Estimation method for railway passengers’ train choice behavior with smart card transaction data

Kusakabe, Takahiko; Iryo, Takamasa; Asakura, Yasuo

doi:10.1007/s11116-010-9290-0

Cited by 132 publications

(65 citation statements)

References 9 publications

(12 reference statements)

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“…Previous studies use discrete choice analysis extensively to predict passenger choice . As demonstrated in the case study, we figure out the key issue of estimating passenger boarding plans, based on which all the route choice, section flow, load factor, and so forth can be deduced, furthermore, and no longer depend on the assumption that smart card data that could not be identified to the possible train choices would be assigned with equal probability (Kusakabe et al, 2010). Furthermore, the proposed approach improves the methodologies of Sun and Schonfeld [19] and Zhou and Xu [20] on calculating passenger boarding plans.…”

Section: Resultsmentioning

confidence: 99%

“…However, in spite of the widespread attention on the use of AFC data, there are fewer studies dealing with the passenger train choice behavior in a URT system. Kusakabe et al [17] developed a methodology for estimating which train would be boarded by each smart card holder using long-term transaction data. Their approach was based on the assumption that smart card data that could not be identified to the possible train choices would be assigned with equal probability.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Zhu

Wang

Huang

2017

Journal of Advanced Transportation

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An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger's travel. Thus, it is the key to estimate passengers' train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger's train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski's paradigm on modelling choice. Then, an integrated framework for estimating individual passenger's train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Zhu

Wang

Huang

2017

Journal of Advanced Transportation

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“…As access to public or shared transport systems becomes increasingly digitised, new datasets have emerged offering opportunities to research travel behaviour in a continuous, large-scale and non-invasive way (Blythe and Bryan 2007;Froehlich, Neumann, and Oliver 2008;Kusakabe, Iryo, and Asakura 2010;P ‡ez, TrŽpanier, and Morency 2011;Lathia, Ahmed, and Capra 2012). The data produced by urban bike share schemes can be regarded as a particular instance of these new datasets.…”

Section: Introductionmentioning

confidence: 99%

Exploring gendered cycling behaviours within a large-scale behavioural data-set

Beecham

Wood

2013

Transportation Planning and Technology

125

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Exploring gendered cycling behaviours within a large-scale behavioural datasetAnalysing over 10 million journeys made by members of LondonÕs Cycle Hire Scheme, we find that female customersÕ usage characteristics are demonstrably different from those of male customers. Usage at weekends and within LondonÕs parks characterises womenÕs journeys, whereas for men, a commuting function is more clearly identified. Some of this variation is explained by geodemographic differences and by an atypical period of usage during the first 3 months after the schemeÕs launch. Controlling for each of these variables brings some convergence between men and women. However, many differences are preserved. Studying the spatio-temporal context under which journeys are made, we find that womenÕs journeys are highly spatially structured. Even when making utilitarian cycle trips, routes that involve large, multi-lane roads are comparatively rare, and instead female cyclists preferentially select areas of the city associated with slower traffic streets and with cycle routes slightly offset from major roads.

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“…The number of tours threshold ranges as [1,2,5,10,15,20,30,40,50,60,70,80,90,100,120,150,180]. For example, a value of 5 indicates that any travelers with less than five tours are disqualified and discarded, while the travelers with 5 or more are qualified and collected.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The first one offers traffic information to impact travelers' decisions, which does not force behavior change [18]. Implementation cases include a comparison study of passengers' travel choice behavior by altering the train timetable, proposed by Kusakabe in Japan [19,20]; a dynamic ridesharing service, Virtual Bus in Italy [21]; a Predict-aTrip traffic information forecast program in San Francisco [22]; and so on. The second category of "soft" measures uses incentives to influence traveler behavior and has recently attracted attention worldwide.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

Zhu

Gonder

Lin

2017

Journal of Advanced Transportation

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With the development of and advances in smartphones and global positioning system (GPS) devices, travelers' long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-toHome and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers' activity behavior features for an extended period. After that, the variability pattern of an individual's travel behavior is used for estimating the individual's social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment's result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

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Estimation method for railway passengers’ train choice behavior with smart card transaction data

Abstract: Smart card data, Travel behavior, Railway passenger, Train timetable,

Cited by 132 publications

References 9 publications

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data

Exploring gendered cycling behaviours within a large-scale behavioural data-set

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

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