Using Cube for Public Transport Planning

An Overview Andreas Köglmaier Regional Director

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

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

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 Rail Rail Platform WalkTime = ~1 min Bus stop node Streets

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 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

AUTO DRIVE ALONE SHARED RIDE SHARED RIDE 2 SHARED RIDE 2+ BUS TRANSIT WALK LIGHT RAIL COMMUTER RAIL CIRCULATOR BUS DRIVE LIGHT RAIL COMMUTER RAIL CIRCULATOR`

Representing the passenger behaviour

Representing the passenger behaviour

Home Bus Stop Rail Platform Transfer Transit path Auto path Toll Parking Work

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

of the routes.

and the costs of each Calculates the % probability of using each path using choice models at each decision point

Report and Visualize PT Results & Inputs

   Reports Graphics Record Processing

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 Escalator link PNR lot–stop access connector Buses Bus Stop PNR Lot Rail Platform 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

4. Exemplos internacionais de utilização do CUBE – Europa

Multimodal model Spain

Passengers: Cars, Railways, Airplanes Freights: Highways and Railways

De Lijn Public Transport Model (North Belgium)

4. Exemplos internacionais de utilização do CUBE – Europa

Master Plan Budapest, Hungary

Multimodal Model Lisboa, Portugal

42 1 18 17 25 16 24 13 14 15 55 54 56 97 52 53 57 58 59 72 60 63 64 68 65 69 62 66 71 70 73 75 76 74 80 67 61 79 83 85 86

4. Exemplos internacionais de utilização do CUBE – Ásia

Calcutta Light Rail Model Calcutta, India

4. Exemplos internacionais de utilização do CUBE – Ásia

Thailand Multimodal Model

• Bangkok • Khisanulok

Multimodal Model Hong Kong

Transport Model Beijing, China

Multimodal Model Jakarta

Multimodal Model Hanoi, Vietnam

Multimodal Model - Melbourne

Highspeed Rail model, California USA

Thoughts on ideal modelling program

  Four inter-related ▪ ▪ ▪ ▪ Data collection program Model design/structure Validation/testing procedures Training elements… …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.

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Thank you!

Andreas Köglmaier Regional Director [email protected]