Bus Passenger Origin-Destination Estimation and Related Analyses Using Automated Data Collection Systems

Wang, Wei; Attanucci, John; Wilson, Nigel H. M.

doi:10.5038/2375-0901.14.4.7

Cited by 175 publications

(121 citation statements)

References 6 publications

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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%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

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Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

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Section: Trip Chaining Modelmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

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“…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%

“…The proposed methodology for estimating demand and OD matrices takes into account the two kinds of transfer trips existent in Montevideo (detailed in Section 2.2). The proposed model is based on reconstructing the trip sequence for passengers that use a smart card, following a similar approach to that applied in the related literature [13][14][15]. We assume that each smart card corresponds to a single passenger, so we use the terms card and user in an indistinct manner.…”

Section: Mobility Patterns and Demand/od Matrices Estimationmentioning

confidence: 99%

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A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

Nesmachnow¹,

Baña²,

Массобрио³

2017

EAI Endorsed Transactions on Smart Cities

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This article proposes a platform for distributed big data analysis in the context of smart cities. Extracting useful mobility information from large volumes of data is crucial to improve decision-making processes in smart cities. This article introduces a framework for mobility analysis in smart cities combining Intelligent Transportation Systems and socioeconomic data for the city of Montevideo, Uruguay. The efficiency of the proposed system is analyzed over a distributed computing infrastructure, demonstrating that the system scales properly for processing large volumes of data for both off-line and on-line scenarios. Applications of the proposed platform and case studies using real data are presented, as examples of the valuable information that can be offered to both citizens and authorities. The proposed model for big data processing can also be extended to allow using other distributed (e.g. grid, cloud, fog, edge) computing infrastructures.

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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%

Evaluating the Impacts of Bus Fare on Social Equity Based on IC Card Data in China

2016

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Abstract:Bus fare equity has attracted significant attention in China in the past few years. Compared with developed countries, China's intelligent transportation systems are in their infancy, with immature bus fare policies being used in many public transit systems. The methods used for evaluating public transit fare equity in developed countries compare different fare policies based on rich data and cannot be directly applied in developing countries like China. In this paper, we present a method that uses IC card data, bus-mounted GPS data, and relevant statistical yearbook data to evaluate the equity of flat bus fare. The method ranks the factors that influence the impacts of bus fare on social equity for different passenger groups and indicates that trip distance and passenger boarding time are the two primary factors for bus fare equity from a resource allocation perspective. Finally, we present a case study that evaluates the flat fare policy for route 204 in Suzhou using the proposed method. The results show that the proposed method is feasible.

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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)

Cited by 175 publications

References 6 publications

Smart Card Data Mining of Public Transport Destination: A Literature Review

Smart Card Data Mining of Public Transport Destination: A Literature Review

A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

Evaluating the Impacts of Bus Fare on Social Equity Based on IC Card Data in China

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