Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Kumar, Pramesh; Khani, Alireza; Davis, Gary A.

doi:10.1177/0361198119845896

Cited by 10 publications

(3 citation statements)

References 26 publications

(38 reference statements)

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“…This technique of recovering a sparse solution planted in an under-determined set of linear equations is known as compressed sensing , proposed by Candès and Tao ( 19 ). Kumar et al showed that the method works better than existing methods such as least-squares minimization and entropy maximization even for high demand ( 14 ). Using this method allows more confidence as, because of COVID-19, the ridership of various transit routes is so low that it makes the resulting flow matrix sparse.…”

Section: Creation Of Passenger Contact Networkmentioning

confidence: 99%

“…The limitation of APC data is that the recorded number of boarding and alighting gives no indication of the movement of individual passengers from one stop location to another. Recent success in origin–destination (O-D) estimation using APC data by Kumar et al can help in estimating the travel patterns of passengers on a public transit route with minimal error ( 14 ).…”

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confidence: 99%

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Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data

Kumar

Khani

Lind

et al. 2021

Transportation Research Record: Journal of the Transportation R

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This paper studies the potential spread of infectious disease through passenger encounters in a public transit system using automatic passenger count (APC) data. An algorithmic procedure is proposed to evaluate three different measures to quantify these encounters. The first two measures quantify the increased possibility of disease spread from passenger interaction when traveling between different origin–destination pairs. The third measure evaluates an aggregate measure quantifying the relative risk of boarding at a particular stop of the transit route. For calculating these measures, compressed sensing is employed to estimate a sparse passenger flow matrix planted in the underdetermined system of equations obtained from the APC data. Using the APC data of Route 5 in Minneapolis/St. Paul region during the COVID-19 pandemic, it was found that all three measures grow abruptly with the number of passengers on board. The passenger contact network is densely connected, which further increases the potential risk of disease transmission. To reduce the relative risk, it is proposed to restrict the number of passengers on-board and analyze the effect of this using a simulation framework. It was found that a considerable reduction in the relative risk can be achieved when the maximum number of passengers on-board is restricted below 15. To account for the reduced capacity and still maintain reasonable passenger wait times, it would then be necessary to increase the frequency of the route.

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Section: Creation Of Passenger Contact Networkmentioning

confidence: 99%

mentioning

confidence: 99%

Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data

Kumar

Khani

Lind

et al. 2021

Transportation Research Record: Journal of the Transportation R

Self Cite

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

Amblard¹,

Dib²,

Cherrier³

et al. 2023

Lecture Notes in Computer Science

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Transit Route Origin–Destination Matrix Estimation using Compressed Sensing

Cited by 10 publications

References 26 publications

Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data

Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data

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

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

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