Search for: All records

Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels (e.g., barges). Although methods to track vessels (e.g., tug and tow boats) are publicly available through Automatic Identification System (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are nonexistent, especially in real-time settings. This study developed a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet were employed to detect the presence of vessels/barges from video and classify them (no vessel or barge, vessel without barge, vessel with barge, barge). A dataset of 331 annotated images was collected from five existing traffic cameras along the Mississippi and Ohio Rivers for model development. YOLOv8 achieved an F1-score of 96%, outperforming YOLOv5, SSD, and EfficientDet at 86%, 79%, and 77%, respectively. Sensitivity analysis was carried out for weather conditions (rain, fog) and location (Mississippi and Ohio River). A background subtraction technique normalized the video images across the various locations for the location sensitivity analysis. This model could be used to detect the presence of barges along river segments, which could be used for anonymous bulk commodity tracking and monitoring. Such data are valuable for long-range transportation planning efforts carried out by public transportation agencies, and for operational and maintenance planning conducted by federal agencies such as the U.S. Army Corps of Engineers. 

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. 

Abstract Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed schedule. Optimizing traffic controllers is time consuming and usually require experienced traffic engineers. Recent research has demonstrated the potential of using deep reinforcement learning (DRL) in this context. However, most of the studies do not consider realistic settings that could seamlessly transition into deployment. In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints. In this framework, we also propose a novel reward function that shows significantly improved traffic performance compared to the typical baseline pre-timed and fully-actuated traffic signals controllers. The framework is implemented and validated on a simulation platform emulating real-life traffic scenarios and sensor data streams.