Predicting risky driving behaviours using the theory of planned behaviour: A meta-analysis.

The current meta-analysis explored the efficacy of the theory of planned behaviour (TPB) in predicting high-risk driving behaviours. Specifically, we examined speeding (in relation to exceeding the limit as well as speed compliance), driving under the influence, distracted driving, and seat belt use. We searched four electronic databases (i.e., PubMed, Web of Science, Scopus, and ProQuest) and included original studies that quantitatively measured the relationships between the TPB variables (attitude, subjective norm, perceived behavioural control [PBC], intention, and prospective/objective behaviour). The study identified 80 records with 94 independent samples. Studies were assessed for risk of bias using the JBI checklist for cross-sectional studies and compliance with the TPB guidelines. Together, attitude, subjective norm and PBC explained between 30 % and 51 % of variance found in intention, with attitude showing as the strongest predictor for intention across the different driving behaviours. The findings also showed that the model explained 36 %-48 % variance found in predicting the observed and/or prospective behaviours for distracted driving, speed compliance and speeding. Understanding the varying strengths and thus relative importance of TPB constructs in predicting different risky driving behaviours is crucial for developing targeted road safety interventions.

A comparison of actual and perceived risk of apprehension for speeding in Norway.

This paper compares actual and perceived risk of apprehension for speeding in Norway. Actual risk of apprehension was estimated by relying on data on the number of citations for speeding and the percentage of vehicles speeding when passing automatic traffic counting stations. It was defined as the number of detected violations per million kilometres driven while committing a violation. Perceived risk of apprehension was estimated as a mean annual frequency of getting detected by the police, based on survey answers given by samples of drivers surveyed in 2010, 2014 and 2024. Actual risk of apprehension was converted into a mean annual frequency of detection by relying on estimates of the mean annual driving distance. Thus, perceived mean annual frequency of detection could be compared to actual mean annual frequency of detection. Drivers were found to overestimate the risk of apprehension considerably, but the size of the overestimation declined from 2010 to 2014 and further again to 2024. In 2024, mean perceived risk of apprehension was about 2.4 times higher than actual risk of apprehension. Drivers were also found to overestimate the number of speed cameras deployed in Norway. Only a small minority of drivers had a correct perception of how the risk of apprehension for speeding varied according to the level of speeding. The decisions drivers make about speeding are based on their perceived risk of apprehension; hence it is advantageous to compliance that drivers overestimate the risk of apprehension.

Understanding speeding behavior on interstate horizontal curves and ramps using networkwide probe data.

INTRODUCTION: Lane departure collisions account for many roadway fatalities across the United States. Many of these crashes occur on horizontal curves or ramps and are due to speeding. This research investigates factors that impact the odds of speeding on Interstate horizontal curves and ramps. METHOD: We collected and combined two unique sources of data. The first database involves comprehensive curve and ramp characteristics collected by an automatic road analyzer (ARAN) vehicle; the second database includes volume, average speed, and speed distribution gathered from probe data provided by StreetLight Insight®. We evaluated the impacts of level of service (LOS), which reflects traffic density or level of congestion, time of the day (morning, evening, and off-peak hours), time of the week (weekdays and weekends), and month of the year (Jan-Dec), and various information about geometric characteristics, such as curve radius, arc angle, and superelevation, on odds of speeding. RESULTS: The results show that the odds of speeding increases at horizontal curves with improved levels of service, as well as those with larger radii and superelevation. The odds of speeding decreases on curves with larger arc angles and during the winter months of the year. The findings indicate a reduction in odds of speeding at diagonal/loop ramps with larger arc angles and narrower lane widths. CONCLUSION: The results show the importance of using speed enforcement and other countermeasures to reduce speeding on curves with low traffic volumes, high speed limits, and large radius and superelevation, especially for those in rural areas. PRACTICAL APPLICATION: The results could be used to prioritize locations for the installation of speed countermeasures or dispatch enforcement resources to high-priority locations and times.

Identification of the factors influencing speeding behaviour of food delivery e-bikers in China with the naturalistic cycling data.

With the rapid growth of the gig economy in China, millions of food delivery e-bikers are making their living by rushing on the street. Speeding is one of their most common risky riding behaviours, leading to severe traffic crashes. Based on 2-month naturalistic cycling data of 46 full-time food delivery e-bikers in Changsha, their speeding behaviour is deeply studied with the individual daily speeding proportion being taken as the speeding indicator. A beta regression model is built to identify the factors significantly influencing the indicator. The estimation results reveal that female riders, middle-aged riders and riders with a bachelor's degree are less likely to engage in speeding. The same result is indicated for those working longer or experiencing more crashes. Additionally, holidays and riding distance are found to have significantly positive influences. Finally, some countermeasures are proposed to prevent speeding among food delivery e-bikers.

Improvement of riding licensing criteria based on rider's braking capability: A preliminary study.

Speeding is the factor that usually associated with fatal accident. However, riders have tendency to exceed their vehicle's speed above the regulated speed. Therefore, the likelihood of traffic accidents is significantly influenced by braking ability. Unfortunately, the braking capability has not been accommodated properly in the accident risk management, such as riding license obtaining mechanism. This paper focuses on the possibility of the development of riding licensing criteria based on rider's braking capability. The parameters used in the analysis are the safety factor and margin of safety, due to the differences in riders' braking capability. All the input data were collected from the result of previous related studies. Although the sample size is varied but data source was taken from relevant objects studies. The result of this study showed that impact speed and/or rider's involvement in fatal crashes could be reduced by increasing their braking ability. It strongly indicates that each rider should realize that their speed choices should be suited to their braking ability which could be increased during the riding licensing practical test. The utilization of a rider's braking abilities, which could provide a minimum margin of safety, should therefore be taken into consideration as a basis for the criteria used to obtain a riding license.

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics.

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000-10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Naturalistic driving analysis of situational, behavioral, and psychosocial determinants of speeding.

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself.

Interactive effects analysis of road, traffic, and weather characteristics on shared e-bike speeding risk: A data-driven approach.

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

An Improved and Simplified Agrobacterium-Mediated Genetic Transformation Protocol for Solanum nigrum with a Shorter Growth Time.

Solanum nigrum (Solanaceae family) is widely consumed as a fruit or local leafy vegetable after boiling; it also serves as a medicinal plant. Although Agrobacterium-mediated genetic transformation has been established in S. nigrum, the transformation period is long. Specifically, induction of roots takes approximately five weeks for tetraploid and hexaploid S. nigrum, and 7 weeks for diploid Solanum americanum. In this study, we developed an improved rooting-induced method that requires only about 1 week and avoids the use of tissue culture. After generating the transgenic shoots, they were directly transplanted into the soil to facilitate root formation. Remarkably, 100% of the transgenic shoots developed roots within 6 days. Our improved method is time-saving (saving more than 1 month) and simpler to operate. The improved rooting-induced step can be applied to induce roots in various plants using tissue culture, exemplified by the carnation (Dianthus caryophyllus L.). Furthermore, we applied the improved method to generate S. americanum plants expressing AcMYB110 from kiwifruit (Actinidia chinensis spp.). This method will contribute to speeding up gene functional analysis and trait improvement in S. nigrum and might have potential in fast plant molecular breeding processes in crops and rapid rooting induction in tissue culture.

Analyzing fatal crash patterns of recidivist drivers across genders and age Groups: A hazard-based duration approach.

Identifying factors that significantly affect drivers that are repeatedly involved in traffic violations or non-fatal crashes (defined here as recidivist drivers) is very important in highway safety studies. This study sought to understand the relationship between a set of variables related to previous driving violations and the duration between a previous non-fatal crash and a subsequent fatal crash, taking into account the age and gender of the driver. By identifying the characteristics of this unique driver population and the factors that influence the duration between their crash events strategies can be put in place to prevent the occurrence of future and potentially fatal crashes. To do this, a five-year (2015-2019) historical fatal crash data from the United States was used for this study. Out of 15,956 fatal crashes involving recidivist drivers obtained, preliminary analysis revealed an overrepresentation of males (about 75%). It was also found that the average duration between the two crash events was about a year and a half, with only an average of one month difference between male and female drivers. Using hazard-based duration models, factors such as number of previous crashes, previous traffic violations, primary contributing factors and some driver demographic characteristics were found to significantly be associated with the duration between the two crash events. The duration between the two events increased with driver's age for drivers who were involved in only one previous crash and the duration was shorter for those that were previously involved in multiple crashes. Previous DUI violations, license suspensions, and previous speeding violations were found to be associated with shorter durations, at varying degrees depending on the driver's age and gender. The duration was also observed to be longer if the fatal crash involved alcohol or drug use among younger drivers but shorter among middle-aged male drivers. These findings reveal interesting dynamics that may be linked to recidivist tendencies among some drivers involved in fatal crashes. The factors identified from this study could help identify crash countermeasures and programs that will help to reform such driver behaviors.

Exploring spatial heterogeneity in factors associated with injury severity in speeding-related crashes: An integrated machine learning and spatial modeling approach.

Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is essential for implementing countermeasures aimed at preventing severe injury incidents and achieving Vision Zero goals. With the wealth of traffic crash data collected by various agencies, researchers have a valuable opportunity to conduct data-driven studies and employ various modeling methods to gain insights into the correlated factors affecting injury severity in traffic crashes. Machine learning models, owing to their superior predictive power compared to statistical models, are increasingly being adopted by researchers. These models, in conjunction with interpretation techniques, can reveal potential relationships between crash injury severity and contributing factors. Traffic crashes are inherently tied to geographic locations, distributed across road networks influenced by diverse socioeconomic and geographical factors. Recognizing spatial heterogeneity in traffic safety is crucial for tailored safety measures to address speeding-related crashes, as a one-size-fits-all approach may not work effectively everywhere. However, most existing machine learning models are unable to incorporate the spatial dependency among observations, such as traffic crashes, which hinders their ability to uncover spatial heterogeneity in traffic safety. To address this gap, this study introduces the Geographically Weighted Neural Network (GWNN) model, a spatial machine-learning model that integrates neural network (NN) and geographically weighted modeling approaches to investigate spatial heterogeneity in speeding-related crashes. Unlike the traditional NN model, which trains a single set of model parameters for all observations, the GWNN trains a local NN model for each crash location using a spatially weighted subsample of nearby crashes, allowing for the quantification of corresponding local effects of features through calculating local marginal effects. To understand the spatial heterogeneity in speeding-related crashes, this study extracted two years (2020 and 2021) of speeding-related crash data from Alabama for the development of the GWNN local models. The modeling results show significant spatial variability among several factors contributing to injury severity in speeding-related crashes. These factors include driver condition, vehicle type, crash type, speed limit, weather, crash time and location, roadway alignment, and traffic volume. Based on the GWNN modeling results, this study identified three types of spatial variations in relationships between contributing factors and crash injury severity: consistent positive associations, consistent negative associations, and inverse associations (i.e., marginal effects can vary between positive and negative depending on the location). This study contributes by integrating advanced machine learning and spatial modeling approaches to uncover intricate spatial patterns and factors influencing injury severity in speeding-related crashes, thereby facilitating the development of targeted policy implementations and safety interventions.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means.

INTRODUCTION: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. METHOD: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. RESULTS: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. CONCLUSIONS: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. PRACTICAL APPLICATIONS: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Speeding behavior and associated factors in Buenos Aires, Argentina.

OBJECTIVE: Excessive speed is a major risk factor for serious injuries and death. However, speeding remains a pervasive problem around the world. This study aimed to investigate the factors associated with speeding behavior in the city of Buenos Aires, Argentina. METHODS: A sample of vehicles (n = 34,967) from ten locations in the city was observed in two waves during 2021. Measurements were made at different times and days of the week. Observation sites were free of intersections, traffic lights, speed bumps and cameras, allowing drivers to speed freely. Data on speed, drivers and vehicle types were collected. Factors associated with speeding were identified through logistic regression analyses. RESULTS: 15.3% of vehicles were observed to be speeding. Roads with posted speed limits of 40 km/h showed higher speeding compared to 60 km/h roads. 77% of vehicles traveled above 30 km/h on local roads, and 30% above 50 km/h on avenues. Motorcycles, both commercial and private, showed a higher percentage of speeding compared to all other vehicles. Speeding was lower among women, among adults over 60 years of age, and among those using cell phones. CONCLUSION: It is crucial to strengthen strategies for increased compliance with speed limits. Actions targeting motorcyclists must be a priority.

The dynamic-static dual-branch deep neural network for urban speeding hotspot identification using street view image data.

The visual information regarding the road environment can influence drivers' perception and judgment, often resulting in frequent speeding incidents. Identifying speeding hotspots in cities can prevent potential speeding incidents, thereby improving traffic safety levels. We propose the Dual-Branch Contextual Dynamic-Static Feature Fusion Network based on static panoramic images and dynamically changing sequence data, aiming to capture global features in the macro scene of the area and dynamically changing information in the micro view for a more accurate urban speeding hotspot area identification. For the static branch, we propose the Multi-scale Contextual Feature Aggregation Network for learning global spatial contextual association information. In the dynamic branch, we construct the Multi-view Dynamic Feature Fusion Network to capture the dynamically changing features of a scene from a continuous sequence of street view images. Additionally, we designed the Dynamic-Static Feature Correlation Fusion Structure to correlate and fuse dynamic and static features. The experimental results show that the model has good performance, and the overall recognition accuracy reaches 99.4%. The ablation experiments show that the recognition effect after the fusion of dynamic and static features is better than that of static and dynamic branches. The proposed model also shows better performance than other deep learning models. In addition, we combine image processing methods and different Class Activation Mapping (CAM) methods to extract speeding frequency visual features from the model perception results. The results show that more accurate speeding frequency features can be obtained by using LayerCAM and GradCAM-Plus for static global scenes and dynamic local sequences, respectively. In the static global scene, the speeding frequency features are mainly concentrated on the buildings and green layout on both sides of the road, while in the dynamic scene, the speeding frequency features shift with the scene changes and are mainly concentrated on the dynamically changing transition areas of greenery, roads, and surrounding buildings. The code and model used for identifying hotspots of urban traffic accidents in this study are available for access: https://github.com/gwt-ZJU/DCDSFF-Net.

Speeding behavior among teenage drivers during the learner and early independent driving stage: A case study approach.

INTRODUCTION: Speed is a primary contributing factor in teenage driver crashes. Yet, there are significant methodological challenges in measuring real-world speeding behavior. METHOD: This case study approach analyzed naturalistic driving data for six teenage drivers in a longitudinal study that spanned the learner and early independent driving stages of licensure in Maryland, United States. Trip duration, travel speed and length were recorded using global position system (GPS) data. These were merged with maps of the Maryland road system, which included posted speed limit (PSL) to determine speeding events in each recorded trip. Speeding was defined as driving at the speed of 10 mph higher than the posted speed limit and lasting longer than 6 s. Using these data, two different speeding measures were developed: (1) Trips with Speeding Episodes, and (2) Verified Speeding Time. Conclusions & Practical Applications: Across both measures, speeding behavior during independent licensure was greater than during the learner period. These measures improved on previous methodologies by using PSL information and eliminating the need for mapping software. This approach can be scaled for use in larger samples and has the potential to advance understanding about the trajectory of speeding behaviors among novice teenage drivers.

Post-loss speeding or post-win slowing? An empirical note on the interpretation of decision-making time as a function of previous outcome.

Differences in response time following previous losses relative to previous wins are robust observations in behavioural science, often attributed to an increased (or decreased) degree of cognitive control exerted after negative feedback, hence, post-loss slowing (or post-loss speeding). This presumes that the locus of this effect resides in the specific modulation of decision time following negative outcomes. Across two experiments, I demonstrate how the use of absolute rather than relative processing speeds, and the sensitivity of processing speeds in response to specific experimental manipulations (Experiment 1: win rate, Experiment 2: feedback), provide clarity as to the relative weighting of post-win and post-loss states in determining these behavioural effects. Both experiments show that the speeding or slowing of decision-time is largely due to the flexibility generated by post-win cognitive states. Given that post-loss speeding may actually represent post-win slowing, conclusions regarding the modulation of decision-making time as a function of previous outcomes need to be more carefully considered.

Drivers' performance assessment approaching pedestrian crossings through the analysis of the speed and perceptive data recorded during on-field tests.

Pedestrian fatalities in road accidents represent one of the biggest causes of death in the world despite the great efforts that have been made to decrease the involvement of vulnerable road users in road accidents. Literature analysis revealed the presence of several studies aimed at investigating the phenomenon and proposing strategies to improve pedestrian safety, but this is still not enough to considerably reduce the number of pedestrians killed on the road. In this context, with the aim to take a step forward in the topic, this paper describes a naturalistic driving assessment carried out in Firenze aimed at evaluating the effect of different pedestrian crossing configurations on the drivers' behavior, especially concerning the reduction of the speeding phenomenon approaching a pedestrian crossing. The experiment was conducted on a section of an urban collector road within the Firenze suburban area. Crucially, over the past few years, different traffic calming interventions have been implemented along this street. Among the different traffic calming countermeasures, both the presence of a traffic light and trapezoidal deflection have been considered to assess their effect on drivers' behavior, also with reference to specific aspects related to the drivers' perception. During the experiment, thirty-six users drove their own vehicles along the street, encountering different pedestrian crossing configurations. During the driving speed, deceleration and ocular fixation were recorded. This study shows the difference in drivers' behavior in response to different traffic calming countermeasures. It demonstrates also that the raised pedestrian crossing caused a significant effect on reducing the speed approaching a pedestrian crossing. Moreover, it is observed that, when perceptive countermeasures are present, the drivers' behavior changes only if the pedestrian crossing configuration is perceived in foveal vision; suggesting that the correct identification of the configuration is crucial to implement a congruent and safe driving behavior.

Exploring the influence of drivers' visual surroundings on speeding behavior.

Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways. By segmenting and analyzing these images along with vehicle-related variables, the study examines factors affecting speeding behavior. After filtering the images, to ensure an accurate representation of the driver's view, 1,340,035 driver view images were used for analysis. Statistical models, including hurdle beta and bivariate probit models with random driver effects as well as Machine Learning's eXtreme Gradient Boosting (XGBoost), were employed to estimate speeding behavior. The results indicate that factors within the driver's visual environment, weather conditions, and driver heterogeneity significantly impact speeding. Speeding behavior also varies across geographic locations, even within the same city, suggesting a connection between local context and speeding. The study highlights the importance of the driver's environment, showing that more open spaces encourage speeding, while areas with trees and buildings are associated with reduced speeding. Notably, this research differs from previous studies by utilizing real-time data from dash cameras, providing a dynamic and accurate representation of the driver's visual surroundings. This approach enhances the reliability of the findings and empowers transportation engineers and planners to make informed decisions when designing roadways and implementing interventions to address effectively excessive speeding. In addition to examining speeding behavior, the study also analyzes time-headway, a key factor affecting safety and risky driver behavior, to explore its relationship with speeding. The findings offer valuable insights into the factors influencing speeding and the driver's visual environment. These insights can inform efforts to create environments that discourage speeding (and close car following) and ultimately reduce severe accidents caused by excessive speed (and tailgating).

Examining factors affecting driver injury severity in speeding-related crashes: a comparative study across driver age groups.

This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.