Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas

Abstract

Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads.

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

“…The prediction errors of the SP-STGCNN model using different spatial correlations are as follows: SP-STGCNN based 𝑊 1 𝑎𝑏𝑔 < SP-STGCNN based 𝑊 2 𝑎𝑏𝑔 < SP-STGCNN based 𝑊 𝑑𝑖𝑠 < SP-STGCNN based 𝑅 𝑖𝑛𝑓𝑜 . As the travel mode of the last kilometer, the demand for shared bicycles will be affected by the distance factor and road network characteristics (Guo et al, 2022;Jiao et al, 2021;Bai et al, 2021, El-Assi et al, 2017. However, when using geographic information to describe the spatial similarity of different stations, the prediction accuracy is higher.…”

Differences in perception of safety in driving environment according to shared PMD user experience through multi-criteria analysis

“…Maiti et al analysed data on collisions between shared PMDs and pedestrians on a university campus to identify elements that could affect the safety of pedestrians [ 47 ]. Jiao and Choi examined data on traffic accidents caused by PMDs and derived Moran's I by considering spatial autocorrelation; they verified a spatial correlation between traffic accidents caused by the collision between shared PMDs [ 48 ]. Specifically, traffic accidents caused by shared PMDs were more likely to occur on primary street due to traffic signals than general traffic accidents.…”