A vehicle routing model based on large-scale radio frequency identification data

Wang, Yuming; Xu, Zhongzhi; Du, Ruxu; Li, Haifeng; Wang, Pu

doi:10.1080/15472450.2019.1598863

Cited by 7 publications

(6 citation statements)

References 45 publications

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“…Meanwhile, congestion tracing research has been extended from tracing microscopic roadway congestion sources to macroscopic regional congestion sources [8]. Depending on the transportation system under study, Wang et al [5,7] elaborated the data and methods required for traceability, which can use mobile cellular call detail records (CDRs), cellular signaling data, radiofrequency identification (RFID) data, and metro card data to estimate travel demand, assign traffic flows on the basis of the travel OD matrix and the corresponding road network, locate the major congestion sources, construct a road use dichotomous network to explore the relationship between congestion sources and road sections, and finally propose congestion optimization methods on this basis. For macro-regional congestion traceability, Wang et al [8] proposed to select the source parcels with the largest proportion as the city-wide congestion sources according to the travel time delay caused by each source.…”

Section: Tracing the Source Of Congestionmentioning

confidence: 99%

“…The main method of congestion traceability studies is to trace the source of traffic flow causing road congestion by analyzing the traffic flow OD of the surrounding traffic area and constructing a dichotomous network of road use to explore the relationship between the source of congestion and the congested road [4][5][6][7][8][9][10][11]. Such congestion traceability traces the source mainly to the traveler's residence and departure traffic cell outside the road system, and there are also studies that consider the few travelers causing congestion as the source of congestion.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach

Wang

Chen

et al. 2023

Sustainability

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Traffic congestion is a critical problem in urban areas, and understanding the relationship between land use and congestion source is crucial for traffic management and urban planning. This study investigates the relationship between land-use characteristics and congestion pattern features of source parcels in the Second Ring Road of Xi’an, China. The study combines cell-phone data, POI data, and land-use data for the empirical analysis, and uses a spatial clustering approach to identify congested road sections and trace them back to source parcels. The correlations between building factors and congestion patterns are explored using the XGBoost algorithm. The results reveal that residential land and residential population density have the strongest impact on congestion clusters, followed by lands used for science and education and the density of the working population. The study also shows that a small number of specific parcels are responsible for the majority of network congestion. These findings have important implications for urban planners and transportation managers in developing targeted strategies to alleviate traffic congestion during peak periods.

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Section: Tracing the Source Of Congestionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach

Wang

Chen

et al. 2023

Sustainability

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“…Congestion sources can be identified at both demand and supply levels. From a demand perspective, this involves pinpointing travelers' congregation points and their abnormal movement timings, identifying specific travelers as congestion sources [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Li,

Wang,

Chen

et al. 2024

Land

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Analyzing the factors influencing traffic congestion is essential for urban planning and coordinated development. Previous research frequently focuses on the internal aspects of traffic systems, often overlooking the impact of external factors on congestion sources. Therefore, this study utilizes a geospatial dataset and mobile signaling data, firstly applying the Fuzzy C-Means (FCM) algorithm to identify congested roads of different levels and trace the localization of travelers’ origins on regional congested roads. Furthermore, it employs the LightGBM method to study the influence of the built environment of various congestion sources on network-level congestion. The findings are as follows: (1) There is a positive correlation between traffic congestion and geographical location, with congestion predominantly caused by a few specific plots and demonstrating a concentrated trend in city centers. (2) Residential population density is the most critical factor, accounting for over 12% of the congestion contribution, followed by road density and working population density. (3) Both residential and working population densities show a non-linear positive correlation with congestion contribution, while the mixture of land use displays a non-linear V-shaped influence. Additionally, when residential population density is between 8000 and 11,000, it notably exacerbates congestion contribution. Significantly, by emphasizing land use considerations in traffic system analysis, these findings illuminate the intricate linkages between urban planning and traffic congestion, advocating for a more comprehensive approach to urban development strategies.

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“…In the freeway transportation context, driver sources include the toll stations which a large number of vehicles pass through, creating congested freeway sections [26,27]. Moreover, researchers have developed on-ramp control methods and route guidance methods based on driver source information [27][28][29], finding that driver source information can improve the congestion mitigation effect and reduce the difficulty of implementing traffic control schemes [27][28][29]. Yet, the spatiotemporal patterns of freeway driver sources, which are crucial for deploying effective traffic control countermeasures and developing sustainable regional transportation systems, are still not well understood.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the Spatiotemporal Patterns of Regional and Local Driver Sources in a Freeway Network

Wang,

et al. 2024

Sustainability

Self Cite

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We propose a method to identify the congestion driver sources contributing to the major traffic congestion of a regional (Hunan province) freeway network. The results indicate that the congestion driver sources are mostly observed during heavy traffic periods and mainly distributed in the regions surrounding Changsha (the capital of Hunan province) and the regions adjacent to other provinces and freeway interconnecting hubs. Moreover, we develop a method to analyze the major driver sources of a local freeway section. Using the method, the trips affected by traffic accidents or road maintenance works can be identified well. Our findings and the proposed methods could facilitate the deployment of effective traffic control countermeasures and the development of sustainable regional transportation.

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A vehicle routing model based on large-scale radio frequency identification data

Cited by 7 publications

References 45 publications

Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach

Exploring the Relationship between Land Use and Congestion Source in Xi’an: A Multisource Data Analysis Approach

Analysis of Urban Congestion Traceability: The Role of the Built Environment

Uncovering the Spatiotemporal Patterns of Regional and Local Driver Sources in a Freeway Network

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