Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning

Utsunomiya, Mariko; Attanucci, John; Wilson, Nigel H. M.

doi:10.1177/0361198106197100114

Cited by 69 publications

(34 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although the main purpose is to make charging and management more convenient [2], massive and continuous smart card data also can be recorded and served, which can provide lots of precious opportunities for researchers. The data can be used in various fields such as analysis of transit riders' travel patterns [3][4][5], behavior analysis [6][7][8][9], performance assessment of bus transport reform [10][11][12][13] and planning of the public transportation system [14][15][16][17]. In the study of smart card data, the spatio-temporal information on boarding and alighting is very important [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

View full text Add to dashboard Cite

Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

View full text Add to dashboard Cite

show abstract

“…In recent years, automatic fare collection (AFC) data such as smart card data have been used by transit service providers to analyze passenger demand and system performance. These data have been used for O-D matrices estimation [8,9], demand analysis [10,11], travel behavior analysis [12], operational management, and public transit planning [13][14][15], and so forth. In particular, there are emerging studies dealing with AFC data of URT systems.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…By analyzing smart card data collected in Outaouis, Canada, Agard et al [1] have identified different trip habits based on the predefined user types and variabilities of trips against time. Utsunomiya et al [12] pointed out that demand pattern varies with day in week, therefore, different operation schedules should be provided for each day. Some researchers also focus on data processing methods and aim to get more meaningful information from smart card data.…”

Section: Introductionmentioning

confidence: 99%

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Sun

Lee

Erath³

et al. 2012

Proceedings of the ACM SIGKDD International Workshop on Urban Computing

View full text Add to dashboard Cite

Rapid tranit systems are the most important public transportation service modes in many large cities around the world. Hence, its service reliability is of high importance for government and transit agencies. Despite taking all the necessary precautions, disruptions cannot be entirely prevented but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, information about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportunity to investigate and understand the demand pattern of passengers and service level from transit operators.In this present study, we present a methodology to analyze smart card data collected in Singapore, to describe dynamic demand characteristics of one case mass rapid transit (MRT) service. The smart card reader registers passengers when they enter and leave an MRT station. Between tapping in and out of MRT stations, passengers are either walking to and fro the platform as they alight and board on the trains or they are traveling in the train. To reveal the effective position of the passengers, a regression model based on the observations from the fastest passengers for each origin destination pair has been developed. By applying this model to all other observations, the model allows us to divide passengers in the MRT system into two groups, passengers on the trains and passengers waiting in the stations. The estimation model provides the spatio-temporal density of passengers. From the density plots, trains' trajectories can be identified and passengers can be assigned to single trains according to the estimated location.

show abstract

Potential Uses of Transit Smart Card Registration and Transaction Data to Improve Transit Planning

Cited by 69 publications

References 2 publications

Smart Card Data Mining of Public Transport Destination: A Literature Review

Smart Card Data Mining of Public Transport Destination: A Literature Review

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

Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

Contact Info

Product

Resources

About