scholarly journals Intermodal mobility analysis with smart-card data. Spatio-temporal analysis of the bus-metro network of Rennes metropole

Cybergeo ◽  
2019 ◽  
Author(s):  
Cyprien Richer ◽  
Etienne Come ◽  
Mohamed Khalil El Mahrsi ◽  
Latifa Oukhellou
Keyword(s):  
Smart Card ◽  
Temporal Analysis ◽  
Mobility Analysis ◽  
Smart Card Data ◽  
Spatio Temporal ◽  
Metro Network

Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data

2017 ◽  
Vol 18 (11) ◽  
pp. 3135-3146 ◽  
Author(s):  
Juanjuan Zhao ◽  
Qiang Qu ◽  
Fan Zhang ◽  
Chengzhong Xu ◽  
Siyuan Liu
Keyword(s):  
Smart Card ◽  
Temporal Analysis ◽  
Travel Patterns ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Spatio Temporal

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

Clustering Smart Card Data for Urban Mobility Analysis

2017 ◽  
Vol 18 (3) ◽  
pp. 712-728 ◽  
Author(s):  
Mohamed K. El Mahrsi ◽  
Etienne Come ◽  
Latifa Oukhellou ◽  
Michel Verleysen
Keyword(s):  
Smart Card ◽  
Urban Mobility ◽  
Mobility Analysis ◽  
Smart Card Data

scholarly journals Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao Fu ◽  
Yu Gu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Rapid Development ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Mobility Patterns ◽  
Passenger Flow ◽  
Smart Card Data ◽  
The Impact ◽  
Metro Network

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

scholarly journals Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

2019 ◽  
Vol 11 (24) ◽  
pp. 7069
Author(s):  
Enhui Chen ◽  
Zhirui Ye ◽  
Hui Bi
Keyword(s):  
Built Environment ◽  
Smart Card ◽  
Temporal Relationship ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Temporal Autocorrelation ◽  
Smart Card Data ◽  
Travel Behaviors ◽  
Spatio Temporal ◽  
Travel Distances

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.

Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data

Cities ◽  
2019 ◽  
Vol 95 ◽  
pp. 102359 ◽  
Author(s):  
Enhui Chen ◽  
Zhirui Ye ◽  
Chao Wang ◽  
Wenbo Zhang
Keyword(s):  
Built Environment ◽  
Smart Card ◽  
Smart Card Data ◽  
Spatio Temporal
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