Re-visiting crash–speed relationships: A new perspective in crash modelling

Abstract

Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash-speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash-speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed-crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

“…Road segments are considered as decision making units (DMUs) and the basic target is to identify the best-performing road segments and worst as well. Best are those with minimum output levels given the input exposure variables, and other segments' risk is then measured relative to this frontier [20].…”

“…Road segments are considered as decision making units (DMUs) and the basic target is to identify the best-performing road segments and worst as well. Best are those with minimum output levels given the input exposure variables, and other segments' risk is then measured relative to this frontier [20]. Mathematically, to use DEA for road safety evaluation, the model is shown as follows: Mathematically, to use DEA for road safety evaluation, the model is shown as follows:…”

“…Because of that reason, hotspot identification of accident-prone locations remains a focal point of interests for transportation engineers. That is why a series of researches were involved in relationship analysis of accidents [12][13][14][15][16][17][18][19][20][21][22][23] in context of volume/capacity (V/C) [18,19,24,25], vehicles miles travelled (VMT) [17,[26][27][28][29], vehicles hours travelled (VHT) [25,30,31], speed [18,20,24,[32][33][34][35][36][37][38], flow [12,13,32,35,36,39], and geometric design [35,[40][41][42][43][44]. Different methods were applied to locate the risky road segments to be treated for safety level enhancement.…”

Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

“…The model results indicated that average travel speed was not significantly related to crash frequency during the off-peak period, however, during the peak period, a significant positive relationship between average speed and crash frequency was demonstrated. More recently, Imprialou et al (Imprialou et al, 2016) proposed a new condition-based approach to aggregate the crashes according to the similarity of their pre-crash traffic and geometric conditions, and then compared it with the traditional segment-based aggregation approach. The results showed that average speed was significantly positively associated with crash occurrence in the conditionbased model, while the relationship was found to be negative in the segment-based model.…”

Optimization of the Allowable Speed on Iran’s Freeways to Reduce Violations and Accidents, Using Zero-Truncated Poisson Regression Model

“…Though the speed has been considered as one of the leading causes of the accidents, the result of different studies is inconsistent. Imprialou et al [2] modeled a set of data using a multivariable lognormal Poisson regression to indicate part of these inconsistencies. The speed management is an essential strategy for improving the safety on freeways, due to the sharp relationship between speed and accident occurrence and its intensity.…”