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Estimating the potential of a warning system preventing road accidents at pedestrian crossings

Ess Juri, Luppin Janek, Antov Dago

LogForum

2021

Vol. 17, no. 3

441--452

Background: The safety of pedestrians is one of the main traffic safety issues today and despite measures being applied, the number of pedestrian deaths in traffic is not changing. According to the Pareto Rule, 80% of consequences come from 20% of the causes and here the question arises whether we have already used these 20% of the most efficient measures. Today the European Union (EU) puts big hopes are on contemporary technologies, such as Advanced Emergency Braking Systems (AEB) and cooperative intelligent transport systems (C-ITS). This decade, we can expect smarter vehicles with automatic brakes, and smarter infrastructure which can communicate with vehicles. Along this other profits technological development provides new opportunities for improving pedestrian safety. One of the most promising solutions is deployment of C-ITS systems at uncontrolled crossings. It would monitor the situation and warn the road users of potential dangers as well as make the vehicles brake automatically. However, before making large investments into this field, one has to be sure that this approach will work. The aim of this paper is to describe typical vehicle-pedestrian crash scenarios and to estimate whether a C-ITS warning system is able to prevent them. Research estimates the potential of this system and provides insights to its must-have features. Methods: To understand the situations in which the warning system should function, researchers carried out traffic conflict studies at uncontrolled crossings with traffic filmed in both winter and summer. They determined and described serious conflicts and, based on their scenarios, classified them into three types. Then, researchers selected the most critical conflict of each type and analysed whether warning signals can be provided to the vehicle and the driver early enough to prevent collisions. For these purposes, researchers used a modelling software for traffic accident investigation. To access the efficiency of the C-ITS warning system, researchers estimated the probability of preventing collisions and used the efficiency parameters of classical traffic calming measures. Results: The C-ITS warning system has good potential in preventing vehicle-pedestrian collisions at uncontrolled pedestrian crossings. It is remarkable and very promising that it would be able to prevent all types of conflicts analysed in the scope of this study by warning AEB-equipped vehicles. Warning the driver would be also effective, but the system work will largely depend on the quality of warning signals. An effective C-ITS warning system should be capable of predicting the trajectories and acceleration of road users as well as calculating the stopping distance of vehicles based on the coefficient of static friction. Study showed that in some cases, the system will have to give false positive alarms, but the fewer such alarms will be given, the more efficient the system will be. A disturbing or annoying C-ITS warning system cannot be considered effective. Conclusions: Road accident statistics contain general data about vehicle-pedestrian collisions at uncontrolled crossing, but there is few information about behavioral patterns leading to accidents. Based on large-scaled traffic studies, researchers were able to determine these patterns and described how road users act when being involved in a dangerous situation. This knowledge helped to model typical vehicle-pedestrian collisions as well as their possible scenarios. Researchers used the conflict models totest the C-ITS warning system and to understand its efficiency. The study results were implementedin a prototype that has been developed in Estonia and is being tested it in real traffic conditions of a smart city in the scope of the Finnish-Estonian project “FinEst Twins”. The next steps are to analyze the test results and to conduct research to understand how to warn drivers (and pedestrians) most effectively.