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 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

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

2012 ◽  
Vol 2276 (1) ◽  
pp. 48-55 ◽  
Author(s):  
Flavio Devillaine ◽  
Marcela Munizaga ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
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

MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA

2006 ◽  
Vol 39 (3) ◽  
pp. 399-404 ◽  
Author(s):  
Bruno Agard ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
User Behaviour ◽  
Smart Card Data

scholarly journals Rationalizing Urban Transportation using Smart Card Data

2019 ◽  
Vol 8 (11S) ◽  
pp. 507-515
Keyword(s):  
Smart Card ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Population ◽  
Transport Systems ◽  
Living Standard ◽  
Transit System ◽  
Advantages And Disadvantages ◽  
Smart Card Data ◽  
The Impact

The urban population in 2014 accounted for 54% of the total global population, up from 34% in 1960, and continues to grow. The global urban population is expected to grow approximately 1.84%, 1.63% and 1.44% between 2015 and 2020, 2020 and 2025, and 2025 and 2030 respectively. This growing population puts pressure on government not only to accommodate the current and potential citizens but also provide them facilities and services for a better living standard. Providing a sustainable growing environment for the citizens is the biggest challenge for the government. As the populations increase, complexity network of transportation, water and sanitation, emergency services, etc. will increase many folds. SMART CITY Mission is being implemented to resolve this issue. As the cities turn smart, so should the transportation facilities. India on June 2018 had only 20 cities with populations of over 500,000 have organized public transport systems, pointing to the large gap to be bridged in their journey to turn smart. The aim of this paper is to examine the impact of smart card data from public transport for improving the predictions and planning of public transport usage and congestions. The mobile apps like M-Indicator, Google Maps don’t interlink, do not have a real time tracking of vehicles, fare distribution, congestion-based route mapping for public transportation. These factors are addressed in the paper with its advantages and disadvantages. This paper also talks about how information from smart card is to be extracted, how Big Data is to be managed and finally come to a smart, sustainable Urban Transit System. This paper also brings into light the data security issues and measures to curb those issues. This paper proposes and emphasizes on a single smart card for all modes of public transport

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