Cooperative Control of Traffic Signals and Vehicle Trajectories
The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructur...
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| Published in: | Informatika i avtomatizaciâ (Online) Vol. 22; no. 1; pp. 5 - 32 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
27.01.2023
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| ISSN: | 2713-3192, 2713-3206 |
| Online Access: | Get full text |
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| Abstract | The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructure is to manage traffic flows, incl. by controlling traffic signals at signalized intersections. One of the trends in the development of intelligent transportation systems is the creation of vehicular ad hoc networks that allow the exchange of information between vehicles and infrastructure, as well as the development of autonomous vehicles. As a result, it becomes possible to formulate the problem of cooperative control of vehicle trajectories and traffic signals to increase the capacity of intersections and reduce fuel consumption and travel time. This paper presents a method for managing traffic flow at an intersection, which consists of the cooperative control of traffic signals and trajectories of connected/autonomous vehicles. The developed method combines an algorithm for the adaptive control of traffic signals based on a deterministic model for predicting the movement of vehicles and a two-stage algorithm for constructing the trajectory of vehicles. The objective optimization function used to construct the optimal trajectories takes into account fuel consumption, travel time on the road lane, and waiting time at the intersection. Experimental studies of the developed method were carried out in the microscopic traffic simulation package SUMO using three simulation scenarios, including two synthetic scenarios and a scenario in a real urban environment. The results of experimental studies confirm the effectiveness of the developed method in terms of fuel consumption, travel time, and waiting time in comparison with the adaptive traffic signal control algorithm. |
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| AbstractList | The transportation system is one of the most important parts of the country's economy. At the same time, the growth in road traffic has a significant negative impact on the economic performance of the industry. One of the ways to increase the efficiency of using the transportation infrastructure is to manage traffic flows, incl. by controlling traffic signals at signalized intersections. One of the trends in the development of intelligent transportation systems is the creation of vehicular ad hoc networks that allow the exchange of information between vehicles and infrastructure, as well as the development of autonomous vehicles. As a result, it becomes possible to formulate the problem of cooperative control of vehicle trajectories and traffic signals to increase the capacity of intersections and reduce fuel consumption and travel time. This paper presents a method for managing traffic flow at an intersection, which consists of the cooperative control of traffic signals and trajectories of connected/autonomous vehicles. The developed method combines an algorithm for the adaptive control of traffic signals based on a deterministic model for predicting the movement of vehicles and a two-stage algorithm for constructing the trajectory of vehicles. The objective optimization function used to construct the optimal trajectories takes into account fuel consumption, travel time on the road lane, and waiting time at the intersection. Experimental studies of the developed method were carried out in the microscopic traffic simulation package SUMO using three simulation scenarios, including two synthetic scenarios and a scenario in a real urban environment. The results of experimental studies confirm the effectiveness of the developed method in terms of fuel consumption, travel time, and waiting time in comparison with the adaptive traffic signal control algorithm. |
| Author | Agafonov, Anton Yumaganov, Alexander |
| Author_xml | – sequence: 1 givenname: Anton surname: Agafonov fullname: Agafonov, Anton – sequence: 2 givenname: Alexander surname: Yumaganov fullname: Yumaganov, Alexander |
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| Cites_doi | 10.1109/TITS.2020.3008612 10.1109/TITS.2020.3023788 10.3390/su14031542 10.1155/2020/1456207 10.1016/j.neunet.2021.03.015 10.1016/j.trc.2021.103416 10.1109/TCYB.2020.3015811 10.15439/2021F109 10.1109/FISTS.2011.5973594 10.1016/j.scitotenv.2013.01.074 10.1145/3219819.3220096 10.1145/3467707.3467767 10.1177/0361198119845363 10.1109/TCST.2010.2047860 10.1061/(ASCE)0733-947X(2003)129:3(278) 10.1109/TITS.2011.2178836 10.1109/ITNT55410.2022.9848651 10.1016/j.trb.2016.06.010 10.15622/sp.2019.18.3.557-581 10.1016/j.trb.2014.09.014 10.1145/3068287 10.1016/j.trb.2016.05.007 10.3141/1683-15 10.1109/ITSC.2010.5625066 10.1109/ITSC.2018.8569938 10.3390/vehicles3030032 10.1016/j.trc.2017.04.001 10.1016/j.envint.2017.11.025 10.1109/JPROC.2003.819610 10.1016/j.trc.2016.04.009 10.1007/978-1-4614-6243-9_2 10.1016/j.trb.2019.03.002 |
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