Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data

Vittorio Astarita,Vincenzo Pasquale Giofrè,Ciro Caliendo,Isidoro Russo

doi:10.3390/su12176974

Vittorio Astarita, Vincenzo Pasquale Giofrè + Show 2 more

Open Access

https://doi.org/10.3390/su12176974

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Journal: Sustainability	Publication Date: Aug 27, 2020
Citations: 18	License type: CC BY 4.0

Affiliation: University of Calabria, University of Salerno

Abstract

The traditional analysis of road safety is based on statistical methods that are applied to crash databases to understand the significance of geometrical and traffic features on safety, or in order to localize black spots. These classic methodologies, which are based on real crash data and have a solid background, usually do not explicitly consider the trajectories of vehicles at any given location. Moreover, they are not easily applicable for making comparisons between different traffic network designs. Surrogate safety measures, instead, may enable researchers and practitioners to overcome these limitations. Unfortunately, the most commonly used surrogate safety measures also present certain limits: Many of them do not take into account the severity of a potential collision and the dangers posed by road-side objects and/or the possibility of drivers being involved in a single-vehicle crash. This paper proposes a new surrogate safety indicator founded on vehicle trajectories, capable also of considering road-side objects. The validity of the proposed indicator is assessed by means of comparison between the calculation of surrogate safety measures on micro-simulated trajectories and the real crash risk obtained with data on real crashes observed at several urban intersection scenarios. The proposed experimental framework is also applied (for comparison) to classical indicators such as TTC (time to collision) and PET (post-encroachment time).

Highlights

The crash data were extracted from the official reports of the urbane police of the city of Salerno, while traffic flows were computed by using video cameras placed at each intersection
Since we found from a preliminary statistical analysis that α converged to zero, a univariate correlated random-parameter Poisson (CRPP) model was used in the present paper
We investigate and compare two different CRPP models, which include different sets of covariates: (i) model based on collisions (Model A) establishes a relationship between: Collisions (C) calculated in simulation on the basis of the safety indicators such as the Time to collision (TTC) and Post-encroachment time (PET), traffic flow (TF, indicated in the text as VHPe;i), and dummy variable (Du) for accounting for the occurrence of a much higher number of crashes at the Scenario I in the afternoon peak hours; (ii) Model B involves the following explanatory variables: Mean energy (Ea), traffic flow (TF), and dummy variable (Du)

Summary

Introduction

Analytical methods have been applied to address traffic congestion problems by making efforts to shift demand from private vehicles to transit systems [1] and to put into operation better road traffic control implementing tools, such as traffic simulation [2,3,4,5,6,7], network dynamic equilibrium models [8,9,10], and other methodologies that are devoted to change user route choice [11,12,13,14,15]. Analytical methods based on traffic network simulation have rarely been implemented in order to produce an evaluation of the level of safety of a given traffic scenario. Traffic simulation is applied to assess safety levels related to the risk of road crashes

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