Peter Hewitt. Transport for London

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1 Peter Hewitt Transport for London

2 What the modelling community needs to crack! Peter Hewitt 23 NOVEMBER 2016

3 Surface Transport s recent history on modelling TfL strategic planning: - LTS, SATURN through LoHAM models TfL Tactical/ Operational: - ONE (Visum), Vissim, Aimsun, LinSig, TRANSYT TfL Visualisation - 3ds Max, Photoshop, After Efects, Unity 3D, VR/AR Other Regional/ City strategic planning: - Regional transport SATURN models Tactical/ Operational: - Vissim, Aimsun, Paramics, LinSig, TRANSYT Visualisation - Vissim 3D

4 London s transport changes heading our way People demands in London Population in M (c. 27 million trips/ day) Population forecast in million (c.27.5 million trips/ day) Population forecast in million (c. 30 million trips/ day) Population forecast in million (c.32 million trips/ day) Growth up 19% on 2000 & higher than MTS forecasting Daytime population in 2014 was 9.6M (+11.6%) The city s housing situation is focusing on regeneration in opportunity & growth areas East London, Lee Valley, Old Oak Common & other Metropolitan town centres such as Croydon Social changes People are living longer More people are staying, cities are becoming ever greater hubs of people. Older age groups are stating, families are staying, less are moving to home counties etc Source: GLA online portal More single/ low occupancy households, more shared living

5 Social City wide changes for consideration Social changes - Growth & housing intensification along the eastern areas of the River Thames, the Lee Valley, Heathrow area & town centres such as Croydon & Old Oak Common (already known and factored into long term planning) - Land use change is seeing more use of the public highways as a just in time mobile freight facility - Expansion of Central Area Zone beyond Inner Ring Road Technology changes - Greater use of door to door freight deliveries (e.g. Amazon deliveries) & implications of just in time deliveries (greater population likely to see increased) - Private hire/ app capability service provider increasing - Apps and information assist facilitate the use of private vehicles & CAVs & private hire more (trips previously used by more sustainable modes) All London trips in 2014 Surface Rail Underground (inc. DLR) Bus (inc Tram) Taxi/Private Hire Car driver Car passenger Motorcycle Cycle Walk Source: GLA online portal & TfL Surface Planning

6 Planned transport strategies to meet these changes Pot ent ial London Net work delay (2016 baseline = 5.5Billion/ annum) Increase in delay Effect of Crossrail 1 (Elizabeth line), Silvertown tunnel inc. Effect of Surface schemes such as Wandsworth Town Centre, Cycle superhighway Route 11 and 4 Effect of sustainable policies such as cycle, walking, freight management million people Effect of Crossrail 2 tunnel inc million people Source: GLA online portal & RSM Insight & Analysis Team million people Hypothetical trends to invoke thought & discussion London base delay posit ion over t ime Forecasting the known ones!

7 The unknown considerations Connected vehicles arrive in 2018 & full autonomous vehicle capability from 2020 onwards [interaction between CAV, non CAV capabilities & other road users] Data processing capabilities moving at a faster pace than ever [TfL development of SITS (Surface Intelligent Transport System) & predictive modelling capabilities] Freight management through: - Drone capability moving forward [security risks of air space. Singapore are developing a drone air traffic control] - Printers used to be about ink now they are 3D - healthcare prosthetics & automotive industry components, construction industry fabrication Mobility as a Service public transport intent for cities such as Helsinki and Lisbon.

8 Increase in delay million people Is this a plausible demand scenario? Pot ent ial London Net work delay (2016 baseline = 5.5Billion/ annum) Source: GLA online portal & RSM Insight & Analysis Team million people Effect of Crossrail 2 tunnel inc million people London base delay posit ion over t ime Effect of Surface schemes such as Wandsworth Town Centre, Cycle superhighway Route 11 and 4 Effect of Crossrail 1 (Elizabeth line), Silvertown tunnel inc. Effect of sustainable policies such as cycle, walking, freight management Effect of CAV capabilities & other Smart technologies (inc empty running) Effect of MaaS optioning Hypothetical trends to invoke thought & discussion and approaches (inc empty Effects of data processing running) with SITS being the obvious beneficary Effects of greater commerical penetration into transport Potential benefits due to through Apps, car club drone capability replacing advising on road network surface freight management benefits Potential benefits of 3D printer capabilities replace need for material supply Potential public Transport benefits - Modelling community needs access to credible research and accepted data to the expansion of use of new technologies. - Assurance roles need to be established Forecasting the known ones & adding in the less known!

9 Corridor Management of London s strategic road network TfL manages its key network on a corridor basis (e.g. A1203 & A3211 Limehouse link to Parliament Square) London s Street network - 14,800km TLRN 580km (40% of all London s traffic use) By focusing on the holistic operation of the corridor its weakest points are neutralised for optimum efficiency Today locations such as Tower Hill, Southwark Bridge, Northumberland Avenue and Parliament Square are the critical locations that dictate upstream and downstream traffic patterns Will the strategic use of the network remain the same? - Who controls the routing decision internet engines, App services, OEMs, TfL/ traffic authorities? Q: Year by year can network constraint locations (e.g Tower Hill gyratory) distribute more vehicles through them? If yes, can the next downstream constraint location achieve this (e.g. Southwark Bridge) Source: RSM Insight & Analysis Team

10 2020 Potential effects over time at critical locations Example to challenge A network congestion zone with high volumes of pedestrians & cyclists Network constraint location high volumes of pedestrians & cyclists can junction staging arrangements be modified or will they remain largely unchanged? E Over time: - Greater Nos. of CAV 3 & 4/ 5 capability on the network - Vehicle platoons become more connected - Greater volumes of empty vehicles materialise Key: Would the performance of CAVS be as efficient in a dense urban location? Effect of none motorised people behaviours on autonomous vehicles? Performance transition zone on approach to complex urban interchanges Expected corridor benefit zones through connectivity Present day vehicle CAV3 vehicle CAV4+ vehicle Public transport vehicle Intensified pedestrian & cycle zone Q: The output of this would have a significant effect on the role buses move forward their performance in a city where 15% of all travel is bus traffic is critical! E CAV4+ empty vehicle

11 It s complex! A city such as London has to get it right! Scenario 1 (Central Umbrella control) Scenario 2 (Market forces control) Scenario 3 (Central control of network & access) Scenario 4 (Slow time development model) High population & Growth High Population & Growth High population and growth Stagnant population and growth TfL or similar becoming all providing & control of network access TfL give over access to market forces TfL takes on prescriptive roles e.g. from buses to MaaS?, London Underground, policy direction for all new transport modes TfL facilitates balance between market forces and facilitating network control to promote growth Demand managed through allocation of time, space and potential pricing differential TfL provide an overall umbrella control, but individual organisations left to managed interfaces Demand controlled and managed centrally Demand managed through allocation of time, space Highly managed network access Pressures likely at many high profile locations (e.g. Central area, retail) Streets could become used quite differently Very effective use of the network Highly managed network access

12 The question to ask ourselves? Q: The pace of data processing is rapid, it will change industries. Is the traditional modelling software Industry able to keep a pace of developments to meet a multitude of potential outcomes? Q: There is a move away from traditional modelling to agent based and transfer of significant volumes of data and potentially from multiple organisations what does this mean for the modelling community? Q: If the Industry is going to find it difficult to keep pace, where do the threats come from is it the motor manufacturers, internet providers? Q: What comes into scope in the Surface transport world? Is the effect of drone developments? Is it the evolving landscape of MaaS? Is it the social changes through 3D printers? The influence of external providers of Apps, Car clubs, private hire trends? Evolving big data network operational tools?

13 For more information please contact: Peter Hewitt

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