An efficient adaptive intelligent routing system for multi-intersections

Abstract

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car-following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections.

A review on vehicle to vehicle communication system applications

“…With decentralized coordination between vehicles based on the freedom degrees that each vehicle has available to avoid collision [60]. A design of traffic simulation models for such vehicles for exchanging information to reduce the average delay and minimize traffic congestion for vehicles could be clearly seen in [48,[61][62][63][64][65]]. An experimental model and simulation for pertaining the performance of IEEE 802.11p communication standard is used in [28] and robust decision support tool for cooperative traffic simulation [46].…”

A Deep Review and Analysis of Data Exchange in Vehicle-to-Vehicle Communications Systems: Coherent Taxonomy, Challenges, Motivations, Recommendations, Substantial Analysis and Future Directions

“…In addition, [93] introduced an evaluation method for describing the emergency degree of ongoing collisions by using particle swarm optimization to calculate the target acceleration and control the vehicle to prevent unexpected collisions. The authors in [94]- [97] evaluated how vehicles can exchange information with each other by V2V communications integrated in the vehicular architecture to get more information about oncoming road, arranges appropriate vehicle routes based on queuing theory. The performance of short range vehicular communications by using real world personal portable devices (smartphones, tablets, and laptops), two different PHY standards (IEEE 802.11g and IEEE 802.11a), and vehicles is evaluated in [98].…”

“…Other topics include a wide measuring of incoming traffic [69] and full distribution of traffic information systems that enable traffic selforganisation in smart cities [75], [132]. Research has been performed on designing efficient microscopic traffic simulation model for such vehicles, including a robust protocol for exchanging information to improve and reduce traffic congestion, minimise average delay for vehicles [44], [56], [57], [73], [94], [100], simulations and an experimental model pertaining to the performance of IEEE 802.11p communication standard to reduce traffic congestion [66] and robust decision support tool for cooperative traffic simulation [65].…”

“…High traffic volume and variability of traffic conditions on the road results in large queue length, which causes long waiting time for cars. Under heavy traffic conditions, fixed signals can cause congestion due to their inability to adapt according to the dynamic requirements of the real-time density of vehicles [73], [94]; thus, up-to-date traffic data must be collected to control traffic lights before vehicles pass by them [75], [100]. Providing accurate traffic information is becoming a major challenge for public institutions and private companies, which result in the rapid growth of intelligent transportation systems [17].…”

“…A robust set of protocols for information dissemination and collection that is adaptive to vehicular traffic density and equipment deployment rate is defined in [131]. Securing the main phases of a protocol, especially the data collection and aggregation phases [41] can reduce the cost of packet routing [21] and provide an Adaptively Intelligent Routing System (AIRS) for future target with routing path to make a realistic simulation [94]. A forward topology to meet the requirement of vehicle and perform further experimentation with combinations of routing protocols and other wireless technologies is provided in [121].…”