkWise initiatives and support, it is also possible to use the number of WorkWise passes issued as a baseline, as it can be assumed that if more passes are issued, more of the local population are aware of WorkWise initiatives and support. Therefore, the baseline figures for 2012/13 of 519 interview/day passes issued and 1440 people supported to travel to a new job, as shown in Table 2.2 in Section 2.2.1 , should also be used as an indicator to assess if the SNSC programme increases the awareness of WorkWise initiatives and support.

26 N=1102

3.4.2 Second Order Outcomes

Increased share of public transport/walking and cycling trips for short/local journeys: The resident’s panel survey collected data on how respondent’s travel to their local centre, and this data can be used as a baseline to establish if the SNSC programme leads to an increased share of public transport, walking and cycling trips for short/local journeys. Figure 3.15 shows that the most popular mode of transport used by respondents to travel to their local centres is the car, with 49.6% 27 of respondents travelling by car, either as a driver or passenger. 37.7% of respondents walk to their local centres, and 11.0% travel by bus.

Figure 3.15: Mode of transport usually used to travel to local centre

Source: Resident’s Panel Baseline Data

3.4.3 Third Order Outcomes

All of the third order outcomes relating to the marketing and communications smarter choices workstream are long- term, network-wide outcomes which are monitored through the network wide baseline datasets that are discussed in Section 2 .

27 N=6315

3.5 Community and Residential Workstream Data

Figure 3.16 shows the logic map for the community and residential smarter choices workstream and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 3.12 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is working towards these overall objectives. Following the logic map approach, these outcomes are separated into first order, second order and third order outcomes.

Table 3.12: Community and Residential Monitoring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Increased awareness of Change in road traffic accident Reduced peak period congestion, sustainable travel modes (see rates/severity and associated costs journey times and delays (see Figures 3.2-3.5, Section 3.2.1 ) (see Table 2.11 & Figures 2.1-2.2, Section 2.2.2 ) Section 2.2.2 ) Increased awareness of public Reduced unemployment among transport services/routes (see Reduced car based mileage (see local population (see Table 2.1, Figures 3.2-3.5, Section 3.2.1 ) Figure 3.6, Section 3.2.2 ) Section 2.2.1 ) Increased prominence of walking Perceptions of improved accessibility Change in modal share of trips to and cycling in mode choice set to skills/employment/services (see work Figures 3.7-3.9, Table 3.4, Section Increased skills and confidence Increased physical activity and 3.2.2 ) with cycling improved general health (see Increased public transport use Figures 3.10 & 3.11, Section 3.2.3 ) Increased walking and cycling

Figure 3.16: Community and Residential Engagement

3.5.1 First Order Outcomes

Increased awareness of sustainable transport modes: Although this data has been reported in Section 3.2.1 as shown in Table 3.12, data collected as part of the further Personalised Travel Planning baseline survey will also be able to be used to assess whether the SNSC programme leads to an increase in awareness of sustainable transport modes, as this survey is collecting data on respondents’ awareness of walking, cycling and public transport within their local area. This data is still in the process of being collected, and so is not currently available to report, although this data will be available to report in future annual monitoring reports.

Increased prominence of walking and cycling in mode choice set: Data collected as part of further Personalised Travel Planning baseline survey will be able to be used to assess whether the SNSC programme leads to an increased prominence of walking and cycling in the local populations’ mode choice set, as this survey is collecting data on how often a respondent travels by different transport modes in a typical week. This data is still in the process of being collected, and so is not currently available to report, although this data will be available to report in future annual monitoring reports.

Increased skills and confidence with cycling: In order to assess if there has been an increase in skills and confidence of the local population with relation to cycling, it is necessary to measure levels of skills and confidence prior to a person receiving cycling training and support, and then again after training with a follow up survey. This method of assessing if cycle training does improve a person’s skills and confidence is already in place by BikeRight!, the SNSC cycle training provider. However, this data is not currently available but will be available for future annual reports.

3.5.2 Second Order Outcomes

Increased public transport use/ Increased walking and cycling:

Personalised Travel Planning data provides current levels of public transport use and number of walking and cycling trips, indicated in Table 3.13 which shows the number of respondents who made at a journey by public transport, walking and cycling on the day of the survey. It is therefore possible to analyse these figures year on year to deduce if the SNSC programme increases the number of travel diary journeys undertaken by public transport, walking or cycling.

Table 3.13: Modes of Transport used for at least One Travel Diary Journey Mode of Transport (used for at least 1 journey) Number of Respondents % of Respondents Car Driver 1668 57.6 Car Passenger 246 8.5 Bus 534 18.4 Walk 345 11.9 Train 40 1.4 Cycle 21 0.7 Taxi 21 0.7 Metro 12 0.4 Motorcycle 10 0.3 Base 2897 100

Source: Personalised Travel Planning Baseline Data

Data collected as part of the Personalised Travel Planning baseline survey will also be able to be used to assess whether the SNSC programme leads to an increase in public transport use and an increase in walking and cycling, as this survey is collecting data on how often a respondent travels by different transport modes in a typical week. By analysing the baseline figures of how often public transport, walking and cycling are used to make trips within a typical week against future years data collection, it will be possible to deduce if the SNSC programme increases public transport use and walking and cycling. This data is still in the process of being collected, and so is not currently available to report, although this data will be available to report in future annual monitoring reports.

3.5.3 Third Order Outcomes

Change in modal share of trips to work: Table 3.14 shows Personalised Travel Planning baseline data in relation to the modal split for work journeys undertaken on the day of respondents’ travel diaries. The most popular mode of transport used to travel to work by respondents was the car, with 80.3% 28 of respondents travelling to work either as a car driver or a car passenger. 11.3% of respondents travelled to work by bus, and 4.4% walked to work. By analysing the modal split to work through Personalised Travel Planning data collection year on year it will be possible to deduce if the SNSC programme results in a change in the modal share of trips to work.

Table 3.14: Modal Split for Work Journeys on the Day of Respondents’ Travel Diaries Mode of Transport to Work (Day of Travel Diary) Number of Respondents % of Respondents Car Driver 679 76.6 Car Passenger 33 3.7 Bus 100 11.3 Walk 39 4.4 Train 17 1.9 Metro 5 0.6 Motorbike 5 0.6 Cycle 4 0.5 Taxi 4 0.5 Base 886 100

Source: Personalised Travel Planning Baseline Data

Data collected as part of the Personalised Travel Planning baseline survey will also be able to be used to assess whether the SNSC programme leads to a change in modal share of trips to work as this survey is collecting data on the normal mode of transport used by respondents to travel to and from work on a weekly basis. By analysing the baseline figures of respondents’ normal mode of transport to travel to and from work against future years data collection, it will be possible to deduce if the SNSC programme leads to a change in modal share commuting purposes. This data is still in the process of being collected, and so is not currently available to report, although this data will be available to report in future annual monitoring reports.

28 N=886

Scheme Specific Baseline Data – Infrastructure Improvements

4 Scheme Specific Baseline Data – Infrastructure Improvements

4.1 Introduction

This chapter presents scheme specific data for the LSTF infrastructure improvements, collected through SNSC initiatives. The aim of this chapter is to establish a baseline for infrastructure improvements data and will form the benchmark for a future comparison of travel patterns relating to infrastructure improvement interventions. In order to establish how the SNSC programme is progressing towards the programme objectives, the causal chains present in the infrastructure improvements package logic map, as shown in Figure 4.1 , has been mapped against SNSC data collection schemes to establish key monitoring outcomes. The SNSC scheme specific datasets that have been used to inform the baseline for the infrastructure improvements package are shown in Table 4.1 .

Table 4.1: SNSC Scheme Specific Datasets, Infrastructure Improvements Baseline Dataset Baseline Data Collection Period Next Period of Data Collection Resident Panel Survey February - Mid July 2013 (Coventry Interim Survey June 2014 South Corridor baseline collection September 2013) Station Travel Plan Monitoring Suburban stations baseline Suburban stations November 2014 collection November 2012 Major stations April-May 2015 Major stations baseline collection April – May 2013 Personalised Travel Planning Collected March – May 2013 and; 1 October – November 2013 and; 11 th August – 8th November 2013 Follow up customer satisfaction 1 st October – 13 th December 2013 Stakeholder Panel Survey June - September 2013 Interim Panel Refreshment Activities September – December 2014

Section 4.2 will present the baseline data for the Infrastructure improvements package.

4.2 Infrastructure Improvements Data

Figure 4.1 shows the logic map for the infrastructure improvements package and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 4.2 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is progressing towards meeting these overall objectives. Following the approach set out in the logic map, these outcomes are separated into first order, second order and third order outcomes.

Table 4.2: Infrastructure Improvements Monitoring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Increased awareness of sustainable transport modes (see Reduced car-based mileage (see Figure 3.2-3.5, Section 3.2.1 ) Figure 3.6, Section 3.2.2 ) Increased proportion of shorter Increased awareness of bus distance trips to local areas by Increased bus patronage (see Table services/routes (see Figure 3.2- sustainable transport (see Figure 2.3, Section 2.2.1 ) 3.5, Section 3.2.1 ) 3.15, Section 3.4.2 ) Reduction in bus crimes (see Table Increased perception of safety and Increased reliability of bus services 2.8, Section 2..12) security for pedestrians and cyclists (see Table 2.6, Section 2.2.1 ) Reduced casualty rates for Increased perception of local Increased customer satisfaction with vulnerable road users (see Table environment for pedestrians and bus services (see Table 2.7, 2.11 & Figures 2.1-2.2, Section cyclists Section 2.2.1 ) 2.2.2 ) Increased availability of accurate Increased share of trips to rail Improved air quality (see Section information for bus services/routes stations undertaken by sustainable 2.2.2 ) Perceptions of improved transport Reduced congestion, journey times accessibility to rail stations via and delays (see Section 2.2.2 ) sustainable modes

Figure 4.1: Infrastructure Improvements Package

4.2.1 First Order Outcomes

Increased perception of safety and security for pedestrians and cyclists/ Increased perception of local environment for pedestrians and cyclists:

In order to assess if the SNSC programme increases the perceptions of safety and security and the local environment for pedestrians and cyclists, it is necessary to establish a baseline to benchmark progress against. The resident’s panel survey data relating to the satisfaction of walking and cycling facilities will provide the baseline for these indicators.

The data in Figure 4.2 shows respondent’s satisfaction of walking facilities within their local areas. It can be seen that respondent’s are most satisfied with their personal safety when walking around their local area (72.2% 29 ). 66.5% of respondents are also very satisfied or satisfied with their safety from moving vehicles, indicating that the perception of safety and security for pedestrians is high within repondents’ local areas. Although 68.9% of respondents are satisfied with the direct routes/convenience of pavements, respondents are least satisfied with the upkeep and maintenance of pavements and walkways (59.7%) and signage for showing walking routes (58.3%), indicating that the perception of the local environment for pedestrians is lower then the perception of safety and security within respondents’ local areas.

Figure 4.2: How satisfied are you with the following walking facilities in you local area?

Source: Resident’s Panel Baseline Data

29 N=6438

The data in Figure 4.3 shows respondent’s satisfaction with cycling facilities within their local areas. It can be seen that the level of satisfaction with cycling facilities is lower than the level of satisfaction with walking facilities. However, there is a larger proportion of respondent’s stating they ‘don’t know’ how satisfied they are with cycling facilities within their local area, which could be a result of them being unfamiliar with the cycling infrastructure in their area. In relation to the perception of safety and security for cyclists, 26.4% of respondents were either very satisfied or satisfied with the safety of cycle lanes in their local area, indicating that the perception of safety and security is lower for cyclists than pedestrians. In relation to the perception of the local environment for cyclists, respondent’s were most satisfied with the maintenance of segregated cycle paths (33.7% 30 ), but least satisfied with the provision of advanced stop lines at signal controlled junctions (16.4% 31 ) and cycle parking at local rail stations (16.1% 32 ). Such low levels of satisfaction indicate that the perception of the local environment is lower for cyclists then pedestrians.

Figure 4.3: Please say how satisfied you are with the following cycling facilities in your local area

Source: Resident’s Panel Baseline Data

30 N=918 31 N=915 32 N=913

Increased availability of accurate information for bus services/routes:

In order to assess whether the SNSC programme increases the availability of accurate information for bus service and routes, it is first necessary to establish a baseline position across the network. Whilst physical infrastructure improvements, such as upgrading bus shelters and installing real time information, will increase the availability of information for bus services, it is also necessary to understand peoples awareness of, and satisfaction with, bus service information, which is data collected through the resident’s panel survey.

The data in Table 4.3 shows that 45.8% 33 of respondents are either very satisfied or satisfied with the provision of bus service information at home, whilst 53.4% 34 of respondents are either very satisfied or satisfied with the provision of bus service information available at bus stops.

Table 4.3: Please say how satisfied you are with the following in your local area

Very Satisfied Neither Dissatisfied Very Don’t Satisfied (%) (%) (%) Dissatisfied Know (%) (%) (%) Provision of bus service 10.4 35.4 17.9 6.3 1.1 29.0 information available at home Provision of bus service 12.8 40.6 15.9 4.5 0.8 25.5 information available at bus stops Source: Resident’s Panel Baseline Data

Perceptions of improved accessibility to rail stations via sustainable modes:

The station travel plan survey provides perceptions of interconnectivity of rail stations with sustainable transport modes, which is used as the baseline for this SNSC outcome indicator. The data in Table 4.4 shows that 67% 35 of respondents are satisfied (rated very good or good) with the connectivity of rail stations with other forms of public transport. 12% of respondents do not feel that there is good connectivity between rail stations and other public transport modes; 9% rated connectivity as poor and 3% rated connectivity as very poor.

33 N=6438 34 N=6435 35 N=1823

Table 4.4: Rating of Connections between Rail Stations and Other Forms of Public Transport

Rating Number of Respondents % of Respondents Very Good 440 24 Good 780 43 Adequate 364 20 Poor 168 9 Very Poor 71 3 Base 1823 100 Source: Station Travel Plan Baseline Data

Respondents of the station travel plan survey were also asked to rate the cycling accessibility of rail stations, and Table 4.5 shows that 46% 36 of respondents feel that cycle accessibility of stations is adequate. Only 16% of respondents rated cycle accessibility as very good, however only 8% of respondents felt that cycle access was poor or very poor.

Table 4.5: Rating of Cycle Accesssibility of Rail Stations

Rating Number of Respondents % of Respondents Very Good 176 16 Good 333 30 Adequate 516 46 Poor 64 6 Very poor 27 2 Base 1117 100 Source: Station Travel Plan Baseline Data

Perceptions of walking accessibility of rail stations was also collected through the station travel plan survey and Table 4.6 shows that 86% 37 of respondents are satisfied (rated very good or good) that rail stations are accessible to those travelling by foot. 6% of respondents do not feel that rail stations have good walking accessibility; 4% rated walking access as poor and 2% rated walking access as very poor.

Table 4.6: Rating of Walking Accessibility of Rail Stations

Rating Number of Respondents % of Respondents Very Good 783 40 Good 901 46 Adequate 171 9 Poor 81 4 Very Poor 32 2 Base 1968 100 Source: Station Travel Plan Baseline Data

36 N=1117 37 N=1968

4.2.2 Second Order Outcomes

As the majority of the second order outcomes are long-term, network-wide outcomes, these are monitored through the network wide baseline datasets that are discussed in Section 2 . However, scheme specific second order baseline data relating to the infrastructure improvement package is presented within this section.

Increased share of trips to rail stations undertaken by sustainable transport:

In order to establish a baseline for the modal share used by the local population to travel to rail stations a SNSC scheme specific station travel plan survey was undertaken. Table 4.7 shows that the car is the most popular form of transport to rail stations with 41.4% 38 of respondents travelling to the rail station by car; 24% as drivers and 17.4% as passengers. Respondents walk marginally less to rail stations, with walking accounting for 41% of journeys, whilst 9.4% of respondents travel to rail stations by bus.

Table 4.7: Mode of transport used to travel to rail stations

Mode of Transport Number of % of Respondents Respondents Walk 4986 41.0 Car (as driver) 2922 24.0 Car (as passenger) 2114 17.4 Bus 1140 9.4 Train 678 5.6 Taxi 191 1.6 Cycle 121 1.0 Tram 8 0.1 Motorbike/Scooter 4 0.0 Total 12164 100 Source: Station Travel Plan Baseline Data

4.2.3 Third Order Outcomes

As all of the third order outcomes are long-term, network-wide outcomes, these are monitored through the network wide baseline datasets that are discussed in Section 2 .

38 N=12164

Scheme Specific Baseline Data – Technology Showcase

5 Scheme Specific Baseline Data – Technology Showcase

5.1 Introduction

This chapter presents technology showcase scheme specific data which has been collected through SNSC initiatives. The aim of this chapter is to establish a baseline for technology showcase data and will form the benchmark for a future comparison of travel patterns relating to technology showcase interventions. In order to establish how the SNSC programme is progressing towards the programme objectives, the causal chains present in the technology showcase package logic map , as shown in Figure 5.1 , has been mapped against SNSC data collection schemes to establish key monitoring outcomes. The SNSC scheme specific datasets that have been used to inform the baseline for the technology showcase package are shown in Table 5.1 .

Table 5.1: SNSC Scheme Specific Datasets, Technology Showcase Baseline Dataset Baseline Data Collection Period Next Period of Data Collection Resident Panel Survey February - Mid July 2013 (Coventry Interim Survey June 2014 South Corridor baseline collection September 2013) Workplace Establishment Annual Baseline to close March 2014 (data April 2014 – March 2015 Monitoring Surveys presented within this report was collected to August 2013)

Section 5.2 will present the baseline data for the technology showcase package.

5.2 Technology Showcase Data

Figure 5.1 shows the logic map for the technology showcase package and the causal chains which lead to the monitoring outcomes to assess the success of SNSC in achieving various programme objectives. Using this logic map, Table 5.2 highlights the outcomes which will be monitored on an annual basis to assess how the SNSC programme is working towards these overall objectives. Following the logic map approach, these outcomes are separated into first order, second order and third order outcomes.

Table 5.2: Technology Showcase Monitoring Outcomes 1st Order Outcomes 2nd Order Outcomes 3rd Order Outcomes Improved bus reliability and punctuality (see Table 2.6, Section

2.2.1 ) Increased awareness of passenger Perceptions of improved accessibility Improved air quality (see Section information availability (see Figure to employment (see Figures 3.7, 3.9 2.2.2 ) 3.2-3.4, Section 3.2.1 ) & Table 3.4, Section 3.2.2 ) Reduced peak period congestion, Change in modal share for journey times and delays (see commuter trips (see Table 3.3, Increased awareness of public Section 2.2.2 ) Section 3.2.2 ) transport (see Figure 3.2-3.4, Reduce car-based mileage (see Section 3.2.1 ) Long term modal shift to public Figure 3.6, Section 3.2.2 ) transport for commuter trips (see

Increased patronage levels for public Table 3.3, Section 3.2.2 ) Improved infrastructure for bus transport (see Tables 2.3 – 2.5, Increase in take up amongst bus users Section 2.2.1 ) operators of Smartcards Increase in the number of people using Smartcards and use of WIFI shelters

Figure 5.1: Technology Showcase Package

5.2.1 First Order Outcomes

Improved infrastructure for bus users:

In order to assess whether the SNSC programme delivers improved infrastructure for bus users it is necessary to establish a baseline for infrastructure improvements. As part of the Technology Showcase package solar powered bus flags and real time information is to be implemented along two of the SNSC corridors. Table 5.3 shows that as of September 2013 there have been no solar powered RTI displays installed and have been 59 mains powered RTI displays installed as part of the LSTF programme.

Table 5.3: Implemented RTI Displays Baseline Figures, September 2013

Type of RTI Display Number of Displays Ins talled as Part of the LSTF Programme Solar Powered Bus Flags 0 Mains Powered 59 Source: Centro

5.2.2 Second Order Outcomes

As the majority of the second order outcomes are long-term, network-wide outcomes, these are monitored through the network wide baseline datasets that are discussed in Section 2. However, scheme specific second order outcome baseline data relating to the technology showcase package is presented within Table 5.4 . Table 5.4: Baseline Smartcards on Issue & WIFI Shelters, September 2013

Increase in the number of people using Smartcards and use of WIFI shelters: Type of RTI Display Number of Displays Installed as Part of the LSTF Programme No. SWIFT Smartcards 13 No WIFI Shelters 59 Source: Centro

5.2.3 Third Order Outcomes

As the majority of third order baseline data has been presented in Section 3.2.2 , as shown in Table 5.2 , only third order baseline data relating to the Technology Showcase package is presented within this section.

Increase in take up amongst bus operators of Smartcards:

Table 5.5 shows that as of September 2013 there are currently 10 operators accepting Swift Pay as You Go smartcards.

Table 5.5: Baseline Take Up Figures Amongst Bus Operators of Smartcards, September 2013

Year Number of Operators A ccepting Swift Pay as You Go Smartcards 2013 10 Source: Centro

Appendix A

Appendix A Figure A.1 : Location of Delays, Corridor 1

Figure A.2: Location of Delays, Corridor 2

Figure A.3: Location of Delays, Corridor 3

Figure A.4: Location of Delays, Corridor 4

Figure A.5: Location of Delays, Corridor 5

Figure A.6: Location of Delays, Corridor 6

Figure A.7: Location of Delays, Corridor 7

Figure A.8: Location of Delays, Corridor 8

Figure A.9: Location of Delays, Corridor 9

Figure A.10: Location of Delays, Corridor 10