Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human 3 driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12-47%, 50-80%, 82-92% and 90-94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five "W" questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users' demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems.
Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a fiveminute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures.
Secondary crashes (SCs) or crashes that occur within the boundaries of the impact area of prior, primary crashes are one of the incident types that frequently affect highway traffic operations and safety. Existing studies have made great efforts to explore the underlying mechanisms of SCs and relevant methodologies have been evolving over the last two decades concerning the identification, modeling, and prevention of these crashes. So far there is a lack of a detailed examination on the progress, lessons, and potential opportunities regarding existing achievements in SC-related studies. This paper provides a comprehensive investigation of the state-of-the-art approaches; examines their strengths and weaknesses; and provides guidance in exploiting new directions in SC-related research. It aims to support researchers and practitioners in understanding well-established approaches so as to further explore the frontiers. Published studies focused on SCs since 1997 have been identified, reviewed, and summarized. Key issues concentrated on the following aspects are discussed: (i) static/dynamic approaches to identify SCs; (ii) parametric/non-parametric models to analyze SC risk, and (iii) deployable countermeasures to prevent SCs. Based on the examined issues, needs, and challenges, this paper further provides insights into potential opportunities such as: (a) fusing data from multiple sources for SC identification, (b) using advanced learning algorithms for real-time SC analysis, and (c) deploying connected vehicles for SC prevention in future research. This paper contributes to the research community by providing a one-stop reference for research on secondary crashes.
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