LAUSR

Lane reservation strategies are widely used to ensure the right of way for eco-friendly vehicles and encourage people to green their commute. In most lane reservation problems (LRPs), the parameters underlying the system traffic conditions (e.g., vehicle speed and traffic flow) cannot be effectively specified. This paper addresses a new dynamic lane reservation problem (DLRP), which aims to optimize lanes that need to be reserved in different time periods based on trajectory data for existing transit network optimization. For passengers in reserved lanes, their travel time is minimized to guarantee traffic priority. Considering that the reserved lanes cause travel time growth on regular lanes, an improved multiobjective mixed integer nonlinear programming (MINLP) is established to minimize the delay. The problem complexity of this paper is NP-hard. This paper applies a preprocessing method for the actual traffic flow data analysis for link travel time calculation. We developed a hybrid evolutionary algorithm decomposing a multiobjective optimization problem (MOP) to a collection of simple MOPs. These subproblems are collaboratively solved. The hybrid crossover strategy exploits the advantages of different crossover operators for a better performance. The experimental results of MOEA/D and NSGA-II with standard test functions show that the proposed algorithm can improve the convergence and distribution of the results. Through numerous analyses and calculations of instances, the proposed algorithm is proven to be effective.