Objective: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain. Method: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach. Results: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit. Conclusions: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.

The real-time kinematic differential Global Positioning System (GPS) has facilitated a new horizon in traffic engineering. Multiple car-following experiments conducted with a real-time kinematic GPS with 10 vehicles participating in a probing field gave high-quality results in headway, speed, relative speed, and acceleration. The expected accuracies for measuring position and speed were 10 mm and 0.16 km/h, respectively. The vehicles were driven in a loop consisting of two parallel straight sections connected by two semicircular curves. Different driving conditions were induced in the platoon by instructing the leading driver to follow predetermined speed variations. The experiments yielded sets of continuous observations. Headway, speed, and acceleration were measured using conventional equipment for the purpose of comparing accuracy. The accuracy of the data obtained using the GPS was superior to that of the same data obtained using conventional measurements. The variation in driving characteristics down the stream of vehicles was studied using the experimental data. The results showed that the reaction time between a change in relative speed and the corresponding change in acceleration varies during the driving process. The reaction time of individual drivers also changes along the platoon. The good-quality data were able to give high-resolution plots of acceleration and relative speed illustrating that both the reaction time and the functional relationship between acceleration and relative speed do not remain constant.

The performance of six microscopic traffic flow models was investigated on the basis of how well these models fit with the real-time kinematic Global Positioning System (GPS) measurements. Ten passenger cars equipped with the GPS receivers participated in the car-following experiments, conducted at a test track. The genetic algorithm-based approach is adopted to optimize the model parameters for two different cases: using speed and headway data. The optimized performance of each model is analyzed for various driving conditions introduced by the different level of disturbances to the lead vehicle's speed, which include half-wave, one-wave, two-wave, three-wave, random, and constant speed patterns. In the former case with speed data, five models performed well with the average percentile error ranging from 3.87% to 4.71% and standard deviation ranging from 1.09% to 1.64%. In the latter case with headway data, only three models performed well with the average percentile error ranging from 12.04% to 12.91% and standard deviation ranging from 4.53% to 5.13%. All models performed better in the former case than in the latter case. The interpersonal variations are significant compared with the intermodel variations and indicate individual drivers' influence on the car-following phenomena.

Car-following is the activity of safely driving behind a leading vehicle. Traditional mathematical car-following models capture vehicle dynamics without considering human factors, such as driver distraction and the reaction delay. Consequently, the resultant model produces overly safe driving traces during simulation, which are unrealistic. Some recent work incorporate simplistic human factors, though model validation using experimental data is lacking. In this paper, we incorporate three distinct human factors in new compositional car-following model called modal car-following model, which is based on hybrid input output automata (HIOA). HIOA have been widely used for the specification and verification of cyber-physical systems. HIOA incorporate the modeling of the physical system combined with discrete mode switches, which is ideal for describing piece-wise continuous phenomena. Thus, HIOA models offer a succinct framework for the specification of car-following behavior. The human factors considered in our approach are estimation error (due to imperfect distance perception), reaction delay, and temporal anticipation. Two widely used car-following models called Intelligent Driver Model (IDM) and Full Velocity Difference Model (FVDM) are used for extension and comparison purpose. We evaluate the root mean square (rms) error of the following vehicle position using the traces obtained from human drives through different driving scenarios. The result shows that our model reduces the rms error in IDM and FVDM by up to 48.8% and 7.41%, respectively.