Vehicular ad hoc networks (VANETs) hold significant potential for enhancing road safety and traffic efficiency. The performance of VANETs heavily relies on the strategic deployment of roadside units (RSUs), which gather and disseminate critical information. A key challenge is that different types of information possess varying timeliness requirements, rendering delayed information ineffective. However, this crucial aspect has not been sufficiently addressed in the existing RSU deployment literature. To bridge this gap, we first classify operational scenarios based on discretized traffic flows and information types, analyzing their corresponding transmission time constraints. We then formulate an RSU deployment model that explicitly incorporates these heterogeneous timeliness requirements. To solve this complex problem, we develop a Branch-and-Benders decomposition (BBD) algorithm, which partitions the problem into a master problem for determining RSU locations and multiple subproblems for allocating vehicle communication demands in each scenario. The master problem is solved using a branch-and-cut procedure. Upon finding an integer feasible solution, the dual subproblems are solved to generate optimality and feasibility cuts that are dynamically added to the master problem. Furthermore, we introduce valid inequalities to accelerate convergence. Numerical experiments demonstrate that the proposed BBD algorithm can efficiently generate provably high-quality solutions.

Vehicular Ad hoc Networks (VANETs) in intelligent transportation systems have been regarded as an effective means to alleviate traffic congestion, reduce traffic accidents and save fuel. A proper roadside unit (RSU) deployment strategy is essential to improve the efficiency and stability of VANETs’ communication. However, the RSU deployment strategy that combines coverage and quality of service is still limited. To provide city planners with decision-making support, a multi-objective optimization model is built to optimize the deployment of RSUs under a limited budget. Two objective functions are proposed to maximize the number of communication tasks served and minimize the total task-weighted delay. Through a simple transformation of the delay matrix, we transform the proposed multi-objective model into a p-median problem. Then, we design a Lagrangian relaxation algorithm in a multi-objective framework to solve the model. Moreover, simulation examples are presented to demonstrate and validate the proposed model and solution algorithm. We analyze the gap between the algorithm result and the optimal solution, and characterize the Pareto front. The simulation results verify the feasibility and effectiveness of the proposed model and algorithm.

With the rapid development of Internet of Things and communication technologies, the connected vehicle technology with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is seen as the most promising solution for reducing traffic accidents and alleviating traffic congestion. Roadside Units (RSUs) play a crucial role in enabling V2I communication, as they can gather and broadcast traffic event information in the road network. Implementing a well-thought-out RSU strategy can significantly improve the stability and efficiency of traffic event information transmission in the road network. However, there is a lack of effective strategies for RSU deployment in stochastic scenarios. The presence of unpredictable factors like equipment failures and network attacks may disrupt the normal functioning of the RSUs. To guide the deployment of roadside facilities, we analyze the transmission time of traffic events in the road network and construct a multi-objective RSU deployment model considering the possibility of RSU failures. This model aims to maximize the number of connected vehicles served and minimize the expected transmission time of events. Through fuzzy programming theory and different decision preferences, we transform the two objectives into a single objective function. A random-key genetic algorithm (RKGA) is designed to solve the transformed model. Moreover, several simulation examples are presented to verify the feasibility and effectiveness of the proposed model and algorithm. The results indicate that decentralized deployment is preferred for low RSU failure probabilities, while centralized deployment is preferred for high failure probabilities.

The roadside unit (RSU), which enables vehicle-to-infrastructure communication, is essential for improving the communication performance of vehicular ad hoc networks. However, optimizing the deployment of RSUs while considering their deployment at both fixed locations and on mobile vehicles remains a challenging issue. To bridge this gap, we develop two spatio-temporal networks derived from vehicle trajectories. Subsequently, the joint deployment challenge of static and mobile RSUs is articulated as a mixed-integer programming model. After linearization, the model can be directly solved by CPLEX. Additionally, the integrated optimization problem can also be formulated as a route-based model. Due to the exponential growth of route numbers, a branch-and-price (BAP) algorithm is designed to solve the route-based model. Within the framework of the BAP, we develop a heuristic technique for generating effective initial solutions. Based on the characteristics of the spatio-temporal network, a directed acyclic graph shortest path algorithm is utilized to accelerate the solution of column generation pricing problem at each node. Simulation examples are presented to demonstrate the proposed algorithm. The results indicate that the BAP can generate verifiable high-quality solutions and has a significant speed advantage over CPLEX for large-scale problems. Furthermore, a series of sensitivity analyses are conducted to assess the system’s responses to various influencing factors.

Aiming at improving the operation of the bottleneck area of the highway in the environment of a connected and automated vehicle, this paper proposes an integrated lane-level control (ILC) method by combining the variable speed limit control method and lane selection method into a comprehensive framework. A lane-level variable speed limit (LVSL) control method is proposed for the mixed traffic flow based on a deep deterministic policy gradient algorithm. Then, a lane-level short-term traffic prediction (LSTP) model based on hybrid deep learning is built to forecast the traffic state in a next control horizon. Finally, a lane selection method using a dynamic programming algorithm is established for the connected automated vehicle (CAV) to look for the optimal lane-level route by considering the estimated traffic speeds and limit speeds from LSTP and LVSL respectively. Comprehensive simulation-based evaluation experiments were conducted in various scenarios e.g., with different traffic demands, penetration rates of CAVs, length of control horizons, and the number of lanes. The evaluation results reveal that the proposed LSTP model outperforms the state-of-the-art traffic prediction models in terms of accuracy and stability. In addition, the proposed ILC method is capable of improving the traffic operation of the bottleneck efficiently by synthetically taking the advantage of the LVSL method and lane selection method. Compared with the no-control strategies, the ILC method can reduce total travel time by more than 30% in various traffic scenarios.