An Optimization Method for Internal Vehicle-traffic Organization in Off-street Parking Lot Considering Safety and Efficiency

To improve the efficiency of off-street parking lots and to ensure road traffic, this study proposes an optimization method for internal vehicle-traffic organization in off-street parking lots. A directed weighted graph is utilized to represent the layout of exit/entrance and passages of a parking lot and the traffic organization within the parking lot, thereby transforming the optimization problem of traffic organization into the optimization problem of adjacency matrix. With safety and efficiency as the optimization objectives, three evaluation indicators, i.e., potential conflict risk, parking travel time and node equilibrium coefficient are used. Thereby the optimization model for internal traffic organization of parking lots is established, considering the constraints of the number of parking spots and passage capacity. The optimization problem is solved using genetic algorithm. To compare the effects of traffic organization before and after optimization, VISSIM-simulation is adopted based on data from an empirical case. The parameters such as queue length at entrances/exits, individual parking time, distribution of conflict points, and use ratio of parking spots are selected for comparison, along with sensitivity analysis of model parameters and traffic flow. The results show that: ① the model can compensate for the limitations of qualitative research and subjective empirical judgements, achieving quantitative optimization of the internal traffic organization in off-street parking lots. ② The queue lengths at entrances/exits reduces by 25.8% on average; the number of parking spots with use ratio in the range of 0 to 1.8 decreases by 5.89%; and the kernel density of conflict points also reduces. ③ The model is relatively stable as it is insensitive to the variations of parameters of potential conflict risks within the range of ± 0.1 to ±0.3. 4) Within a range of -20% to +20% regarding the variation of traffic volume, the optimized solution ensures the corresponding variations of individual parking time and the average queue length remain within 10%, which shows that the solution is adaptable to fluctuating traffic volumes in real-world scenarios.