Evaluation of penalty and enforcement strategies to combat speeding offences among professional drivers: A Hong Kong stated preference experiment

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework

The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors.

What would affect drivers' stop-and-go decisions at yellow dilemma zones? A driving simulator study in Hong Kong

Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviours are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviours at the yellow dilemma zone are considered. In this study, drivers' propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviours are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers' propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma.

Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances

Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.

Who might encounter hard-braking while speeding? Analysis for regular speeders using low-frequency taxi trajectories on arterial roads and explainable AI

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Drivers' behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change

Utilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18-0.23) and 0.57% (0.54-0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95-10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21-0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from - 3.44 to - 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Promoting passenger behaviour change with provision of occupancy information to help moderate train overcrowding: A cognitive work analysis approach

Passengers' travel behaviour is one of the significant factors affecting train overcrowding. Train occupancy information has been introduced as a tool to stimulate passengers' behaviour change to ease in-vehicle crowding. However, there are limitations to this strategy as it often fails to consider other elements in the complex rail system that influence behaviour. This research provides insights to service providers to promote passenger behaviour change by revealing the behavioural constraints in the environment. Cognitive Work Analysis (CWA) was applied to systematically analyse passengers' behaviour and related constraints in the environment. Specifically, Work Domain Analysis (WDA) and Social Organisation and Cooperation Analysis (SOCA) were conducted and presented in the forms of Abstraction Hierarchy (AH) and Contextual Activity Template (CAT). Results showed that a wide range of informational, navigational and physical support alongside provision of occupancy information could better encourage passengers to select and use less busy carriages and trains. Behaviour change goals are likely to be achieved more effectively when the constraints of the system are better understood.

Driving Fine and its Relationship with Dangerous Driving Behaviour Among Heavy Vehicle Drivers

Context

There is a significant difference between actual and existing statistics of traffic fines; since some invisible fines and most of the visible traffic violations cannot be recorded by traffic officers. Therefore, dealing with driving fines and road fatalities is considered an important issue in social and public management worldwide.

Aims

Explore the factors associated with unsafe behaviors and getting traffic fines among a sample of Iranian heavy-vehicle professional drivers.

Settings and Design

The present cross-sectional study was conducted in Iran, from February 2019 to September 2020.

Methods and Material

This study used the driver behavior questionnaire (DBQ), demographic and driving characteristics, the number of fines, and structural equation modeling. Also, in this study 320 professional drivers participated.

Statistical Analysis Used

This article used structural equation modeling for Statistical analysis.

Results

The results of structural equation modeling analysis indicated that the data fit well with the theoretical model proposed in this study. The number of fines was directly predicted by both demographic and driving characteristics and risky driving behaviors. A significant relationship was observed between, driving hours, driving experience, and smoking, respectively, with a mistake, slip, and risky violation. There was a negative correlation between education and all four sub-scales of risky driving behaviors.

Conclusions

In order to reduce traffic fines, training courses on increasing attention and precision in drivers' observations and judgments are useful. The courses can decrease traffic violations by trying to change beliefs, attitudes, and social norms. It is therefore helpful to understand the ways to change the drivers' attitudes.

The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong

This study quantifies the effects of health control measures at the airport on passenger behaviour related to business travel. A stated preference survey was conducted over potential air travellers in Hong Kong in the context of COVID-19 pandemic. Panel latent class models were estimated to understand passenger preference toward new travel requirements given the applicability of online meeting. Online meeting is applicable in cases where it is a good substitute of air travel and achieves the same outcomes of a trip, and inapplicable otherwise. Empirical results indicate that traveller subgroups are affected in different ways. When an online meeting is inapplicable, nearly 75% of the respondents prefer to travel for business and undertake health screenings. These passengers (identified as "captive" business travellers) perceive such measures necessary to lower health related risks during air travel. As such, they are willing to spend up to 21 to 38 min on the health control measures such as vaccination record requirements and test involving sample collection. When an online meeting is applicable, the share of "choice" business travellers is about 45%, among whom the attitudes towards health control measures become more averse. The average weighted willingness-to-pay for the time saved at health checkpoints increase significantly. The aviation industry thus faces a "double-hit" problem: operation costs will increase due to pandemic control measures, and the resultant inconvenience, extra time and costs further reduces travel demand. Unlike previous short pandemics, business travel is likely to suffer with an extended decline until the pandemic is fully controlled. These identified challenges call for financial and operational support to help the aviation industry reach a sustainable "new normal". The high value of time saved at check points also justifies investments that make the pandemic control and health measures efficient and smooth. Travellers' time spent on airport health control should be within 20 min to avoid substantial negative impacts on business travel demand.

The effect of risk perception and other psychological factors on mobile phone use while crossing the street among pedestrians

Pedestrians may be the most vulnerable group among road users, and mobile phone use while crossing the street is ubiquitous worldwide in this information era. However, previous studies have found that such distracting behaviors may increase the risk of injury and death. The present study primarily aimed to explore the effect of reinforcement sensitivity theory components (i.e., Behavioral Inhibition System (BIS), Behavioral Approach System (BAS)), risk perception, attitudes towards red light running, and fear of missing out (FoMO) on mobile phone use while crossing the street among pedestrians. Risk perception was measured in three ways (i.e., assessing the probability of a negative outcome (RP-Pro), judging the severity of the consequence (RP-Se), and evaluating the general riskiness of the behavior (RP-Ri)). An online questionnaire survey was conducted, and only valid responses (N = 425) were used for subsequent data analyses. The results indicated significant differences in the responses across the risk perception questions with different focuses. Participants who reported engaging in more distracted street-crossing (i.e., high-risk takers) perceived a significantly lower risk, and this difference did not depend on the focus of risk perception. Three path analysis models with differential risk perception constructs (RP-Pro, RP-Se, and RP-Ri) were developed to examine the relationship between risk perception and distracted street-crossing. The results suggest that the relationship between these two variables does not depend on the focus of the risk perception questions. Moreover, FoMO was a predictor of mobile phone use while crossing the street, while attitudes had both direct and indirect effects on behavior. BIS and BAS had the lowest total effect on mobile phone use among pedestrians. In particular, a direct association between BAS and distracted street-crossing was found only in the model in which risk perception was measured by judging the severity of crashes caused by mobile phone use distraction. This study may be meaningful for understanding the associations between psychological factors and mobile phone use among pedestrians. The implications of the findings for the development of safety interventions are discussed in this study.

A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros

Public bus constitutes more than 70% of the overall road-based public transport patronage in Hong Kong, and its crash involvement rate has been the highest among all public transport modes. Though previous studies had identified explanatory factors that affect the crash risk of buses, use of considerably imbalanced crash data with excessive zero observations could lead to inaccurate parameter estimation. This study aims to resolve the excess zero problem of disaggregate analysis of bus-involved crashes based on synthetic data using a Synthetic Minority Over-Sampling Technique for panel data (SMOTE-P). Dataset comprising crash, traffic, and road inventory data of 88 road segments in Hong Kong during the period from 2014 to 2017 is used. To assess the data balancing performance, other common data generation approaches such as Random Under-sampling of the Majority Class (RUMC) technique, Cluster-Based Under-Sampling (CBUS), and mixed resampling, are also considered. Random effect Poisson (REP) models based on synthetic data and random effect zero-inflated Poisson (REZIP) model based on original data are estimated. Results indicate that REP model based on synthetic data using SMOTE-P outperforms REZIP model based on original data and REP models based on synthetic data using RUMC, CBUS and mixed approaches, in terms of statistical fit, prediction error, and explanatory factors identified. Results of model estimation based on SMOTE-P suggest that factors including morning peak, evening peak, hourly traffic flow, average lane width, road length, bus stop density, percentage of bus in the traffic stream, and presence of bus priority lane all affect the bus-involved crash frequency. More importantly, this study provides a feasible solution for disaggregate crash analysis with imbalanced panel data.

The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach

Pedestrian safety has been a major concern in Hong Kong, where walking is an important access mean to urban transportation services and pedestrian-vehicle conflicts are prevalent. Red light running violation of pedestrians is a leading cause of pedestrian-vehicle crashes at the signal intersections. It is necessary to examine the possible factors including personal characteristics and road environments that affect the propensities of red light running violation of pedestrians. Therefore, effective traffic control and enforcement measures can be implemented to deter against the red light running behaviors of pedestrians. This study attempts to examine the roles of trade-off between safety and time, as well as situational features and personality traits, in the red light running behaviors of pedestrians using a stated preference survey method. Then, a regret-based panel mixed multinomial logit model is established for the association measure between propensities of red light running violation and possible factors, with which the effects of unobserved heterogeneity and correlation in the choices between different scenarios of the same person are considered. Results indicate that the choice decision of pedestrians are more sensitive to a reduction in time loss, as compared to the equivalent increase in safety risk. In addition, the safety versus time trade-off may vary between pedestrian groups. Furthermore, presence and type of another violator also significantly affect the propensities of red light running violation. Such findings are indicative to effective policy interventions that can deter against the red light running behaviors of vulnerable pedestrian groups. Therefore, overall pedestrian safety level can be improved in the long term.

Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model

In previous research, the effects of commercial vehicle proportions (CVP) on overall crash propensity have been found to be significant, but the results have been varied in terms of the effect direction. In addition, the mediating or moderating effects of roadway attributes on the CVP-vs-safety relationships, have not been investigated. In addressing this gap in the literature, this study integrates databases on crashes, traffic, and inventory for Hong Kong road segments spanning 2014-2017. The classes of commercial vehicles considered are public buses, taxi, and light-, medium- and heavy-goods vehicles. Random-parameter Tobit models were estimated using the crash rates. The results suggest that the CVP of each class show credible effects on the crash rates, for the various crash severity levels. The results also suggest that the interaction between CVP and roadway attributes is credible enough to mediate the effect of CVP on crash rates, and the magnitude and direction of such mediation varies across the vehicle classes, crash severity levels, and roadway attribute type in four ways. First, the increasing effect of taxi proportion on slight-injury crash rate is magnified at road segments with high intersection density. Second, the increasing effect of light-goods vehicle proportion on slight-injury crash rate is magnified at road segments with on-street parking. Third, the association between the medium- and heavy-goods vehicle proportion and killed/severe injury (KSI) crash rate, is moderated by the roadway width (number of traffic lanes). Finally, a higher proportion of medium- and heavy-goods vehicles generally contributes to increased KSI crash rate at road segments with high intersection density. Overall, the findings of this research are expected not only to help guide commercial vehicle enforcement strategy, licensing policy, and lane control measures, but also to review existing urban roadway designs to enhance safety.

Processes of a Transformation of Young Drivers' Responsibility for Health-Carpe Diem

Research most often deals with the relationship between risky behaviour on the road and other aspects of young adults’ lifestyle. It is rare that the sense of responsibility for one’s own life and health and for that of other people on the road is understood, due to the limitation of perceptual data. In this study, we researched 198 young adults (M = 19.75; SD = 1.11) using the Multidimensional Health Locus of Control, the Inventory of Health Behaviours, the Criteria of Health List and a student health survey. The chance of risky driving will increase by 50.7% among young adults as their understanding of health as a state increases. On the other hand, when young people feel that others are responsible for their lives, the likelihood of risky driving will drop by 6.4%. The hedonistic life orientation of a young adult had a significant impact on the results obtained which was connected with their understanding of health.

Propensities of red light running of pedestrians at the two-stage crossings with split pedestrian signal phases

Two-stage crossing with a median refuge island is commonly installed at the busy urban signalized intersections. To accommodate for the high traffic flow in different directions, split phasing is often applied for pedestrians' right of way of the two crossing stages. Previous studies mainly focus on the time delay, capacity and operation efficiency of two-stage crossings. It is rare that safety of two-stage crossings is investigated. Crossing behaviors and propensities of red light running at the two crossing stages (i.e. from the curbside to a central island, and then to another side of the road) are different from that of single stage crossing. Also, there could be interferences in the crossing behaviors and situational features between the two stages. This study aims to identify the personal characteristics, traffic attributes and environmental factors that affect the red light running propensities of pedestrians at the two-stage crossings, based on the video observation surveys at six urban signal intersections in Hong Kong. Random parameter logit regression approach is applied to measure the association between possible factors and propensities of red light running of pedestrians, with which the effect of unobserved heterogeneity is accounted. Results indicate that, other than the pedestrians' characteristics, pedestrian signal of the second stage, maximum waiting time and traffic flow significantly affect the propensity of red light running in the first stage. Also, there are significant interaction effects between pedestrians' characteristics and situational features on the propensity. On the other hand, pedestrians' waiting time before crossing the first stage significantly affects the propensity of red light running in the second stage. Findings are indicative to the design of pedestrian crossing, signal time plan and effective enforcement and education strategies that can deter against the red light running behaviors of pedestrians. Therefore, safety of two-stage signalized crossings can be enhanced.

A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics

Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong.

Urban road space allocation incorporating the safety and construction cost impacts of lane and footpath widths

INTRODUCTION

Walkability continues to attract great attention from urban planners, designers, and engineers as they recognize not only the merits of pedestrian facilities in terms of the health benefits but also their demerits in terms of accident risk to pedestrians. Wide footpaths improve the pedestrian environment and experience, and thereby motivate travelers to walk as much as possible. However, if footpaths are too wide, they may leave a smaller space for the roadway. On the other hand, wide road lanes may lead to higher road vehicle safety but are costly to construct and maintain and also may leave little space for the footpath. Evidently, for a fixed urban space, what is needed is an optimal balance between the vehicle lane and pedestrian path. This problem is encountered routinely in dense cities including Hong Kong where land availability is severely limited.

METHOD

To address the issue, this paper first establishes safety performance functions (SPFs) for the pedestrian space and the road space, using the random-parameter negative binomial regression. The results indicate the extent to which road lane and footpath width changes are associated with changes in in-vehicle occupant and pedestrian casualties. Then the paper uses the SPFs to develop a methodology for optimizing the width allocations to the road lanes and footpaths, duly considering the user (safety) costs and agency (construction) costs associated with each candidate allocation of the widths. Finally, the paper analyzes the sensitivity of the optimal solution to the relative weights of user cost and agency cost.

RESULTS

When user and agency costs are considered equally important, the optimal lane width is 5.4 m.

CONCLUSION

It is observed that the road space allocation ratio used by the Hong Kong road agency suggests that the agency places a higher weight to user cost compared to agency cost. Practical Application: The findings can help incorporate design-safety relationships, and the stakeholders (agency and users) perspectives in urban road and footpath design.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.