Assessing the public transport travel behavior consistency from smart card data

Espinoza, Catalina; Munizaga, Marcela; Bustos, Benjamín; Trépanier, Martin

doi:10.1016/j.trpro.2018.10.008

Cited by 14 publications

(9 citation statements)

References 17 publications

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“…In our case, as all the vertices in a clique are connected by edges, it can be argued that the clustering coefficients of the vertices in the polishing graph increase on average when the clique is generated. The cluster coefficient of vertex, i,, is defined by (4), where e i denotes the number of edges connecting the neighborhoods of vertex i and k i denotes the number of vertices adjacent to i.…”

Section: The Thresholdmentioning

confidence: 99%

A method for extracting travel patterns using data polishing

2021

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With recent developments in ICT, the interest in using large amounts of accumulated data for traffic policy planning has increased significantly. In recent years, data polishing has been proposed as a new method of big data analysis. Data polishing is a graphical clustering method, which can be used to extract patterns that are similar or related to each other by identifying the cluster structures present in the data. The purpose of this study is to identify the travel patterns of railway passengers by applying data polishing to smart card data collected in the Kagawa Prefecture, Japan. To this end, we consider 9,008,709 data points collected over a period of 15 months, ranging from December 1st, 2013 to February 28th, 2015. This dataset includes various types of information, including trip histories and types of passengers. This study implements data polishing to cluster 4,667,520 combinations of information regarding individual rides in terms of the day of the week, the time of the day, passenger types, and origin and destination stations. Via the analysis, 127 characteristic travel patterns are identified in aggregate.

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Section: The Thresholdmentioning

confidence: 99%

A method for extracting travel patterns using data polishing

2021

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“…Several studies questioned if one day observation records would be sufficient to build mobility models. "In this context, Huff and Hanson (1986) [1] discussed the relation between regularity and variability in human mobility behavior and define some metrics to measure both phenomena. In their research, they observed high regularity around few places (home, work and shopping), but also a high travel variability between each day.…”

Section: Need/background Of the Studymentioning

confidence: 99%

“…With the advent of smart card data, public transport authorities can now have access to a better view of the use of their service [1]. As the card is linked with information provided by the users, it gives a better idea of the socioeconomic and demographic profile of the users.…”

Section: Data Recordedmentioning

confidence: 99%

Rationalizing Urban Transportation using Smart Card Data

2019

IJITEE

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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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“…The first refers to users’ tendency to board a given station for their trips, while the later refers to users’ tendency to travel at a particular hour of the day i.e., trip frequency [ 41 ]. Espinoza et al [ 42 ] measured PT users’ behavior change in travel over time by splitting data into different time windows using three algorithms (Transition Probability Matrix (TPM), Spatiotemporal Edit Distance Method (EDM) and Regions of Interest and Feature Vector (RoIs-FV)). It was found that the results obtained for the same users can be different using different algorithms.…”

Section: Introductionmentioning

confidence: 99%

Exploring Equity in Public Transportation Planning Using Smart Card Data

Ghasemlou

Ergun

Dadashzadeh

2021

Sensors

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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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Assessing the public transport travel behavior consistency from smart card data

Cited by 14 publications

References 17 publications

A method for extracting travel patterns using data polishing

A method for extracting travel patterns using data polishing

Rationalizing Urban Transportation using Smart Card Data

Exploring Equity in Public Transportation Planning Using Smart Card Data

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