Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Ding, Shenzhen; Chen, Xumei; Yu, Lei

doi:10.1007/s11771-022-5023-8

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…The TC of intersections is critical for evaluating the performance of the road network in general. Many methods have been proposed for assessing and measuring the TC of signal-controlled intersections and many models for optimizing traffic at intersections have been developed [6][7][8][9]. However, most studies are based on the assumption that the traffic flow is homogeneous (all vehicles are standard) and are accepted for calculations according to the Highway Capacity Manual (HCM) as a passenger car equivalent (PCE) [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

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Section: Literature Reviewmentioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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“…Owing to the difficulty of acquiring realistic road data, video data acquisition is susceptible to interference from factors such as vegetation and building shading, rain, fog, ice, snow, and nontraffic participant objects that affect the quality of data acquisition. Therefore, VISSIM is selected to build a simulation system [27][28][29] to collect experimental data. To improve the accuracy of target identification, subjects in the traffic simulation system were limited to three types of vehicles, cars, buses, and trucks, and the intersection video was collected using VISSIM's built-in camera.…”

Section: Dat Processingmentioning

confidence: 99%

A Moving Target Tracking Framework Based on a Set and Its Topological Space

et al. 2023

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Moving target tracking is a technology that matches frames and images based on target characteristics. This technology is widely utilized in intelligent transportation, logistics transportation, public security, sports event broadcasting, and other fields. Existing research focuses primarily on improving target detection and tracking algorithms to improve target retrieval and tracking efficiency. However, the majority of studies focus on global and full-range retrieval. More importantly, in large video scenes with multiple camera collaborations, these methods rarely consider the efficiency of target retrieval and tracking. Based on relevant theories and methods of video GIS, set theory, and topology, in this paper, a set and its topology space covering road networks, cameras, videos, and key frames were constructed. Additionally, the positioning, tracking, and track representation of a moving target based on the set and its topology space were solved. Compared to the feature vector algorithm, video summarization and Meanshift algorithm, the experimental findings reveal that the target retrieval performance, algorithm stability, and robustness are improved.

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“…Some researchers determine whether the vehicles are running abnormally by entering information about pedestrians and obstacles on the road where the target vehicles are running [23]. This method has limitations because it does not take into account the running information of the vehicles themselves [24][25]. It can only determine certain abnormal running behavior caused by pedestrians or obstacles on the road [26].…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Road cameras, can collect the time and space information of the target vehicles in real time. This paper adopted the YOLOV5-DEEPSORT algorithm to effectively extract the temporal and spatial information of the detected target vehicles in real-time [24].…”

Section: ⅰ Introductionmentioning

confidence: 99%

An R–A dual network detection model for abnormal behavior of running vehicles

Liu,

Sun,

Fan

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Detection of abnormal behavior of running vehicles plays an essential role in road traffic safety. In this paper, the R-A (ResNet-Adaboost) dual network detection algorithm to cope with the problem that the existing detection algorithm of abnormal running behavior cannot detect different kinds of abnormal running behavior and cannot adapt to different detection scenarios. Firstly, this paper utilizes the YOLOV5-DEEPSORT algorithm to collect the spatial and temporal information of the target vehicle. In this way, various kinds of abnormal running behavior can be detected efficiently. Secondly, based on the information matrix, the entropy method is used to determine the dynamic weight of various abnormal running information, and then to determine the type of information input to the judgment model so as to obtain the information more accurately. Finally, the accurate vehicle running information is input into the R-A detection model. In this paper, an algorithm to estimate the abnormal running information is established as the information classification basis of the R-A detection model. The algorithm calculates the difference between the input vehicle running information matrix and the normal one to judge whether the information matrix is abnormal. Moreover, it is proved by the field experiments and NGSSIM datasets that the R-A detection model is able to detect different abnormal running behavior vehicle in different scenarios. The experimental results show that the R-A model with an accuracy of 90%-95% is better than the existing detection model. Additionally, it can more accurately detect various abnormal behavior of running vehicles.

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Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Cited by 7 publications

References 42 publications

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

A Moving Target Tracking Framework Based on a Set and Its Topological Space

An R–A dual network detection model for abnormal behavior of running vehicles

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