Time Integrated Time-to-collision Research Articles

Analysis of the impact of cyberattacks on the lane changing behavior of connected automated vehicles

The characteristics of connected automated vehicles (CAVs) make them more susceptible to various cyberattacks, resulting in traffic congestion, delays, and even serious traffic accidents. In recent years, the safety issues of CAVs have become a hot research topic. The current research mostly focuses on studying and evaluating the impact of cyberattacks on the car-following behavior of single lane vehicles. However, lane changing behavior is also one of the most common traffic behaviors on the road, and compared to car-following behavior, lane changing is more complex, vehicles are more likely to collide with surrounding vehicles, and traffic accidents are more likely to occur. Therefore, it is necessary to explore the impact of cyberattacks on vehicles in lane changing environments. The purpose of this study is to investigate the effect of cyberattacks on the lane-changing behavior of two-lane vehicles and evaluate the security. First, this study establishes a framework for dynamics under cyberattacks by combining the intelligent driver model (IDM) and the minimizing overall braking induced by lane changes (MOBIL) model. Then, the traffic effects of different severity of attacks on the vehicle are simulated under three attack scenarios (bogus information, replay/delay and collusion cyberattacks), respectively. Finally, we use the conflict measures to analyze the collision risk of various cyberattacks, including time-to-collision (TTC), time-integrated time-to-collision (TIT) and the standard deviation (SD) of velocity. The simulation results indicate that different cyberattacks may cause changes in lane-changing time and refuse to change lanes. In addition, the CAV platoon of two lanes will lead to varying degrees of speed oscillations and collision risks. Among these, collusion attacks cause the most serious damage. These findings help to better understand the impact of cyberattacks on CAVs and lay the foundation for enhanced cybersecurity.

Risk prediction for cut-ins using multi-driver simulation data and machine learning algorithms: A comparison among decision tree, GBDT and LSTM

The cut-ins (one kind of lane-changing behaviors) have result in severe safety issues, especially at the entrances and exits of urban expressways. Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences. This paper makes some efforts on these purposes. In this paper, twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection. The surrogate measures, Time Exposure Time-to-Collision (TET) and Time Integrated Time-to-collision (TIT) were employed to quantify the risk of cut-ins, then k-means clustering was applied for risk classification of 3 levels. Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables. Based on these variables, three prediction models including decision tree (DT), gradient boosting decision tree (GBDT) and long short-term memory (LSTM) are used for predicting the risks of cut-ins. Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data. From results of risk prediction models, the LSTM, with an overall accuracy of 87%, outperforms the GBDT (80.67%) and DT (76.9%). Despite this, this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.

Simulation modeling and safety analysis of the mixed traffic flow with connected automated trucks platoon and human-driven vehicles

The rapid development of intelligent connected vehicles makes it possible to achieve breakthroughs in the application of technology in the connected automated trucks platoon(CATP) in the scenarios of ports, logistics parks, etc., but the safety of the mixed traffic flow formed by it and traditional human-driven vehicles has not been fully recognized. This paper builds a simulation scene based on SUMO(Simulation of Urban Mobility) simulation software, builds traditional human-driven vehicles and CATP car-following model, and studies the safety analysis of the mixed traffic flow of CATP and human-driven vehicles. According to the Traci interface of SUMO and PYTHON programming language, the functions of connected automated environment configuration and real-time data acquisition are realized, and the safety of mixed traffic flow is evaluated using multiple safety surrogate measures(SSM) such as time exposed time-to-collision(TET) and time-integrated time-to-collision(TIT). The simulation results show that when the CATP penetration rate is less than 0.5, the security risk of mixed traffic flow increases with the increase of penetration rate; When the CATP penetration rate exceeds 0.5, the safety risk of mixed traffic flow will be significantly reduced. The research conclusion of this paper has reference significance for the safety and practical implementation of CATP in ports and logistics parks.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Traffic safety evaluation of mixed traffic flow considering the maximum platoon size of connected automated vehicles

In this paper, we investigate the influence of the maximum platoon size of connected automated vehicles (CAVs) on the traffic safety of traffic flow mixed with human-driven vehicles (HDVs) and CAVs. First, the spatial distributions of vehicles within the mixed traffic flow are analyzed. On this basis, the car-following characteristics of different vehicles are obtained. Second, the distribution probability of the four car-following modes in mixed traffic flow is derived based on the maximum platoon size of CAVs. Then, the intelligent driver model (IDM), gap-regulating model, cooperative adaptive cruise control (CACC) model, and adaptive cruise control (ACC) model are used to capture the four different car-following modes, respectively. Finally, multiple safety measures, such as the coefficient of variation of the vehicle speed, time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT), are adopted to evaluate the safety of mixed traffic flow. Moreover, the impact of platoon size on the safety of mixed traffic flows is evaluated based on a numerical simulation. The results demonstrate that the magnitude of the impact of the platoon size of CAVs on the safety of mixed traffic flow is related to the penetration rate of CAVs.

Exploring the impact of damping on Connected and Autonomous Vehicle platoon safety with CACC

Connected and Autonomous Vehicle (CAV) platooning has gained attention for its potential for vastly enhancing capacity and safety of future transport systems. Previous research on CAV platoon control approximates stability as safety during CAV operations and primarily addressed instability and stability properties. Nevertheless, it is vital to note that stability does not stand for absolutely safe, since the behaviors of CAV platoons could also be impacted by the oscillation characteristics of damping that are essential for CAV platoons to boost reliability against variant continuous disturbances. To explore the impact of damping on CAV platoon safety, the time-domain analysis approach is introduced to reveal the relationship between damping characteristics and CAV platoon safety performance with Cooperative Adaptive Cruise Control (CACC) based on the constant time headway policy under stable conditions. Numerical simulations are carried out to verify the impacts of the damping on CAV platoon performance and evaluate the safety of a platoon possessing stability under different damping properties. Further, different control parameters based on damping and stability properties are applied to CAV platoons and their safety is evaluated by calculating the Deceleration Rate to Avoid a Crash (DRAC), Time Exposed Time-to-Collision (TET) and Time-Integrated Time-to-Collision (TIT) as surrogate safety measures of the rear-end collision risk. Numerical analysis results show that it could better maintain platoon safety, when control parameters make the damping ratio between 1 and 1.1.

Risk-based merging decisions for autonomous vehicles

Introduction: The safe freeway merging operation for fully Autonomous Vehicles (AVs) in mixed traffic (i.e., the presence of AVs and non-AVs in a traffic stream) is a challenging task. Under a mixed traffic environment, an AV merging operation could significantly increase conflict risks and reduce operational efficiency. Method: This study quantifies the freeway merging conflict risk and develops a freeway merging decision strategy based on conflict risk assessment for an AV attempting to merge to a traffic stream with non-AVs on the freeway. The performance of the risk-based merging decision strategy is evaluated in uncongested, near-congested, and congested traffic conditions. Results: The analyses show that the risk-based merging strategy causes less abrupt deceleration of an AV’s immediate upstream vehicle in the target lane on the freeway compared to the based models (i.e., two models based on gap acceptance concepts and a safe gap model based on a surrogate measure, ‘Time-to-Collision (TTC)’). The risk-based merging strategy meets the minimum safe gap between an AV intending to merge and its immediate downstream vehicle in the target lane. The risk-based merging strategy produces lower conflict risk in terms of ‘Time Exposed Time-to-Collision (TET)’ and ‘Time Integrated Time-to-Collision (TIT)’ compared to the base models. Moreover, the risk-based merging strategy has a lower impact on the average speed of traffic in the target lane compared to the base models considered in this study. Conclusions: The risk-based merging strategy shows higher safety benefits for an AV’s merging operation compared to base models. Practical Applications: The findings of this research would help design AV controllers for improving the safety of an AV merging operation in a mixed traffic stream.

Safety Impact of Connected Vehicles on Driver Behavior in Rural Work Zones under Foggy Weather Conditions

Work zone safety is one of the paramount goals of the safety community. Safety in WZs is a particular concern under foggy conditions as they represent an exogenous factor contributing to high variability in driver behavior. In line with the Connected Vehicle (CV) Pilot Deployment Program on Interstate-80 (I-80) in Wyoming, this study investigates the safety benefits of CV Work Zone Warning (WZW) applications on driver behavior during foggy weather conditions. A work zone (WZ) was simulated using VISSIM in four sequential areas, including the advance warning, transition, activity, and termination area. The effect of drivers’ increased situational awareness under the effect of WZW was calibrated in VISSIM based on the results of a high-fidelity driving simulator experiment. Various Surrogate Measures of Safety (SMoS), including Time-To-Collision (TTC), Time Exposed Time-to-collision (TET), Time-Integrated Time-to-collision (TIT), and Modified Deceleration Rate to Avoid Crash (MDRAC), were employed to quantify the safety performance of CVs under varying CV Market Penetration Rates (MPRs). According to the results of TTC and MDRAC, it was found that an increase in CV-MPR enhances the safety performance of the WZ area. Findings showed that, under foggy weather conditions, the advance warning area had the highest TIT and TET values. Furthermore, it was revealed that an increase in MPR of up to 60% on I-80 would reduce mean speeds and the standard deviation of speed at each of the WZ areas, leading to more speed harmonization and minimizing the crash risk in WZs.

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.

Effectiveness of the compensatory strategy adopted by older drivers: Difference between professional and non-professional drivers

It has been a controversial issue for the effect of ageing population on driving safety. Apparently, drivers’ physiological and cognitive performances deteriorate with age. However, older drivers may compensate for the elevated risk by adjusting their behaviors, known as compensatory strategy. Despite the extensive research on this topic, the compensatory strategy of older professional drivers is not well understood since many studies focused on the differences in compensatory behavior between older and young drivers. Professional drivers tend to be more skillful and able to cope with the unfavorable driving environments, thus presenting a higher capability to mitigate the risk. This study attempts to examine the compensatory behavior and its safety effect amongst older professional drivers, as compared to those of older non-professional drivers, using the driving simulator approach. In the driving simulator experiment, participants were asked to follow a leading vehicle for one hour, and two sudden brake events were presented. 41 (mid-aged and older) drivers completed the driving tests. Each participant was required to complete a car-following test, either under high or low traffic flow conditions. Performance indicators include driving capability (i.e. lateral control, longitudinal control, and brake reaction time) and compensatory behavior (i.e. average speed, and time headway). Additionally, two modified traffic conflict measures: time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) are applied to indicate the traffic conflict risk. The random parameter Tobit models were estimated to measure the association between conflict risk and driver attributes, and random intercept models were used to assess other driving performance indicators. Results show that despite the impaired lateral control performance and longer brake reaction time of older drivers, the likelihood of severe traffic conflict of older drivers is lower than that of mid-aged drivers. Furthermore, though both older professional and older non-professional drivers adopted longer time headway, the reduction in the risk of severe traffic conflict is more profound among the older professional drivers. Such findings suggest that older professional drivers are more capable of mitigating the possible collision risk by adopting the compensatory strategy, as compared to older non-professional drivers. This justifies the existence of compound effect by the compensatory strategy of older driver and better driving skills of professional driver. This research provides useful insights into driver training and management strategies for employers, as older drivers would become a major cohort in the transportation industry.

Calibration and evaluation of the Responsibility-Sensitive Safety model of autonomous car-following maneuvers using naturalistic driving study data

Safety guarantees are vital to the dependability of the automated vehicle (AV), so are of primary concern to the AV industry and regulatory bodies. Responsibility-Sensitive Safety (RSS), proposed by Mobileye, is a rigorous mathematical model that defines the real-time safety distance that the AV needs to maintain in relation to surrounding vehicles, and helps the AV respond to dangerous situations. However, RSS’s performance has not yet been tested with real driving data. This study thus calibrates and evaluates the RSS model based on car-following scenarios created from safety-critical events (SCEs) detected in the Shanghai Naturalistic Driving Study. SCEs were identified from the raw data with a trigger method; they were manually validated and then converted into virtual scenarios in which the human-driven subject vehicle was replaced with an AV controlled by a model predictive control based adaptive cruise control (ACC) system embedded with RSS. The RSS model was calibrated with the goal of achieving optimal safety performance, that is, of generating the lowest time-integrated time-to-collision (TIT) after the AV algorithms were implemented in the safety-critical scenarios. By comparing the performance of RSS-embedded ACC, ACC-only, and the unassisted human driver, this study found that: 1) RSS-embedded ACC performed best in eliminating underlying safety-critical situations by controlling the average TIT for each 15-second event at 2.65 s2; 2) RSS-embedded ACC generated the highest average relative speed and relative distance, and the lowest standard deviation of speed; and 3) on average, RSS-embedded ACC was able to perceive the SCEs 2.31 s ahead of the human driver.

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.

Optimal off-ramp terminal locating strategy based on traffic safety and efficiency

ABSTRACT Off-ramps are key nodes which connect urban elevated highways and auxiliary roads, and the distance between an off-ramp terminal and an intersection (ORTI-D) relates directly to traffic safety and efficiency. The authors introduced a new off-ramp terminal locating strategy considering traffic safety and efficiency synthetically. After building a basic VISSIM simulation model which included two-lane off-ramp, one-lane auxiliary road before the weaving area and four-lane entrance road before a no-signal intersection, the functional relationships of the LWA (Length of Weaving areas)-TIT (Time integrated time-to-collision) and the LWA-TTD (Total traffic delay) were developed. On this basis, the genetic algorithm was used to obtain the traffic safety-efficiency optimal ORTI-D and a positive correlation between the weaving proportion and the optimal ORTI-D was found. The methods developed in this paper can satisfy the requirements of off-ramps engineering design and contribute to solve traffic safety and efficiency problems in traffic management.

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Cooperative adaptive cruise control (CACC) is a promising technology to improve traffic efficiency and enhance road safety. In this paper, a modified CACC control model considering the communication time delay is proposed, which is used to investigate the longitudinal safety impacts of the communication time delay to the CACC platoon. Then, the communication time delay model is integrated into the CACC model to simulate the realistic information transfer process in the CACC platoon. Then a microscopic CACC platoon simulation is designed and conducted to verify the feasibility and reliability of the modified CACC control algorithm. The obtained results reveal that the modified CACC control algorithm can not only reduce about 96.6% of inter-vehicle spacing error, but also enhance the vehicles’ ability to sense the upstream traffic changes. Furthermore, to quantitatively analyze the longitudinal safety influence of the time delay caused by representative communication systems, sensitivity analysis experiments of headway time were designed and conducted. In the sensitivity analysis, the time exposed time-to-collision (TET) and the time-integrated time-to-collision (TIT) were introduced as the key performance indicators (KPIs) to quantify the rear-end collision risks. Sensitivity analysis results demonstrate that the performance of the CACC platoon is strictly related to the applied wireless communication style. Furthermore, the CACC system supported by the 5th generation (5G) communication system shows great advantages in narrowing the minimal headway time gap and reducing the rear-end collision risks.

Short-term prediction of safety and operation impacts of lane changes in oscillations with empirical vehicle trajectories

Lane changes made during traffic oscillations on freeways largely affect traffic safety and could increase collision potentials. Predicting the impacts of lane change can help to develop optimal lane change strategies of autonomous vehicles for safety improvement. The study aims at proposing a machine learning method for the short-term prediction of lane-changing impacts (LCI) during the propagation of traffic oscillations. The empirical lane-changing trajectory records were obtained from the Next Generation Simulation (NGSIM) platform. A support vector regression (SVR) model was trained in this study to predict the LCI on the crash risks and flow change using microscopic traffic variables such as individual speed, gap and acceleration on both original lanes and target lanes. Sensitivity analyses were conducted in the SVR to quantify the contributions of correlative lane changing factors. The results showed that the trained SVR model achieved an accuracy of 72.81 % for the risk of crashes and 95.34 % in predicting the flow change. The sensitivity analysis explored the optimal speed and acceleration for the lane changer to achieve the lowest time integrated time-to-collision (TIT) value for safety maximization. Finally, we compared the LCI for motorcycles, automobiles and trucks as well as the LCI for both lane-changing directions (from left to right and from right to left). It was found that motorcycles conducted lane changes with smaller gaps and larger speed differences, which brings the highest crash risks. Passenger cars were found to be the safest when they conduct lane changes. Lane changes to the right had more negative impacts on traffic flow and crash risks.

Analysis of impact of elderly drivers on traffic safety using ANN based car-following model

The purpose of this study is to quantitatively investigate the impact of elderly drivers on traffic safety. Based on the data collected from a driving simulator, this study utilizes artificial neural network (ANN) to establish a car-following model with consideration of elderly drivers. The model validation demonstrates that the ANN based car-following model has a high fitting degree and strong predictive capacity. Furthermore, two surrogate safety indicators, number of potential collisions (NPC) and Time Integrated Time-to-collision (TTT) which characterize the number of potential collisions and the degree of potential crash risk respectively, are used to quantify the road traffic safety with elderly drivers. The simulation results show that car-following behavior of elderly drivers made a strong impact on the stability of the following platoon. The increase of elderly drivers could make the variability in the speed of all the followers greater, and lead to drastic increases in both NPC and TTT per vehicle per second. Besides, the two indicators increase firstly and then decrease as the stationary time of the first vehicle increases. And they decrease as the absolute value of deceleration for the first vehicle increases when the proportion of elderly drivers is higher. We hope the results in this study may be helpful to the development of road safety interventions under the aging population background.

Evaluation of cooperative systems on driver behavior in heavy fog condition based on a driving simulator

This paper studies the effectiveness of fog warning systems on driving performance and traffic safety in heavy fog condition. A comparison study was conducted for four scenarios in heavy fog condition. First, a series of indexes corresponding to driving speed adjustments and surrogate measures of safety was obtained to explore the impacts that fog warning systems have on driving behavior and traffic safety when approaching a fog area. This study divided the analyzed road into three different zones (clear zone, transition zone, and fog zone) according to visibility levels. Then, multivariate analysis of variance (MANOVA) was conducted, and the effects of drivers’ individual characteristics on driving behavior were also investigated. Moreover, the linear mixed model with random effects was estimated to consider the contributing factors of the drivers’ speed adjustment behaviors. In addition, the standard deviation of speed, TET (time exposed time-to-collision), and TIT (time integrated time-to-collision) were selected to evaluate the longitudinal safety. To obtain the driving data, an empirical driving simulator platform was established based on a real-world road in Beijing. Thirty-five drivers were recruited to participate in the driving experiment. The results showed that the cooperative vehicle-infrastructure warning systems could be beneficial to better driving behavior and safer traffic operations. The results revealed that the warning systems could be beneficial to speed reduction before entering a fog area. In addition, the On-Board Unit (OBU) had a significant impact on individual speed adjustment. Moreover, the results showed that scenarios with fog warning systems improve safety significantly over the no warning system scenario. The study results could also facilitate the selection of a proper information release format in the context of connected vehicles.

Impacts of reduced visibility under hazy weather condition on collision risk and car-following behavior: Implications for traffic control and management

This paper examines the impacts of reduced visibility under hazy weather conditions on collision risk and car-following behavior to enhance the understanding of relationships among weather conditions, driving performance and road safety. A high-fidelity driving simulator is applied to collect vehicle trajectory data concerning a number of driving performance metrics under both clear and hazy weather conditions. The collision risks under different weather conditions as indicated by two surrogate measures – time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) – are compared. Results indicate that hazy weather conditions have significant impacts on traffic safety in terms of increased collision risk and impaired car-following performance. The TET and TIT with a critical time-to-collision threshold value of 5 s under hazy weather conditions are 35.9% and 43.0% higher, respectively, than those under clear weather conditions. The increase in the low collision risk is more noticeable than that of the high collision risk. For car-following behavior, under hazy weather conditions, the average reaction time is higher and the sensitivity to the change in car-following spacing is lower. In addition, the interaction effects of vehicular speed on the relationship between weather conditions and some driving performance metrics are significant. Both the distance and time headways under hazy weather conditions are lower than those under clear weather conditions when the vehicular speed is high. However, no significant evidence is found for the relationship between weather conditions and distance or time headways when the vehicular speed is low or medium. Moreover, the variations in speeds under hazy weather conditions are higher than those under clear weather conditions when the speed is high or medium. The plausible causal relationships among the weather conditions, driving performance changes and collision risk are analyzed and discussed. Findings of this study contribute to the effective traffic control and management to enhance road safety under hazy weather conditions.

Longitudinal safety impacts of cooperative adaptive cruise control vehicle's degradation

Introduction: The adaptive cruise control (ACC) and cooperative ACC (CACC) systems are critical parts of self-driving vehicles. The ACC vehicles detect front vehicle' information via vehicle-mounted sensors and make longitudinal reactions automatically, while CACC vehicles enhance the performance by vehicle-to-vehicle (V2V) wireless communication. However, CACC vehicles may abruptly degrade to ACC mode in reality due to various reasons, including communication failures, driver manipulations, and cyber-attacks. The sudden degradation will definitely bring negative influences on safety. Method:This study quantitatively evaluated the longitudinal safety impacts of vehicles' degradation in a CACC fleet based on microscopic simulations. The realistic CACC and ACC models proposed by the California Partners for Advanced Transit and Highways (PATH) were used for simulation experiments. The time integrated time-to-collision (TIT) was measured to quantify the collision risks. Extensive simulations were conducted via a fleet of 10 CACC vehicles and speed profiles of vehicles in different scenarios were compared. Key factors, including the leading vehicle's deceleration rate, the number of vehicles between degraded vehicles (NVDVs), threshold of TTC, and visibility were also examined via sensitivity analyses. Results and conclusions: Simulation results indicate that degradation has significant negative influences on longitudinal safety of degraded vehicles under the driving state of deceleration. Degradation at middle positions in a CACC fleet, such as fourth and fifth positions, is much safer than that at others. Moreover, nonadjacent degradation is much riskier than adjacent degradation at the front positions of a fleet. NVDVs can bring inverse impacts on safety with different degradation positions. Speed profiles imply that the hysteresis of degraded vehicles' speed control is the major reason for high collision risks. Practical applications: Appropriately, hierarchical countermeasures have the potential to reduce the longitudinal safety impacts of degradation. Findings of this study can contribute to determining the applicable length of CACC fleets.

Safety benefits of arterials’ crash risk under connected and automated vehicles

This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from microsimulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.