Presentation on theme: "Using Cube for Public Transport Planning"— Presentation transcript:
1 Using Cube for Public Transport Planning An OverviewAndreas KöglmaierRegional DirectorAs some of our clients use Cube mainly for the modelling of road traffic and are less familiar with the aspects of public transport modelling I thought it is a good idea to provide an overview of what needs to be considered when using Cube for public transport planning processes. Obviously this presentation has not the intention to provide a comprehensive step by step guide to develop a pt model. It rather should be first step to get involved in PT modelling for new users and provide other aspects to consider for existing PT modellers.
2 Content Reasons for modelling public transport Public transport data availabilityConsiderations regarding network representationRepresenting passenger behaviour (mode choice and route choice)Public transport model development in CubeSpecific considerations (fare, park and ride, crowding)Examples of Cube public transport modelsWhy should we model PT? What data do we need to have? How can we represent the network (Supply)?How can we represent the demand?
3 Reasons for building a public transport model Forecast public transport demand and revenueAnalyse PT projects (economic analysis)Optimise the routing of PT linesTesting different ticket systemsRevenue allocation for integrated ticket systems with multi operatorsEstimate future run timesBefore we build a model we should always consider what is the purpose of the model
4 Public transport data availability System data (Supply)Network data (line routing, stopping pattern, timetable)Observed vehicle speeds (delays)Reliability (delays, cancelations, accessibility of vehicle)Quality of vehicles and station infrastructure (comfort)Ticket and tariff System
5 Public transport data availability Passenger data (Demand)Boarder and alighter informationVehicle loadings (crowding information)Passenger origin and destination informationDemographic Information (age group, car ownership, …)Usage of different ticket typesMode choice and route choice behaviour (stated and revealed preference surveys)
6 Representing the public transport system Physical infrastructure (roads, tracks, stations)Stick network or shape networkStation single node or detailed modelling of interchangesSpeeds taken from network or from service definitionWalkTime = ~1 minStreetsBus stop nodeRail PlatformRail
7 Representing the public transport system Service definition and routingDecision on which services to include (special school runs)Consider simplification of networkCoding of services as one or two way lines (one way streets)Organisation of the system (operators, modes, fares)Understand which parameters influence route and mode choiceLevel of representation depends on set up of demand model
8 Cube represents complexity of PT Systems The route, where it stops (boarding and alighting: both, only boarding, only alighting), and its variations (sub-routes) by period of the day. Details such as boarding delays at selected stops..The type of vehicle providing the service (bus, light rail, heavy rail, ferry…etc.) and its capacity (seated and crush). (capacity restraint is provided in PT)Information about multiple PT operators and their operating/fare policiesFull representation of circular routesUses function of roadway speed, set travel times via time points, used fixed time.Highway networkTransit lineCube provides the possibility to have this representation:It can do a everything therefore it provides the following files:Highway networkTransit lineSystem data
9 Representing the passenger behaviour Understanding route and mode choice behaviourChoosing between bus and rail (mode or route choice?)Considerations for mode choice modelGroup passengers in homogenous groups with similar route choice behaviourConsider ticket choice modelConsiderations for route choiceValue of different trip componentsEach user-class may have unique factors which determine:Which routes are reasonable…How much walking?How many transfers?What are the access characteristics?What is the maximum cost?What is the maximum travel time?Which routes are desirable…What is the perceived time for walking, transferring, waiting, riding (by mode)?Are there rebates or special fare systems?What is the sensitivity to crowding/capacity restraint?What is the value of time?What is the willingness to pay?AUTODRIVE ALONESHARED RIDESHARED RIDE 2SHARED RIDE 2+TRANSITWALKBUSLIGHT RAILCOMMUTER RAILCIRCULATORDRIVECIRCULATOR`
10 Representing the passenger behaviour Deviding the trip in its components
11 Representing the passenger behaviour HomeRail PlatformBus StopTransferTransit pathMode Choice, Route Choice and Wait curveAuto pathTollWorkParking
12 PT MethodologyCompiles Data and Simplifies Network (Produces NET file)Enumerates “Acceptable” Discrete Routes for every O-D (RTE file)Finds least cost routeEnumerates all other routes within defined limitsEvaluates Routes and Performs Analysis:Decisions on access and assignment to individual lines through a series of logit choice models.Route evaluation through a hierarchical logit choice model.
13 Route EnumerationEnumeration finds a traveler's reasonable public transport routes from origin to destinationIdentifies full discrete routesThe route should move progressively from the origin to the destinationTravelers tend to select journeys that are simpler – that are direct or involve few interchangesTravelers are unwilling to walk very long distancesThese principals are used to constrain the potentially huge computational task of identifying all reasonable routesThe process can be considered analogous to a traveler using a map to reject routes which are very long relative to more direct alternativesCreates a dataset of the ‘reasonable’ routes between each origin and destination by user class
14 Route EvaluationEvaluation ‘qualitatively judges’ the routes calculated in the route enumeration stage of PTThe elements that can be used in this processLimit on the number of transfersThe difference (actual & percentage) difference between the minimum cost route and the evaluated routeLimit on non-transit cost (walk/drive access)Limit on waiting and transfer timesLimit on In-vehicle costsSpecified by user class – or – market segmentProvides attractive and reasonable routes along with their probability of being used and the costs of each of the routes.Calculates the % probability of using each path using choice models at each decision point
15 Report and Visualize PT Results & Inputs ReportsGraphicsRecord ProcessingSelect Link Analysis
16 Fare System Representation All sorts of complex fare systems can be modeled by user classFREE – No cost incurredFLAT – One fixed cost per useDISTANCE – Possible boarding cost + unit cost per distance or cost lookup tableFROMTO –Fare zone matrix based on the start/end zonesCOUNT –Counts number of fare zones crossed, sum number of zones crossedACCUMULATE – Each fare zone has a fare and when crossed adds to cost
17 Representing Park and Ride Input Car & PT cost matricesDefine Catchment Area, City zone & Station zoneCalculate Car cost: Catchment to Station & oppositeCalculate PT cost: Station to City & oppositeCombine to calculate Catchment to City via station cost & oppositeConsider cost penalty for parking time.Where there is more than one station in a catchment, the process is repeated for each station to determine the station with the minimum cost for each zone in the catchment.Output a Park and Ride cost matrix
18 Representing Park and Ride Rail StationRailBus StopPNR LotRail PlatformEscalator linkPNR lot–stop access connectorBusesStreetsDriveway link18
19 Representing crowding Vehicle capacity required as inputUnderstand changes in comfortSeating capacity and crush capacityDefine crowding curvePenaltiesIn-vehicle penaltyBoarding penaltyIterative process: Loaded demand from an iteration is used to update the following for the next iterationLink travel timesThe probability of boarding a line at a particular stopLink travel time adjustmentThe link travel time adjustment models passenger perceptions that travel time is more onerous when they have to stand (rather than sit), or when the vehicle is crowded. The adjustment is represented by a “crowding factor,” which is multiplied by in-vehicle time to give the perceived value of “crowded in-vehicle time.”Wait time adjustmentIn the context of a transit leg from a boarding point to an alighting point with several lines operating, demand (without crowding) is allocated using the service frequency model (SFM), or service frequency & cost model (SFCM). The wait time adjustment redistributes the initial SFM or SFCM loadings whenever any line does not have the available capacity to take its allocated demand. This excess demand is reallocated to other lines which have spare unused capacity, and incurs additional wait time.The additional wait time (due to not being able to take the first service) may make this route less attractive, and so cause diversion of demand to other enumerated routes for the origin-destination pair.
20 Multimodal model Spain 4. Exemplos internacionais de utilização do CUBE – EuropaMultimodal model SpainPassengers: Cars, Railways, AirplanesFreights: Highways and RailwaysMAIN FEATURES AND PECULIARITIESA tool enabling to test nationwide and/or regional transport policies, as well as specific network/services scenarios.Multimodal model:Passengers: road (car-bus)-rail (conventional-high speed) and air.Freight: road-rail (loading only).2 ½ step model: modal split + assignment + pre-load-calculationHighly aggregated data: 390 zones. Short-mid distance trips matrices (<50 km) are not included in the modelling.
21 De Lijn Public Transport Model (North Belgium) Autonomous Public Transport Company in Flanders ( habitants)508 million travelers in 2008More than busses – 360 trams50% service by external operatorsDe Lijn = decentralized organizationHeadquarters: support, coordination & strategy5 operational regional entities and over 60 workplaces
22 Master Plan Budapest, Hungary 4. Exemplos internacionais de utilização do CUBE – EuropaMaster Plan Budapest, HungarySupport establishment of project selection and project prioratization
23 Multimodal Model Lisboa, Portugal demand and revenue forecast multimodal model, for a 30 year period, with the following characteristics:Traffic counts and passenger counts in all transit modes;Surveys in both public and private systems;Reveled preferenceStated preferenceMultimodal model with separated assignment models for public and private transport;Peak hourOff peak hour.Complex fare systemSpecific modeling for park & ride usersModal split model.
24 Calcutta Light Rail Model Calcutta, India 4. Exemplos internacionais de utilização do CUBE – ÁsiaCalcutta Light Rail Model Calcutta, IndiaCapital of the Indian state of West Bengal.The city of Kolkata has 4.5 million residents, and the metropolitan area, including suburbs, has a population of approximately 15.7 million, making it the third most populous metropolitan area in India and the 13th most populous urban area in the world
25 Thailand Multimodal Model 4. Exemplos internacionais de utilização do CUBE – ÁsiaThailand Multimodal ModelUrban Master PlansBangkokKhisanulokModelos urbanos
26 Multimodal Model Hong Kong Developed a series of hierarchical models:A strategic model representing interaction with mainland China, called the Cross Boundary Model (CBM)A conventional 4-stage model for HK Special Administrative RegionDetailed models for PT and rail assignmentDetailed modeling of highway route choiceLocal area models including microsimulation (Dynasim)
27 Transport Model Beijing, China Key Statistics:10m populationOver 20m daily tripsRapidly growing car availabilityTransport networks:Metro with plans for extensive expansionExpresswaysBRTExtensive Bus NetworkSome 30% of trips in Beijing are made by bicycle: Cube allows them to be modeled as a separate mode with an appropriate pcu factor.Dedicated bicycle lanes are modeled in Cube as a separate link class which is banned to other vehicle types in the assignment; bicycles also have a separate speed-flow curveAs some bicycle lanes are only separated by a road markings (rather than physical segregation), a special mechanism has been set up in Cube to check the vol/cap ratio of these links and reflect overspill to the main carriageway
28 Multimodal Model Jakarta Key Statistics:8.5m Jakarta22m total in Greater Jakarta (Jabodetabek)More than 30m daily tripsLarge public transport market – but nearly all on busesMotorcycle and carsHighly congested road networkTransport networks:Toll Road network on key arterials and around CBDHeavily used other roadsCube Analyst (Matrix estimation) used to refine matrices by multi-user classesHighways assignment model – using multi-user class equilibrium assignment in HIGHWAY with capacity constrained congestion modelingTolls represented by fixed point toll or distance based component (depending on toll road)
29 Multimodal Model Hanoi, Vietnam Key Statistics:3m Population6m daily trips- Around 75% of trips made by motorcycle10% public transport; grown from 2% only 5 years agoAverage road speeds <20kphTransport networks:Bus network – modernized and expanded in past 5 yearsBus Rapid Transit and Rail Lines under planning
30 Multimodal Model - Melbourne Train tram busTwo user classes PT walk access and PT park and RideRegional Rail Services and use of airport shuttle bus handled by submodels4 time periods; clustered run to bring down run time to less than 24 hours on 4 core machineTaking account crowding increases run time significantly
31 Highspeed Rail model, California USA Evaluate HSR alternativesStatewideInto and out of the San Francisco Bay AreaProduce performance and evaluation measuresRidership and revenues, user benefitsTime and cost savings for new ridersImpacts on other modesUse existing models to build high speed rail networksDevelop Logit mode choice models from new dataPerform Assignment to look at ridershipUse Cube PT (Public Transport) Module to:Code transit route networksAccess and Egress to trainsPark & Ride and Pedestrian / Bike Catchment AreaDefine Fares and penaltiesModel Service Scenarios
32 Thoughts on ideal modelling program Four inter-related elements…Data collection programModel design/structureValidation/testing proceduresTraining…where each element is developed with the other three elements in mindData collection program captures sufficient information for validation/testingValidation/testing procedures correspond with the model design/structureTraining is sufficient to allow for proper execution or maintenance of the model design/structureEtc.32