An Adaptive Route Planning Method of Connected Vehicles for Improving the Transport Efficiency

Abstract

In recent years, the route-planning problem has gained increased interest due to the development of intelligent transportation systems (ITSs) and increasing traffic congestion especially in urban areas. An independent route-planning strategy for each in-vehicle terminal improves its individual travel efficiency. However, individual optimal routes pursue the maximization of individual benefit and may contradict the global benefit, thereby reducing the overall transport efficiency of the road network. To improve traffic efficiency while considering the travel time of individual vehicles, we propose a new dynamic route-planning method by innovatively introducing a bidding mechanism in the connected vehicle scenario for the first time. First, a novel bidding-based dynamic route planning is proposed to formulate vehicle routing schemes for vehicles affected by congestion via the bidding process. Correspondingly, a bidding price incorporating individual and global travel times was designed to balance the travel benefits of both objectives. Then, in the bidding process, a new local search algorithm was designed to select the winning routing scheme set with the minimum bidding price. Finally, the proposed method was tested and validated through case studies of simulated and actual driving scenarios to demonstrate that the bidding mechanism would be conducive to improving the transport efficiency of road networks in large-scale traffic flow scenarios. This study positively contributes to the research and development of traffic management in ITSs.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

“…It has better convergence ability compared to other algorithms. Liu et al [8] innovatively introduced a bidding mechanism in the scenario of connected vehicles and proposed a new dynamic route-planning method. Experiments showed that in large-scale traffic flow scenarios, the bidding mechanism was beneficial for improving the transportation efficiency of the road network.…”

“…Reference [7] also optimizes and improves the algorithm to improve its convergence performance, which is different from the goal of the method we constructed. Reference [8] is mainly aimed at optimizing the transportation efficiency of the road network and improving the transportation capacity of vehicles. Our method has different research objectives, aiming to match tourist interests and reduce travel costs.…”

“…Node n sub (7) is found, then a feasible path is formed, keep n sub (7) , as shown in the Figure 9(4). (5) Step 4: Select n sub (8) or n sub (11) . Node n sub (11) is found, then a feasible path is formed, keep n sub (11) , as shown in the Figure 9(5).…”

“…(7) (8). Step 7: Arrive at the POI P (j) and form a pathway n sub(i) → 1, 4, 7, 11, 10, 12 , which constitutes one individual fruit fly f R sub(i,j) , as shown in the Figure9(8).…”

Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

“…However, the complexity is high. Furthermore, to increase travel efficiency, a dynamic route planning method 34 was proposed. This approach uses a local search algorithm to choose the route in large‐scale traffic conditions.…”