Proactive assessment of road curve safety using floating car data: An exploratory study

Abstract

Driving speed is an important risk factor, especially when negotiating horizontal curves. Therefore it may be useful in extracting surrogate measures to proactively safety assessment, a practice consistent with a current shift towards a Safe System approach to addressing road trauma. Review of previous literature indicated two categories of studies: (1) studies focusing on a safe driving perspective, i.e. studies primarily interested in finding the cut-off point in FCD data characteristics between safe and unsafe driving; (2) studies focusing on relating meaningful risk rates (percentages of exceeding the risk thresholds) to specific locations, and thus identify safety critical sites. However, no study was found that specifically focused on the relationship between kinematic characteristics (other than just speed) and road curves. The presented study focused on exploring the relationship between acceleration and jerk thresholds and crashes occurring on road curves. The first objective was to determine meaningful acceleration and jerk thresholds to utilize in explaining safety performance when negotiating curves. For this purpose floating car data (FCD) from a fleet of company vehicles, driving in rural sections of national roads in the Czech Republic, was collected and used to derive and validate potential surrogate safety measures. FCD presents in-vehicle information with several benefits compared to traditional techniques, such as feasibility of data collection, relatively unlimited spatial coverage, and availability of historical data. In the analysis, lateral acceleration and longitudinal jerk were found to be the most influential measures of curve safety performance. To sum up, the exploratory study outlined a practical approach to proactive evaluation of road curve safety: FCD data can generate useful surrogate measures of curve safety (acceleration and jerks) associated with crash history. A larger study is required to strengthen robustness of the results and provide confidence necessary for practical application. Potential use cases may include conducting interim evaluations of curve road safety treatments, or in-vehicle monitoring devices for detection of potentially unsafe manoeuvers and providing real-time feedback to drivers based on a combination of identified safety thresholds.

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

“…These experiments are a quite similar alternative to smartphone data but much more expensive as there are significant costs that depend on the equipment used [48] and the duration of the experiment [49]. The majority of the SSMs collected through instrumented vehicles range in a similar concept to the data collected by smartphones and concern harsh driving behavior events [44,45,[50][51][52][53][54][55][56][57][58]. Apart from these studies that focus on harsh driving behavior events, traffic conflicts and related measures for rating their severity have also been examined in other naturalistic driving experiments using instrumented vehicles [59,60].…”

“…However, there are certain studies that focus on serious or fatal road crashes [62,66]. Several studies attempt to correlate SSMs with specific road crash types such as rear-end, angle and single-vehicle crashes [52,56,64,76]. Other research studies focus on specific road crash characteristics such as the weather conditions, and the time or the day of the crash, which usually correspond to the conditions of SSM collection [33,68].…”

“…Generalized Linear Models (GLMs) have been implemented widely in the road safety literature for many years, as they assume that crashes are independent, random, and sporadic countable events [77]. Based on Table A1, it is observed that Poisson [56,65,78] and Negative Binomial (NB) models [38,45,52,53,60,66,69] are the most common forms of GLMs among studies exploiting SSMs for historical crash record investigations, with NB models being more prevalent than Poisson models. The key difference between these two GLM forms has to do with the fact that NB models relax the equal mean and variance assumption of the Poisson model, which can account for overdispersion resulting from unobserved heterogeneity and temporal dependency [79].…”

“…In these studies, the time period of road crashes is estimated to be between 12 and 130 times longer (mean: 50, st.dev: 36.3) than that of the SSMs. In addition, there are also some studies located in the central and upper right part of Figure 2 for which the time period of road crashes is 4-9 times longer than that of SSMs (mean: 7, st.dev: 2.3) [38,44,52,59,86].…”

“…It appears that the technological development in recent years has significantly contributed to making smartphones a key choice for collecting SSMs [32][33][34][35][36][37][38][39]. The indicators collected through smartphones' sensors can be quite similar to those collected by instrumented vehicles [52,54,56]. However, the cost of collecting SSMs via smartphones is significantly lower compared to that of instrumented vehicles.…”