Using Cube for Public Transport Planning An Overview Andreas Köglmaier Regional Director As 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.

Content Reasons for modelling public transport Public transport data availability Considerations regarding network representation Representing passenger behaviour (mode choice and route choice) Public transport model development in Cube Specific considerations (fare, park and ride, crowding) Examples of Cube public transport models Why should we model PT? What data do we need to have? How can we represent the network (Supply)? How can we represent the demand?

Reasons for building a public transport model Forecast public transport demand and revenue Analyse PT projects (economic analysis) Optimise the routing of PT lines Testing different ticket systems Revenue allocation for integrated ticket systems with multi operators Estimate future run times Before we build a model we should always consider what is the purpose of the model

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

Public transport data availability Passenger data (Demand) Boarder and alighter information Vehicle loadings (crowding information) Passenger origin and destination information Demographic Information (age group, car ownership, …) Usage of different ticket types Mode choice and route choice behaviour (stated and revealed preference surveys)

Representing the public transport system Physical infrastructure (roads, tracks, stations) Stick network or shape network Station single node or detailed modelling of interchanges Speeds taken from network or from service definition WalkTime = ~1 min Streets Bus stop node Rail Platform Rail

Representing the public transport system Service definition and routing Decision on which services to include (special school runs) Consider simplification of network Coding 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 choice Level of representation depends on set up of demand model

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 policies Full representation of circular routes Uses function of roadway speed, set travel times via time points, used fixed time. Highway network Transit line Cube provides the possibility to have this representation: It can do a everything therefore it provides the following files: Highway network Transit line System data

Representing the passenger behaviour Understanding route and mode choice behaviour Choosing between bus and rail (mode or route choice?) Considerations for mode choice model Group passengers in homogenous groups with similar route choice behaviour Consider ticket choice model Considerations for route choice Value of different trip components Each 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? AUTO DRIVE ALONE SHARED RIDE SHARED RIDE 2 SHARED RIDE 2+ TRANSIT WALK BUS LIGHT RAIL COMMUTER RAIL CIRCULATOR DRIVE CIRCULATOR`

Representing the passenger behaviour Deviding the trip in its components

Representing the passenger behaviour Home Rail Platform Bus Stop Transfer Transit path Mode Choice, Route Choice and Wait curve Auto path Toll Work Parking

PT Methodology Compiles Data and Simplifies Network (Produces NET file) Enumerates “Acceptable” Discrete Routes for every O-D (RTE file) Finds least cost route Enumerates all other routes within defined limits Evaluates 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.

Route Enumeration Enumeration finds a traveler's reasonable public transport routes from origin to destination Identifies full discrete routes The route should move progressively from the origin to the destination Travelers tend to select journeys that are simpler – that are direct or involve few interchanges Travelers are unwilling to walk very long distances These principals are used to constrain the potentially huge computational task of identifying all reasonable routes The process can be considered analogous to a traveler using a map to reject routes which are very long relative to more direct alternatives Creates a dataset of the ‘reasonable’ routes between each origin and destination by user class

Route Evaluation Evaluation ‘qualitatively judges’ the routes calculated in the route enumeration stage of PT The elements that can be used in this process Limit on the number of transfers The difference (actual & percentage) difference between the minimum cost route and the evaluated route Limit on non-transit cost (walk/drive access) Limit on waiting and transfer times Limit on In-vehicle costs Specified by user class – or – market segment Provides 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

Report and Visualize PT Results & Inputs Reports Graphics Record Processing Select Link Analysis

Fare System Representation All sorts of complex fare systems can be modeled by user class FREE – No cost incurred FLAT – One fixed cost per use DISTANCE – Possible boarding cost + unit cost per distance or cost lookup table FROMTO –Fare zone matrix based on the start/end zones COUNT –Counts number of fare zones crossed, sum number of zones crossed ACCUMULATE – Each fare zone has a fare and when crossed adds to cost

Representing Park and Ride Input Car & PT cost matrices Define Catchment Area, City zone & Station zone Calculate Car cost: Catchment to Station & opposite Calculate PT cost: Station to City & opposite Combine to calculate Catchment to City via station cost & opposite Consider 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

Representing Park and Ride Rail Station Rail Bus Stop PNR Lot Rail Platform Escalator link PNR lot–stop access connector Buses Streets Driveway link 18

Representing crowding Vehicle capacity required as input Understand changes in comfort Seating capacity and crush capacity Define crowding curve Penalties In-vehicle penalty Boarding penalty Iterative process: Loaded demand from an iteration is used to update the following for the next iteration Link travel times The probability of boarding a line at a particular stop Link travel time adjustment The 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 adjustment In 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.

Multimodal model Spain 4. Exemplos internacionais de utilização do CUBE – Europa Multimodal model Spain Passengers: Cars, Railways, Airplanes Freights: Highways and Railways MAIN FEATURES AND PECULIARITIES A 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-calculation Highly aggregated data: 390 zones. Short-mid distance trips matrices (<50 km) are not included in the modelling.

De Lijn Public Transport Model (North Belgium) Autonomous Public Transport Company in Flanders (6.161.000 habitants) 508 million travelers in 2008 More than 4.000 busses – 360 trams 50% service by external operators De Lijn = decentralized organization Headquarters: support, coordination & strategy 5 operational regional entities and over 60 workplaces

Master Plan Budapest, Hungary 4. Exemplos internacionais de utilização do CUBE – Europa Master Plan Budapest, Hungary Support establishment of project selection and project prioratization

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 preference Stated preference Multimodal model with separated assignment models for public and private transport; Peak hour Off peak hour. Complex fare system Specific modeling for park & ride users Modal split model.

Calcutta Light Rail Model Calcutta, India 4. Exemplos internacionais de utilização do CUBE – Ásia Calcutta Light Rail Model Calcutta, India Capital 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

Thailand Multimodal Model 4. Exemplos internacionais de utilização do CUBE – Ásia Thailand Multimodal Model Urban Master Plans Bangkok Khisanulok Modelos urbanos

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 Region Detailed models for PT and rail assignment Detailed modeling of highway route choice Local area models including microsimulation (Dynasim)

Transport Model Beijing, China Key Statistics: 10m population Over 20m daily trips Rapidly growing car availability Transport networks: Metro with plans for extensive expansion Expressways BRT Extensive Bus Network Some 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 curve As 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

Multimodal Model Jakarta Key Statistics: 8.5m Jakarta 22m total in Greater Jakarta (Jabodetabek) More than 30m daily trips Large public transport market – but nearly all on buses Motorcycle and cars Highly congested road network Transport networks: Toll Road network on key arterials and around CBD Heavily used other roads Cube Analyst (Matrix estimation) used to refine matrices by multi-user classes Highways assignment model – using multi-user class equilibrium assignment in HIGHWAY with capacity constrained congestion modeling Tolls represented by fixed point toll or distance based component (depending on toll road)

Multimodal Model Hanoi, Vietnam Key Statistics: 3m Population 6m daily trips - Around 75% of trips made by motorcycle 10% public transport; grown from 2% only 5 years ago Average road speeds <20kph Transport networks: Bus network – modernized and expanded in past 5 years Bus Rapid Transit and Rail Lines under planning

Multimodal Model - Melbourne Train tram bus Two user classes PT walk access and PT park and Ride Regional Rail Services and use of airport shuttle bus handled by submodels 4 time periods; clustered run to bring down run time to less than 24 hours on 4 core machine Taking account crowding increases run time significantly

Highspeed Rail model, California USA Evaluate HSR alternatives Statewide Into and out of the San Francisco Bay Area Produce performance and evaluation measures Ridership and revenues, user benefits Time and cost savings for new riders Impacts on other modes Use existing models to build high speed rail networks Develop Logit mode choice models from new data Perform Assignment to look at ridership Use Cube PT (Public Transport) Module to: Code transit route networks Access and Egress to trains Park & Ride and Pedestrian / Bike Catchment Area Define Fares and penalties Model Service Scenarios

Thoughts on ideal modelling program Four inter-related elements… Data collection program Model design/structure Validation/testing procedures Training …where each element is developed with the other three elements in mind Data collection program captures sufficient information for validation/testing Validation/testing procedures correspond with the model design/structure Training is sufficient to allow for proper execution or maintenance of the model design/structure Etc. 32

Thank you! Andreas Köglmaier Regional Director akoeglmaier@citilabs.com