Behavioural data mining of transit smart card data: A data fusion approach

Kusakabe, Takahiko; Asakura, Yasuo

doi:10.1016/j.trc.2014.05.012

Cited by 229 publications

(100 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

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

“…3 years Behavioral trip purpose estimation [23] Bayesian classifier 20 months Relation of arrival and departure at certain station [20] -1 week…”

Section: Relationship Extractionmentioning

confidence: 99%

“…Existing studies that fall into this category either relate passenger journeys with their travel purpose [21] using additional survey data; or relate the attractiveness of bus stops, stations or travel modes in terms of passenger inflow [22,23]. Zeng et al linked the arrivals and departures at stations with activities in the area based on the time of arrivals and departures [20].…”

Section: Flow Orientationmentioning

confidence: 99%

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Singh

Jung

2017

IJGI

View full text Add to dashboard Cite

Analyzing public movement in transportation networks in a city is significant in understanding the life of citizen and making improved city plans for the future. This study focuses on investigating the flow orientation of major activity regions based on smart card transit data. The flow orientation based on the real movements such as transit data can provide the easiest way of understanding public movement in the complicated transportation networks. First, high inflow regions (HIRs) are identified from transit data for morning and evening peak hours. The morning and evening HIRs are used to represent major activity regions for major daytime activities and residential areas, respectively. Second, the directional orientation of flow is then derived through the directional inflow vectors of the HIRs to show the bias in directional orientation and compare flow orientation among major activity regions. Finally, clustering analysis for HIRs is applied to capture the main patterns of flow orientations in the city and visualize the patterns on the map. The proposed methodology was illustrated with smart card transit data of bus and subway transportation networks in Seoul, Korea. Some remarkable patterns in the distribution of movements and orientations were found inside the city. The proposed methodology is useful since it unfolds the complexity and makes it easy to understand the main movement patterns in terms of flow orientation.

show abstract

“…It is one of the most fundamental research topics to many real-world applications, including Active Traffic and Demand Management (ATDM), Mobility-as-a-Service, and transportation demand management. The activity-travel behavior pattern is derived from either manually collected traveler activity diaries in travel surveys or passively obtained data, like global positioning system (GPS) trajectory data [1][2][3][4][5], geolocation data [6,7], and transit smart card data [8,9].…”

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

View full text Add to dashboard Cite

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.

show abstract

Behavioural data mining of transit smart card data: A data fusion approach

Cited by 229 publications

References 24 publications

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

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

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

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

Contact Info

Product

Resources

About