EXPLORATION OF NATURALISTIC DRIVING DATA FOR IDENTIFYING HIGH CRASH RISK HIGHWAY LOCATIONS

摘要

This paper describes a project that was undertaken using naturalistic driving data collected via Global Positioning System (GPS) devices to examine the relationship between long-term crash frequency and repeated occurrences of high magnitude jerks while decelerating in the driving data. The motivation to look for correlations between abrupt/abnormal driving maneuvers and long-term crash frequency was to demonstrate a proof-of-concept for proactive safety assessments of crash prone locations. Linear referencing in ArcMap was used to link the GPS data with roadway characteristic data from a roadway base map. The linear referencing methodology was the key to relate the GPS driving data with the freeway corridor of interest, i.e., US 101 northbound (NB) and southbound (SB) in San Luis Obispo California. The process used to merge GPS data with quarter-mile freeway segments for traditional crash frequency analysis is also discussed in the paper. Negative binomial regression analyses showed that proportion of high magnitude jerks while decelerating on freeway segments (from the driving data) was significantly related with the long-term crash frequency of those segments. Applying the same model, average daily traffic (ADT), roadway curvature and presence of an auxiliary lane were found to be insignificant. The results from this exploration are promising since the data used to derive the variable(s) used in the analysis can be collected using most off-the-shelf GPS devices, including many smartphones.