A resilience-oriented evaluation and identification of critical thresholds for traffic congestion diffusion

Chen, Hengrui; Zhou, Ruiyu; Chen, Hong; Lau, Albert

doi:10.1016/j.physa.2022.127592

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…Shojaat et al [20] determined the speed threshold as the boundary between the fuid and congested trafc by analysing the speed and fow time series. Given this, from the perspective of road network resilience, we used the previous research results to determine the critical threshold of trafc congestion difusion by using the percolation theory to determine the critical speed of the road [21,22].…”

Section: Methodsmentioning

confidence: 99%

“…As mentioned before, from the perspective of road network resilience, we used the previous research results to determine the critical threshold of trafc congestion difusion by using the percolation theory to determine the critical speed of the road [21,22]. Te critical speed of reserved road was approximately 30 km/h.…”

Section: Calculation Of Reservation Road Orvmentioning

confidence: 99%

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Optimal Reservation Volume of Urban Roads Based on Travel Reservation Strategy

Zhou,

Chen,

Chen

2024

Journal of Advanced Transportation

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Improving the spatial-temporal balance between the supply and demand of urban transportation and alleviating traffic congestion are important ways to build sustainable cities. The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. This study aims to determine the optimal reservation volume (ORV) for urban roads and verify the effectiveness of the TRS. First, we employed the sustained flow index to estimate the ORV from the degree of trade-off between the road breakdown probability and capacity. Then, a bilevel programming model based on ORV constraints was established to analyse the effectiveness of the TRS. The results indicated that the ORV range is 0.79–0.89 times the road capacity. The TRS can achieve the best steady benefit when the demand for reservation travel reaches at least 40%. Selecting the most congested critical roads in the network to implement TRS is more effective than on a large area. The driver default behavior will increase the V/C ratios and travel costs of all roads in the network. It has been proven that the reservation transportation mode will promote the spatial-temporal balance between supply and demand to alleviate traffic congestion.

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Section: Methodsmentioning

confidence: 99%

Section: Calculation Of Reservation Road Orvmentioning

confidence: 99%

Optimal Reservation Volume of Urban Roads Based on Travel Reservation Strategy

Zhou,

Chen,

Chen

2024

Journal of Advanced Transportation

Self Cite

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“…To enhance the objectivity of trafc indicators, we should show the most comprehensive and critical information with the help of the least number of indicators. Adequate credentials for trafc management control, trafc volume, average travel speed, and space occupancy could comprehensively refect the road trafc operation [38]. Te average travel speed is the most direct refection of the trafc state [39].…”

Section: Research On Evaluation Indicatorsmentioning

confidence: 99%

Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach

Zhao

Rui

2023

Journal of Advanced Transportation

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Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.

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