書籍詳細

書籍詳細




洋書

倉内文隆(岐阜大学)共著/スマートカード・データによる公共交通計画

Public Transport Planning with Smart Card Data

Kurauchi, Fumitaka (EDT)   Schmocker, Jan-dirk (EDT)

CRC Pr I LLC 2016/11
264 p. illustrations (some color) ; 24 cm   
装丁: Hrd    装丁について
テキストの言語: ENG    出版国: GB
ISBN: 9781498726580
KCN: 1023672332
紀伊國屋書店 選定タイトル
標準価格:¥27,313(本体 ¥24,830)   
Web販売価格あり    Web販売価格について

為替レートの変動や出版社の都合によって、価格が変動する場合がございます。

この商品は提携先の海外出版社在庫からの取り寄せとなります。品切れの場合、恐れ入りますがご了承下さい。

納期について
DDC: 388
KDC: F93 交通・運輸工学
ご購入を希望される方は、
下のリンクをクリックしてください。

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 Naive 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