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<p>With the level of motorization on the rise, road accidents are increasing predominantly in developing countries. Many countries have developed strategies to ensure road safety, but the problem persists. In the case of Kenya, the country recorded 3369 deaths due to road accidents during the first nine months of the year 2024, with pedestrians and motorcyclists being the most affected groups. The government has integrated intelligent transport systems (ITS) to mitigate traffic congestion and, to some extent, prevent accidents, especially in Nairobi. Other countries have proposed vehicle-to-infrastructure (V2I) technology, a subset of ITS, as a better solution to reduce road accidents. The implementation of V2I necessitates having roadside units (RSUs) on the road network, and it is said to be very expensive in terms of deployment, operation, and maintenance costs. RSUs communicate with vehicles equipped with an onboard unit, and the exchange between them must be established with optimal performance by considering the connectivity, the packet delivery ratio, the average downlink end-to-end delay, and the energy consumption of RSUs. This research aims to develop an optimal RSU deployment scheme for urban areas based on artificial intelligence. The objective is to optimally deploy RSUs operating in energy-saving mode using a hybrid genetic algorithm–particle swarm optimization (GA–PSO) technique. The Kilimani–Hurlingham road network, a section of the Nairobi Road network, is used to test this model. The simulation results demonstrate the effectiveness of the hybridization of GA and PSO for the optimal deployment of RSUs with significant communication results concerning connectivity, packet delivery ratio, and average downlink end-to-end delay. Two scenarios of packet exchange with 200 bytes (small packets) and 1024 bytes (large packets) were used for the simulation, and in both, the GA–PSO could obtain the best nodes to allocate the RSUs. For example, looking at the packet delivery ratio, it was possible to obtain up to 62.36% for large packets and 79.08% for small packets. Also, looking at packets’ end-to-end delay, the hybrid GA–PSO could place RSUs such that the maximum end-to-end delay was 3.51&#xa0;ms for large packets, which is far less than the maximum acceptable delay of 20&#xa0;ms in the V3I network. The validation of this method was done as an analytical comparison between the results obtained when using GA and PSO individually, with those obtained when using the hybrid GA–PSO. Comparisons showed the superiority of the hybridization of both techniques over using them standalone. </p>

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Strategic deployment of roadside units for effective vehicle-to-infrastructure communication to limit road accidents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Paule Kevin Nembou Kouonchie,
  • Willy Stephen Tounsi Fokui,
  • Danube Kirt Ngongang Wandji

摘要

With the level of motorization on the rise, road accidents are increasing predominantly in developing countries. Many countries have developed strategies to ensure road safety, but the problem persists. In the case of Kenya, the country recorded 3369 deaths due to road accidents during the first nine months of the year 2024, with pedestrians and motorcyclists being the most affected groups. The government has integrated intelligent transport systems (ITS) to mitigate traffic congestion and, to some extent, prevent accidents, especially in Nairobi. Other countries have proposed vehicle-to-infrastructure (V2I) technology, a subset of ITS, as a better solution to reduce road accidents. The implementation of V2I necessitates having roadside units (RSUs) on the road network, and it is said to be very expensive in terms of deployment, operation, and maintenance costs. RSUs communicate with vehicles equipped with an onboard unit, and the exchange between them must be established with optimal performance by considering the connectivity, the packet delivery ratio, the average downlink end-to-end delay, and the energy consumption of RSUs. This research aims to develop an optimal RSU deployment scheme for urban areas based on artificial intelligence. The objective is to optimally deploy RSUs operating in energy-saving mode using a hybrid genetic algorithm–particle swarm optimization (GA–PSO) technique. The Kilimani–Hurlingham road network, a section of the Nairobi Road network, is used to test this model. The simulation results demonstrate the effectiveness of the hybridization of GA and PSO for the optimal deployment of RSUs with significant communication results concerning connectivity, packet delivery ratio, and average downlink end-to-end delay. Two scenarios of packet exchange with 200 bytes (small packets) and 1024 bytes (large packets) were used for the simulation, and in both, the GA–PSO could obtain the best nodes to allocate the RSUs. For example, looking at the packet delivery ratio, it was possible to obtain up to 62.36% for large packets and 79.08% for small packets. Also, looking at packets’ end-to-end delay, the hybrid GA–PSO could place RSUs such that the maximum end-to-end delay was 3.51 ms for large packets, which is far less than the maximum acceptable delay of 20 ms in the V3I network. The validation of this method was done as an analytical comparison between the results obtained when using GA and PSO individually, with those obtained when using the hybrid GA–PSO. Comparisons showed the superiority of the hybridization of both techniques over using them standalone.