Urban green space accessibility data and urban traffic congestion data

Previous studies have often used network analysis methods in ArcGIS or single time period network map path planning methods that do not include the impact of traffic conditions to obtain travel cost data (Pei et al., 2022). However, the data obtained by these two methods ignore the dynamic resistance effects in actual urban traffic flow, such as traffic congestion during peak hours, waiting time for traffic lights, and traffic restrictions on different roads, resulting in a lack of data accuracy. To address this issue, this study collected complete travel cost data including road congestion information and real-time traffic data, and obtained the road congestion index and average vehicle speed of the streets where the road network is located at different time periods to obtain a complete dataset for subsequent research.

In order to ensure the scientific and rigorous nature of the dataset, real-time traffic cost data was collected at a total of 49 time points during different time periods from Monday to Friday and on weekends. The data for this study comes from Baidu Maps（ map.baidu.com/ ）The real-time path planning interface (API) of Baidu Maps provides real-time traffic conditions based path travel time, road congestion index, and more complete road condition information.