This project aims to relieve traffic pressure and enhance the parking experience for attendees during planned special events (PSEs). The objective is to develop an optimal strategy for efficiently allocating parking spaces during PSEs in parking lots. PSEs, such as football games or large concerts, typically result in concentrated vehicle arrivals within a limited time period, leading to increased traffic flow, potential disruptions, elevated emissions, and safety concerns in nearby areas. By optimizing parking space allocation strategies in the parking lot, this project seeks to improve overall traffic management and relieve these challenges. To achieve this, a linear programming (LP) algorithm and a simulation-based genetic algorithm (GA) are employed to search for the optimal solution. While the LP model offers computational efficiency, it has limitations in incorporating different route conditions. To address this, an agent-based simulation is constructed to depict the interaction and movement of vehicles within the parking lot. The simulation-based GA utilizes objective values derived from the simulation, providing a more comprehensive basis for finding the optimal solution. The allocation process considers factors such as parking lot layout, vehicle entry time step, and specific parking rules including road directions within the parking lot. Results demonstrate that the optimal strategy obtained from the simulation-based GA outperforms comparison groups. The simulation-based GA showcases its ability to converge on the optimal solution within a large solution area. The optimal strategy saving time for all vehicles, particularly during periods of high demand. Effective parking is achieved by allocating parking spaces according to the arrival order and positioning vehicles on the left or right based on their arrival order and parking space location. By employing these methods, this project offers a valuable contribution to the field of parking space allocation in the parking lot during PSEs, enhancing the overall parking experience for event attendees.

The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.@en