Optimization of probe vehicle deployment for traffic status estimation

Chu, Kang-Ching

doi:10.1109/coase.2013.6654046

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Studies conducted in Shanghai, with 4000 taxis, have shown that hidden structures with traffic condition matrices underlie the probe data. Kang-Ching et al [93] also exploited probe vehicles equipped with GPS devices to optimize traffic status estimation. Data assimilation techniques and Newtonian relaxation methods are considered to integrate probe data into macroscopic traffic models.…”

Section: Traffic Prediction In Vehicular Networkmentioning

confidence: 99%

Network Management and Monitoring Solutions for Vehicular Networks: A Survey

et al. 2020

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Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions.

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Section: Traffic Prediction In Vehicular Networkmentioning

confidence: 99%

Network Management and Monitoring Solutions for Vehicular Networks: A Survey

et al. 2020

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“…Because of these advantages, trajectory data enjoy broad application prospects in performance monitoring, signal control optimization, and other areas (Ban et al, 2011, Chu and Saitou, 2013, Guo et al, 2019, Xu et al, 2018, Ma et al, 2020. A large number of existing methods have been developed for short-term traffic volume estimation (Nanthawichit et al, 2003, Zhan et al, 2017, Duan et al, 2018, Emami et al, 2019, Luo, et al, 2019, Zhang et al, 2020 or cycle-based traffic volume estimation (Zheng and Liu, 2017, Wang et al, 2020, Yao et al, 2020, Zhang et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

A Matrix Completion Method for Cycle-based Traffic Volume Estimation Using Sampled Trajectory Data

Tan

Yao

Tang

2022

Preprint

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Cycle-based volume can describe the fluctuation of traffic demand in a more sophisticated manner, which is helpful for performance evaluation and optimization of signalized intersections. Solely using sampled vehicle trajectory data for traffic volume estimation has received increasing attention recently. However, most existing methods commonly rely on historical data or site-specific vehicle arrival distributions. This study proposes a data-driven method for cycle-based volume estimation at signalized intersections. Using the sampled trajectory data, the cyclic vehicle arrival is represented as an arrival flow rate vector to discretize the continuous arrival flow. So for a TOD (time-of-day) period, an incomplete arrival rate matrix can be constructed with a rasterization standardization step to ensure equal sizes of the arrival rate vector for each cycle. The cycle-based volume estimation problem is then transformed into a matrix completion problem, and solved through the Singular Value Thresholding (SVT) algorithm. The proposed method is evaluated through an empirical case and two simulation cases. Empirical case results show that given a penetration rate of 8.4%, the proposed method can reach 5.7% for hourly volume estimation and 17.6% for cycle-based volume estimation. Even under a penetration rate of 3.4%, it shows better robustness than three existing methods, A simulation case is done to explore the sensitivity of the proposed method to the degree of saturation, the number of lanes and the penetration rate, while another simulation case demonstrates the applicability of the proposed method in large-scale network, which is also one advantage of the proposed data-driven approach.

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