Assessing temporal-spatial characteristics of urban travel behaviors from multiday smart-card data

The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

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Space–time classification of public transit smart card users’ activity locations from smart card data

Public Transport ◽

10.1007/s12469-021-00274-0 ◽

2021 ◽

Author(s):

Li He ◽

Martin Trépanier ◽

Bruno Agard

Keyword(s):

Smart Card ◽

Public Transit ◽

Space Time ◽

Smart Card Data ◽

Transit Smart Card

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Examining public transport usage by older adults with smart card data: A longitudinal study in Japan

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2021.103046 ◽

2021 ◽

Vol 93 ◽

pp. 103046

Author(s):

Shasha Liu ◽

Toshiyuki Yamamoto ◽

Enjian Yao ◽

Toshiyuki Nakamura

Keyword(s):

Older Adults ◽

Longitudinal Study ◽

Smart Card ◽

Public Transport ◽

Smart Card Data

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Exploring Equity in Public Transportation Planning Using Smart Card Data

Sensors ◽

10.3390/s21093039 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3039

Author(s):

Kiarash Ghasemlou ◽

Murat Ergun ◽

Nima Dadashzadeh

Keyword(s):

Smart Card ◽

Public Transportation ◽

Transportation Planning ◽

Demand Characteristic ◽

Monthly Data ◽

Smart Card Data ◽

Planning Methods ◽

Public Transportation Planning ◽

Daily Travel ◽

A Share

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

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Detection of Activities of Public Transport Users by Analyzing Smart Card Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2276-06 ◽

2012 ◽

Vol 2276 (1) ◽

pp. 48-55 ◽

Cited By ~ 65

Author(s):

Flavio Devillaine ◽

Marcela Munizaga ◽

Martin Trépanier

Keyword(s):

Smart Card ◽

Public Transport ◽

Smart Card Data

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Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211013349 ◽

2021 ◽

pp. 036119812110133

Author(s):

Eun Hak Lee ◽

Kyoungtae Kim ◽

Seung-Young Kho ◽

Dong-Kyu Kim ◽

Shin-Hyung Cho

Keyword(s):

Travel Time ◽

Smart Card ◽

Public Transportation ◽

Gradient Boosting ◽

Log Data ◽

Smart Card Data ◽

Extreme Gradient Boosting ◽

Express Train ◽

Total Travel Time ◽

The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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