Impact of E-Scooters on Pedestrian Safety: A Field Study Using Pedestrian Crowd-Sensing

TL;DR

This study empirically investigates pedestrian safety issues related to e-scooter usage in urban areas through crowd-sensed encounter data, identifying unsafe zones and providing a data-driven approach for future safety improvements.

Contribution

It introduces a novel field study using crowd-sensed data to analyze e-scooter and pedestrian interactions, offering a first-of-its-kind blueprint for safety-related research.

Findings

Identification of unsafe spatio-temporal zones for pedestrians

Empirical data on e-scooter and pedestrian encounters

Analysis of mobility trends related to safety concerns

Abstract

The popularity and proliferation of electric scooters (e-scooters) as a micromobility solution in our cities and urban communities has been rapidly rising. Rent-by-the-minute pricing and a healthy competition between micromobility service providers is also benefiting riders with low trip costs. However, an unprepared urban infrastructure, combined with uncertain operation policies and poor regulation enforcement, has resulted in e-scooter riders encroaching public spaces meant for pedestrians, thus causing significant safety concerns both for themselves and the pedestrians. As a consequence, it has become critical to understand the current state of pedestrian safety in our urban communities vis-\`{a}-vis e-scooter services, identify factors that impact pedestrian safety due to such services, and determine how to support pedestrian safety going forward. Unfortunately, to date there have been no realistic, data-driven efforts within the research community that address these issues. In this work, we conduct a field study to empirically investigate crowd-sensed encounter data between e-scooters and pedestrian participants on two urban university campuses over a three-month period. We also analyze encounter statistics and mobility trends that could identify potentially unsafe spatio-temporal zones for pedestrians. This first-of-its-kind work provides a preliminary blueprint on how crowd-sensed micromobility data can enable safety-related studies in urban communities.