Tours-Based LUTI Modelling

Transcription

1 Tours-Based LUTI Modelling Leicester and Leicestershire Integrated Transport Model Mark Dazeley, Associate Director 16 th October 2014

2 Presentation Structure Introduction Team technical skills, clients and overview of some Emme models Leicester and Leicestershire Integrated Transport Model Context Model structure Model implementation Model usage and the future Summary

3 Introduction M1 J21, Enderby Park-and-Ride, Fosse Park & Lubbesthorpe Sustainable Urban Extension (SUE) Site, Leicester

4 Model Development and Research Technical Skills Model Development Demand models Matrix development Network models Model suite integration Statistical analysis Parameter estimation Forecasting Scheme appraisal Business case development Research Data analysis & application New methods Contributing to guidance

5 Model Development and Research Current Clients

6 Examples of Emme-Based Models (1) Regional Models East of England Regional Model (includes a SATURN highway model) Leeds Transport Model (includes a tiered Emme highway model & CUBE public transport model) Lower Thames Crossing Model (includes a SATURN highway model) National Models Bulgaria National Model Romania National Model LUTI Models Forecasting Land-Use, Transport and the Economy (FLUTE) (includes a SATURN highway model) Leicester and Leicestershire Integrated Transport Model (LLITM) (includes a tiered Emme highway model)

7 Examples of Emme-Based Models (2)

8 LLITM: Context Leicester and Leicestershire Integrated Transport Model Market Harborough Railway Station

9 Local Context Geography Contains Ordnance Survey data Crown copyright and database right 2014

10 Local Context Images

11 Local Context National Policy and Local Issues National Policy transport a catalyst for growth competition for national scheme funding devolving of other funding local authorities required to: o develop local development and delivery strategies o develop and monitor local transport plan indicators Local Issues significant decline in bus patronage investment in Smarter Choices (sustainable travel and marketing) significant new developments (SUEs) strategic freight rail interchange (SRFI) Highways Agency needs a strategic modelling capability

12 LLITM: Model Structure Leicester and Leicestershire Integrated Transport Model Cycle Route Signage, Coalville

13 Model Structure Demand Model Trip Frequency Emme EMME How? Active Mode Choice (motorised vs. active) When? Public Transport Time Period Choice Motorised Mode Choice (car vs. public transport) Time Period Choice Car Time Period Choice Walk+Cycle Trip Distribution Trip Distribution Trip Distribution Where? Parking Choice Public Transport Mode Choice (rail vs. bus) PNR CUBE Emme VOYAGER Rail Bus On-street Off-street Park-and-Ride

14 Model Structure LUTI Model 2008 Supply Models Highway Network Public Transport Network 2011 Supply Models Highway Network Public Transport Network 20xx Supply Models Highway Network Public Transport Network 2008 Demand Model Network Assumptions Growth Assumptions Economic Assumptions Parking Assumptions 2011 Demand Model Network Assumptions Growth Assumptions Economic Assumptions Parking Assumptions 20xx Demand Model Network Assumptions Growth Assumptions Economic Assumptions Parking Assumptions 2009 Planning Data 2010 Planning Data 2011 Planning Data DELTA Land-Use Model (David Simmonds Consultancy) 20xx-4 Planning Data 20xx-3 Planning Data 20xx-2 Planning Data 20xx-1 Planning Data 20xx Planning Data Analysis and Reporting Key Network data Cost data Planning data Model outputs Planning Forecasts Network Statistics Matrix Statistics TUBA EASE

15 Model Dimensions Zones and Networks Model zoning: consistent across models 973 zones 850 zones in Leicestershire Highway (SATURN) model: 19,000 simulation links 9,400 simulation nodes Public transport model: all bus, coach and rail services intersecting Leicestershire

16 No Car, Part Car, Full Car Model Dimensions Demand Segmentation Car Own. Journey Purpose Income HB Commute (Home-Work) Low Med High 19 demand segments HB Education HB Shopping 3 car ownership levels HB Other NHB Other (non-business) HB Employers Business 4 modes: highway, rail, bus, active (walk & cycle) NHB Employers Business Light Goods Vehicles Heavy Goods Vehicles

17 Model Dimensions Time Periods and Tours Home-based trips assumed to form 2-legged tours: links outbound and return trips allows consistent model choices mode choice and distribution but greater data requirements Time Period Duration Early 00:00-07:00 AM Period 07:00-10:00 Interpeak 10:00-16:00 PM Period 16:00-19:00 Late 19:00-00:00 We assume that no tour returns earlier in the day than it departs: 98% of tours do this significant reductions in model size reductions in run times Early AM IP PM Late Early AM IP PM Late

18 LLITM: Model Implementation Leicester and Leicestershire Integrated Transport Model Peak Hour Congestion, Leicester City Centre

19 Implementation Hardware and Processing Implemented using Emme/3 Dedicated modelling servers 15k rpm SAS Drives (x3) RAID0 configuration 12/16 core processing Separate databanks for each demand segment partly due to Emme/3 dimension limits allows parallel running, reducing model run times Separate databanks for assignments simplified (tiered) Emme highway assignment replaces SATURN within demand model iterations parallel running reduces model run times Demand Segments Commuting Low Income Commuting Med Income Commuting High Income Education Low Income Education Med Income Education High Income Shopping Low Income Shopping Med Income Shopping High Income HB Business HB Other Low Income HB Other Med Income HB Other High Income NHB Business NHB Other Low Income NHB Other Med Income NHB Other High Income Light Goods Vehicles Heavy Goods Vehicles Highway Model AM Peak Hour Average Interpeak Hour PM Peak Hour Average Night (OP) Hour Public Transport Model AM Peak Hour Average Interpeak Hour PM Peak Hour Walk/Cycle Model All Day

20 Tiered Highway Model Overview Rationale: SATURN expected by client but estimated run times were unacceptably high in 2009 issue reducing with hardware and software improvements Tiered Emme highway assignment used to: reduce model run times improve demand-supply convergence reduces data transfer between Emme and SATURN Need for validation of Emme vs. SATURN costs

21 Tiered Highway Model Implementation LUTI Model Pre-demand model SATURN assignments used to calculate: link VDFs turn VDFs Transport Model Land-Use Model Trip End Model These SATURN assignment data then converted to Emme networks identical topography reflect networks and reference demand for a given scenario remove dynamic junction modelling from the demand model iterations Create Reference Demand Pre-Assignment (SATURN) Demand Model (tiered assignments) Post-Assignment (SATURN)

22 Tiered Highway Model Validation Absolute cost comparison shows slope and R 2 values close to 1 Cost change comparison has slopes around 1, and R 2 values around 0.95

23 Tiered Highway Model Performance Summary Tiered Emme highway assignment provides: very good fit in terms of absolute costs some scatter when comparing cost changes between Emme and SATURN Main source of scatter due to the calculated turn VDFs: within SATURN these are recalculated in response to flows at a given junction however, turn VDFs are fixed within Emme can lead to inconsistencies where the junction is at or above capacity Soar Valley Way, South-West Leicester

24 Other Emme-based Techniques Developed in LLITM Automatic calibration of synthetic matrices for merging with observed demand data Marginal social cost pricing algorithm uses the tiered approach used to internalise the externalities of road users calculates a marginal social cost of an additional trip: noise accidents air pollution carbon emissions road maintenance fuel costs time Developed process to calibrate public transport tour matrices based on demadjt.mac used to reconcile OD and PA tours matrices

25 LLITM: Model Usage and the Future Leicester and Leicestershire Integrated Transport Model M1 Motorway, West of Leicester

26 LLITM Applications Government funding bids m ($45.5m) inner relief road and PT improvements m ($23.8m) sustainable travel initiatives 8.5m ($13.7m) new development access infrastructure - 5m ($8m) Local government development policy / strategy informing decisions on the location and quantum of development developing area-wide transport and land-use strategy Assessment of individual development proposals assessing transport system impacts developing mitigation packages

27 LLITM Model Forecasts Drivers of Travel Growth Changes in traffic vs (vehicle kilometres) Growth Driver Population Growth 3% 8% 14% 19% 24% Car Ownership / Economics 0% 2% 3% 5% 6% Fuel Cost per km 3% 4% 8% 12% 15% Car Passenger Occupancy 1% 1% 2% 3% 4% Smarter Choices 0% -2% -2% -2% -2% Highway Congestion -1% -3% -7% -11% -14% Total Growth (from 2008) 3% 11% 18% 25% 32% Other Modes Public Transport Passengers 2% 4% 7% 8% 10% Walk / Cycle Trips 4% 8% 12% 15% 19%

28 LLITM Model Forecasts Outputs

29 LLITM 2014 Model Update Migration to Emme/4 New data 2014 base year new planning data (inc Census) extensive survey data mobile data Updated network models New functionality unified highway assignment public transport sub-mode choice

30 LLITM: Summary Leicester and Leicestershire Integrated Transport Model Competing Buses in Leicester (First and Arriva)

31 Summary of LLITM LUTI Model Model strengths: provides policy makers with a consistent evidence base underlying data, demand segmentation and spatial detail o allows a variety of applications to be undertaken o provides evidence to inform transport and land-use policy relatively few fully integrated transport and land-use models in UK Model weaknesses: integrated model results in significant run times model scope can encourage inappropriate use Key benefits of Emme: flexible platform to control model components and interfaces macro language allows efficient coding of model processes multiple databanks provide significant time savings

32 Any Questions? Bradgate Park, North-West of Leicester

33

34 Automatic Calibration of Synthetic Models Bradgate Park, North-West of Leicester

35 Automatic Calibration of Synthetic Models As part of base year matrix development an Emme-based synthetic demand model was developed Function takes the form: Need to calibrate α and based on observed data Often the values of α and can differ between short and long trips

36 Automatic Calibration of Synthetic Model Iterative matrix build process developed using combinations of α and parameters Goodness of fit measured using comparisons of average and standard deviation of trip-cost Process homes in on best solution by using smaller increments around the current best values through the process

37 Public Transport Tours Calibration Bradgate Park, North-West of Leicester

38 Matrix Estimation demadjt.mac AM Peak Interpeak PM Peak Screenline Direction Observed Modelled Diff Observed Modelled Diff Observed Modelled Diff Central Transport Inbound 5,491 3, % 3,353 1, % 1,784 1, % Central Transport Outbound 1,587 1, % 2,599 2, % 4,244 2, % City Centre Inbound 5,236 5, % 4,063 3, % 2,267 2, % City Centre Outbound 4,196 4, % 4,677 3, % 5,925 4, % N-S Screenline Eastbound 2,712 3, % 1,869 1, % 1,041 1, % N-S Screenline Westbound 1,252 1, % 1,948 1, % 2,828 2, % Outer Ring Road Inbound 2,747 3, % 1,677 1, % 1,219 1, % Outer Ring Road Outbound 1,844 1, % 1,608 1, % 2,580 2, % Hinckley Inbound % % % Hinckley Outbound % % % Loughborough Inbound % % % Loughborough Outbound % % % AM Peak Interpeak PM Peak Coalville Inbound % % 99 Screenline % Direction Observed Modelled Diff Observed Modelled Diff Observed Modelled Diff Coalville Outbound % % 163 Central 220Transport 34.8% Inbound 5,491 5, % 3,353 3, % 1,784 1, % Ashby Inbound % % 71 Central 32Transport -54.0% Outbound 1,587 1, % 2,599 2, % 4,244 4, % Ashby Outbound % % 84 City Centre % Inbound 5,236 5, % 4,063 4, % 2,267 2, % Lutterworth Inbound % % 34 City Centre % Outbound 4,196 3, % 4,677 4, % 5,925 5, % Lutterworth Outbound % % 36 N-S Screenline % Eastbound 2,712 2, % 1,869 1, % 1,041 1, % Market Harborough Inbound % % 86 N-S Screenline % Westbound 1,252 1, % 1,948 2, % 2,828 2, % Market Harborough Outbound % % 83 Outer 38 Ring Road -54.5% Inbound 2,747 2, % 1,677 1, % 1,219 1, % Melton Mowbray Inbound % % 104 Outer 68 Ring Road -34.9% Outbound 1,844 1, % 1,608 1, % 2,580 2, % Melton Mowbray Outbound % % 90 Hinckley % Inbound % % % 27,717 26, % 23,851 17, % 23,805 Hinckley 16, % Outbound % % % Loughborough Inbound % % % Loughborough Outbound % % % Coalville Inbound % % % Coalville Outbound % % % Ashby Inbound % % % Ashby Outbound % % % Lutterworth Inbound % % % Lutterworth Outbound % % % Market Harborough Inbound % % % Market Harborough Outbound % % % Melton Mowbray Inbound % % % Melton Mowbray Outbound % % % 27,717 26, % 23,851 21, % 23,805 20, %

39 The Problem Too Low Too High

40 Some Less Elegant Solutions Apply factors to each leg of a tour 1.47 a b 0.66 Break link between demand and assignment models

41 PM Peak Interpeak AM Peak A Better Approach Tour-Based Estimation AM Peak AM Peak Interpeak PM Peak Interpeak PM Peak

42 Marginal Social Cost Pricing Algorithm Bradgate Park, North-West of Leicester

43 Elements of Marginal Cost Noise pollution Accident costs Air pollution Carbon emissions Road maintenance Fuel costs Time savings

44 Travel time (mins) Marginal Cost of One Vehicle Number of Cars