Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data

Tian, Xiancai; Zheng, Baihua

doi:10.1109/bigdata47090.2019.9005575

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Hence, Commute (move between the workspace and the place of residence) is the most common journey of a common person who makes together profound patterns of mobility that often characterize the area central mobility characteristicmorning flows generally (residence to workplace) and night movement (workplace to residential) [20]. Intentionally and periodically, group travel behavior (GTB) is classified as two or more switches from one place to another, moving together [21]. The fundamental concept is: if two or more commuters tap at the same bus/metro stop in a very short time window and at least one such syncing takes place they are known as community travelers a certain number of times [21].…”

Section: Related Workmentioning

confidence: 99%

“…Intentionally and periodically, group travel behavior (GTB) is classified as two or more switches from one place to another, moving together [21]. The fundamental concept is: if two or more commuters tap at the same bus/metro stop in a very short time window and at least one such syncing takes place they are known as community travelers a certain number of times [21]. The study of migration therefore covers different fields of science, including anthropology, sociology, economics, mathematics, physics and more recently informational.…”

Section: Related Workmentioning

confidence: 99%

“…It has the so-called "social big data" of people created by mobile, web, Online Social Networks (OSN), web-based devices via things [22]. This investigation focus on three separate migration phases: the journey -analysis of stocks and flows of migration; Stay -study of multiculturalism and changes in participating communities; return -study of returning migrants to their country of origin [21]. The car is a vital part of Vehicle Ad hoc Network (VANET), which is an evolving sub-class of MANET.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, focuses on a collection of real-world tweets in the city of London metropolitan area give a good result in extracting spatio-temporal features [39]. Additionally, in [21], the analysis has used fully the city-scale data obtained from the Automated Fare Collection (AFC) system. To determine motifs, three separate datasets are used: a Paris survey and data billing for cell phones and a Chicago survey [19].…”

Section: Data Descriptionmentioning

confidence: 99%

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A Review of Flow Migration Through Mobile Networks

Alqaysi

Behadili²

2022

eijs

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The interesting new sources of data for official statistics are cell phone data. Electronic media has defined the way of research human behavior rapidly over the last decade. As data storage and sensing technology progressed, electronic records now cover a diverse variety of human activities from localized data (phone) to open source contributions on Wikipedia and the Open Area Map. Electronic records now encompass the numerous fields of activity. The ad hoc vehicle network is a research community-based wireless technology for the implementation of intelligent transport applications. It is necessary to estimate migration flows and predict future trends to understand the causes and effects of migration and to enforce policies to deliver certain services. Several studies have exposed in this review with their datasets such as Credit card records (CCRs) provide deep insight into buying behavior; Call Details Records (CDRs) present new possibilities for under-implemented human mobility. Therefore, various forms of transportation and other travel behavior, various travel-related events such as "Home-Tour-Work -Tour-Tour” and the corresponding travel-related motifs have also been distinguished by the inclusion of land use details in the GIS data. This review also investigated migration trips residence between cities and inside a single city. The review concluded clear results for the adoption of the mentioned data, for example, mobile phone data (CDRs), because it is very useful as it provides real big data or real time big data without additional cost and is available in telecommunications companies, from which it is possible to analyze the movement of communities and deduce the activity of a particular city. In the future, there is a tendency to use this type of data from Korek Telecom Company in Iraq to the flow migration of Iraq governorates by using the gravity model. As well as, an attempt to study the conditions of cities and the movement of individuals in urban places to clarify the needs of the city in its need for new improvements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

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A Review of Flow Migration Through Mobile Networks

Alqaysi

Behadili²

2022

eijs

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“…PT smart card data benefits scholars and practitioners in understanding urban dynamics and human activities [ 12 , 33 ]. For instance, smart card data can be used to estimate the Origin-Destination (OD) of PT users, long-term network planning, demand forecasting, operational purposes like timetable and schedule adjustments as well as PT funding and investment decisions [ 13 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. PT smart card data includes information on boarding numbers, their times, and locations.…”

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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Context-Aware Graph Convolutional Network for Dynamic Origin-Destination Prediction

Nathaniel

Zheng

2021

2021 IEEE International Conference on Big Data (Big Data)

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Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data

Cited by 3 publications

References 8 publications

A Review of Flow Migration Through Mobile Networks

A Review of Flow Migration Through Mobile Networks

Exploring Equity in Public Transportation Planning Using Smart Card Data

Context-Aware Graph Convolutional Network for Dynamic Origin-Destination Prediction

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