Affiliation:
1. School of Freeway, Chang’an University, Xi’an 710064, China
2. Xi’an University of Posts and Telecommunications, Xi’an 710064, China
3. Hangzhou Communications Investment Group CO., LTD, Hangzhou 310051, China
Abstract
On fully enclosed freeways, the service area is the only key node for certain crowd activities such as resting, toileting, and dining. In these activities, using the toilet is the most important purpose for people to enter the service area. A reasonable toilet location and size in the service area is the basis for its efficient operation. This study conducts field surveys on several service areas, including Meicun, Tai’an, and Qianxian, through video recording and crowd tracking questionnaires. We summarize and analyze the characteristics of population flow in Chinese freeway service areas. The key data obtained through field surveys are reported, such as the priority and length of stay of customers in the facilities during peak hours at weekends, to analyze the characteristics of pedestrian traffic in the service areas. Finally, based on a large number of pedestrian simulation methods used in studies on the layout of subways and airport facilities, the AnyLogic simulation software is applied to establish a crowd movement model for these areas. The study not only obtained the pedestrian characteristics of the service area but also showed that only changing the location of toilets has no effect on the efficiency of toilet use without changing the level of service, whereas changing the number of toilets has a significant impact on the efficiency. Most importantly, this paper establishes a relationship model between the toilet size of the service area and the service area pedestrians flow; when the toilet seats 266/158/148 people, the maximum flow of people in the service area is about 3,115/2200/2000 p/h, which can provide help for the scale design of the service area.
Funder
Transport Technology Project of Shaanxi Province
Subject
Computer Science Applications,Software
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04