Network-wide road crash risk screening: A new framework

Network-wide road crash risk screening is a crucial issue for road safety authorities in governing the impact of road infrastructures over road safety worldwide. Specifically, screening methods, which also enable a proactive approach (i.e., pinpointing critical segments before crashes occur), would be extremely beneficial. Existing literature provided valuable insights on road network screening and crash prediction models. However, no research tried to quantify the risk of crash on the road network by considering its main components together (i.e., probability, vulnerability, and exposure). This study covers this gap by a new framework. It integrates road safety factors, prediction models and a risk-based method, and returns the risk value on each road segment as a function of the probability of a crash occurrence and the related severity as well as the exposure model. Next, road segments are ranked according to the risk value and classified by a five-level scale, to show the parts of road network with the highest crash risk. Experiments show the capability of this framework by integrating base map data, context information, road traffic data and five years of real-world crash data records of the whole non-urban road network of the Province of Brescia (Lombardy Region - Italy). This framework introduces a valid support for road safety authorities to help identify the most critical road segments on the network, prioritise interventions and, possibly, improve the safety performance. Finally, this framework can be incorporated in any safety managerial system.

Parkinson's Disease and Driving Fitness: A Systematic Review of the Existing Guidelines

BACKGROUND

Motor/nonmotor symptomatology and antiparkinsonian drugs deteriorate the driving ability of Parkinson's disease (PD) patients.

OBJECTIVES

Treating neurologists are frequently asked to evaluate driving fitness of their patients and provide evidence-based consultation. Although several guidelines have been published, the exact procedure along with the neurologist's role in this procedure remains obscure.

METHODS

We systematically reviewed the existing guidelines, regarding driving fitness evaluation of PD patients. We searched MEDLINE and Google Scholar and identified 109 articles. After specified inclusion criteria were applied, 15 articles were included (nine national guidelines, five recommendation papers, and one consensus statement).

RESULTS

The treating physician is proposed as the initial evaluator in 8 of 15 articles (neurologist in 2 articles) and may refer patients for a second-line evaluation. The evaluation should include motor, cognitive, and visual assessment (proposed in 15, 13, and 8 articles, respectively). Specific motor tests are proposed in eight articles (cutoff values in four), whereas specific neuropsychological and visual tests are proposed in seven articles each (cutoff values in four and three articles, respectively). Conditional licenses are proposed in 11 of 15 articles, to facilitate driving for PD patients. We summarized our findings on a graphic of the procedure for driving fitness evaluation of PD patients.

CONCLUSIONS

Neurological aspects of driving fitness evaluation of PD patients are recognized in most of the guidelines. Motor, neuropsychological, visual, and sleep assessment and medication review are key components. Clear-cut instructions regarding motor, neuropsychological, and visual tests and relative cutoff values are lacking. Conditional licenses and periodical reevaluation of driving fitness are important safety measures.

Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

Real-time monitoring of driver distraction: State-of-the-art and future insights

Driver distraction and inattention have been found to be major contributors to a large number of serious road crashes. It is evident that distraction reduces to a great extent driver perception levels as well as their decision making capability and the ability of drivers to control the vehicle. An effective way to mitigate the effects of distraction on crash probability, would be through monitoring the mental state of drivers or their driving behaviour and alerting them when they are in a distracted state. Towards that end, in recent years, several inexpensive and effective detection systems have been developed in order to cope with driver inattention. This study endeavours to critically review and assess the state-of-the-art systems and platforms measuring driver distraction or inattention. A thorough literature review was carried out in order to compare and contrast technologies that can be used to detect, monitor or measure driver's distraction or inattention. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The results indicated that in most of the identified studies, driver distraction was measured with respect to its impact to driver behaviour. Real-time eye tracking systems, cardiac sensors on steering wheels, smartphone applications and cameras were found to be the most frequent devices to monitor and detect driver distraction. On the other hand, less frequent and effective approaches included electrodes, hand magnetic rings and glasses.

From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safety

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

Driving Behaviour in Depression Based on Subjective Evaluation and Data from a Driving Simulator

Road traffic collisions are a major issue for public health. Depression is characterized by mental, emotional and executive dysfunction, which may have an impact on driving behaviour. Patients with depression (N = 39) and healthy controls (N = 30) were asked to complete questionnaires and to drive on a driving simulator in different scenarios. Driving simulator data included speed, safety distance from the preceding vehicle and lateral position. Demographic and medical information, insomnia (Athens Insomnia Scale, AIS), sleepiness (Epworth Sleepiness Scale, ESS), fatigue (Fatigue Severity Scale, FSS), symptoms of sleep apnoea (StopBang Questionnaire) and driving (Driver Stress Inventory, DSI and Driver Behaviour Questionnaire, DBQ) were assessed. Gender and age influenced almost all variables. The group of patients with depression did not differ from controls regarding driving behaviour as assessed through questionnaires; on the driving simulator, patients kept a longer safety distance. Subjective fatigue was positively associated with aggression, dislike of driving, hazard monitoring and violations as assessed by questionnaires. ESS and AIS scores were positively associated with keeping a longer safety distance and with Lateral Position Standard Deviation (LPSD), denoting lower ability to keep a stable position. It seems that, although certain symptoms of depression (insomnia, fatigue and somnolence) may affect driving performance, patients drive more carefully eliminating, thus, their impact.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

One year of COVID-19: Impacts on safe driving behavior and policy recommendations

INTRODUCTION

In the unprecedented year of 2020, the rapid spread of COVID-19 disrupted everyday activities worldwide, leading the majority of countries to impose lockdowns and confine citizens in order to minimize the exponential increase in cases and casualties. To date, very few studies have been concerned with the effect of the pandemic on driving behavior and road safety, and usually explore data from a limited time span.

METHOD

This study presents a descriptive overview of several driving behavior indicators as well as road crash data in correlation with the strictness of response measures in Greece and the Kingdom of Saudi Arabia (KSA). A k-means clustering approach was also employed to detect meaningful patterns.

RESULTS

Results indicated that during the lockdown periods, speeds were increased by up to 6%, while harsh events were increased by about 35% in the two countries, compared to the period after the confinement. However, the imposition of another lockdown did not cause radical changes in Greek driving behavior during the late months of 2020. Finally, the clustering algorithm identified a "baseline," a "restrictions," and a "lockdown" driving behavior cluster, and it was shown that harsh braking frequency was the most distinctive factor.

POLICY RECOMMENDATIONS

Based on these findings, policymakers should focus on the reduction and enforcement of speed limits, especially within urban areas, as well as the incorporation of active travelers in the current transport infrastructure.

Analysis of the impact of COVID-19 on collisions, fatalities and injuries using time series forecasting: The case of Greece

The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (from January 2010 to August 2020. The chosen time period contained normal operations, as well as the period of the first COVID-19-induced lockdown period in Greece. Three different Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models were implemented in order to compare the observed measurements to forecasted values that were intended to depict assumed conditions; namely, without the appearance of the COVID-19 pandemic. Modelling results revealed that the total number of road collisions, fatalities, and slightly injured were decreased, mainly due to the sharp traffic volume decrease. However, the percentage reduction of the collision variables and traffic volume were found to be disproportionate, which probably indicates that more collisions occurred with regard to the prevailing traffic volume. An additional finding is that fatalities and slightly injured rates were significantly increased during the lockdown period and the subsequent month. Overall, it can be concluded that a worse performance was identified in terms of road safety. Since subsequent waves of COVID-19 cases and other pandemics may reappear in the future, the outcomes of the current study may be exploited for the improvement of road safety from local authorities and policymakers.

Effect of Apolipoprotein E4 on the Driving Behavior of Patients with Amnestic Mild Cognitive Impairment or Mild Alzheimer's Disease Dementia

BACKGROUND

The driving behavior of patients with mild Alzheimer's disease dementia (ADD) and patients with mild cognitive impairment (MCI) is frequently characterized by errors. A genetic factor affecting cognition is apolipoprotein E4 (APOE4), with carriers of APOE4 showing greater episodic memory impairment than non-carriers. However, differences in the driving performance of the two groups have not been investigated.

OBJECTIVE

To compare driving performance in APOE4 carriers and matched non-carriers.

METHODS

Fourteen APOE4 carriers and 14 non-carriers with amnestic MCI or mild ADD underwent detailed medical and neuropsychological assessment and participated in a driving simulation experiment, involving driving in moderate and high traffic volume in a rural environment. Driving measures were speed, lateral position, headway distance and their SDs, and reaction time. APOE was genotyped through plasma samples.

RESULTS

Mixed two-way ANOVAs examining traffic volume and APOE4 status showed a significant effect of traffic volume on all driving variables, but a significant effect of APOE4 on speed variability only. APOE4 carriers were less variable in their speed than non-carriers; this remained significant after a Bonferroni correction. To further examine variability in the driving performance, coefficients of variation (COV) were computed. Larger headway distance COV and smaller lateral position COV were observed in high compared to moderate traffic. APOE4 carriers had smaller speed COV compared to non-carriers.

CONCLUSION

The lower speed variability of APOE4 carriers in the absence of neuropsychological test differences indicates reduced speed adaptations, possibly as a compensatory strategy. Simulated driving may be a sensitive method for detecting performance differences in the absence of cognitive differences.

Driving and Alzheimer's dementia or mild cognitive impairment: a systematic review of the existing guidelines emphasizing on the neurologist's role

BACKGROUND

Driving is a complex task requiring the integrity and the cooperation of cognition, motor, and somatosensory skills, all of which are impacted by neurological diseases.

OBJECTIVE

Identification of neurologist's role when assessing fitness to drive of cognitively impaired individuals.

METHODS

We performed a systematic review of the guidelines/recommendations (G/Rs) regarding the evaluation of driving fitness of patients with mild cognitive impairment (MCI) and/or dementia. Emphasis was put on the neurological and neuropsychological aspects of the evaluation.

RESULTS

Eighteen G/Rs were included in the review (9 national guidelines, 5 recommendation papers, 3 consensus statements, and 1 position paper). All G/Rs referred to drivers with dementia and 9/18 referred to drivers with MCI. A common approach among G/Rs is the initial trichotomization of patients in safe to drive, unsafe to drive, and undetermined cases, which are referred to a second-line evaluator. First-line evaluators are general practitioners in 10/18 G/Rs; second-line evaluators are neurologists in 7/18 G/Rs. Specific neuropsychological tests are proposed in 11/18 G/Rs and relative cut-off values in 7/18. The most commonly used tests are the MMSE, TMT, and CDT. A thorough neurological examination is proposed in only 1/18 G/R.

CONCLUSION

Although extensive multi-disciplinary research has provided useful information for driving behavior of cognitively impaired individuals, we are still far from a widely accepted approach of driving ability evaluation in this increasing population. A comprehensive assessment from a multi-disciplinary team in which the neurologist plays a critical role seems to be required, although this has not yet been implemented in any G/Rs.

Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

INTRODUCTION

COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lockdown is yet to be provided. This gap forms the motivation for this paper, which aims at comparing observed values concerning three indicators (average speed, speeding, and harsh braking), with forecasts based on their corresponding observations before the lockdown in Greece.

METHOD

Time series of the three indicators were extracted using a specially developed smartphone application and transmitted to a back-end platform between 01/01/2020 and 09/05/2020, a time period containing normal operations, COVID-19 spreading, and the full lockdown period in Greece. Based on the collected data, XGBoost was employed to identify the most influential COVID-19 indicators, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models were developed for obtaining forecasts on driving behavior.

RESULTS

Results revealed the intensity of the impact of COVID-19 on driving, especially on average speed, speeding, and harsh braking per 100 km. More specifically, speeds were found to increase by 2.27 km/h on average compared to the forecasted evolution, while harsh braking/100 km increased to almost 1.51 on average. On the bright side, road crashes in Greece were reduced by 49% during the months of COVID-19 compared to the non-COVID-19 period.

Impact of socioeconomic and transport indicators on road safety during the crisis period in Europe

The risk of being involved in a road crash is typically influenced by mobility, which in turn is influenced by various socioeconomic indicators. This study aims to investigate the impact of socioeconomic and transport indicators on road safety during the economic crisis period in Europe. A database containing Human Development Index (HDI), suicides, passenger-kilometers and road fatalities per population was developed. Linear Mixed Models were applied for all the examined countries and the different groups that were selected for the period 2006-2015. The results led to the conclusion that HDI has the most important impact and its increase leads to road fatalities decrease. Moreover, the evolution of human development affects the outcomes of road crashes more than suicides and passenger-kilometers travelled. After the end of the crisis, the impact of human development is even higher. Concerning passenger-kilometers travelled, there is an increase in the relative impact on road fatalities after the end of the crisis.

Investigation of the speeding behavior of motorcyclists through an innovative smartphone application

OBJECTIVE

The objective of the present study is twofold: (i) to explore the riding behavior of motorcyclists while speeding, based on detailed riding analytics collected by smartphone sensors, and (ii) to investigate whether personalized feedback can improve motorcyclist behavior.

METHODS

In order to achieve the objective, a naturalistic riding experiment with a sample of 13 motorcyclists based on a smartphone application developed within the framework of the BeSmart project was conducted. Using risk exposure and riding behavior indicators calculated from smartphone sensor data, Generalized Linear Mixed-Effects Models are calibrated to correlate the percentage of riding time over the speed limit with other riding behavior indicators. An overall model was developed for all trips, as well as separate models for the parts of trips realized on different road types (urban and rural).

RESULTS

Results indicate that the parameters of trip duration, distance driven during risky hours, morning peak hours and the number of harsh accelerations are all determined as statistically significant and positively correlated with the percentage of speeding time. Additionally, the provision of rider feedback and riding during afternoon peak hours are statistically significant and correlated with decreased percentages of speeding time.

CONCLUSIONS

The outcomes of this study entail both scientific and social impacts. The present research contributes a preliminary example of the quantitative documentation of the impact of personalized rider feedback on one of the most important human risk factors; speeding. The ultimate objective when providing feedback to riders is to: (i) trigger their learning and self-assessment process, thus enabling them to gradually improve their performance and (ii) monitor the shift of riding behavior as the application provides feedback. The present results capture and quantify the positive effects of rider feedback, thus providing needed impetus for larger-scale applications as well as relevant policy interventions.

Examining the relationship between impaired driving and past crash involvement in Europe: Insights from the ESRA study

Driving under the influence of alcohol, drugs and fatigue are all important factors of crash causation. Exploring the link between driver attitudes and crash involvement provides understanding on these important issues. To that end, questionnaire answers of car drivers disclosing their attitudes on the impacts of driving under the influence of alcohol, drugs and fatigue, and their relationship with past crash involvement as car drivers were analysed. A two-step approach is adopted: Principal Component Analysis (PCA) was employed to consolidate relative questions in numeric factor quantities. Afterwards, binary logistic regression was implemented on the calculated component scores to determine the impact of perspectives of road users for each factor on past crash involvement of car drivers. Data from the international ESRA2015 survey were utilized. PCA indicated that it is possible to meaningfully merge 29 ESRA2015 questions relevant to driving under the influence of alcohol, drugs and fatigue into 8 informative components accounting for an adequate percentage of variance. Binary logistic analysis indicated that components involving overall personal and communal acceptance of impaired driving, overall and past year personal behaviour towards impaired driving and frequency of typical journey checks by traffic police were all quantities positively correlated with past crash involvement.

Post-trip safety interventions: State-of-the-art, challenges, and practical implications

INTRODUCTION

Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip.

METHOD

The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation.

RESULTS

The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform.

CONCLUSIONS

Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.

Temporal analysis of driving efficiency using smartphone data

This paper attempts to shed light on the temporal evolution of driving safety efficiency with the aim to acquire insights useful for both driving behavior and road safety improvement. Data exploited herein are collected from a sophisticated platform that uses smartphone device sensors during a naturalistic driving experiment, at which the driving behavior from a sample of two hundred (200) drivers during 7-months is continuously recorded in real time. The main driving behavior analytics taken into consideration for the driving assessment include distance travelled, acceleration, braking, speed and smartphone usage. The analysis is performed using statistical, optimization and machine learning techniques. The driver's safety efficiency index is estimated both in total and in several consecutive time windows to allow for the investigation of safety efficiency evolution in time. Initial data analysis results to the most critical components of microscopic driving behaviour evolution, which are used as inputs in the k-means algorithm to perform the clustering analysis. The main driving characteristics of each cluster are identified and lead to the conclusion that there are three main driving groups of the a) moderate drivers, b) unstable drivers and c) cautious drivers.

Investigation of accidents involving powered two wheelers and bicycles - A European in-depth study

INTRODUCTION

The number of road fatalities have been falling throughout the European Union (EU) over the past 20 years and most Member States have achieved an overall reduction. Research has mainly focused on protecting car occupants, with car occupant fatalities reducing significantly. However, recently there has been a plateauing in fatalities amongst 'Vulnerable Road Users' (VRUs), and in 2016 accidents involving VRUs accounted for nearly half of all EU road deaths.

METHOD

The SaferWheels study collected in-depth data on 500 accidents involving Powered Two-Wheelers (PTWs) and bicycles across six European countries. A standard in-depth accident investigation methodology was used by each team. The Driver Reliability and Error Analysis Method (DREAM) was used to systematically classify accident causation factors.

RESULTS

The most common causal factors related to errors in observation by the PTW/bicycle rider or the driver of the other vehicle, typically called 'looked but failed to see' accidents. Common scenarios involved the other vehicle turning or crossing in front of the PTW/bicycle. A quarter of serious or fatal injuries to PTW riders occurred in accidents where the rider lost control with no other vehicle involvement.

CONCLUSIONS

Highly detailed data have been collected for 500 accidents involving PTWs or bicycles in the EU. These data can be further analyzed by researchers on a case-study basis to gain detailed insights on such accidents. Preliminary analysis suggests that 'looked but failed to see' remains a common cause, and in many cases the actions of the other vehicle were the critical factor, though PTW rider speed or inexperience played a role in some cases. Practical Applications: The collected data can be analyzed to better understand the characteristics and causes of accidents involving PTWs and bicycles in the EU. The results can be used to develop policies aimed at reducing road deaths and injuries to VRUs.

Off-road assessment of cognitive fitness to drive

Road safety is a major issue in every society. The assessment of driving ability with a real vehicle is a lengthy and costly process; therefore, there is a growing need for the development of a neuropsychological battery that can provide a fast and reliable evaluation of a person's cognitive fitness to drive. In the present study, we examined the relationship of an off-road lab-type test, namely, the Driving Scenes test, with performance on a driving simulator, as well as the influence of cognitive factors on driving ability as evaluated by Driving Scenes. Our results demonstrated a relationship between Driving Scenes and driving simulator performance. They also showed that some cognitive factors (namely, selective attention and verbal memory), were predictive of driving ability (as determined by the Driving Scenes test), but not others (namely visuospatial perception/memory, working memory, and visuospatial recognition). In addition, age strongly predicted performance on this test (younger age was associated with better performance). The conclusions derived from the present study highlight the need to identify off-road tools with high predictive value in assessing driving ability.

A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety

The spread of the new coronavirus COVID-19, has led to unparalleled global measures such as lockdown and suspension of all retail, recreation and religious activities during the first months of 2020. Nevertheless, no scientific evidence has been reported so far with regards to the impact on road safety and driving behavior. This paper investigates the effect of COVID-19 on driving behavior and safety indicators captured through a specially developed smartphone application and transmitted to a back-end platform. These indicators are reflected with the spread of COVID-19 and the respective governmental countermeasures in two countries, namely Greece and Kingdom of Saudi Arabia (KSA), which had the most completed routes for users of the smartphone applications. It was shown that reduced traffic volumes due to lockdown, led to a slight increase in speeds by 6-11%, but more importantly to more frequent harsh acceleration and harsh braking events (up to 12% increase) as well mobile phone use (up to 42% increase) during March and April 2020, which were the months where COVID-19 spread was at its peak. On the bright side, accidents in Greece were reduced by 41% during the first month of COVID-19-induced measures and driving in the early morning hours (00:00-05:00) which are considered dangerous dropped by up to 81%. Policymakers should concentrate on establishing new speed limits and ensure larger spaces for cycling and pedestrians in order to enlarge distances between users in order to safeguard both an enhanced level of road safety and the prevention of COVID-19 spread.

Combined impact of road and traffic characteristic on driver behavior using smartphone sensor data

The objective of this research is to exploit high resolution driving behavior data collected via sensors of smartphones from 303 drivers in order to examine driver behavior at road segment and junction level. These sensor data are combined with traffic and road geometry characteristics and subsequently depicted spatially using Geographical Information System software. Events of harsh driver behavior (8592 harsh accelerations and 3946 harsh brakings) were mapped to delimited segments and junctions of two urban expressways in Athens, Greece. For the analysis, two multiple linear regression models and two log-linear regression models were developed. Results indicate that in road segments there is an increase in the number of harsh events if average traffic flow per lane increases in the respective areas. Furthermore, as the average occupancy increases in junctions, there is an increase in harsh accelerations, and as the average speed increases, more harsh deceleration events occur. It is evident that traffic characteristics (traffic flow & speed) have the most statistically significant impact on the frequency of harsh events compared to factors related to road geometry and driver behavior.

Predictors of accidents in people with mild cognitive impairment, mild dementia due to Alzheimer's disease and healthy controls in simulated driving

OBJECTIVES

To examine the driving variables that predict accident probability in mild dementia due to Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy older control drivers in simulated driving. To compare the three groups in mean performance and in frequency of scores exceeding 1.5 SD from the mean.

METHODS/DESIGN

Participants were 37 drivers with MCI, 16 drivers with AD, and 21 control drivers over the age of 52. Driving measures were derived from four rural driving conditions: moderate traffic without and with distraction and high traffic without and with distraction. The measures were z-transformed based on the performance of 90 control drivers of different ages. Two unexpected incidents occurred per condition, requiring the sudden breaking to avoid an accident.

RESULTS

Drivers with AD showed significantly lower average speed, speed variability, greater headway distance, headway variability and average reaction time (RT) than control drivers. Drivers with MCI showed significantly lower average speed, greater headway distance and average RT than control drivers in the two conditions of distraction. No differences were found in accident probability. Drivers with AD had more deviant scores than both control drivers and drivers with MCI in most comparisons. Predictors of accident probability were average RT, speed variability and lateral position variability but MCI and AD status were not significant predictors in any of the regression models.

CONCLUSIONS

Despite significant differences in performance, drivers with MCI and AD did not differ in accident probability from control drivers. An individualized approach of examining individual driving performance is recommended.

Investigation of traffic and safety behavior of pedestrians while texting or web-surfing

Objective: More and more pedestrians use mobile phones in their daily traffic activities by the roadside or even when crossing the street. The objective of this research is to examine pedestrians' traffic and safety behavior while texting or web-surfing, when crossing signalized intersections.Methods: In order to compare the behavior of distracted and non-distracted pedestrians, an experimental process through video recording was carried out in real road conditions, in three signalized intersections in the center of Athens in Greece. Demographic and behavioral characteristics were observed, including use of mobile device. For the statistical analysis, two multiple linear regression models were developed to investigate the association of pedestrians' speed and distraction caused by mobile phone use. Additionally, binary logistic regression models were developed in order to determine the influence of distraction on pedestrians' safety characteristics and more specifically on near misses with oncoming vehicles.Results: Observers recorded crossing behaviors for 2,280 pedestrians and noticed that nearly one-fifth (16.6%) of them performed a phone-distracting activity while crossing. Distractions included texting or web-surfing (6.3%), listening to music (5.4%) and using a handheld phone (4.9%). Τhis research indicated that distraction caused by texting or web-surfing had a negative impact on pedestrians' main traffic and safety characteristics. Results pointed out that in high pedestrian traffic, distracted pedestrians who were texting or web-surfing on their mobile phone present lower speed than non-distracted pedestrians, regardless of their age, as they may be not aware of traffic conditions due to distraction and therefore, they have higher crossing times. Furthermore, their probability of a near miss increases with increasing pedestrian volume as the more pedestrians who occupy the pedestrian crossing the more difficult is for them to observe carefully the rest traffic.Conclusions: Mobile phones are integral to contemporary daily life and their use and penetration is increasing rapidly as well. For this reason, it is crucial to investigate the impacts of distracted walking on pedestrians' traffic and safety behavior. Various measures and strategies should be implemented and further research should be conducted as texting and web-surfing distraction is associated with a rather high risk.

A review of spatial approaches in road safety

Spatial analyses of crashes have been adopted in road safety for decades in order to determine how crashes are affected by neighboring locations, how the influence of parameters varies spatially and which locations warrant interventions more urgently. The aim of the present research is to critically review the existing literature on different spatial approaches through which researchers handle the dimension of space in its various aspects in their studies and analyses. Specifically, the use of different areal unit levels in spatial road safety studies is investigated, different modelling approaches are discussed, and the corresponding study design characteristics are summarized in respective tables including traffic, road environment and area parameters and spatial aggregation approaches. Developments in famous issues in spatial analysis such as the boundary problem, the modifiable areal unit problem and spatial proximity structures are also discussed. Studies focusing on spatially analyzing vulnerable road users are reviewed as well. Regarding spatial models, the application, advantages and disadvantages of various functional/econometric approaches, Bayesian models and machine learning methods are discussed. Based on the reviewed studies, present challenges and future research directions are determined.

A critical overview of driver recording tools

INTRODUCTION

Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal.

METHOD

In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed.

RESULTS

A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters.

CONCLUSIONS

New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events

Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway ("Attiki Odos") located in Greater Athens Area in Greece for the 2008-2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways.

Driving difficulties as reported by older drivers with mild cognitive impairment and without neurological impairment

Objective: Considerable evidence indicates that medical conditions prevalent among older individuals lead to impairments in visual, cognitive, or psychomotor functions needed to drive safely. The purpose of this study was to explore the factors determining driving difficulties as seen from the viewpoint of 30 older drivers with mild cognitive impairment (MCI) and 30 age-matched controls without cognitive impairment. Methods: Perceptions of driving difficulties from both groups were examined using data from an extensive questionnaire. Samples of drivers diagnosed with MCI and age-matched controls were asked to report the frequency with which they experienced driving difficulties due to functional deficits and knowledge of new traffic rules and traffic signs. Results: The analysis revealed that 2 factors underlie MCI perceptions of driving difficulties, representing (1) difficulties associated with late detection combined with slowed response to relevant targets in the peripheral field of view and (2) difficulties associated with divided attention between tasks requiring switching from automatic to conscious processing particularly of long duration. The analysis for healthy controls revealed 3 factors representing (1) difficulties in estimating speed and distance of approaching vehicles in complex (attention-dividing) high-information-load conditions; (2) difficulties in moving head, neck, and feet; and (3) difficulties in switching from automatic responses to needing to use cognitive processing in new or unexpected situations. Conclusions: Though both group analyses show difficulties with switching from automatic to decision making, the difficulties are different. For the control group, the difficulty in switching involves switching in new or unexpected situations associated with high-information-load conditions, whereas this switching difficulty for the MCI group is associated with divided attention between easier tasks requiring switching. These findings underline the ability of older drivers (with MCI and without cognitive impairment) to indicate probable impairments in various driving skills. The patterns of difficulties perceived by the MCI group and the age-matched healthy control group are indicative of demanding driving situations that may merit special attention for road designers and road safety engineers. They may also be considered in the design of older drivers' fitness to drive evaluations, training programs, and/or vehicle technologies that provide for older driver assistance.

The European road safety decision support system on risks and measures

The European Road Safety Decision Support System (roadsafety-dss.eu) is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs.

Review and ranking of crash risk factors related to the road infrastructure

The objective of this paper is the review and comparative assessment of infrastructure related crash risk factors, with the explicit purpose of ranking them based on how detrimental they are towards road safety (i.e. crash risk, frequency and severity). This analysis was carried out within the SafetyCube project, which aimed to identify and quantify the effects of risk factors and measures related to behaviour, infrastructure or vehicles, and integrate the results in an innovative road safety Decision Support System (DSS). The evaluation was conducted by examining studies from the existing literature. These were selected and analysed using a specifically designed common methodology. Infrastructure risk factors were structured in a hierarchical taxonomy of 10 areas with several risk factors in each area (59 specific risk factors in total), examples include: alignment features (e.g. horizontal-vertical alignment deficiencies), cross-section characteristics (e.g. superelevation, lanes, median and shoulder deficiencies), road surface deficiencies, workzones, junction deficiencies (interchange and at-grade) etc. Consultation with infrastructure stakeholders (international organisations, road authorities, etc.) took place in dedicated workshops to identify user needs for the DSS, as well as "hot topics" of particular importance. The following analysis methodology was applied to each infrastructure risk factor: (i) A search for relevant international literature, (ii) Selection of studies on the basis of rigorous criteria, (iii) Analysis of studies in terms of design, methods and limitations, (iv) Synthesis of findings - and meta-analysis, when feasible. In total 243 recent and high quality studies were selected and analysed. Synthesis of results was made through 39 'Synopses' (including 4 original meta-analyses) on individual risk factors or groups of risk factors. This allowed the ranking of infrastructure risk factors into three groups: risky (11 risk factors), probably risky (18 risk factors), and unclear (7 risk factors).

Safety assessment of control design parameters through vehicle dynamics model

An existing vehicle dynamics model was utilized to define design parameters up to which steady state cornering conditions apply and consequently lift the restrictions of the point mass model. Aiming to assess critical safety concerns in terms of vehicle skidding, the motion of a passenger car was examined over a range of design speed values paired with control design elements from AASHTO 2011 Design Guidelines as well as certain values of poor pavement friction coefficients. Two distinct cases were investigated; the determination of the maximum attainable constant speed (termed as safe speed) at impending skid conditions as well as the case of comfortable curve negotiation where lower constant speed values were utilized. The overall objective was to define the safety margins for each examined case. From the interaction between road geometry, pavement friction and vehicle characteristics, many interesting findings are reported, where some of them are beyond the confined field of road geometry parameters; such as demanded longitudinal and lateral friction values and horse-power utilization rates. From the road geometry point of view, it was found that control alignments on steep upgrades consisting of low design speed values and combined with poor friction pavements are critical in terms of safety. Such cases should be treated very cautiously through certain actions. These actions include the adoption of acceptable arrangements for the above values regarding new alignments, posted speed management for existing but also scheduling friction improvement programmes more accurately for both cases.

How many crashes are caused by driver interaction with passengers? A meta-analysis approach

INTRODUCTION

Conversation and other interactions with passengers while driving induce a level of distraction to the person driving.

METHOD

This paper conducts a qualitative literature review on the effect of passenger interaction on road safety and then extends it by using meta-analysis techniques.

RESULTS

The literature review indicates that the distraction due to passengers is a very frequent risk factor, with detrimental effects to various driving behavior and safety measures (e.g., slower reaction times to events, increased severity of injuries in crashes), associated with non-negligible proportions of crashes. Particular issues concern the effect of passenger age (children, teenagers) on which the literature is inconclusive. Existing studies vary considerably in terms of study methods and outcome measures. Nevertheless, a meta-analysis could be carried out regarding the proportion of crashes caused by this distraction factor. The selection of studies for the meta-analysis was based on a rigorous method including specific study selection criteria. The findings of the random-effects meta-analyses that were carried out showed that driver interaction with passengers causes a non-negligible proportion of road crashes, namely 3.55% of crashes regardless of the age of the passengers and 3.85% when child and teen passengers are excluded. Both meta-estimates were statistically significant, revealing the need for further research, especially considering the role of passenger age.

PRACTICAL APPLICATIONS

Stakeholders could make good estimates on future crash numbers and causes and take action in order to counter the effects of passenger interaction.

Incidental and Intentional Memory: Their Relation with Attention and Executive Functions

Objective

The aim of the current study was to investigate the impact of gender and age on incidental and intentional memory in healthy participants and to explore the strength of the association of incidental and intentional memory with attentional and executive functioning.

Method

A total number of 47 participants underwent a driving simulation experiment and went through detailed neuropsychological testing. Incidental memory was assessed with a questionnaire that evaluated the memorization of information related to the driving simulator task while intentional memory was assessed using the Hopkins Verbal Learning Test-Revised and the Brief Visuospatial Memory Test-Revised.

Results

The analysis revealed a greater impact of age on incidental as compared to intentional memory. Gender did not appear to have such an effect on either incidental or intentional memory. Finally, attentional and executive functioning were more strongly associated with incidental memory than the intentional memory measures that were utilized in the current study.

Conclusions

Ageing appears to affect incidental rather than intentional memory to a greater extent. In addition, attentional and executive functioning seem to play a more important role in incidental than intentional encoding and consolidation processes.

A simplified transport model for the ex ante evaluation of road pricing on a project basis

The objective of this research is the development of a diversion transport model that adopts a specific technique for forecasting changes in demand when examining road pricing schemes for two generic types of road projects: a new road project and the upgrade of an existing roadway. It is assumed that the key drivers of demand are roadway capacity and generalised cost of travel. The model incorporates most of the transport behaviours and sensitivity desired in a transport model, yet it has a simple structure (sketch-based model) and manageable data requirements. Due to its simple structure, the model is developed for use mainly by public authorities, with limited resources or know-how in transport modelling. The model was applied in order to produce a (defensible) simulation of the ‘immediate’ impacts of road pricing policies (traffic volume, amount of travel, journey time, speed, etc.) on given road projects. Model results were used for the estimation of traffic-related impacts that are usually included in an ex ante evaluation of a road project (reduction of traffic congestion, improvement of safety level, improvement of air quality and reduction of noise annoyance).

Meta-analysis of the effect of road work zones on crash occurrence

There is strong evidence that work zones pose increased risk of crashes and injuries. The two most common risk factors associated with increased crash frequencies are work zone duration and length. However, relevant research on the topic is relatively limited. For that reason, this paper presents formal meta-analyses of studies that have estimated the relationship between the number of crashes and work zone duration and length, in order to provide overall estimates of those effects on crash frequencies. All studies presented in this paper are crash prediction models with similar specifications. According to the meta-analyses and after correcting for publication bias when it was considered appropriate, the summary estimates of regression coefficients were found to be 0.1703 for duration and 0.862 for length. These effects were significant for length but not for duration. However, the overall estimate of duration was significant before correcting for publication bias. Separate meta-analyses on the studies examining both duration and length was also carried out in order to have rough estimates of the combined effects. The estimate of duration was found to be 0.953, while for length was 0.847. Similar to previous meta-analyses the effect of duration after correcting for publication bias is not significant, while the effect of length was significant at a 95% level. Meta-regression findings indicate that the main factors influencing the overall estimates of the beta coefficients are study year and region for duration and study year and model specification for length.

Driving in mild cognitive impairment: The role of depressive symptoms

OBJECTIVES

Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

METHODS

Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

RESULTS

Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

CONCLUSIONS

Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.

Mild Cognitive Impairment and driving: Does in-vehicle distraction affect driving performance?

OBJECTIVES

In-vehicle distraction is considered to be an important cause of road accidents. Drivers with Mild Cognitive Impairment (MCI), because of their attenuated cognitive resources, may be vulnerable to the effects of distraction; however, previous relevant research is lacking. The main objective of the current study was to explore the effect of in-vehicle distraction on the driving performance of MCI patients, by assessing their reaction time at unexpected incidents and accident probability.

METHODS

Thirteen patients with MCI (age: 64.5±7.2) and 12 cognitively intact individuals (age: 60.0±7.7), all active drivers were introduced in the study. The driving simulator experiment included three distraction conditions: (a) undistracted driving, (b) conversing with passenger and (c) conversing through a hand-held mobile phone.

RESULTS

The mixed ANOVA models revealed a greater effect of distraction on MCI patients. Specifically, the use of mobile phone induced a more pronounced impact on reaction time and accident probability in the group of patients, as compared to healthy controls. On the other hand, in the driving condition "conversing with passenger" the interaction effects regarding reaction time and accident probability were not significant. Notably, the aforementioned findings concerning the MCI patients in the case of the mobile phone were observed despite the effort of the drivers to apply a compensatory strategy by reducing significantly their speed in this driving condition.

CONCLUSION

Overall, the current findings indicate, for the first time, that a common driving practice, such as the use of mobile phone, may have a detrimental impact on the driving performance of individuals with MCI.

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

OBJECTIVE

Understanding the various factors that affect accident risk is of particular concern to decision makers and researchers. The incorporation of real-time traffic and weather data constitutes a fruitful approach when analyzing accident risk. However, the vast majority of relevant research has no specific focus on vulnerable road users such as powered 2-wheelers (PTWs). Moreover, studies using data from urban roads and arterials are scarce. This study aims to add to the current knowledge by considering real-time traffic and weather data from 2 major urban arterials in the city of Athens, Greece, in order to estimate the effect of traffic, weather, and other characteristics on PTW accident involvement.

METHODS

Because of the high number of candidate variables, a random forest model was applied to reveal the most important variables. Then, the potentially significant variables were used as input to a Bayesian logistic regression model in order to reveal the magnitude of their effect on PTW accident involvement.

RESULTS

The results of the analysis suggest that PTWs are more likely to be involved in multivehicle accidents than in single-vehicle accidents. It was also indicated that increased traffic flow and variations in speed have a significant influence on PTW accident involvement. On the other hand, weather characteristics were found to have no effect.

CONCLUSIONS

The findings of this study can contribute to the understanding of accident mechanisms of PTWs and reduce PTW accident risk in urban arterials.

Innovative motor insurance schemes: A review of current practices and emerging challenges

The objective of this paper is to provide a review of the most popular and often implemented methodologies related to Usage-based motor insurance (UBI). UBI schemes, such as Pay-as-you-drive (PAYD) and Pay-how-you-drive (PHYD), are a new innovative concept that has recently started to be commercialized around the world. The main idea is that instead of a fixed price, drivers have to pay a premium based on their travel and driving behaviour. Despite the fact that it has been implemented only for a few years, it appears to be a very promising practice with a significant potential impact on traffic safety as well as on traffic congestion mitigation and pollution emissions reduction. To this end, the existing literature on UBI schemes is reviewed and research gaps are identified Findings show that there is a multiplicity and diversity of several research studies accumulated in modern literature examining the correlation between PAYD (based on driver's travel behaviour and exposure) and PHYD (based on driving behaviour) schemes and crash risk in order to determine crash risk. Moreover, there is evidence that UBI implementation would eliminate the cross-subsidies phenomenon, which implies less insurance costs for less risky and exposed drivers. It would also provide a strong motivation for drivers to improve their driving behaviour, differentiate their travel behaviour and reduce their degree of exposure by receiving feedback and monitoring their driving preferences and performance, which would result in crash risk reduction both totally and individually. The paper finally discussed the current and emerging challenges on this research field.