Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, the...
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ISBN:
(纸本)9781509028337
Traffic congestion control is pivotal for intelligent transportation systems. Previous works optimize vehicle speed for different objectives such as minimizing fuel consumption and minimizing travel time. However, they overlook the possible congestion generation in the future (e.g., in 5mins), which may degrade the performance of achieving the objectives. In this paper, we propose a vehicle Trajectory based driving speed OPtimization strategy (TOP) to minimize vehicle travel time and meanwhile avoid generating congestion. Its basic idea is to adjust vehicles' mobility to alleviate road congestion globally. TOP has a framework for collecting vehicles' information to a central server, which calculates the parameters depicting the future road condition (e.g., driving time, vehicle density, and probability of accident). The server then formulates a non-cooperative Stackelberg game considering these parameters, in which when each vehicle aims to minimize its travel time, the road congestion is also proactively avoided. After the Stackelberg equilibrium is reached, the optimal driving speed for each vehicle and the expected vehicle density that maximizes the utilization of the road network are determined. Our real trace analysis confirms some characteristics of vehicle mobility to support the design of TOP. Extensive trace-driven experiments show the effectiveness and superior performance of TOP in comparison with other driving speed optimization methods.
Connectivity in vehicularnetworks is continuously changing due to high mobility of vehicles, causing rapid changes in the network topology. This has a direct negative impact on the throughput and packet transmission ...
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ISBN:
(纸本)9781538638392
Connectivity in vehicularnetworks is continuously changing due to high mobility of vehicles, causing rapid changes in the network topology. This has a direct negative impact on the throughput and packet transmission delay. Many algorithms and approaches have been proposed to address this problem. However, most of them are not practical due to their dependence on a centralized infrastructure, which are attributed with high cost for installation and maintenance. vehicularnetworks are composed of all types of vehicles including autonomous and non-autonomous vehicles. In this paper, we propose addressing the problem of VANET connectivity through controlling the routes and speed of autonomous vehicles according to the density and speed of non-autonomous vehicles. This is achieved through a decentralized cooperative game theory approach that provides a balance between autonomous vehicles' trip duration, and enhancements to the VANET connectivity. We evaluate our proposed approach through extensive simulations using real traffic data, and observe substantial enhancements of up to 46% in the network connectivity using our approach.
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