Huang Yan, Li Zongzhi, Zhang Shengrui, Zhou Bei, Zhang Lei
College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi 710064, China.
Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, 3201 South Dearborn Street, Chicago, 60616, IL, United States.
Data Brief. 2023 Jul 20;49:109423. doi: 10.1016/j.dib.2023.109423. eCollection 2023 Aug.
This article presents the data utilized in a study focused on identifying an optimal bus dispatching strategy in light of epidemic impacts. The study specifically examines the Xi'an Xiaozhai central business district (CBD) street network, which consists of 33 major signalized intersections and 112 bus stops associated with 12 bus routes. The dataset includes details of intersection and bus stop geospatial data, street segment and intersection design, intersection signal timing plans, bus route operational properties such as dispatching frequencies, fleet sizes, loading bay capacities, and bus-specific parameters. It also encompasses data on passenger boarding and alighting counts, as well as travelers' origin and destination (O-D) locations, routes, and departure times during three time periods: 10:00-11:00 PM, 1:00-2:00 PM, and 7:00-8:00 PM on Monday, June 7, 2021. These times represent off-peak (10:00 PM-1:00 AM the next day), adjacent-to-peak (9:00-11:00 AM, 1:00-4:00 PM, and 8:00-10:00 PM), and peak (7:00-9:00 AM, 11:00 AM-1:00 PM, and 4:00-8:00 PM) periods, respectively. Data collection involves searching government and organizational records, utilizing Alibaba Cloud's Amap platform, conducting onsite measurements, and performing a field survey. The dataset is a valuable resource for studying the integrated operations of various urban mass transit services, including buses, bus rapid transit (BRT), and fixed guideway transit, under both normal and epidemic-affected travel conditions. Additionally, it can be used to investigate multimodal integrated urban passenger services offered by automobiles, transit, ridesharing, and active transportation modes.
本文介绍了一项研究中所使用的数据，该研究旨在根据疫情影响确定最佳公交调度策略。该研究具体考察了西安小寨中央商务区（CBD）的街道网络，它由33个主要信号控制交叉口和与12条公交线路相关的112个公交站点组成。数据集包括交叉口和公交站点的地理空间数据细节、路段和交叉口设计、交叉口信号配时计划、公交路线运营属性，如调度频率、车队规模、候车亭容量以及公交特定参数。它还涵盖了乘客上下车人数数据，以及2021年6月7日星期一三个时间段（晚上10:00 - 11:00、下午1:00 - 2:00、晚上7:00 - 8:00）的出行者起讫点（O - D）位置、路线和出发时间。这些时间分别代表非高峰时段（晚上10:00 - 次日凌晨1:00）、临近高峰时段（上午9:00 - 11:00、下午1:00 - 4:00、晚上8:00 - 10:00）和高峰时段（上午7:00 - 9:00、上午11:00 - 下午1:00、下午4:00 - 8:00）。数据收集包括搜索政府和组织记录、利用阿里云的高德平台、进行现场测量以及开展实地调查。该数据集是研究正常和受疫情影响的出行条件下包括公交车、快速公交（BRT）和固定导轨交通在内各种城市公共交通服务综合运营的宝贵资源。此外，它还可用于研究汽车、公共交通、拼车和主动交通方式提供的多模式综合城市客运服务。
