Measuring the Diversity and Dynamics of Mobility Patterns Using Smart Card Data

2018 ◽  
pp. 438-451 ◽  
Author(s):  
Chengmei Liu ◽  
Chao Gao ◽  
Yingchu Xin
Keyword(s):  
Smart Card ◽  
Mobility Patterns ◽  
Smart Card Data

scholarly journals Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149222 ◽  
Author(s):  
Chen Zhong ◽  
Michael Batty ◽  
Ed Manley ◽  
Jiaqiu Wang ◽  
Zijia Wang ◽  
...  
Keyword(s):  
Smart Card ◽  
Mobility Patterns ◽  
Smart Card Data

scholarly journals Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas

2018 ◽  
Vol 10 (10) ◽  
pp. 3489 ◽  
Author(s):  
Cristina Pronello ◽  
Davide Longhi ◽  
Jean-Baptiste Gaborieau
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Methodological Approach ◽  
User Needs ◽  
Time Interval ◽  
Mobility Patterns ◽  
Suburban Areas ◽  
Latent Demand ◽  
Smart Card Data ◽  
Bus Services

This paper aims to define an algorithm capable of building the origin-destination matrix from check-in data collected in the extra-urban area of Torino, Italy, where thousands of people commute every day, using smart cards to validate their travel documents while boarding. To this end, the methodological approach relied on a survey over three months to record smart-card validations. Peak and off-peak periods have been defined according to validation frequency. Then, the origin-destination matrix has been estimated using the time interval between two validations to outline the different legs of the journey. Finally, transport demand has been matched with existing bus services, showing which areas were not adequately covered by public transport. The results of this research could assist public transport operators and local authorities in the design of a more suitable transport supply and mobility services in accordance with user needs. Indeed, tailoring public transport to user needs attracts both more customers and latent demand, reducing reliance on cars and making transport more sustainable.

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.

Measuring variability of mobility patterns from multiday smart-card data

2015 ◽  
Vol 9 ◽  
pp. 125-130 ◽  
Author(s):  
Chen Zhong ◽  
Ed Manley ◽  
Stefan Müller Arisona ◽  
Michael Batty ◽  
Gerhard Schmitt
Keyword(s):  
Smart Card ◽  
Mobility Patterns ◽  
Smart Card Data

scholarly journals Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

2019 ◽  
Vol 8 (10) ◽  
pp. 434 ◽  
Author(s):  
Tong Zhang ◽  
Jianlong Wang ◽  
Chenrong Cui ◽  
Yicong Li ◽  
Wei He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Card ◽  
Travel Behavior ◽  
Public Transit ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Learning Methods ◽  
Transit Network ◽  
Machine Learning Methods ◽  
Smart Card Data

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

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 SPATIOTEMPORAL DYNAMICS OF PUBLIC TRANSPORT DEMAND: A CASE STUDY OF RIGA

Transport ◽  
2021 ◽  
Vol 35 (6) ◽  
pp. 576-587
Author(s):  
Dmitry Pavlyuk ◽  
Nadežda Spiridovska ◽  
Irina Yatskiv (Jackiva)
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Clustering Algorithms ◽  
Demand Forecasting ◽  
Spatiotemporal Dynamics ◽  
Mobility Patterns ◽  
Data Set ◽  
Daily Mobility ◽  
Smart Card Data ◽  
Passenger Flows

Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.

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

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
Sign in / Sign up

Export Citation Format

Share Document