Unraveling Latent Transfer Patterns Between Metro and Bus From Large-Scale Smart Card Data

Chen, Enhui; Zhang, Wenbo; Ye, Zhirui; Yang, Min

doi:10.1109/tits.2020.3035719

Cited by 11 publications

(5 citation statements)

References 54 publications

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“…In addition to the behavioral modeling based on RP and SP survey data, many studies focus on data-driven methods to investigate route choice attributes of URT trips. This body of route-related research uses smart card data, global positioning system (GPS) data, or mobile phone signaling data to infer passengers' journeys and analyze their route choice patterns (26)(27)(28)(29)(30)(31). These provide novel approaches for estimating the access and egress station choices, identifying the relatively integral routes of mode chains, and understanding passengers' route choice behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Generalized Cost Model Considering the Whole Trip for Urban Rail Transit Station Choices

Qin,

Yang,

Ling

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Convenient transfer between urban rail transit (URT) and feeder modes could be fundamental for improving the URT service. In the field of URT, most studies have attempted to estimate the station choice of sub-trips like access trips and egress trips rather than the whole trip, which may be locally optimal and inconsistent with the traveler’s behavior of maximizing the utility of the whole trip. In this study, we build an integrated generalized cost model for URT to estimate the access and egress station choices of URT travelers from the perspective of the whole trip and apply a genetic algorithm to estimate the model parameters. Data used in this study include a revealed preference (RP) mode chain survey conducted in Nanjing, China, and the travel cost data obtained from an online navigation application program interface (API), thus reducing the transit networking modeling work as the conventional traffic assignment process. The proposed model has promising station choice prediction accuracies for whole trips. The results show that: (a) travelers do not always choose the nearest station, and the whole trip’s generalized cost would affect their choices; (b) travelers make heterogeneous station choices. They have a varied perception of time and fare and are more sensitive to access trips; and (c) in forecasting-oriented applications, the model parameters can be set flexibly, and the model can maintain acceptable forecasting accuracy. The findings contribute to understanding station choice mechanism, transfer optimization, and feeder demand forecast for stations.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Generalized Cost Model Considering the Whole Trip for Urban Rail Transit Station Choices

Qin,

Yang,

Ling

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…Assessing the status of public transport is covered by a vast number of related studies in different study cases (urban rail, bus, metro, or even multimodal) worldwide, where accessibility, comfort, security, and other factors are considered relevant to improve the network [32][33][34][35]. For this purpose, researchers and operators recurrently perform inquiries, inference (e.g., gravity model) or datadriven analysis (e.g., data mining on smart card data) to extract users' behaviour.…”

Section: Transfer and Trip Status Indicatorsmentioning

confidence: 99%

“…Chen et al [34] provide useful methods to capture transfer patterns between metro and bus transports in order to assess the accessibility in Nanjing, China. Employing a multidimensional representation (cube) with three dimensions, time of the day, day of the week and stations, the author cuts the cube into two dimensions (matrix) to visualize the transfers.…”

Section: Transfer and Trip Status Indicatorsmentioning

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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“…The high cost of constructing and operating metro systems means that in the majority of cities there will be a continued role for extensive feeder modes, such as walking, cycling, bus, and so on (Brons et al, 2009). Among these feeder options, ground-level bus offers flexible services with relatively inexpensive fares and high-level accessibility, thus is the dominant feeder mode for passengers traveling from/to metro stations (Chen, Zhang, et al, 2020). Besides walking, over 70% of metro trips was extended by bus in Shanghai, China (Shanghai Urban and Rural Construction and Transportation Development Research Institute, 2015).…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China

Shi,

Yan

2023

Transactions in GIS

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Ground‐level bus offers flexible services with relatively inexpensive fares and high‐level accessibility, thus is the dominant feeder mode for metro in the majority of metropolitan areas in China. Intermodal transfer between the metro and bus networks is therefore a crucial element in the successful operation of an integrated transit system. In this circumstance, a key challenge is lack of appropriate methodologies to evaluate the spatial–temporal disparities of intermodal transfers between metro and bus. To address this issue, this research aims to promote an existing two‐step floating catchment area (2SFCA) model by incorporating the temporal variability of service supply, demand, and travel time to provide a more realistic estimation of accessibility using smart card data and automatic vehicle location data. The proposed methodology was applied in a case study of the metropolitan area of Shanghai in the specific context of metro‐stop based accessibility to bus services. The results of the case study show that the daily fluctuations in output metro‐to‐bus transfer accessibility values are highly sensitive to temporal dynamics of transfer time, service supply, and demand. It is hoped the results output here could give planners and policymakers greater insight into spatiotemporal dynamics on transfer accessibility, and help to establish an effective and efficient integrated transit system.

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Unraveling Latent Transfer Patterns Between Metro and Bus From Large-Scale Smart Card Data

Cited by 11 publications

References 54 publications

Integrated Generalized Cost Model Considering the Whole Trip for Urban Rail Transit Station Choices

Integrated Generalized Cost Model Considering the Whole Trip for Urban Rail Transit Station Choices

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

A spatiotemporal analysis of metro‐stop based accessibility to bus services in Shanghai, China

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