Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.

This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected to capture within-group and between-group characteristics of the stages. Besides, this study also examines the prediction performance of conflict stage identification using specific driving behaviour characteristics associated with each stage. Results reveal variations in dominant driving characteristics and predictive importance across stages. Heterogeneity exists within stages, with differences among clusters. Drivers slow down during in-conflict, with decreasing speed reduction as stages progress. Reaction time increases in post-conflict. Insufficient space gaps contribute to rear-end conflicts in the in-conflict stage. Furthermore, the prediction performance of conflict stage identification, based on the specific driving behaviour characteristics associated with each stage, is commendable. This study enhances understanding and prediction of conflict stage identification in rear-end conflicts.Practitioner summary: This study explores driving behaviour in rear-end conflict stages using trajectory data. It identifies pre-, in-, and post-conflict stages via time-to-collision indicator and assesses within-group and between-group characteristics. Besides, prediction performance for conflict stage identification based on these characteristics is commendable. This research enhances understanding and prediction of rear-end conflicts.

Classifying safe following distance for motorcycles to prevent rear-end collisions.

This study aims to classify motorcycle (MC) following distance based on trajectory traffic data and identify the risks associated with MC following distances to prevent rear-end collisions. A total of 8,223 events of a MC following a vehicle were investigated in Pathum Thani, Thailand, and 41 cases of MC rear-end crashes were analyzed between 2017 and 2021. Time headway (TH), safe stopping distance (SSD) and time to collision (TTC) were applied to the proposed concept to determine safe following distance (SFD). Speed and following distance for actual rear-end crashes were applied to validate SFD. Results showed that the proposed SFD model identified the causes of MC rear-end collision events as mostly due to longitudinal critical area (38 cases, 92.68%), implying insufficient MC rider reaction and decision time for evasive action. The longitudinal warning area had relatively few chances for rear-end collisions to occur, with only 3 cases recorded. VDO clip extracts from MC rear-end crashes illustrated 11 cases (26.83%) of rider fatality. The study findings revealed that the SFD concept can help to prevent MC rear-end collision events by developing reminder systems when the rider reached the following distances of both warning and critical areas.

Modeling the influence of safety aid market penetration on traffic safety: Case of collision warning system for powered two-wheelers.

Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU "Vision Zero" goal: "reduce road deaths to almost zero by 2050". MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving.

OBJECTIVE: To practically apply level 2 automated driving in complex traffic conditions, it is necessary to prompt driver behaviors to prevent potential accidents in areas where manual interventions are frequently required. METHODS: A driving simulator experiment with 20 participants was conducted to evaluate the impact of different human machine interfaces (HMIs) on drivers' interventions in terms of braking to avoid rear-end collisions during level 2 automated driving when a motorcycle abruptly cut in near intersections. Two types of HMIs were tested: a static HMI that informed drivers about approaching intersections, and a sensor HMI that displayed real-time object recognition results. Each driver participated in five experimental conditions, which varied the presence or absence of the static and sensor HMIs during level 2 automated driving, with manual driving serving as the baseline condition. RESULTS: The maximum deceleration in terms of braking to avoid rear-end collisions was significantly larger when level 2 automated driving was used without any HMI, compared to that of manual driving. However, when the sensor HMI was applied together with the static HMI during level 2 automated driving, a comparable time to collision could be achieved with a significantly smaller deceleration, compared to that without any HMI. Drivers' eye-gaze behaviors revealed that no significant difference existed in the percentages of gaze to the road center area, indicating that they were not distracted by the HMIs. Finally, drivers' attention levels to surrounding traffic and feeling of safety were significantly higher when level 2 automated driving was used in combination with the static and sensor HMIs. CONCLUSIONS: The results demonstrated that the combination of static and sensor HMIs successfully aided drivers in ensuring driving safety with a significantly smaller deceleration to avoid rear-end collisions during level 2 automated driving. Furthermore, drivers' attention levels were maintained, and their feeling of safety was improved when both HMIs were used in combination.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety.

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle's (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

Factors contributing to the severity of motorcycle rear-end crashes in Thailand.

OBJECTIVE: Motorcycle (MC) rear-end collisions cause many serious injuries and deaths for MC riders. In Thailand, the MC crash investigation data revealed that 18% of all MC crashes were rear-end collisions, which accounted for 18% of all fatalities as well. The aim of this study was to investigate the causes of injuries and deaths from MC rear-end collisions and factors that contribute to their severity level. Between 2016 and 2020, 141 MC rear-end crashes were thoroughly investigated throughout Thailand. METHOD: The ordinal logistic analysis was conducted to analyze factors contributing to severe injuries. The analysis to rear-end collision models comprised four categories: M1 (n = 141) is all types of rear-end collisions to MC, and M2 (n = 114) is the rear-end collision due to other vehicles (OV) collided by MC, M3 (n = 72) is the rear-end collisions for traveling OV collided by MC, and M4 (n = 42) is the rear-end collision for MC hitting the parked OV. The outcomes are verified by the likelihood and Pseudo R2. RESULT: When a MC collides with the rear of another vehicle, there are more fatalities than when other vehicles collide with the rear of a MC. Furthermore, the probability of death is higher if MCs collide with the rear-end of parking vehicles. As for the primary crash contributing factor, motorcyclists' perception failure was the most frequent. Experience, license status, driving conditions, speed, the time of the crash, the areas of the crash, and types of other vehicles involved significantly influence the severity of rear-end crashes. CONCLUSION: In severe crashes, riders with perception failure are more likely to be involved. Based on the findings of this study, some policies and countermeasures can be drawn to prevent MC rear-end crashes and reduce their severity.

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

A reliability-based weather-responsive variable speed limit system to improve the safety of rural highways.

Weather-responsive Variable Speed Limit (WRVSL) systems treat speed limits as weather-dependent random variables, as opposed to the conventional static speed limits. This study (i) evaluates drivers' response to a fixed speed limit in different road-weather conditions, and (ii) proposes an effective approach to set WRVSLs, for rural divided highways located in extremely cold regions. Study data: road-weather, and speed data, collected from a rural highway (fixed speed limit = 110 km/h), are used to (i) estimate the 85th percentile speeds of population-level speed distributions, and (ii) develop WRVSLs based on the reliability theory. More specifically, the WRVSLs are set based on reliability: the probability of a speed being (i) likely complied by drivers, and (ii) adequate to avoid a rear-end collision. The study results reveal that merely 73 % of the drivers at the study site comply with the existing posted speed limit under normal road-weather conditions i.e., no precipitation and dry pavements. The reliability of the current speed limit is revealed to be approximately-one under normal road-weather conditions; thus, the current speed limit is perceived credible under such road-weather conditions. Yet, reliability of the current speed limit is substantially reduced in the presence of slight snow, and ice warning pavement conditions. A set of reliability-based WRVSLs ranging from 80 to 110 km/h is proposed. Jurisdictions experiencing extreme road-weather conditions may adapt the proposed methodology to effectively manage speed, particularly in rural highways under adverse road-weather conditions to enhance the probability of speed limits being safe and complied by drivers and as a result reduce crash propensity.

In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment.

Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers' performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle's brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers' speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies.

Characterizing Lumbar Spine Kinematics and Kinetics During Simulated Low-Speed Rear Impact Collisions.

BACKGROUND: Recent work has demonstrated that low back pain is a common complaint following low-speed collisions. Despite frequent pain reporting, no studies involving human volunteers have been completed to examine the exposures in the lumbar spine during low-speed rear impact collisions. METHODS: Twenty-four participants were recruited and a custom-built crash sled simulated rear impact collisions, with a change in velocity of 8 km/h. Randomized collisions were completed with and without lumbar support. Inverse dynamics analyses were conducted, and outputs were used to generate estimates of peak L4/L5 joint compression and shear. RESULTS: Average (SD) peak L4/L5 compression and shear reaction forces were not significantly different without lumbar support (compression = 498.22 N [178.0 N]; shear = 302.2 N [98.5 N]) compared to with lumbar support (compression = 484.5 N [151.1 N]; shear = 291.3 N [176.8 N]). Lumbar flexion angle at the time of peak shear was 36° (12°) without and 33° (11°) with lumbar support. CONCLUSION: Overall, the estimated reaction forces were 14% and 30% of existing National Institute of Occupational Safety and Health occupational exposure limits for compression and shear during repeated lifting, respectively. Findings also demonstrate that, during a laboratory collision simulation, lumbar support does not significantly influence the total estimated L4/L5 joint reaction force.

An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment.

INTRODUCTION: To assist drivers in avoiding rear-end collisions, many early warning systems have been developed up to date. Autonomous braking technology is also used as the last defense to ensure driver's safety. METHOD: By taking the accuracy and timeliness of automatic system control into account, this paper proposes a rear-end Real-Time Autonomous Emergency Braking (RTAEB) system. The system inserts brake intervention based on drivers' real-time conflict identification and collision avoidance performance. A driving simulator-based experiment under different traffic conditions and deceleration scenarios were conducted to test the different thresholds to trigger intervention and the intervention outcomes. The system effectiveness is verified by four evaluation indexes, including collision avoidance rate, accuracy rate, sensitivity rate, and precision rate. RESULTS: The results showed that the system could help avoid all collision events successfully and enlarge the final headway distance, and a TTC threshold of 1.5 s and a maximum deceleration threshold of -7.5 m/s2 could achieve the best collision avoidance effect. The paper demonstrates the situations that are more inclined to trigger the RTAEB (i.e., a sudden brake of the leading vehicle and a small car-following distance). Moreover, the study shows that driver characteristics (i.e., gender and profession) have no significant association with system trigger. Practical Applications: The study suggests that development of collision avoidance systems design should pay attention to both the real-time traffic situation and drivers' collision avoidance capability under the present situation.

Passive stiffness changes in the lumbar spine following simulated automotive low speed rear-end collisions.

BACKGROUND: Historically, there has been a lack of focus on the lumbar spine during rear impacts because of the perception that the automotive seat back should protect the lumbar spine from injury. As a result, there have been no studies involving human volunteers to address the risk of low back injury in low velocity rear impact collisions. METHODS: A custom-built crash sled was used to simulate rear impact collisions. Randomized collisions were completed with and without lumbar support. Measures of passive stiffness were obtained prior to impact (Pre), immediately post impact (Post) and 24 h post impact (Post-24). Low back pain reporting was monitored for 24 h following impact exposure. FINDINGS: None of the participants developed clinically significant levels of low back pain after impact. Changes in the passive responses persisted after impact for the length of the low stiffness flexion and extension zone. The length of the low stiffness zone was longer in the Post and Post-24 trial for low stiffness flexion and longer in the Post-24 for low stiffness extension. INTERPRETATION: Findings from this investigation demonstrate that during a laboratory-simulation of an 8 km/h rear-impact collision, young healthy adults did not develop low back pain. Changes in the low stiffness zone of the passive flexion/extension curves were observed following impact and persisted for 24 h. Changes in passive stiffness may lead to changes in the loads and load distributions during movement within the passive structures such as the ligaments and intervertebral discs following impacts.

An optimal control-based vehicle speed guidance strategy to improve traffic safety and efficiency against freeway jam waves.

Freeway jam waves create many problems, including capacity reduction, travel delays, and safety risks. The development of cooperative vehicle infrastructure system (CVIS) has prompted numerous new strategies, which can resolve jam waves by implementing microscopic car-following control actions to individual vehicles. However, most of those strategies aimed at eliminating freeway jam waves without considering the safety risks induced by the car-following control. This paper proposes an optimal control-based vehicle speed guidance strategy to improve both traffic efficiency and safety against jam waves. The optimal controller is developed based on a discrete first-order traffic flow model formulated in Lagrangian coordinates. The optimization of vehicles' driving speed is formulated as a linear programming problem, where the constraints concerning threshold safety measures are imposed. The proposed vehicle speed guidance strategy is tested using a modified Intelligent Driving Model (IDM+), which represents real traffic dynamics in CVIS environment. The proposed speed guidance strategy is compared with a state-of-the-art jam-absorption driving strategy, which also aimed to eliminate freeway jam waves. Simulation results show that the proposed strategy outperforms that strategy in terms of both total time spent saving and surrogate safety measures' reduction. The time exposed time-to-collision (TET) is reduced by 31%, and the time integrated time-to-collision (TIT) is reduced by 9.5% on average. Furthermore, the computation time of the linear optimization is only a few seconds, which is fast enough for the online application of the proposed strategy.

Analysis of effects of driver's evasive action time on rear-end collision risk using a driving simulator.

INTRODUCTION: Driver's evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver's evasive action. Thus, this study determines the driver's evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver's evasive action time (i.e., duration of evasive action) on rear-end collision risk. METHOD: Fifty drivers' car-following behavior on a freeway was observed using a driving simulator. An SSM called "Deceleration Rate to Avoid Crash (DRAC)" and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios - Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. RESULTS: DRAC decreased with the evasive action time and DRAC was closely related to drivers' gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. CONCLUSIONS: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver's evasive action and the speed variations among different drivers.

Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach.

The time-to-collision (TTC) index and its extended variants have been widely utilized to assess rear-end collision risks, but the characteristics of the time-series data have not been fully explored, especially for the transition from safe to risky conditions. This study proposes a novel approach in rear-end collision risk analysis based on the concept of transition durations. The vehicle trajectory data were extracted and the TTC index was used to identify risky and safe conditions. Three important transition durations are defined and their rationalities for evaluating rear-end collision risks are examined by developing random-parameters accelerated failure time (AFT) survival models. Furthermore, a typical case from real trajectory data is taken to discuss the limitations of using TTC and its variants, and the advantage of the proposed transition durations. The results of random-parameters AFT models reveal contributing factors affecting the length of three durations and demonstrate the rationality of transition durations in rear-end collision risks analysis. It is indicated that the proposed method outperforms TTC and its variants in evaluating rear-end collision risks, because it could not only provide the information of time point but also the variation of time-series data.

Rear-end collision warning of connected automated vehicles based on a novel stochastic local multivehicle optimal velocity model.

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system.

Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data.

Time-to-collision (TTC) index has been extensively utilized to evaluate rear-end collision risks, but few studies have focused on the special transition process that vehicles change from a safe to a dangerous situation. This study conducts an in-depth analysis of the transition condition of rear-end collisions. Realistic vehicle trajectory data were extracted from the Federal Highway Administration's Next Generation Simulation (NGSIM) datasets. The TTC index was utilized to pinpoint dangerous and transition conditions. A total of 13 types of transition conditions were categorized and a novel indicator, the derivative of TTC (TTCD), is proposed to evaluate changing rate of TTCs. Three types of TTCDs, corresponding to different time point or interval, were further analyzed based on developed regression models. The results indicate that: (1) although theoretically there are a total of 13 types of transition conditions, three types are dominant in practice; (2) the TTCD(t0) values at transition start points are significantly smaller than the TTCD(t1) at end points and the average TTCD(t0,t1), which indicates the quickest change of TTC values, while the TTCD(t1) has the slowest changes of TTC values; and (3) the following vehicle's speed and acceleration rate, and speed difference and acceleration difference between two vehicles have significant effects on TTCDs. The influences are more remarkable of TTCD(t0) than those of TTCD(t1), and the TTCD(t0,t1) always shows the average characteristic. Lastly, corresponding countermeasures are discussed based on findings above.

Investigating rear-end collision avoidance behavior under varied foggy weather conditions: A study using advanced driving simulator and survival analysis.

Previous studies have focused on the impact of visibility level on drivers' behavior and their safety in foggy weather. However, other important environmental factors such as road alignment have not been considered. This paper aims to propose a methodology in investigating rear-end collision avoidance behavior under varied foggy conditions, with focusing on changes in visibility and road alignment in this study. A driving simulator experiment with a mixed 2 × 4 × 6 factor design was conducted using an advanced high-fidelity driving simulator. The design matrix includes two safety-critical conditions, four visibility conditions, and six road alignment situations (in terms of the road curve and slope). Behavior variables from different dimensions were identified and compared under varied conditions. To estimate the safety of drivers, a time-based measurement, speed reduction time, is selected among the variables as a measure of safety. The survival analysis approach was introduced to model the relationship between environmental factors and driver safety, using speed reduction time as the survival time. Both the Kaplan-Meier method and the COX model were applied and compared. Results generally suggest that reduced visibility leads to more dangerous rear-end collision avoidance behavior from different aspects. Though findings are mixed regarding the road alignment, the impact of the road alignment was found to be significant. Interestingly, conditions of downward slope were found to be safer. Overall, the COX model outperformed the Kaplan-Meier method in understanding the impact of environmental factors, and it can be applied to investigate other contributing factors for freeway safety under foggy weather conditions.

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions.

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.

Optimal jam-absorption driving strategy for mitigating rear-end collision risks with oscillations on freeway straight segments.

Traffic oscillations (or stop-and-go waves) in freeway traffic jam cause large variation of vehicle speed and remarkably reduce travel safety. Previous jam-absorption driving strategies focused on the operational side and did not consider the safety effects caused by the controlled vehicle on freeways. In this paper, we proposed an optimal jam-absorption driving strategy to mitigate traffic oscillations and rear-end collision risks on freeway straight segments. Firstly, the proposed strategy determined the starting and ending point of an oscillation at the temporal and spatial dimensions based on the Wavelet Transform (WT) and the steady equilibrium condition of car-following driving. Then different controlled vehicles were evaluated by the given absorbing speeds. Various measurements were considered to evaluate the safety performance of the strategies. The optimal solution was obtained which guided the controlled vehicle to move slowly at the optimal jam-absorbing speed, and created a gap to eliminate the downstream oscillation timely but avoid causing secondary wave in the upstream traffic. The Intelligent Driver Model (IDM) was modified to build the simulation platform in a connected environment. The results showed that our proposed strategy effectively reduced the severity of traffic oscillations, or even fully eliminate the oscillations. The optimal strategy reduced the surrogate safety measures by 93.53 %-94.78 %, and decreased the total travel time by 1.27 %. We also compared our strategy with previous strategies and the results suggested that ours had better performances.