The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both. This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1,140-min of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multivideo stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based trajectory datasets, CitySim had significantly more safety-critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations’ three-dimensional base maps and signal timings.

This research proposes a method to estimate the practical effectiveness of 6 advanced driver assistance systems (ADAS) in avoiding crashes on different roadway facilities (locations). The systems include blind spot warning/lane change warning (BSW/LCW), forward collision warning (FCW), intersection movement assist (IMA), pedestrian crash avoidance and mitigation (PCAM), lane departure warning (LDW) and left turn assist (LTA) systems. The method involves the use of meta-analysis and quasi-induced exposure methodologies to estimate the distribution of effectiveness factors across the driver population. These factors include system operating conditions, system adoption rates and system usage features. Then a Monte-Carlo procedure is applied to sample these factors for simulating the practical ADAS effectiveness. The research shows that the practical effectiveness could be as much as 48.25% lower than the ideal effectiveness proposed by previous research. It is also found that BSW/FCW, PCAM, FCW and LDW systems exhibited the highest effectiveness when operating on urban expressways/freeways. On rural multilane highways, such systems are demonstrated to have the least effectiveness. Furthermore, LTA systems are found to perform better at rural intersections than at urban intersections while the conclusion is contrary regarding IMA systems. Among the six systems, BSW/LCW has the most consistent performance among facilities, while LTA is most likely to be affected by facility type. The integrated system of six ADAS systems could prevent at most 29.50% of total crashes on urban multi-lane highways, while its lowest practical effectiveness is 13.72% on rural two-lane highways. Finally, technical and policy implications of the results are discussed.

In the context of pro-active traffic management, real-time crash risk evaluation is one of the most critical components. Signalized intersections are well-known high-risk locations because of the variety of traffic movements, modes, and their interactions. Unlike access-controlled freeways, the traffic flow at signalized intersections presents cyclical characteristics, which are temporally separated by traffic signals. Therefore, the data preparation for real-time crash risk prediction at signalized intersections should be based on the signal cycle rather than a predefined fixed time interval (e.g., 5 minutes). In this research, the corresponding signal cycles where crashes occurred were verified based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). Six types of real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather. Two undersampling strategies, matched case-control and random undersampling, were utilized to develop conditional logistic and binary logistic models, respectively. Model results indicate that the random undersampling performs better than the matched case-control method. It was found that higher cycle volume, overall average flow ratio across lanes, arrivals on yellow ratio, traffic volatility across approach sections, as well as longer cycle length and lower green ratio could significantly increase the odds of crash occurrence at signalized intersections. Moreover, longer maximum queue length, bigger shockwave, and higher absolute queuing shockwave speed tend to increase the odds of crash occurrence.