Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

Abstract

Despite the fact that semi-autonomous vehicles will become more and more prevalent in the coming decades, recent studies have highlighted that traffic accidents will persist as a core issue for road users, insurers, and policy makers alike. Researchers and industry players see potential in the technology embedded in semi-autonomous vehicles to combat this challenge by reliably predicting locations with a high likelihood of traffic accidents. This technology can be leveraged to detect accidents and 'near miss incidents', such as heavy braking and evasive manoeuvres, otherwise known as Critical Driving Events (CDEs). The locations of CDEs could identify areas of high accident exposure, offering automotive insurers a unique opportunity to reduce traffic accidents through the adoption of active loss prevention business models, such as providing saferouting services and in-vehicle warnings. To date, there is limited empirical evidence on whether the Crash Frequency and Crash Rate of locations can be accurately identified through CDEs. To address this research gap, an 18-week naturalistic driving field study of 72 vehicles was conducted in Switzerland, covering over 690,000 km. Data collected from the CAN Bus of these vehicles indicate that there is a proportional relationship between the CDEs of the fleet, and the Crash Frequency and Crash Rate of a location. Furthermore, a nationwide spatial regression analysis was applied to determine Crash Frequency across the majority of the Swiss road network. We identify the relationship between Crash Frequency, and the CDEs and Trip Frequency of the fleet, along with additional explanatory variables for urban and highway locations. These insights provide first evidence that insurance companies and other industry players with access to a nationwide semi-autonomous fleet can determine existing and emerging locations of high accident probability, enabling more proactive business models and safety focused services.

A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots

“…These same thresholds were taken for both acceleration and turning. Recent research also considered the jerk-rate as a good indicator for critical driving events [29] and values of −2 m/s 3 were found to be good classifier thresholds when brake event locations were correlated with historical accident locations [32]. Moreover, a sophisticated categorization of dangerous driving has been given by the "jerk feature", calculated via the standard deviation of the jerk rate within a specific time window over the average jerk rate of the current road type [25].…”

“…However, since the system collected the vehicle accelerometer measurements in addition to the video sequences, we can assign the maximum magnitude acceleration and maximum jerk rates a-posteriori to each video. The following Figures will show situations, which are characterized either by a high acceleration value following the definition of [4], or by a high jerk rate, following the definition of [29,32] as a dangerous situation. Similar to the classification of historic hotspots, the classification of a locations where dangerous driving maneuvers occurred is demonstrable from the recorded video sequences.…”

Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

“…The development of computers and software leads to the development of new types of vehicles. In the current market of new vehicles, safer driving can be recognized through vehicle tracking and smartphone applications that detect risky driving patterns such as speeding or inappropriate lane changing [11]. Furthermore, the appearance of partly or fully autonomous vehicles can significantly contribute to road safety, reducing the frequency of RTAs [11].…”

Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution

“…Therefore, urban traffic loads are becoming increasingly serious. Then there are problems such as traffic congestion, safety accidents and insufficient allocation of traffic resources [2].…”