Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable

Li, Hong; Li, Wei; Zhu, Wei

doi:10.1155/2017/4373871

Cited by 18 publications

(14 citation statements)

References 17 publications

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“…The data points automatically get together in local synchronized status, leading centroids to be merged slowly in the following parts. It can be seen that, with the clustering process, data points gradually merge to form cluster centers, and noisy data are isolated obviously at the same time, when reaching the optimal domain distance as (13)- (16). The final result is shown in Figure 9.…”

Section: Case Studymentioning

confidence: 98%

“…Ma et al [15] developed a data mining method to identify the spatiotemporal commuting patterns of Beijing public transit riders using transit smart card data. Hong et al [16] proposed a methodology for assigning passenger flows on a metro network based on Automatic Fare Collection (AFC) data and realized timetable. Briand et al [17] analyzed the behavioral habits of public transport passengers using a real dataset of smart card data covering a period of five years.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Luo

Cai

et al. 2018

Journal of Advanced Transportation

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The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.

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Section: Case Studymentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Luo

Cai

et al. 2018

Journal of Advanced Transportation

Self Cite

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“…e AFCS data has been widely applied in public transit systems for OD demand estimation [12], timetable design [13], passenger flow assignment [14,15], passenger behavior analysis [16], and transfer coordination [17][18][19]. Since passengers swipe the metro card only at the gates of origin and destination stations, detailed information, such as the location where a passenger makes a transfer, is unknown.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Xiaoyue

Chien

2020

Journal of Advanced Transportation

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The service quality of public transit, such as comfort and convenience, is an important factor influencing ridership and fare revenue, which also reflects the passengers’ perception to the transit performance. Passengers are frustrated while waiting to board a crowded train especially during the peak hours, while the fail-to-board (FtB) situation commonly exists. The service performance measures determined by deterministic passenger demand and service frequency cannot reflect the perceived service of passengers. With the automatic fare collection system data provided by Chengdu Metro, we develop a data-driven approach considering the joint probability of spatiotemporal passenger demand at stations based on posted train schedule to approximate passenger travel time (e.g., in-vehicle and out-of-vehicle times). It was found that the estimated wait time can reflect the actual situation as passengers FtB. The proposed modeling approach and analysis results would be useful and beneficial for transit providers to improve system performance and service planning.

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“…In RUE method instead of using expected travel time for optimum path search they used travel time budget. (Hong et al, 2017) developed a passenger assignment in metro network model based on Automatic Fare Collection (AFC) and realized time table. With the help of AFC and realized time table observed travel time can be known which is directly related to origin destination (OD) pairs.…”

Section: Literature Reviewmentioning

confidence: 99%

Macro-Simulation Based Passenger Assignment of Delhi Metro

2019

IJTTE

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Metro is the most efficient and sustainable mode of transport in Delhi. Delhi metro helps in transferring a large volume of commuters in an efficient manner. With a growing population and development of infrastructure in Delhi, need for metro is increasing in all the regions of the city. Thus, Delhi Metro Corporation planned and executed further metro expansion from phase II to phase III. This paper studies the metro passenger assignment using Time table based approach in macroscopic simulation software VISUM. This study has been carried out to analyze the effect of increase in ridership and to deduce the factors which are responsible for the change in traffic assignment in the current metro network (2018). The objectives of this study are to compare the Phase II metro network with phase III, and to study the effects due to the extension and addition of metro lines under Phase III. From the study, it was observed that with the implementation of phase III metro plan, both 'journey time' and 'in vehicle travel time' are reduced and 'average speed' of journey through metro is increased. Further, 'passenger kilometer travel' and 'boarding and alighting' have increased tremendously.

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Assigning Passenger Flows on a Metro Network Based on Automatic Fare Collection Data and Timetable

Cited by 18 publications

References 17 publications

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Macro-Simulation Based Passenger Assignment of Delhi Metro

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