Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots

Abstract

This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid-cell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of "similar" sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones.

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

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Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

“…This special issue highlights how analysts can use big data to define hypotheses, incorporate more accurate and new data, model the data, and apply the findings to policy formation. However, the authors are cautious about using their new data, highlighting obstacles in collecting, storing, and applying big data to societal issues (Chiu, Pun, & Wong, 2017;Lin, Margolin, & Wen, 2017;Xie, Ozbay, Kurkcu, & Yang, 2017;Zhou, Sheu, & Wang, 2017).…”

Analytical Assessment of Effective Maintenance Operations on At-Grade Unsignalized Intersections

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