Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Sex-related disparities in vehicle crash injury and hemodynamics.

Objective: Multiple studies evaluate relative risk of female vs. male crash injury; clinical data may offer a more direct injury-specific evaluation of sex disparity in vehicle safety. This study sought to evaluate trauma injury patterns in a large trauma database to identify sex-related differences in crash injury victims. Methods: Data on lap and shoulder belt wearing patients age 16 and up with abdominal and pelvic injuries from 2018 to 2021 were extracted from the National Trauma Data Bank for descriptive analysis using injuries, vital signs, International Classification of Disease (ICD) coding, age, and injury severity using AIS (Abbreviated Injury Scale) and ISS (Injury Severity Score). Multiple linear regression was used to assess the relationship of shock index (SI) and ISS, sex, age, and sex*age interaction. Regression analysis was performed on multiple injury regions to assess patient characteristics related to increased shock index. Results: Sex, age, and ISS are strongly related to shock index for most injury regions. Women had greater overall SI than men, even in less severe injuries; women had greater numbers of pelvis and liver injuries across severity categories; men had greater numbers of injury in other abdominal/pelvis injury regions. Conclusions: Female crash injury victims' tendency for higher (AIS) severity of pelvis and liver injuries may relate to how their bodies interact with safety equipment. Females are entering shock states (SI > 1.0) with lesser injury severity (ISS) than male crash injury victims, which may suggest that female crash patients are somehow more susceptible to compromised hemodynamics than males. These findings indicate an urgent need to conduct vehicle crash injury research within a sex-equity framework; evaluating sex-related clinical data may hold the key to reducing disparities in vehicle crash injury.

Epidemiology and Characteristics of Ankle and Foot Injuries in Electric Scooter-Related Accidents.

Background: The use of electric scooters (e-scooters) continues to increase as a simple, inexpensive means of transport, resulting in a sharp increase in the incidence of scooter-related accidents. No study to date has closely examined the injury extent to the lower leg, joints, and extremities from e-scooter-related accidents. Here, we investigated the epidemiology and injury patterns of such accidents, focusing on injuries to the ankle and foot. Methods: Based on data from a single tertiary hospital's database, the demographics of 563 patients with scooter-associated injuries were analyzed retrospectively. Among the patients, 229 patients who were injured by e-scooter riding were further investigated. Based on the data, the general demographics of whole scooter-associated injuries and the injury characteristics and fracture cases of the lower leg, ankle, and foot were analyzed. Results: During the 4-year study period, the number of patients injured by e-scooters increased every year. Lower extremities were the most common injury site (67.2%) among riders, whereas injuries to the head and neck (64.3%) were more common in riders of non-electric scooters. Among the lower leg, ankle, and foot injuries of riders (52 cases), the ankle joint (53.8%) was the most commonly injured site, followed by the foot (40.4%) and lower leg (21.2%). The fracture group scored significantly higher on the Abbreviated Injury Scale than the non-fracture group (p < 0.001). Among the fracture group (20 cases), ankle fractures (9 cases) were most common, including pronation external rotation type 4 injuries (4 cases) and pilon fractures (2 cases). Five patients (25%) had open fractures, and 12 patients (60%) underwent surgical treatment. Conclusions: The ankle and foot are the most common injury sites in e-scooter-related accidents. Given the high frequency and severity of e-scooter-related ankle and foot injuries, we suggest that more attention be paid to preventing these types of injuries with greater public awareness of the dangers of using e-scooters.

GIS-based intelligent planning approach of child-friendly pedestrian pathway to promote a child-friendly city.

Pedestrian safety, particularly for children, relies on well-designed pathways. Child-friendly pathways play a crucial role in safeguarding young pedestrians. Shared spaces accommodating both vehicles and walkers can bring benefits to pedestrians. However, active children playing near these pathways are prone to accidents. This research aims to develop an efficient method for planning child-friendly pedestrian pathways, taking into account community development and the specific needs of children. A mixed-methods approach was employed, utilizing the Datang community in Guangzhou, China, as a case study. This approach combined drawing techniques with GIS data analysis. Drawing methods were utilized to identify points of interest for children aged 2-6. The qualitative and quantitative fuzzy analytic hierarchy process assessed factors influencing pathway planning, assigning appropriate weights. The weighted superposition analysis method constructed a comprehensive cost grid, considering various community elements. To streamline the planning process, a GIS tool was developed based on the identified factors, resulting in a practical, child-friendly pedestrian pathway network. Results indicate that this method efficiently creates child-friendly pathways, ensuring optimal connectivity within the planned road network.

Treatment Gap in Mental Health Care for Victims of Road Traffic Accidents.

Road traffic accidents (RTAs) are among the most frequent negative life-events. About one in five RTA survivors is susceptible to posttraumatic stress disorder (PTSD). Knowledge about needs for, and usage of, mental health services (MHSs) may improve options for care for RTA victims. The current study aimed to assess rates of victims using different MHSs, including psychotherapy, pharmacotherapy and support groups, and to explore correlates of needs for and use of these MHSs. Further, we aimed to estimate the treatment gap in post-RTA care, defined as including people with probable PTSD who did not use MHSs and people wanting but not getting help from MHSs. Dutch victims of nonlethal RTAs (N = 259) completed self-report measures on needs for and use of MHSs and PTSD. Results showed that 26% of participants had utilized care from psychotherapy, pharmacotherapy or support groups. Among people with probable PTSD, this was 56%. Increased posttraumatic stress was the strongest correlate of MHS use. Forty-eight participants (17.8%) had an unmet care need and represented the treatment gap. Commonly reported reasons and barriers preventing MHS use were perceptions that problems were limited or would disappear without care and financial worries. Regarding possible future care, participants reported a preference for face-to-face (over online) help from a psychologist (over other professionals). The treatment gap for Dutch RTA victims may be limited. However, a significant number of RTA victims need care but do not obtain this care. Care options may be improved by reducing practical barriers to MHSs and increasing mental health literacy and acceptability of different forms of care (besides face-to-face care).

Injury Patterns and Gender in Italy.

Introduction: Globally, injuries pose significant public health challenges, with road traffic accidents in particular being responsible for considerable morbidity, mortality, and economic distress. Italy has been significantly impacted due to its high population density and frequency of road traffic and domestic incidents. Method: This study set out to investigate the incidence of self-reported road traffic and home and leisure accidents in the Italian general population. A particular emphasis was placed on exploring possible gender differences across varying age groups. The data was obtained from the European Health Interview Survey and a representative sample of the Italian population was analyzed. Results: The analysis revealed that regardless of age, women experienced a reduced risk of road traffic accidents compared to men. However, gender disparities in home-leisure accidents were observed to be age-dependent. Women under the age of 25 exhibited a lower likelihood of home-leisure accidents and serious accidents necessitating hospital admission in comparison to their male counterparts. In contrast, women aged 65 and above had an increased likelihood of home-leisure accidents as opposed to men in the same age category. Conclusions: The findings of this study highlight the importance of considering age and gender as significant factors in the occurrence of different types of accidents, offering insight into how injury rates vary between these demographic groups within Italy.

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

The impact of COVID-19 restrictions on motorcycle crashes in Taiwan.

Taiwan is one of the countries with the highest motorcycle per capita globally, and motorcycle crashes are predominant among traffic crashes. This study examines the impact of coronavirus disease 2019 restrictions on motorcycle crashes. We analyzed the trend of motorcycle crashes in Taipei City from 2019 to 2020 using the dataset provided by the Department of Transportation, Taipei City Government, Taiwan. We found 47,108 and 51,441 motorcycle crashes in 2019 and 2020, involving 61,141 and 67,093 motorcycles, respectively. Mopeds had the highest risk in 2020, followed by heavy motorcycles [≥550 cubic capacity (cc)] and scooters compared to 2019. Food delivery motorcycle crashes increased for scooters (0.93% in 2019 to 3.45% in 2020, P < .0001) and heavy motorcycles (250 < cc < 550) (0.90% in 2019 to 3.38% in 2020, P < .0001). While fatalities remained under 1%, 30% to 51% of motorcyclists sustained injuries. Food delivery with scooters or heavy motorcycles (250 < cc < 550) was significantly associated with motorcyclist injuries and deaths. Compared with 2019, the adjusted odds ratios of motorcyclist injuries and deaths in 2020 were 1.43 (95% confidence interval = 1.05-1.94) for heavy motorcycles (≥550 cc) and 1.07 (95% confidence interval = 1.04-1.09) for scooters. This study shows that coronavirus disease 2019 restrictions was associated with elevated risks of crashes, injuries, and deaths among motorcyclists, reflecting the general preference for private transport over public transport. The popularity of food delivery services also contributed to increased motorcycle crashes.

Risk field modeling of urban tunnel based on APF.

OBJECTIVE: The purpose of this paper is to explore the changing laws of driving safety in the complex and changing driving environment in urban tunnels, to analyze the evolution of driving risk fields caused by changes in adjacent vehicles, driving behavior characteristics and road environment, and to reveal the formation mechanism of tunnel driving danger zones. METHODS: The kinetic field, behavioral field and potential field models are constructed according to the APF theory. The driving safety risks arising from the surrounding vehicles, driving behavior characteristics and changes in the tunnel environment are analyzed in the process of driving from the open section to the exit of the tunnel. RESULTS: The magnitude of the risk field force is inversely proportional to the spacing of the vehicles and the distance between the tunnel sidewalls, and is proportional to the relative speed between the vehicles and the slope of the longitudinal slope. Under the same conditions, the vehicle at the entrance and exit of the tunnel is subjected to a greater force of travel risk than inside the tunnel, and the effect of speed on the force of the risk field is greater than the distance. CONCLUSIONS: The established model better describes the trend of driving risk during the driving of vehicles in urban tunnels, and the research findings can provide theoretical support for the design and traffic management of urban tunnels.

Risk factors associated with driving after marijuana use among West Virginia college students during the COVID-19 pandemic.

OBJECTIVES: The purpose of this study was to assess sociodemographic and behavioral risk factors associated with driving after marijuana use (DAMU) among West Virginia college students. METHODS: Participants were recruited from West Virginia University between September and November 2022. The study sample was restricted to students who were ≥18 years of age; reported recently driving; possessed a current, valid driver's license from any US state; and were enrolled for at least one credit hour in the Fall 2022 semester. RESULTS: Among respondents (N = 772), 28.9% reported DAMU. Students who had a GPA of B (adjusted odds ratio [AOR]: 2.17, 95% confidence interval [CI]: 1.06-4.42), smoked or ingested marijuana in the past year (AOR: 26.51, 95% CI: 10.27-68.39), drove after drinking (AOR: 2.38, 95% CI: 1.18-4.79), and used both marijuana and alcohol concurrently and then drove (AOR: 10.39, 95% CI: 2.32-46.54) associated with DAMU. Individuals who felt the behavior was somewhat dangerous or not dangerous or thought their peers approved of DAMU showed significant associations with DAMU. CONCLUSIONS: As DAMU was prevalent, future interventions that raise awareness of the danger and potential consequences of DAMU may be needed to reduce this risky behavior on college campuses.

Examining the impact of legalization on the prevalence of driving after using cannabis: A comparison of rural and non-rural parts of Canada.

OBJECTIVE: The purpose of this study was to examine the likelihood of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in rural areas and non-rural areas before and after legalization. METHODS: A multi-wave analysis of Canada's National Cannabis Survey was conducted using logistic regression with interactions to predict the prevalence of driving after using cannabis, and of being a passenger with someone who is driving after using cannabis, in relation to place of residence (rural or non-rural) and in the weeks and months before and after legalization. Three time points were compared: pre-legalization, two months following legalization and 1 year after legalization. RESULTS: At the national level, there are no significant differences between the predicted estimates of driving after using cannabis for those who live in rural and non-rural areas. However, when examining the impact of legalization, we found a significant increase in driving after using cannabis among rural residents directly following legalization. Furthermore, it was observed that this increase in driving after using cannabis returns to pre-legalization rates one year after legalization. By contrast, in the weeks and months following legalization, driving after using cannabis decreased among those living in non-rural areas, and slowly increased soon thereafter. No significant differences were observed, in either time period or group, in the prevalence of being a passenger with someone who is driving after using cannabis. CONCLUSIONS: The finding of significantly higher risk of driving after use of cannabis soon after legalization in rural areas suggests a need for more attention to address immediate concerns for public safety. The increased potential for traffic injuries and deaths in other jurisdictions contemplating legalization supports the call for more and better targeted prevention efforts in rural communities that have far too often been overlooked and under-served.

Vehicle front-end geometry and in-depth pedestrian injury outcomes.

OBJECTIVE: Large passenger vehicles have consistently demonstrated an outsized injury risk to pedestrians they strike, particularly those with tall, blunt front ends. However, the specific injuries suffered by pedestrians in these crashes as well as the mechanics of those injuries remain unclear. The current study was conducted to explore how a variety of vehicle measurements affect pedestrian injury outcomes using crash reconstruction and detailed injury attribution. METHODS: We analyzed 121 pedestrian crashes together with a set of vehicle measurements for each crash: hood leading edge height, bumper lead angle, hood length, hood angle, and windshield angle. RESULTS: Consistent with past research, having a higher hood leading edge height increased pedestrian injury severity, especially among vehicles with blunt front ends. The poor crash outcomes associated with these vehicles stem from greater injury risk and severity to the torso and hip from these vehicles' front ends and a tendency for them to throw pedestrians forward after impact. CONCLUSIONS: The combination of vehicle height and a steep bumper lead angle may explain the elevated pedestrian crash severity typically observed among large vehicles.

Energy field-based lane changing behavior interaction model and risk evaluation in the weaving section of expressway.

OBJECTIVE: With the development of intelligent driving assistance systems, the evaluation of driving behavior risk has shifted from traditional single-vehicle studies to multi-vehicle studies. This study aimed to investigate the interaction mechanism between vehicles and to study the microscopic laws of traffic flow operation. METHODS: Firstly, the concept of "driving interaction field" was proposed. The virtual interaction quality and distance were used to define the driving interaction field. The interaction angle distinguished the vehicle interaction between different lanes. Then, the risk mechanism in the interaction process was analyzed by driving risk index. Corresponding thresholds of 50% and 85% quantile values were determined. Finally, the process of the lane-changing simulation experiments was divided into three phases (preparation, execution and adjustment). RESULTS: The driving risk index of the execution phase was larger than the other phases. Meanwhile, the comparison with the classical driving risk indexes revealed that the proposed index was more accurate and intuitive in describing the interaction risks. CONCLUSIONS: The driving interaction model proposed in this study quantified the overall environmental pressure on the vehicle. It overcomes the previous limitation of kinetic interaction parameters. The research provides a new idea for the ITS and autonomous driving systems, contributing to the enhancement of traffic safety and efficiency.

Driving after substance use in Rhode Island adolescents: A cross-sectional analysis of surveillance data.

OBJECTIVE: Existing literature on driving under the influence during adolescence is sparse, especially for driving after the use of non-medical prescription drugs (DAP). This study examines the prevalence of driving after use of alcohol (DAA), cannabis (DAC), and DAP, and examines the role of several potential risk and protective factors. METHODS: This was a secondary analysis of the 2022 Rhode Island Study Survey, a cross-sectional survey of middle and high school students. Separate multivariable regressions were conducted for each outcome among lifetime users for each substance, controlling for current substance use, individual-, perceived parental-, and perceived friend-substance use risk perception, age, sexual/gender minority (SGM) status, race, ethnicity, school level, and town poverty level. RESULTS: Among lifetime users of alcohol (n = 3849), cannabis (n = 2289), and non-medical prescription drugs (n = 611), the prevalence of DAA, DAC, and DAP was 4.9, 14.3, and 16.9%, respectively. Current substance use, high individual risk perception, being nonwhite, and being Hispanic were risk factors for DAA while perceiving parent's risk perception as negative and being heterosexual cisgender-female were protective. Current substance use, negative individual risk perception, and being nonwhite were risk factors for DAC while perceiving parent's risk perception as negative and being in high school were protective. Current substance use and older age were risk factors for DAP while perceiving parent's risk perception as negative and perceiving friend's risk perception as negative were protective. CONCLUSIONS: Driving under the influence is a concern among adolescent substance users. Current substance use and perceived parental risk perception as negative are common risks and protective factors, respectively. Findings suggest substance-specific, heterogeneous interventions are needed. For example, interventions focusing on peer perceptions are most relevant for DAP, while shifting personal perceptions of harm are most relevant for DAC.

Validated numerical unrestrained occupant-seat crash scenarios for high-speed trains integrating experimental, computational, and inverse methods.

OBJECTIVE: Occupant impact safety is critical for train development. This paper proposes a systematic procedure for developing validated numerical occupant crash scenarios for high-speed trains by integrating experimental, computational, and inverse methods. METHODS: As the train interior is the most potentially injury-causing factor, the material properties were acquired by mechanical tests, and constitutive models were calibrated using inverse methods. The validity of the seat material constitutive model was further verified via drop tower tests. Finite element (FE) and multibody (MB) models of train occupant-seat interactions in frontal impact were established in LS-DYNA and MADYMO software, respectively, using the experimentally acquired materials/mechanical characteristics. Three dummy sled crash tests with different folding table and backrest configurations were conducted to validate the numerical occupant-seat models and to further assess occupant injury in train collisions. The occupant impact responses between dummy tests and simulations were quantitatively compared using a correlation and analysis (CORA) objective rating method. RESULTS: Results indicated that the experimentally calibrated numerical seat-occupant models could effectively reproduce the occupant responses in bullet train collisions (CORA scores >80%). Compared with the train seat-occupant MB model, the FE model could simulate the head acceleration with slightly more acceptable fidelity, however, the FE model CORA scores were slightly less than for the MB models. The maximum head acceleration was 30 g but the maximum HIC score was 17.4. When opening the folding table, the occupant's chest injury was not obvious, but the neck-table contact and "chokehold" may potentially be severe and require further assessment. CONCLUSIONS: This study demonstrates the value of experimental data for occupant-seat model interactions in train collisions and provides practical help for train interior safety design and formulation of standards for rolling stock interior passive safety.

Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

Implementation of a realistic artificial data generator for crash data generation.

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

Does the priority of ambulance guarantee no delay? a MIPSSTW model of emergency vehicle routing optimization considering complex traffic conditions for highway incidents.

Globally, traffic accidents on the highway network contribute significantly to a high fatality rate, drawing considerable attention from health institutions. The efficiency of transportation plays a vital role in mitigating the severe consequences of these incidents. This study delves into the issues of emergency vehicles experiencing delays despite having priority. Therefore, we construct mixed-integer linear programming with semi-soft time windows (MIPSSTW) model for optimizing emergency vehicle routing in highway incidents. We analyze the time-varying and complex traffic situations and respectively propose corresponding estimation approaches for the travel time of road segments, intersections on the urban road network, and ramp-weave sections on the highway network. Furthermore, we developed a modified cuckoo search(MCS) algorithm to solve this combinatorial problem. Optimization strategies of Lévy flight and dynamic inertial weight strategy are introduced to strengthen the exploration capability and the diversity of solution space of the CS algorithm. Computational experiments based on the Chinese emergency medical system data are designed to validate the efficacy and effectiveness of the MIPSSTW model and MCS algorithm. The results show that our works succeed in searching for high-quality solutions for emergency vehicle routing problems and enhance the efficacy of strategic decision-making processes in the realm of incident management and emergency response systems.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.