2011
DOI: 10.5038/2375-0901.14.4.7
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Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

Abstract: This research explores the application of archived data from Automated Data Collection Systems (ADCS)

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Cited by 178 publications
(121 citation statements)
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“…The maximum walking distance is various in different papers. Zhao et al [5] set the acceptable walking distance as 1320 feet, that's 402 m. The tolerance distance Trépanier et al [2] selected is 2000 m, while Cui [33] uses 1100 m. Wei Wang et al [36] and Marcela Munizaga et al [25,28] both applied 1000 m as the upper limit. Azalden Alsger et al [35] set 800 m and António A. Nunes et al [1] set 640 m. Azalden Alsger et al [30] accesses the matching rate under different walking time.…”
Section: Trip Chaining Modelmentioning
confidence: 99%
“…The maximum walking distance is various in different papers. Zhao et al [5] set the acceptable walking distance as 1320 feet, that's 402 m. The tolerance distance Trépanier et al [2] selected is 2000 m, while Cui [33] uses 1100 m. Wei Wang et al [36] and Marcela Munizaga et al [25,28] both applied 1000 m as the upper limit. Azalden Alsger et al [35] set 800 m and António A. Nunes et al [1] set 640 m. Azalden Alsger et al [30] accesses the matching rate under different walking time.…”
Section: Trip Chaining Modelmentioning
confidence: 99%
“…Wang et al [14] proposed using a trip-chaining method to infer bus passenger origin-destination from smart card transactions and Automatic Vehicle Location (AVL) data from London, United Kingdom. In the studied scenario, authors needed to estimate both origin and destination of trips.…”
Section: Estimation Of Demand and Od Matricesmentioning
confidence: 99%
“…Later, Munizaga et al [15] presented a similar approach to the one applied by Wang et al [14] for estimating OD matrices in the multimodal transportation system of Santiago, Chile. The scenario considered in the article by Munizaga et al is more general, because passengers can use their smart cards to pay for tickets at metros, buses, and bus stations.…”
Section: Estimation Of Demand and Od Matricesmentioning
confidence: 99%
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“…First, the arrival time data of the vehicles for the stops were used to match a passenger's IC card data to get the starting stop of the trip. Similarly, the next IC card record can be used to obtain the destination stop of the trip [27]. Then the travel distance and FPUD were derived based on the extracted trips.…”
Section: Informationmentioning
confidence: 99%