Analysis of travel pattern changes due to a medium-term disruption on public transit networks using smart card data

Nazem, Mohsen; Lomone, André; Chu, Alfred; Spurr, Timothy

doi:10.1016/j.trpro.2018.10.019

Cited by 18 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evidence has been established for the multimodal impact of postdisaster shifts away from transit ( 15 ). Even short- or medium-term disruptions were found to have long-term impacts on behavior ( 20 ). In response to major disruptions, focused investment in the system improves the speed of recovery ( 13 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of School Reopening on Variations in Local Bus Performance in Sydney

Yan

Yao

Chen

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

During the COVID-19 pandemic, stay-at-home orders in conjunction with working from home, school closures, and event cancellations resulted in a decrease in travel demand. Under normal circumstances, these activities are components of trip chains and utilize a multimodal transport network. The overall performance of the network can be traced through delays in the bus system as buses capture both changes in ridership and fluctuations in mixed traffic conditions. This paper explores the hypothesis that resumption of a single component in trip chains (i.e., school reopening) is sufficient for a measurable change in transport system performance. This study used school reopening in Sydney, Australia as a case study to explore whether school-related trips affected bus system performance directly with higher student patronage or indirectly with heavier road congestion from parental car trips. Both stop dwell times and differences in delays between successive stops were used as bus service indicators. Dwell times reflect the travel demand for buses and delay differences capture local changes in service reliability. We found that increase in ridership had limited impacts on bus punctuality. However, the level of local bus performance worsened after schools reopened, and the effect was more pronounced in commercial areas in the afternoon when schools ended, suggesting secondary trip purposes such as leisure and shopping in addition to school pick-ups. This study revealed the interaction between different trip purposes during the postshutdown period and threw light on changes in travel behavior patterns as travel restrictions were relaxed in pandemic circumstances.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Impacts of School Reopening on Variations in Local Bus Performance in Sydney

Yan

Yao

Chen

et al. 2021

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…The review of relevant research focuses on the following aspects: (a) changes of passenger behavior under station closure; (b) changes of passenger flow volume under station closure; (c) anomaly detection algorithms based on smart card data; and (d) interaction between travel demand and railway services. Many scholars have widely studied the changes of passenger behavior in anomalous scenarios [5], [7]- [12]. Pnevmatikou et al [5] developed the joint RP/SP nested logit model to analyze the mode choice during a longterm subway service disruption.…”

Section: Introductionmentioning

confidence: 99%

“…Pnevmatikou et al [5] developed the joint RP/SP nested logit model to analyze the mode choice during a longterm subway service disruption. Nazem et al [7] analyzed the changes of travel behavior and the impacts on transit customers' behavior due to public transit service disruptions. Nguyen-Phuoc et al [8] studied how public transport users adjusted their travel behaviors if public transport was ceased.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Detect Passenger Flow Anomaly Under Station Closure

Huang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

During daily subway operation, station closure has large impact on subway system organization and has received increasing attention. This article proposes an anomaly detection method based on ensemble algorithms to determine the range of station closure influence on passenger flow. Firstly, Ensemble Algorithm I is developed to identify the stations with passenger flow volume anomaly and origin-destination (OD) pairs with volume anomaly. Secondly, Ensemble Algorithm II is proposed to identify the OD pairs with travel time anomaly. Then, the spatial variation in passenger flow caused by station closure, i.e. shift of passenger flow to neighboring stations and shift of path flow, is analyzed, and the spatial-temporal influence range of station closure is determined. A case study of the Beijing subway system is performed to illustrate the validity of the proposed method.Compared with sub algorithm of ensemble learning and KNN algorithm, Ensemble Algorithm I and II are more robust and have less misjudgment.INDEX TERMS Date driven, passenger flow anomaly, spatial-temporal influence detection, station closure, weighted ensemble algorithm.

show abstract

“…However, most previous studies have focused only on a small number of data items in smart card data: the number of uses by day of the week and time of day, by origin time of day, origin point and destination point, etc [7,8]. Or there are travel behavior analysis focused on single data items and specific elements of data items [9,10,11]. It cannot be said that these studies have considered multiple data items in smart card data simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A Method for Extracting Travel Patterns Using Data Polishing

Hosoe

Kuwano

Moriyama

2020

Preprint

View full text Add to dashboard Cite

With the development of ICT (Information and Communication Technology), interest in using the large amount of accumulated data for traffic policy planning has been increasing. In recent years, data polishing has been proposed as a new methodology for big data analysis. Data polishing is one of the graphical clustering methods. This method can be used to extract patterns that are similar or related to each other by clarifying the cluster structures in the data. The purpose of this study is to reveal travel patterns of railway passengers by applying data polishing to smart card data collected in Kagawa Prefecture, Japan. This study uses 9,008,709 data points collected during the 15 months from December 1st, 2013 to February 28th, 2015. This data set includes such information as trip histories and types of passengers. The study uses the data polishing method to cluster 4,667,520 combinations of information about individual rides: day of the week, time of day, passenger type, origin station, and destination station. As a result, 127 characteristic travel patterns were specified from those combinations.

show abstract

Analysis of travel pattern changes due to a medium-term disruption on public transit networks using smart card data

Cited by 18 publications

References 7 publications

Impacts of School Reopening on Variations in Local Bus Performance in Sydney

Impacts of School Reopening on Variations in Local Bus Performance in Sydney

A Data-Driven Approach to Detect Passenger Flow Anomaly Under Station Closure

A Method for Extracting Travel Patterns Using Data Polishing

Contact Info

Product

Resources

About