Mining metro commuting mobility patterns using massive smart card data

Yong, Juan; Zheng, Linjiang; Mao, Xiao Wen; Tang, Xi; Gao, Ang; Liu, Lu

doi:10.1016/j.physa.2021.126351

Cited by 28 publications

(11 citation statements)

References 15 publications

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“…The conclusion further supports the initial conjecture of this paper that differences in human activities due to land types are prevalent, and that data mining of time series of activities is important for the identification of land use. The findings were similar to those of L Toole et al [12][13][14][15]. However, the factors associated with the mechanisms of change in human activity were not discussed further in those studies.…”

Section: Discussion and Analysissupporting

confidence: 89%

“…L Toole [12] and Noelia et al [13] used cell phone signaling data to extract and analyze the human flow characteristics of different functional types of land, and explored the differences in human flow characteristics between different functional areas as a way to achieve urban neighborhood identification of functional land attributes. Based on the Integrated Circuit (IC) card data, Yong et al [14] clustered the passenger flow of each station based on the K-Means++ algorithm from the perspective of the time series, and established the fitting equations of the passenger flow clustering results with the multidimensional parameters of land use features. Cao et al [15] deduced the temporal distribution of passenger flow at metro stations based on the metro smart card data of Shenzhen, and performed a clustering analysis to achieve the identification of the types of occupational and residential land use around the metro stations.…”

Section: Introductionmentioning

confidence: 99%

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Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities

Zhang²,

Zhang

et al. 2022

Sustainability

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As a social carrier, a city is a place of human activities, and human activities also shape and influence the city through its dynamic demand. The in-depth understanding of urban functions is important for urban planning, and the full utilization of the spatial–temporal variation of human activities can be more effective in the identification of land functions. However, the complete extraction of time series features is one of the difficulties. To solve the above problems, the paper explores the identification of land use types based on human activity feature extraction, by taking Beijing as an example. Firstly, this paper constructs a time series that characterizing the change of passenger flow in the metro station area with AFC data, and realizes the feature extraction and type clustering of the time series. Secondly, this paper forms an index system for land use type identification by introducing POI-based indicators, which achieves a comprehensive representation of population activity data. Finally, this paper constructs a land use type identification model based on multi-source human activity data by using GBDT classifier. The results show that the model has high recognition accuracy. It is found that the fusion application of AFC and POI improves the land use recognition accuracy in the case of “Consistent change in time series but different types of demand”, and the different POI triggers the seemingly overall stable but random activity demand within the city. The research results promote the innovative application of open-source big data in the field of urban planning.

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Section: Discussion and Analysissupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities

Zhang²,

Zhang

et al. 2022

Sustainability

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“…Let N = (η m ) be a normalized matrix with ∑ m η m = 1, such that each entry η m suggests an estimated ratio of trips between mth OD pair to all trips. Normalized trip distribution can be obtained from different data sources, such as travel surveys [35], census of population and housing [21], mobile data [36,37], public transport smart tickets [38,39], or geo-tagged social media posts [40]. We refer to this input as a normalized OD (NOD) matrix.…”

Section: Initial Nod Matrixmentioning

confidence: 99%

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Pourmoradnasseri,

Khoshkhah,

Hadachi

2023

IET Smart Cities

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The emergence of smart cities is set to transform transportation systems by leveraging real‐time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real‐time traffic conditions. A framework for near‐real‐time city‐scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi‐level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high‐dimensional real‐time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.

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“…Let = ( ) be a normalized matrix with ∑ = 1, such that each entry suggests an estimated ratio of trips between th OD pair to all trips. Normalized trip distribution can be obtained from different data sources, such as travel surveys (Egu and Bonnel, 2020), census of population and housing (Arora et al, 2021), mobile data (Pourmoradnasseri, Khoshkhah, Lind and Hadachi, 2019;Hadachi, Pourmoradnasseri and Khoshkhah, 2020), public transport smart tickets (Mohamed, Côme, Oukhellou and Verleysen, 2016;Yong, Zheng, Mao, Tang, Gao and Liu, 2021), or geo-tagged social media posts (Gao, Yang, Yan, Hu, Janowicz and McKenzie, 2014). We refer to this input as normalized OD (NOD) matrix.…”

Section: Initial Nod Matrixmentioning

confidence: 99%

Real-Time Calibration of Disaggregated Traffic Demand

Pourmoradnasseri¹,

Khoshkhah²,

Hadachi³

2022

Preprint

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Real-time traffic demand estimation is essential for intelligent transportation systems and traffic forecasting in urban areas. Hence, this paper presents a simulation-based optimization framework for city-scale real-time estimation and calibration of dynamic demand models by focusing on disaggregated microsimulation in congested networks. The calibration approach is based on sequential optimization demand estimation for short time frames and uses a stream of traffic count data from IoT sensors on selected roads. The proposed method builds upon the standard bilevel optimization formulation. The upper-level optimization problem is presented as a bounded variable quadratic programming in each time frame, making it computationally tractable. The DTA (dynamic traffic assignment) problem at the lower level is approached based on the stochastic route choice model. For every time frame, the probabilistic parameters of the route choice model are obtained through several rounds of feedback loop between the OD optimization problem (at the upper level) and parallel samplings and simulations for DTA (at the lower level). At the end of each time frame, the microsimulation state of the network is transferred to the next time frame to ensure the temporal dependency and continuity of the estimations during the time. In addition, the algorithm's convergence is accelerated by applying the fixed-point method to road-segment travel times.The proposed sequential calibration model presents a drastic computational advantage over available methods by splitting the computations into short time frames and under the assumption that the demand in each time frame only depends on current and previous time frames. The model does not depend on reliable prior demand information. Moreover, with receiving the field measurements as a stream and the efficient time complexity of the algorithm in each time frame, the method successfully presents a solution for high-dimensional real-time and online applications. We validated the method with synthetic data and for a real-world case study in Tartu city, Estonia. The results show high accuracy under a tight computational budget.

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Mining metro commuting mobility patterns using massive smart card data

Cited by 28 publications

References 15 publications

Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities

Type Identification of Land Use in Metro Station Area Based on Spatial–Temporal Features Extraction of Human Activities

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Real-Time Calibration of Disaggregated Traffic Demand

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