Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models

Abstract

Adverse weather conditions can have different effects on different types of road crashes. We quantify the combined effects of traffic volume and meteorological parameters on hourly probabilities of 78 different crash types using generalized additive models. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different crash types in case of precipitation, sun glare and high wind speeds. The largest effect of snow is found in case of single-truck crashes, while rain has a larger effect on single-car crashes. Sun glare increases the probability of multi-car crashes, in particular at higher speed limits and in case of rear-end crashes. High wind speeds increase the probability of single-truck crashes and, for all vehicle types, the risk of crashes with objects blown on the road. A comparison of the predictive power of models with and without meteorological variables shows an improvement of scores of up to 24%, which makes the models suitable for applications in real-time traffic management or impact-based warning systems. These could be used by authorities to issue weather-dependent driving restrictions or situation-specific on-board warnings to improve road safety.

An Econometric Analysis of Weather Effects on Roadway Crash Severity in Bangladesh: Evidence from the Dhaka Metropolitan Area

“…Similar research also supports this finding [26]. Studies show that except for severe storms and huge vehicles, wind is not found to be significant [18,[27][28][29]. And from the graphical representation of wind data in Figure 4, it is found that in Dhaka city, wind speed is not severe.…”

“…But the model did not consider traffic volume; it assumed it was a diurnal cycle. The model only looked at weather-related crashes that the police said were caused by road conditions (e.g., slippery roads caused by water, snow, or ice) [27]. The model included the hourly traffic volume and the effects of rain, temperature, sun, and wind.…”

“…Classic logistic regression models were replaced by generalized additive models (GAMs) for dichotomized target variables. The models were created for 78 different types of crashes in a consistent approach to see how the weather effects and the predictive power of different speed limits, types of crashes, and crash severities compare [27]. Anarkooli et al found that injury severity is significantly correlated with factors such as poor visibility, rainy weather, the presence of a central median, the involvement of a heavy vehicle, improper overtaking, vehicle age, traffic volume and composition, the number of travel lanes, the speed limit, the undulating terrain, and the vehicle type [28].…”

Microscopic traffic models, accidents, and insurance losses

“…Malin et al (2019) investigate the relative accident risk of different road and weather conditions and combinations of conditions using data for major roads in Finland; their analysis is based on the notion of Palm probability. Using generalized additive models, Becker et al (2022) quantify the combined effects of traffic volume and meteorological parameters on probabilities of 78 different crash types. Comi et al (2022) investigate the suitability of various data mining techniques in analyzing the factors underlying accidents and predicting these in case studies based on data collected in Rome.…”

“…Using generalized additive models, Becker et al. (2022) quantify the combined effects of traffic volume and meteorological parameters on probabilities of 78 different crash types. Comi et al.…”