Technical Note
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Technical Note Project: West Coast Mainline Passenger Study Subject: West Coast Mainline - Highway Benefits Client: Campaign for Better Transport Version: 1 Project No: 03959 Author: AK Date: February 2019 Approved: PJ 1 Introduction 1.1.1 PJA has been commissioned by Campaign for Better Transport to undertake an assessment of the highway benefits the West Coast Mainline (WCML) has had on the strategic road network (SRN) across Great Britain. 2 Methodology 2.1.1 Virgin Trains provided PJA with a dataset showing the annual number of railway journeys using any section of the WCML between pairs of origin and destination stations for the years 2009 and 2018. The journeys between each origin-destination pair are the total journeys in both directions. Due to the nature of flexible tickets, there is no way of telling which route passengers travelled on. Therefore, the number of actual journeys associated with the origin-destination pairs was calculated by Virgin Trains based on an assumed proportion of the number of tickets purchased. 2.1.2 To manage the scale of the analysis, PJA filtered the dataset to exclude all origin-destination station pairs with fewer than 10,000 associated annual journeys for both 2009 and 2018. The remaining journey pairs represent 73% of journeys in 2009 and 80% of journeys in 2018. As the excluded journey pairs are likely to follow the same sections of road network, the flows for the journey pairs analysed were factored up by 37% for 2009 and 25% for 2018 to represent the total passengers on the WCML. 2.2 Routing 2.2.1 Each station associated with these origin-destination pairs was geographically plotted using ArcGIS Pro. For some larger cities such as Birmingham and London, the dataset provided did not specify the station. In these cases, PJA plotted the most central station, e.g. London Charing Cross for London. LOCATION Brew House TELEPHONE 0117 325 1520 WEBSITE pja.co.uk Jacob Street EMAIL [email protected] Tower Hill Bristol BS2 0EQ 2.2.2 Using ArcGIS Pro’s Network Analyst extension, the route on the Strategic Highway Network (SRN) was calculated between each origin-destination pair along the road network for both 2009 and 2018. 2.2.3 To understand the theoretical additional traffic demand on the SRN if these railway journeys had been made by road, a join was performed between each origin-destination pair route and the underlying SRN. The output of this was a sum of the total additional demand for road journeys from each origin-destination station pair on each link in the SRN. 2.2.4 To convert these person-journeys into car traffic, a car occupancy value was obtained from the National Travel Survey. The category ‘All purposes’ was used to reflect the variety of purposes for which train journeys are made, the value for 2009 was 1.58 and the value for 2017 was 1.55. This car occupancy value was then applied to the demand on each link on the SRN. The value for 2017 was used for the 2018 data in absence of a more recent figure. 2.2.5 Mapping outputs of the theoretical additional annual traffic flows as well as average annual daily traffic (AADT) on the SRN were then generated. These are reproduced at Appendix A. 2.3 Comparison with Baseline 2.3.1 To establish the highest areas of traffic demand on the SRN, the top 100 road links with the highest additional demand were extracted for both 2009 and 2018. For these locations, PJA downloaded 20 traffic count datasets from the Department for Transport (DfT). 2.3.2 These count locations were then plotted on GIS. The additional flow created by the transfer of WCML journeys at each of these locations was then compared with the DfT’s observed AADT for all vehicles for both 2009 and 2018, the results of which are reproduced at Appendix B. 2.4 Forecast Delay 2.4.1 In order to forecast the traffic delay that would result from the transfer of journeys from the WCML to the SRN, speed-flow curves used in the DfTs COBA appraisal tool have been used to infer the change in speeds due to the additional forecast flows. 2.4.2 A publicly-available report prepared by Atkins (G-BATS3 v2.3 Highway Local Model Validation Report) was used which contains an appendix showing COBA speed-flow curves, and which have been converted for use in SATURN modelling. The report provides the parameters for various road types. For the purposes of our analysis, the speed-flow curve for road type Rural D3 Motorway (Index 1) has been used, which best matches the sections of the SRN that would be affected by a transfer of journeys from the WCML. This is shown in Figure 2-1 overleaf. 2 Figure 2-1: Speed-Flow Curves for Different Road Types (Appendix B Figure B.1 G-BATS3 v2.3 Highway Local Model Validation Report) 2.4.3 The Highways England data for the affected road network showed that the AM peak across the network is between 0800 -0900 and represents 10% of all daily traffic. This 10% factor was then applied to the DfT AADT data to derive the AM peak flows. The peak hour for the predicted flows from the WCML was supplied by Virgin Trains. 2.4.4 From the existing and forecast flows, speeds were derived from the graph above. For each section of road, the time taken for a vehicle to drive along each section was calculated based on the speed. The delay was then derived from the difference in driving time for the existing flows and with the additional forecast flows. 3 3 Results 3.1 Routing 3.1.1 The routing algorithm within Network Analyst uses the shortest path on the road network. Analysis across the UK showed that the roads with highest associated forecast demand are sections of the M40, the M6 Toll and the M6. These roads broadly follow the route of the WCML, as shown in overleaf in Figure 3-1. This simple shortest path assumption was then manually adjusted, as referred to below. 3.1.2 The maps at Appendix A indicate that the largest number of people would travel between London and Birmingham along the M40 in both 2009 and 2018. In the dataset provided by Virgin Trains, the two highest journey numbers are associated with two-way journeys between London and Manchester and London and Birmingham. Both of these journey pairs would use the M40 as the main route into and out of London based on the shortest path assignment. The maps also show significant additional demand would occur on the M6 north of Birmingham. 4 Figure 3-1: West Coast Mainline (Source: Project Mapping) 3.1.3 The station (cluster) with the highest number of journeys associated with it is London. As mentioned previously, a central location, London Charing Cross, has been chosen. Depending on the actual origin of the trip (which would typically have been different to the originating station), there is likely to have been a greater proportion of journeys which would chose to travel on the M1 instead of the M40. The proportion of journeys which would likely choose to follow the M1 cannot be easily determined, therefore, the following analysis in this report will analyse the M1 and M40 flows as one overall highway corridor. 3.1.4 Similarly, due to the shortest path algorithm, the routes travelling to the north of Birmingham are forecast to largely travel on the M6 Toll. In reality, as a result of the toll charge, a large proportion of these vehicles would choose to use the M6. 5 3.1.5 The proportion of journeys which would use the M6 Toll was assessed by comparing traffic flows on the M6 through the West Midlands with the M6 Toll, as a proxy for its overall attractiveness as a route. A DfT traffic count from 2017 gives the AADT for cars and taxis on the M6 north of the junction with the M5 as 84,078, with 42,370 on the M6 Toll at a broadly central location, totalling to 126,448 on both these sections of Motorway. This equates to 34% of all traffic passing through the West Midlands motorways using the M6 Toll. The flows for the shortest path scenario and this manually adjusted scenario are summarised in Table 3-1 below. Table 3-1: Flows on M6 and M6 Toll Scenario Road 2009 2018 Shortest Path M6 1,008 1,257 M6 Toll 5,516 19,028 M6 4,693 14,605 34% of cars / taxis on M6 Toll M6 Toll 1,827 5,680 3.2 Comparison with Baseline 3.2.1 As shown in the mapping outputs at Appendix B, the comparison with DfT count points shows a maximum percentage increase of traffic of 17% in 2009 and 18% in 2018 caused by the transfer of journeys from the WCML to the SRN. 3.2.2 The comparison points for the M40 and M1 are shown diagrammatically between the two roads. The points show graphically the percentage increase in the total flow on the two roads due to the transfer of journeys from the WCML. 3.2.3 As discussed previously, only 34% of cars / taxis between the M6 and the M6 Toll travels on the Toll road. Therefore, this has been amended for the purposes of the analysis and is demonstrated in the outputs at Appendix B and flows in Table 3-2 below. Table 3-2: Comparison of Flows DfT Traffic Count AADT DfT AADT DfT WCML AADT WCML AADT % Increase % Increase Location Codepoint 2009 2018 2009 2018 09 18 M6 6027 117,399 119,888 6,860 18,255 6% 15% M6 6028 175,733 169,187 3,798 6,054 2% 4% M56 6047 140,810 156,952 1,863 10,744 1% 7% M6 6048 73,752 81,373 1,863 10,744 3% 13% M6 16030 102,694 107,521 1,783 5,198 2% 5% M6 26031 60,135 73,834 1,278 3,132 2% 4% M6 73324 62,276 69,973 1,288 3,240 2% 5% 6 DfT Traffic Count AADT DfT AADT DfT WCML AADT WCML AADT % Increase % Increase Location Codepoint 2009 2018 2009 2018 09 18 M6 81271 127,142 110,877 6,573 17,568 5% 16% M42 38718 118,510 141,580 9,812 20,613 8% 15% M6 46023 112,897 113,949 7,467 19,140 7% 17% M6 56025