Traffic Risk Modelling and Analysis under Airport-Like Simulation Environment

Traffic safety plays a crucial role in the development of autonomous vehicles which attracts significant attention in the community. It is a challenge task to ensure autonomous vehicle safety under varied traffic environment interference, especially for airport-like closed-loop conditions. To that aim, we analyze autonomous vehicle safety at typical roadway conditions and traffic state constraints (e.g., car-following state at different speed distributions) by simulating the airport-like traffic conditions. The experimental results suggest that traffic collision risk is in a positive relationship with the speed difference and distance among adjacent vehicles. More specifically, the autonomous vehicle may collide with neighbors when the time to collision (TTC) indicator is lower than 4 s, and vice versa. The research findings can help both research community and practioners obtain additional information for improving traffic safety for autonomous vehicles.

Analysing pedestrian–vehicle conflicts behaviour at urban pedestrian crossings

Pedestrian safety has become a serious problem with the rapid growth of motorised vehicle in transportation system in developing counties. Pedestrians often respond differently to changes in surrounding and traffic conditions. A study was undertaken to investigate pedestrians’ gap acceptance and the parameters affecting their risk-taking behaviours based on time-to-collision and post-encroachment-time indexes. Three signalised intersections and two midblock crossings were selected in Qazvin, Iran. A total of 752 pedestrians were examined by video recording and field observation, and pedestrians’ gap acceptance behaviour was estimated by using binary logit model. Results showed that the average time to collision and post-encroachment time were 4.27 s and 1.44 s, respectively. In addition, the presence of children alongside the older pedestrians led to a less risk-taking crossing. Additionally, pedestrian risk-taking was reduced by increasing both time indexes. Rainy weather also reduced pedestrians’ risk-taking behaviour. Elasticity analysis indicated that parameters such as pedestrians’ conflict with vehicles at the first or second half of the crossings, walking with a child, speed of the approaching vehicle, the crossing type and running while crossing were the most important factors in pedestrian risk-taking.

Intersection Safety Assessment Using Video-Based Traffic Conflict Analysis: The Case Study of Thailand

In the road transport network, intersections are among the most critical locations leading to a risk of death and serious injury. The traditional methods to assess the safety of intersections are based on statistical analyses that require crash data. However, such data may be under-reported and omit important crash-related factors. The conventional approaches, therefore, are not easily applied to making comparisons of intersection designs under different road classifications. This study developed a risk-based approach that incorporates video-based traffic conflict analysis to investigate vehicle conflicts under mixed traffic conditions including motorcycles and cars in Thailand. The study applied such conflict data to assess the risk of intersections in terms of time-to-collision and conflict speed. Five functional classes of intersections were investigated, including local-road/local-road, local-road/collector, collector/arterial, collector/collector, and arterial/arterial intersections. The results showed that intersection classes, characteristics, and control affect the behavior of motorists and the safety of intersections. The results found that the low-order intersections with stop/no control are high risks due to the short time-to-collision of motorcycle-related conflicts. They generate frequent conflicts with low chance of injury. The high-order intersections with signal control are high risks due to high conflicting speeds of motorcycle–car conflicts. They generate few conflicts but at a high chance of injury. The study presents the applicability of video-based traffic conflict analysis for systematically estimating the crash risk of intersections. The risk-based approach can be deemed as a supplement indicator in addition to limited crash data to evaluate the safety of intersections. However, future research is needed to explore the potential of other road infrastructure under different circumstances.

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.

Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

Estimating Lane Change Duration for Overtaking in Nonlane-Based Driving Behavior by Local Linear Model Trees (LOLIMOT)

Lane change (LC) is one of the main maneuvers in traffic flow. Many studies have estimated LC duration directly by using lane-based data. The current research presents an estimate of LC duration for overtaking maneuver in nonlane-based traffic flow. In this paper, the LC duration is estimated implicitly by modeling lateral speed and applying the length of required lateral movement to complete the LC maneuver. In lateral speed modeling, the local linear model tree is applied which consists of three variables: the initial lateral distance, longitudinal speed, and time to collision (TTC), which itself is a function of the relative speed of follower and the distance between the two vehicles. The initial lateral distance is the relative transverse distance from which the following vehicle initializes the LC. The range of lateral speed was estimated between 0.5 and 5 km/h, which resulted in the LC duration between 2.5 and 24 sec. The results indicate that the lateral and longitudinal speed would be inversely related, while the lateral speed and the initial transverse distance as well as TTC would be directly related. The findings also indicate that TTC can be assumed as the most important factor affecting lateral speed. TTC at 8 sec can be considered as the threshold for its effect on the LC duration since at longer TTCs, and the lateral speed has remained almost constant. When TTC is longer than 8 sec, it would not affect the LC duration.