Single bicycle accident originating from unsuccessful interactions

In Sweden, single accidents are the biggest traffic safety problem for cyclists contributing alone with 80 % of their serious injuries. In this respect, the issue of bicycle infrastructure maintenance received significant attention. Slippery, uneven or in other way problematic road surface is reported as a main contributing factor in about half of the single bicycle accidents [l]. This work explores other causal mechanisms for single accidents, primarily those originating from unsuccessful interactions between cyclists and inftastructure elements as well as other objects and road users on the bicycle path. Interactions with the infrastructure take place at locations of rapid change (often reduction) of the effective space available für cyclists. Such examples could be the narrowing of the bicycle paths at the entrance into a tunnel, speed-reducing gates, parking areas für bicycles and e-scooters, poles, manholes, tree branches and other objects banging over the bicycle paths end forcing cyclists to adjust their travel. In such situations, the cyclist can either collide with the objects directly or lose balance as a result of the rapid speed/direction changes necessary to avoid the collision. Another risk factor is the sharp (and unexpected) tums or lateral displacements of the bicycle path itself that can be observed at the entrances to tunnels (often combined with a steep road descent) or intersections. Collisions between cyclists and pedestrians resulting in severe injuries are relatively few, about 10% of all severe injuries [l]. lt is reasonable, however, to expect that many of such unsuccessful interactions result in single falls rather than direct collisions. lt was shown that about 10% of single accidents bad an interaction with other road users (incl. motor vehicles) as a contributing factor 11]. [from Background

Comparison of two simulation methods for testing of algorithms to detect cyclist and pedestrian accidents in naturalistic data

Naturalistic studies can potentially be used to detect accidents of vulnerable road users and thus overcome the large degree of under-reporting in the official accident records. In this study, simulated cycling and walking accidents were performed by a stuntman and with a crash test dummy to test how they differ from each other and the potential implications of using simulated accidents as an alternative to real accidents. The study consisted of simulations of common accident types for cyclists and pedestrians, such as tripping over a curb or falling of the bike after hitting an obstacle. Motion data in terms of acceleration and rotation as well as the state of the screen (turned on/off) was collected via an Android smartphone to use as indicators for the motion patterns during accidents. The results show that dummy data have a distinct peak at the moment of the fall as a result of not being able to break the fall. As opposed to this, the stuntman arranges himself in a way to reduce the impact when hitting the ground. In real accidents, motion patterns will probably lie in-between these two types

Surrogate safety measures and traffic conflict observations.

The chapter primarily focuses on observing traffic conflicts (also known as near-accidents) as a site-based road safety analysis technique. Traffic conflicts are a type of surrogate safety measure. The term surrogate indicates that non-accident-based indicators are used to assess VRU safety instead ofthe more traditional approach focusing on accidents (see chapter 2). The theory underpinning surrogate safety measures is briefly described, followed by a discussion on the characteristics of the traffic conflict technique. Next, guidelines for conducting traffic conflict observations using trained human observers or video cameras are presented. Chapter 4 concludes with examples of the use of the traffic conflict technique in road safety studies focusing on VRUs

FROM SPEED PROFILE DATA TO ANALYSIS OF BEHAVIOUR

Classification of speed profiles is necessary to allow interpretation of automatic speed measurements in terms of road user behaviour. Aggregation without considering variation in individual profile shapes easily leads to aggregation bias, while classification based on exogenous criteria runs the risk of loosing important information on behavioural (co-) variation. In this paper we test how three pattern recognition techniques (cluster analysis, supervised learning and dimension reduction) can be applied to automatically classify the shapes of speed profiles of individual vehicles into interpretable types, with a minimum of a priori assumptions. The data for the tests is obtained from an automated video analysis system and the results of automated classification are compared to the classification by a human observer done from the video. Normalisation of the speed profiles to a constant number of data points with the same spatial reference allows them to be treated as multidimensional vectors. The k-means clustering algorithm groups the vectors (profiles) based on their proximity in multidimensional space. The results are satisfactory, but still the least successful among the tested techniques. Supervised learning (nearest neighbour algorithm tested) uses a training dataset produced beforehand to assign a profile to a specific group. Manual selection of the profiles for the training dataset allows better control of the output results and the classification results are the most successful in the tests. Dimension reduction techniques decrease the amount of data representing each profile by extracting the most typical “features”, which allows for better data visualisation and simplifies the classification procedures afterwards. The singular value decomposition (SVD) used in the test performs quite satisfactorily. The general conclusion is that pattern recognition techniques perform well in automated classification of speed profiles compared to classification by a human observer. However, there are no given rules on which technique will perform best

Application of automated video analysis to road user behaviour

The successful planning, design and management of a traffic system is impossible without knowledge of how the traffic environment affects the behaviour of road users and how the behaviour is related to the main qualities of the traffic system (e.g. safety, efficiency). Automated video analysis is a promising tool for traffic behaviour research in that it enables collection of micro-level behaviour data for large populations of road users and provides a detailed description of their motion. This thesis describes the tests done with an automated video analysis system developed at Lund University. The system was used in two large scale studies with the main task of detecting the presence of road users of a particular type. Accuracy of position and speed estimates were tested in a study specially designed for that purpose. The thesis also elaborates on the problem of relating the behaviour of road users to safety and proposes organising all the elementary events in traffic (defined here as encounters between two road users) into a severity hierarchy. The process of an encounter is described with a set of continuous safety indicators that can handle the various approach angles and transfer between being and not being on a collision course. When an objective measure for an encounter severity is found, the severity hierarchies may be used not only for describing safety but also for studying the balance between safety and other qualities valued by road users

SeeMe at the crosswalk : Before-after study of a pedestrian crosswalk warning system

SeeMe is a pedestrian crosswalk warning system with automatic pedestrian detection that is mounted on crosswalk signs. Amber flashing lights are activated when pedestrians are approaching or crossing the crosswalk. The aim is to attract motorists’ attention, to improve yielding behavior and to reduce conflicts. A before-after study with a matched comparison group has been conducted in the Norwegian municipality of Trondheim. Video observations were made at eight crosswalks (four of which were equipped with SeeMe in the after period) of 1825 pedestrian-motorist interactions. On average, yielding rates at SeeMe equipped crosswalks increased by 14% (statistically significant) when changes at the comparison sites are taken into account. However, the results are inconsistent between crosswalks. At two of the crosswalks with SeeMe in the after period, yielding rates increased by 39% (statistically significant), while they decreased by 4% at the other two crosswalks (not statistically significant). There were several differences between crosswalks with increased and unchanged yielding rates: Initial yielding rates (below vs. above 80%), placement of crosswalk signs (immediately at vs. at some distance from the crosswalk) and false alarm rates (30% vs. 57% on average). These factors may have affected the effect of SeeMe on yielding rates, but the number of crosswalks included in the study is too small to generalize the differences between different types of crosswalks. The results do not indicate that SeeMe has negatively affected pedestrian behavior or provoked conflicts. It is concluded that SeeMe may be effective in increasing motorist yielding rates at crosswalks with similar characteristics as in the present study - two-lane roads in residential areas with moderate motor vehicle volumes and speed limits of 50 kph or below - and that high initial yielding rates and high rates of false alarms may limit its effectiveness