# The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand
## 3 Data
### Variables  
The study utilized ride-hailing order data from Nanjing City for five working days, from 11 April to 15 April 2022 (Monday to Friday). The dataset includes essential fields as shown in Table 1, comprising order ID, vehicle ID, pick-up/drop-off time, and vehicle pick-up/drop-off latitude and longitude information. The data underwent screening and cleaning procedures, such as removing irrelevant fields, excluding orders with missing pick-up/drop-off latitude and longitude or time information, and eliminating orders with trip durations less than 2 min or exceeding 2 h. This type of orders deviates from the typical characteristics of regular ride-hailing trips, and these extreme values can disrupt the training and prediction of the model. Removing them can improve the accuracy and reliability of the model [39; 40]. After these processes, a total of 835,370 valid records were obtained. The dependent variable is the count of ride-hailing pick-up points within each grid. The explanatory variables use \"5 Ds\" to describe the built environment. \"Density\" includes population density and POI density. Using the population grid data of WorldPop with 100 m accuracy ([[https://www.worldpop.org/](https://www.worldpop.org/)]([https://www.worldpop.org/](https://www.worldpop.org/)) for detail, accessed on 12 June 2023), the population density is calculated by dividing the total population in the grid by the grid area. We utilized the Chinese Amap API interface ([[https://lbs.amap.com/api/](https://lbs.amap.com/api/)]([https://lbs.amap.com/api/](https://lbs.amap.com/api/)) for detail, accessed on 12 June 2023) to extract POI data within the main urban area of Nanjing City, including bus stations, subway stations, dining facilities, companies, shopping facilities, financial facilities, accommodation services, science, education, culture facilities, scenic spots, commercial residences, leisure services, and medical facilities. It provides important data support in the research of the urban built environment [23]. By calculating the ratio of each category of POI facilities to the grid area, we obtained their density values. Figure 1: Study area. For \"Design\", we extracted road network data of Nanjing City based on OpenStreetMap ([[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) for detail, accessed on 12 June 2023), including the length of urban roads, pedestrian paths, and descriptions of intersections. We calculated the total length of expressways, main roads, secondary roads, and branch roads to obtain the length of urban roads. Additionally, we calculated the length of pedestrian paths and the non-motorized road length and extracted data on road nodes to determine the number of intersections within each grid. The \"Diversity\" of land use mix was quantified using the entropy index of the 10 categories of POI extracted in the study:  
\[EI=\frac{-\sum A_{is}\ln A_{is}}{\ln S_{i}} \tag{1}\]  
where \(A_{is}\) denotes the share of land utilization category \(s\) within the grid \(i\), while \(S_{i}\) represents the count of distinct land types integrated within the grid \(i\). \"Destination accessibility\" is articulated through the Euclidean distance from a given location to the CBD. Buses and subways are important components of urban public transportation. The Euclidean distance measured from the centroid point to the closest bus or subway station in each grid represents the \"Distance to transit\". The descriptive explanation of variables is shown in Table 2.