Dataset for developing optimal headway-based bus dispatching strategy during epidemic outbreaks

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, 1:002:00 PM, and 7:00-8:00 PM on Monday, June 7, 2021. These times represent off-peak (10:00 PM-1:0 0 AM the next day), adjacent-to-peak (9:00-11:00 AM, 1:00-4:00 PM, and 8:0010:00 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. (c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)