A procedure for public transit OD matrix generation using smart card transaction data

Kang, Masood Jafari; Ataeian, Shervin; Amiripour, S M Mahdi

doi:10.1007/s12469-020-00257-7

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…(3) Return trip assumptions: the same passenger in the same day, two consecutive rides on the same route, the opposite direction of travel, the boarding stop of the first ride is the alighting stop of the second ride, and vice versa [6] .…”

Section: Alighting Stop Calculationmentioning

confidence: 99%

“…Ziwei Cui et al (2020) proposed a method to derive bus passengers' alighting stops based on historical travel records, considering both the generation and selection of historical data to derive more alighting stops and improve accuracy [5] . Masood Jafari Kang et al (2020) applied SC data and GPS data to obtain the OD matrix of the BRT network [6] . Pieroni C et al (2021) applied SC data to analyze the temporal and spatial patterns of urban transport operations for residents of unstable settlements in São Paulo, Brazil [7] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and analysis of public transportation trip-chain based on multi-source big data

Chen,

Yang,

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Alighting Stop Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and analysis of public transportation trip-chain based on multi-source big data

Chen,

Yang,

et al. 2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…Refs. [66,67] present two different methodologies for estimating the destination of passenger journeys from AFC data.…”

Section: Automatic Fare Collectionmentioning

confidence: 99%

Review of Transit Data Sources: Potentials, Challenges and Complementarity

Sarhani

Voß

et al. 2021

Sustainability

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Public transport has become one of the major transport options, especially when it comes to reducing motorized individual transport and achieving sustainability while reducing emissions, noise and so on. The use of public transport data has evolved and rapidly improved over the past decades. Indeed, the availability of data from different sources, coupled with advances in analytical and predictive approaches, has contributed to increased attention being paid to the exploitation of available data to improve public transport service. In this paper, we review the current state of the art of public transport data sources. More precisely, we summarize and analyze the potential and challenges of the main data sources. In addition, we show the complementary aspects of these data sources and how to merge them to broaden their contributions and face their challenges. This is complemented by an information management framework to enhance the use of data sources. Specifically, we seek to bridge the gap between traditional data sources and recent ones, present a unified overview of them and show how they can all leverage recent advances in data-driven methods and how they can help achieve a balance between transit service and passenger behavior.

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“…In the past two decades, many studies have been done to estimate stops level transit origin–destination using Automatic Data Collection (ADC) system ( Barry et al, 2002 , Trépanier et al, 2007 , Li et al, 2011 , Nassir et al, 2011 , Wang et al, 2011 , Munizaga and Palma, 2012 , Alsger et al, 2016 , Kumar et al, 2019 , Jafari Kang et al, 2021 , Etikaf et al, 2021 , Wu et al, 2021 ). The majority of these studies used smart card data to estimate O-D for the bus system.…”

Section: Introductionmentioning

confidence: 99%

An estimation of the effects of social distancing measures on transit vehicle capacity and operations

Kamga

Tchamna

Vicuna

et al. 2021

Transportation Research Interdisciplinary Perspectives

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The COVID-19 pandemic has a direct impact on public transport operations. In this paper, impacts on transit operations of the physical distancing measures deployed to slow the spread of the virus are analyzed and recommendations are provided. At first, two social distancing optimization solutions are provided in order to keep riders at a safe distance. The first is a discrete optimization that can be used in buses with fixed seats, while the second is a continuous optimization that can be used to distribute riders on a grid and be applied on a bus or subway platform. Assuming that the ridership will eventually go back to its level before the pandemic, the second objective of this research is to address the transit operation parameters that need to be changed in order to serve the pre-COVID ridership level, while respecting the social distancing measures. An O-D distribution has been developed in this paper for New York City (NYC) subway line 1, based on the 2018 NYC Travel Survey conducted by the Metropolitan Transportation Authority. Five scenarios of physical distancing are simulated and analyzed in this paper: 3ft, 4ft, 5ft, 5.4ft, and 6ft of separation between passengers. The results show the number of additional trains required to accommodate the hypothetical pre-COVID ridership demand while maintaining social distancing. An interesting key finding is that, by decreasing the minimum distance from 6ft to 5.4ft, the number of additional trains required to serve the transit demand drastically decreases and hence more resources are saved.

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A procedure for public transit OD matrix generation using smart card transaction data

Cited by 15 publications

References 28 publications

Modeling and analysis of public transportation trip-chain based on multi-source big data

Modeling and analysis of public transportation trip-chain based on multi-source big data

Review of Transit Data Sources: Potentials, Challenges and Complementarity

An estimation of the effects of social distancing measures on transit vehicle capacity and operations

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