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Road safety has always been a crucial priority for municipalities, as vehicle accidents claim lives every day. Recent rapid improvements in video collection and processing technologies enable traffic researchers to identify and alleviate potentially dangerous situations. This paper illustrates cutting-edge methods by which conflict hotspots can be detected in various situations and conditions. Both pedestrian–vehicle and vehicle–vehicle conflict hotspots can be discovered, and we present an original technique for including more information in the graphs with shapes. Conflict hotspot detection, volume hotspot detection, and intersection-service evaluation allow us to understand the safety and performance issues and test countermeasures comprehensively. The selection of appropriate countermeasures is demonstrated by extensive analysis and discussion of two intersections in Gainesville, Florida, USA. Just as important is the evaluation of the efficacy of countermeasures. This paper advocates for selection from a menu of countermeasures at the municipal level, with safety as the top priority. Performance is also considered, and we present a novel concept of a performance–safety trade-off at intersections. 

As a part of road safety initiatives, surrogate road safety approaches have gained popularity due to the rapid advancement of video collection and processing technologies. This paper presents an end-to-end software pipeline for processing traffic videos and running a safety analysis based on surrogate safety measures. We developed algorithms and software to determine trajectory movement and phases that, when combined with signal timing data, enable us to perform accurate event detection and categorization in terms of the type of conflict for both pedestrian-vehicle and vehicle-vehicle interactions. Using this information, we introduce a new surrogate safety measure, “severe event,” which is quantified by multiple existing metrics such as time-to-collision (TTC) and post-encroachment time (PET) as recorded in the event, deceleration, and speed. We present an efficient multistage event filtering approach followed by a multi-attribute decision tree algorithm that prunes the extensive set of conflicting interactions to a robust set of severe events. The above pipeline was used to process traffic videos from several intersections in multiple cities to measure and compare pedestrian and vehicle safety. Detailed experimental results are presented to demonstrate the effectiveness of this pipeline. 

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method. 

Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine.