Full Description

Collecting fares through "smart cards" is becoming standard in most advanced public transport networks of major cities around the world. Travellers value their convenience and operators the reduced money handling fees. Electronic tickets also make it easier to integrate fare systems, to create complex time and space differentiated fare systems, and to provide incentives to specific target groups. A less-utilised benefit is the data collected through smart cards. Records, even if anonymous, provide for a much better understanding of passengers’ travel behaviour as current literature shows. This information can also be used for better service planning.

Public Transport Planning with Smart Card Data handles three major topics: how passenger behaviour can be estimated using smart card data, how smart card data can be combined with other trip databases, and how the public transport service level can be better evaluated if smart card data is available. The book discusses theory as well as applications from cities around the world and will be of interest to researchers and practitioners alike who are interested in the state-of-the-art as well as future perspectives that smart card data will bring.

Table of Contents

An Overview on Opportunities and Challenges of Smart Card Data Analysis
Introduction
Smart Card Systems and Data Features
Analysis Challenges
Categorization of Potential Analysis using Smart Card Data
Book Overview, What is Missing and Conclusion
References
Author Biography

PART 1: ESTIMATING PASSENGER BEHAVIOR
Transit Origin-Destination Estimation
Introduction
General Principles
Inference of Destinations
Tour ("Trip Chain") Assumptions
Inference Methods
Transfer vs. Activity Inference
O-D Matrix Methods
Journey and Tour Pattern Analysis
Identification of Routes from Smart Card Data
Journey Pattern Analysis
Activity Inference and Analysis
Areas for Future Research
References
Author Biography

Destination and Activity Estimation
Smart Card Use in Trip Destination and Activity Estimation
Smart Card Data Structure in Seoul 39viii 2. Methodology for Trip Destination Estimation
Data Cleaning
Trips and Trip Legs
Trip Purpose Imputation using Household Travel Survey
Activity Start Time and Duration
Trip Purpose Prediction
Results and Discussion
Illustration of Results with MATSim
References
Author Biography

Modelling Travel Choices on Public Transport Systems with Smart Card Data
Introduction
Theoretical Background
Choice-Set Generation Methods
Discrete Choice Models
Modelling Behaviour with Smart Card Data
Modelling Origins and Destinations
Modelling the Choice-Set
Modelling Travel Times and Fares
Modelling Transfers
Modelling Comfort
Modelling Individual Preferences
Modelling Travel Strategies
Case Study: Santiago, Chile
Choice-Set Generation
Model Specification
Estimation Results
References
Author Biography

PART 2: COMBINING SMART CARD DATA WITH OTHER DATABASES
Combination of Smart Card Data with Person Trip Survey Data
Introduction
Exploration of Smart Card Data Set Using Visualization
Interpretation of Features of Smart Card Data Set
Interpretation of Features Using Data Fusion
Model
Schema of Smart Card Data and Person Trip Survey Data
An Overview of Data Fusion Method
Formulation of Naïve Bayes Probabilistic Model
Estimation of Probability Functions
Empirical Analysis
Data Sets
Validation with Person Trip Survey Data
Application to Data Mining of Smart Card Data
References
Author Biography

A Method for Conducting Before-After Analyses of Transit Use by Linking Smart Card Data and Survey Responses
Introduction
Literature Review
Background
Data Collection
Survey Content
Methodo
The Intervention: Availability of Real-Time Information
Condition 1: Panel Eligibility
Condition 2: Completeness and Uniqueness (One Smart Card = One Person)
Condition 3: Congruence (That Smart card = That Person)
Evaluation of the Intervention
Difference of Mean Differences
Regression Analysis
Areas for Improvement and Future Research
Conclusion
References
Author Biography

Multipurpose Smart Card Data: Case Study of Shizuoka, Japan
Introduction
Multipurpose Smart Cards
Case Study Area and Smart Card Data Overview
Shizuoka and Shizutetsu
Multipurpose Smart Card "LuLuCA"
Overview of Collected Data Stated Preference Survey on Sensitivity to Point System
Survey Structure and Hypotheses
Descriptive Survey Results
OLM and MNL Analysis
References
Author Biography

Using Smart Card Data for Agent–Based Transport Simulation
Introduction
User Equilibrium and Public Transport in MATSim
CEPAS
Suitability of Using CEPAS Data to Describe Public Transport Demand
Combining Agent-based Transport Simulation and CEPAS Data
Method
Reconstruction of Bus Trajectories
Generation of a Public Transit Schedule
Generation of Public Transport Trips
Simplification of the Network and Mobility Simulation
Speed Regression Model
Dwell Time Model
Validation and Performance
Speed
Headways, Dwell Times and Bus Bunching
Passenger Travel Time Measures
Application
Impact on Bus Bunching
Excess Waiting Times
References
Author Biography

PART 3: SMART CARD SATA FOR EVALUATION
Smart Card Data for Wider Transport System Evaluation
Introduction
Level of Service Indicators
Application to Santiago
Global Indicators
Indicators at the Municipality Levels
Indicators at Zone Level
Indicators at the Avenue Level
Bus-stop-level Indicators
Indicators at a Specific OD Pair (i.e., Trip) Level
References
Authors Biography

Evaluation of Bus Service Key Performance Indicators using Smart Card Data
Introduction
Background
Performance Indicators
Destination Estimation Algorithm
Information System
KPI Assessment
Error Detection
KPI Calculation Framework
Some Examples
Commercial Speed and Average Trip Distance and Duration
Passenger-kilometres, Passenger-hours
Load Profile
Service Variability
Service Fit
Schedule Adherence
Fare Evasion
Conclusion
Limitations and Challenges
Perspectives
References
Author Biography

Ridership Evaluation and Prediction in Public Transport by Processing Smart Card Data: A Dutch Approach and Example
Introduction
Smart Cards and Data
Smart Card Data Applications
The Dutch Smart Card System: OV-Chipkaart
Dutch Smart Card Data
Predicting Ridership by Smart Card Data
Introduction
Deriving OD Demand from Smart Card Data
Elasticity Model
Incorporating Comfort Impacts
Case Study: The Tram Network of The Hague
Introduction
Evaluation
Predicting
Reflection
References
Author Biography

Evaluation of Bus Stop Conditions Using a Combination of Probe Data and Smart Card Data
Introduction
Previous Tracking Data Research in Japan and the Positioning of this Study
Development of Evaluation Measures
Procedure for Obtaining Evaluation Measures
Saitama City Case Study
Saitama City
Overview of Tracking Data Use
Results and Discussion
References
Author Biography

Conclusions: Opportunities Provided to Transit Organizations by Automated Data Collection Systems, Challenges and Thoughts for the Future
Automated Data Collection Systems (ADCS)
Automatic Vehicle Location Systems (AVL)
Automatic Passenger Counting Systems (APC)
Automatic Fare Collection Systems (AFC)
Other Pertinent Data Systems
A Conceptual Framework for ADCS in a Transit Organization
ADCS and Key Transit Organization Functions
Analytic Framework
Challenges
Challenges Specific to AFC Data
Other Challenges Related to ADCS (including AFC data)
An Unexplored Area for Research Using Smart Card Data: Elasticities and Pricing Strategy
Conclusions: Looking to the Future