Estimation of Origin-Destination matrices under Automatic Fare Collection: the case study of Porto transportation system

Hora, Joana; Dias, Teresa Galvão; Camanho, Ana S.; Sobral, Thiago

doi:10.1016/j.trpro.2017.12.103

Cited by 23 publications

(12 citation statements)

References 14 publications

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“…The system adopts advanced technology and means to realize automatic identification, analysis and other functions [1][2]. Through the information collection of various areas in the city, sort out and summarize the data [3][4].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Smart City Rail Automatic Fare Collection System Under the Background of Big Data

Xie¹

2022

J. Phys.: Conf. Ser.

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With the rapid development of Internet technology, the process of urban construction is accelerating and the level of urbanization is further improved. In the smart city rail automatic fare collection system, there are a large number of data information and data that need to be processed manually. The traditional manual method not only consumes human, material and financial resources, but also has low efficiency. Therefore, this paper proposes a smart city rail automatic fare collection system based on big data design. Firstly, this paper expounds the concept of smart city rail transit and studies the function of automatic fare collection system. Then it studies the definition and characteristics of big data, designs the method of system development, and tests the performance of the system. The test results show that the system runs smoothly, accounts for a relatively small amount of memory, has a fast response speed and low delay. Most passengers are satisfied with the system.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Smart City Rail Automatic Fare Collection System Under the Background of Big Data

Xie¹

2022

J. Phys.: Conf. Ser.

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“…The assessment of candidate alighting stops depends on a suitable distance equation. For our problem, the haversine distance is used, since it expresses more accurately the walking distance and is widely used in recent studies [8,40]. The candidate stop θ that minimises transfer distance, along a given route, is chosen.…”

Section: Alighting Estimation Of a Stage Tripmentioning

confidence: 99%

“…Secondly, in the transport context, the space dimension can be configured for micro and macroscopic analysis. Some studies perform exhaustive and microscopic analysis, observing the flow of trips between pairs of network stops (subway stations, bus stops, bike stations, among others) [8]. On the other hand, there are matrix studies at the macroscopic level, i.e.…”

Section: Origin Destination Matrix Definitionmentioning

confidence: 99%

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

Cerqueira

Arsénio

Henriques

2022

Eur. Transp. Res. Rev.

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Background The provision of seamless public transport supply requires a complete understanding of the real traffic dynamics, comprising origin-to-destination multimodal mobility patterns along the transport network. However, most current solutions are centred on the volumetric analysis of passengers’ flows, generally neglecting transfer, walking, and waiting needs, as well as the changes in the mobility patterns with the calendar and user profile. These challenges prevent a comprehensive assessment of the routing and scheduling vulnerabilities of (multimodal) public transport networks. Research aims/questions The research presented in this paper aims at addressing the above challenges by proposing a novel approach that extends dynamic Origin-Destination (OD) matrix inference to dynamic OD matrix inference with aggregated statistics, highlighting vulnerabilities and multimodal mobility patterns from individual trip record data. Methodology Given specific spatial and temporal criteria, the proposed methodology extends dynamic Origin-Destination (OD) matrices with aggregated statistics, using smart-card validations gathered from (multimodal) public transport networks. More specifically, three major contributions are tackled; i) the data enrichment in the OD matrices with statistical information besides trip volume (e.g., transfer and trip features); ii) the detection of vulnerabilities on the network pertaining to walking distances and trip durations in a user-centric way and iii) the decomposition of traffic flows in accordance with calendrical rules and user (passenger) profiles. The set of contributions are validated on the bus-and-metro public transport network in the city of Lisbon. Results The proposed approach for inferring OD matrices yields four unique contributions. First, we allow inference to consider multimodal commuting patterns, detecting individual trips undertaken along with different operators. Second, we support dynamic matrices’ OD inference along with parameterizable time intervals and calendrical rules, and further support the decomposition of traffic flows according to the user profile. Third, we allow parameterization of the desirable spatial granularity and visualisation preferences. Fourth, our solution efficiently computes several statistics that support OD matrix analysis, helping with the detection of vulnerabilities throughout the transport network. More specifically, statistical indicators related to travellers’ functional mobility needs (commuters for working purposes, etc.), walking distances and trip durations are supported. The inferred dynamic OD matrices are the outcome of a developed software with strict guarantees of usability. Results from the case study using data gathered from the two main public transport operators (Bus and Metro) in the city of Lisbon show that 77.3% of alighting stops can be estimated with a high confidence degree from bus smart-card data. The inferred OD matrices (Bus and Metro) in the city of Lisbon reveal vulnerabilities along specific OD pairs, offering the bus public operators in Lisbon new knowledge and a means to better understand dynamics and validate OD assumptions.

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“…As passenger transport hubs have the characteristics of wide land occupation and large and complex passenger flows, traditional detection technology has difficulty meeting the requirements in terms of detection ability, cost and so on. Automatic fare collection technology (AFC) [18,19], for example, can be utilized only after passengers swipe their card out of the station, resulting in poor real-time performance. Video active detection technology [20,21] is also impractical due to the high cost of cameras and the complex installation of equipment.…”

Section: Research Reviewmentioning

confidence: 99%

Departure efficiency evaluation of a comprehensive transport hub based on Wi-Fi probe data and a multilayer hybrid model

et al. 2022

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Evaluation of the passenger departure efficiency of a comprehensive transport hub is essential for traffic managers. Through the evaluation, security risks in the hub can be found in time to ensure the safe departure of passengers. The attention of existing studies has focused on the analysis of the overall situation of the hub, and the quantitative description of departure status in different connection areas inside the hub is insufficient. In this study, a multilayer hybrid model based on an analytic hierarchy process and entropy weight method was established. The data collected using Wi-Fi probe technology were clustered by a K-means algorithm. The first level of the model was divided according to the connection areas of the passenger hub, and the second level was based on the number of stranded people, wait time and departure time in each connection area. It was found that the SP index has the greatest impact on departure efficiency. In addition, the impact of passenger flow aggregation on each connection area is different, and the management department should treat it accordingly. The applicability of the proposed multilayer hybrid model was verified in the example of the Chongqing north railway station.

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Estimation of Origin-Destination matrices under Automatic Fare Collection: the case study of Porto transportation system

Cited by 23 publications

References 14 publications

RETRACTED: Smart City Rail Automatic Fare Collection System Under the Background of Big Data

RETRACTED: Smart City Rail Automatic Fare Collection System Under the Background of Big Data

Inference of dynamic origin–destination matrices with trip and transfer status from individual smart card data

Departure efficiency evaluation of a comprehensive transport hub based on Wi-Fi probe data and a multilayer hybrid model

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