In the era of Internet of Vehicle (IoV), traffic data are obtained by wireless sensor networks (WSNs), which provides convenience for real-time traffic signal control and is expected to further improve the efficiency of the whole system. Traffic signal controllers designed for IoV systems usually fully depend on data transmitted from the IoV system and are limited to the penetration rate of connected vehicles. This work proposes a dynamic ant colony optimization algorithm with the look-ahead mechanism (DALM) with eight no-conflict phases. The needed traffic information in the DALM algorithm is small and can be obtained independently by the system, which makes this method does not fully rely on the vehicle trajectories data and can better protect customer privacy and deal with cyber attack. With the look-ahead mechanism, the forthcoming traffic flow together with the current waiting queue are involved to predict the length of waiting vehicles and the optimal phase sequence is dynamically selected according to shortest green-light duration of candidate phase sequences obtained form the collected real-time traffic data. Experiments conducted under five scenarios with different kinds of traffic flow on SUMO platform show that compared with other four methods, both the total waiting time and the total carbon emissions of vehicles at the intersection are greatly reduced by using the DALM algorithm, especially in high traffic flow situations. The proposed algorithm can greatly improve the efficiency of the intersection in high traffic flow situation and is predictable to benefit the real-time traffic signal control in IoV system.

In recent years, there has been a growing trend towards using deep reinforcement learning (DRL) to solve the NP-hard vehicle routing problems (VRPs). While much success has been achieved, most of the previous studies solely focused on single-depot VRPs, which became less effective in handling more practical scenarios, such as multi-depot VRPs. Although there are many preprocessing measures, such as natural decomposition, those scenarios are still more challenging to optimize. To resolve this issue, we propose the multi-depot multi-type attention (MD-MTA) to solve the multi-depot VRP (MDVRP) and multi-depot open VRP (MDOVRP), respectively. We design a multi-type attention in the network to combine different types of embeddings and the state of the environment at each step, so as to accurately select the next node to visit and construct the route. We introduce a depot rotation augmentation to enhance solution decoding. Results show that it performs favorably against various representative traditional baselines and DRL-based baselines.

Outsourcing logistics operation to third‐party logistics has attracted more attention in the past several years. However, very few papers analyzed fuel consumption model in the context of outsourcing logistics. This problem involves more complexity than traditional open vehicle routing problem (OVRP), because the calculation of fuel emissions depends on many factors, such as the speed of vehicles, the road angle, the total load, the engine friction, and the engine displacement. Our paper proposed a green open vehicle routing problem (GOVRP) model with fuel consumption constraints for outsourcing logistics operations. Moreover, a hybrid tabu search algorithm was presented to deal with this problem. Experiments were conducted on instances based on realistic road data of Beijing, China, considering that outsourcing logistics plays an increasingly important role in China’s freight transportation. Open routes were compared with closed routes through statistical analysis of the cost components. Compared with closed routes, open routes reduce the total cost by 18.5% with the fuel emissions cost down by nearly 29.1% and the diver cost down by 13.8%. The effect of different vehicle types was also studied. Over all the 60‐ and 120‐node instances, the mean total cost by using the light‐duty vehicles is the lowest.