Optimization Models for Estimating Transit Network Origin–Destination Flows with Big Transit Data

Liu, Xinyu; Hentenryck, Pascal Van; Zhao, Xingping

doi:10.1007/s42421-021-00050-3

Cited by 8 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are multiple ways of modelling the passenger arrival, boarding, and alighting processes at a station. The very nature of the problem can result in some complex models characterised by several parameters, whose calibration will likely require the availability of vast quantities of data (e.g., Gur and Ben-Shabat, 1997 , Liu et al, 2021 , van Oort et al, 2015 , Wang et al, 2011 , Li et al, 2011 , Ji et al, 2015 , Huang et al, 2020 , Sun et al, 2021 , Tao and Tang, 2019 ). Here, simplified models are developed and employed.…”

Section: On-board Passenger Load Estimation Methodologymentioning

confidence: 99%

“…A related problem that has also received considerable attention has been the estimation and prediction of Origin-Destination (OD) flows and matrices for public transport, usually on the basis of passenger counts or Automatic Fare Collection (AFC) systems. Methods adopted include optimisation ( Gur and Ben-Shabat, 1997 , Liu et al, 2021 ), elasticity ( van Oort et al, 2015 ), trip chaining ( Wang et al, 2011 , Li et al, 2011 ), Iterative Proportional Fitting (IPF) ( Ji et al, 2015 ), clustering ( Huang et al, 2020 ), Bayesian inference ( Sun et al, 2021 ), as well as data fusion and Kalman filtering ( Tao and Tang, 2019 ). Some research has also explored the nature and patterns of prediction and forecasting errors and has created inferential statistics models aiming to address them ( Jung and Casello, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data

Roncoli¹,

Chandakas²,

Kaparias³

2023

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

Section: On-board Passenger Load Estimation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data

Roncoli¹,

Chandakas²,

Kaparias³

2023

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

“…Other studies have also incorporated the use of APC data in their methodology. For example, Liu et al proposed three optimization methods to increase the accuracy of transfer identification within O-D flows ( 12 ). In this study, transfer inferences were based on long-term observations of stop terminals in addition to setting constraints on parameters such as walking distance and transfer time.…”

Section: Introductionmentioning

confidence: 99%

Using General Transit Feed Specification-Ride to Identify Riders’ Use of Transfer Opportunities

Hoover

Kost

Porter

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Understanding how riders use a transit agency’s services is central to providing effective service. Although the ideal experience for riders may not include transfers, these may be necessary to connect them from their origin to their destination. Previous methods have identified key hidden transfer locations within transit networks. However, there has been little effort to develop tools that enable small- to mid-sized agencies that typically lack access to sophisticated data sources to conduct this analysis. This research introduces a methodology for identifying transfer opportunities using a combination of statistical analyses of nonindividually identified automated passenger counter data to compute a transfer metric representing the association one service has with another through shared passengers. A unique aspect of this work is the utilization of ridership data compliant with the data standard General Transit Feed Specification (GTFS)-ride, which captures historic states of the transit network with associated ridership levels. GTFS schedule data from an Oregon transit agency were employed to identify transfer opportunities by assessing the probability of a transfer based on transfer time, an upper limit on transfer distance, and a new metric that measures the geographic coverage gains made by a particular transfer. A final transfer metric was calculated and compared against recently collected survey data. The key contribution of this work is the identification of transfer opportunities that lie outside traditional transfer hub locations. The resultant transfer metric will enable transit service planners to conduct regular analyses of their network, identifying key transfer locations and opportunities for further development.

show abstract

A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction

Amblard¹,

Dib²,

Cherrier³

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Optimization Models for Estimating Transit Network Origin–Destination Flows with Big Transit Data

Cited by 8 publications

References 24 publications

Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data

Estimating on-board passenger comfort in public transport vehicles using incomplete automatic passenger counting data

Using General Transit Feed Specification-Ride to Identify Riders’ Use of Transfer Opportunities

A Bayesian Markov Model for Station-Level Origin-Destination Matrix Reconstruction

Contact Info

Product

Resources

About