Exploring the Use of Drones for Conducting Traffic Mobility and Safety Studies

ABSTRACT Advanced traffic data collection methods, including the application of aerial sensors (drones) as traffic data collectors, can provide real-time traffic information more efficiently, effectively, and safely than traditional methods. Traffic trajectory data like vehicles’ coordinates and point timestamps are challenging to obtain at intersections using traditional field survey methods. The coordinates and timestamps crucial in calculating trajectories can be obtained using drones and their particular integrated software. Thus, this study explores the use of unmanned aerial systems (UAS), particularly tethered drones, to obtain traffic parameters for traffic mobility and safety studies at an unsignalized intersection in Tallahassee, Florida. Tethered drones provided more flexibility in heights and angles and collected data over a relatively larger space needed for the proposed approach. Turning movement counts, gap study, speed study, and Level of Service (LOS) analysis for the stated intersection were the traffic studies conducted in this research. The turning movements were counted through ArcGIS Pro. From the drone footages, the gap study followed by the LOS analysis was carried out. A speed algorithm was developed to calculate speed during a speed study. Based on the results, the intersection operates under capacity with LOS B during the time. Also, the results indicated that the through movement traffic tends to slow down as they approach the intersection while south-bound right and east-bound left-turning traffic increase their speeds as they make a turn. Accuracy assessment was done by comparing the drone footages with the results displayed in ArcGIS software. The drone’s data collection was 100% accurate in traffic movement counting and 96% accurate in traffic movement classification. The level of accuracy is sufficient compared to other advanced traffic data collection methods. In this study, safety was assessed by the surrogate safety measures (SSMs). SSMs can be the viable alternatives for locations with insufficient historical data and indicate potential future conflicts between roadway users. The surrogate measures used in this study include the Time to Collision (TTC), Deceleration-based Surrogate Safety Measure (DSSM), and Post-encroachment Time (PET). TTC and DSSM were used for rear-end conflicts, while PET was used to evaluate cross conflicts and other conflicts such as sideswipes. The number of potential conflicts obtained in a one-hour study period was around 20 per 1000 vehicles traversing the intersection. The number of potential conflicts in one non-peak hour may indicate a safety problem associated with the intersection. This study’s findings can help develop appropriate guidelines and recommendations to transportation agencies in evaluating and justifying the feasibility of using tethered drones as safer and cheaper data collection alternatives while significantly improving intersection safety and operations

Development,Validation, and Integration of AI-Driven Computer Vision System and Digital-twin System for Traffic Safety Dignostics

The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive and microscopic traffic safety analysis. This research presents the development, validation, and integration of three AI-driven computer vision systems for vehicle trajectory extraction and traffic safety research: 1) A.R.C.I.S, an automated framework for safety diagnosis utilizing multi-object detection and tracking algorithm for UAV videos. 2)N.M.E.D.S., A new framework with the ability to detect and predict the key points of vehicles and provide more precise vehicle occupying locations for traffic safety analysis. 3)D.V.E.D.S applied deep learning models to extract information related to drivers\u27 visual environment from the Google Street View (GSV) images. Based on the drone video collected and processed by A.R.C.I.S at various locations, CitySim: a new drone recorded vehicle trajectory dataset that aim to facilitate safety research was introduced. CitySim has vehicle interaction trajectories extracted from 1140- minutes of video recordings, which provide a large-scale naturalistic vehicle trajectory that covers a variety of locations, including basic freeway segments, freeway weaving segments, expressway segments, signalized intersections, stop-controlled intersections, and unique intersections without sign/signal control. The advantage of CitySim over other datasets is that it contains more critical safety events in quantity and severity and provides supporting scenarios for safety-oriented research. In addition, CitySim provides digital twin features, including the 3D base maps and signal timings, which enables a more comprehensive testing environment for safety research, such as autonomous vehicle safety. Based on these digital twin features provided by CitySim, we proposed a Digital Twin framework for CV and pedestrian in-the-loop simulation, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to guide the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins

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 1140 minutes of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic 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, multi-video 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 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.Comment: Transportation Research Record (2023

A Dynamic Data Driven Application System for Vehicle Tracking

AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described

Walking against traffic and pedestrian injuries in the United Kingdom: new insights

Background: Studies from Finland and Taiwan have shown that walking against traffic was beneficial for reducing pedestrian crashes and fatalities. This study examined whether such beneficial effects are consistent across various circumstances. Methods: This study aimed to investigate pedestrian fatalities in walking-against or with-traffic crashes by analysing the UK STATS19 crash data for the period between 1991 and 2020. We firstly employed Chi-square tests to examine risk factors for pedestrian injury severity. These variables were then incorporated into stepwise logistic regression models with multiple variables. We subsequently conducted joint effect analysis to investigate whether the beneficial effects of walking against traffic on injury severity vary across different situations. Results: Our data contained 44,488 pedestrian crashes, of which 16,889 and 27,599 involved pedestrians walking against and with traffic, respectively. Pedestrians involved in with-traffic crashes were more likely to sustain fatalities (adjusted odds ratio [AOR] = 1.542; confidence interval [CI] = 1.139–1.927) compared with those in walking against-traffic crashes. The detrimental effect of walking with traffic on fatalities appeared to be more pronounced in darkness-unlit conditions (AOR = 1.48; CI = 1.29–1.70), during midnight hours (00:00–06:59 am) (AOR = 1.60; CI = 1.37–1.87), in rural areas (AOR = 2.20; CI = 1.92–2.51), when pedestrians were elderly (≥ 65 years old) (AOR = 2.65, CI = 2.16–3.26), and when heavy goods vehicles were crash partners (AOR = 1.51, CI = 1.28–1.78). Conclusions: Walking against traffic was beneficial in reducing pedestrian fatalities compared with walking with traffic. Furthermore, such a beneficial effect was more pronounced in darkness-unlit conditions, at midnights (00:00–06:59 am), in rural areas, when pedestrians were elderly, and when heavy goods vehicles struck pedestrians

Towards Next Generation of Pedestrian and Connected Vehicle In-the-loop Research: A Digital Twin Simulation Framework

Digital Twin is an emerging technology that replicates real-world entities into a digital space. It has attracted increasing attention in the transportation field and many researchers are exploring its future applications in the development of Intelligent Transportation System (ITS) technologies. Connected vehicles (CVs) and pedestrians are among the major traffic participants in ITS. However, the usage of Digital Twin in research involving both CV and pedestrian remains largely unexplored. In this study, a Digital Twin framework for CV and pedestrian in-the-loop simulation is proposed. The proposed framework consists of the physical world, the digital world, and data transmission in between. The features for the entities (CV and pedestrian) that need digital twined are divided into external state and internal state, and the attributes in each state are described. We also demonstrate a sample architecture under the proposed Digital Twin framework, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to provide guidance to the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development of ITS applications on CV and pedestrian

Complexity of external distractors from small unmanned aircraft systems

The integration of small unmanned aircraft system (sUAS) into the transportation system has presented a unique opportunity to investigate the effects these operations will have on other areas of the system. Specifically, as a potential distraction to terrestrial vehicle operators. The current study investigated the potential of sUAS operations as a perturbation variable on the human attention system. Using methods from a dynamical system perspective evaluation of the potential in the increased complexity sUAS operations might introduce as an external distraction to drivers. The primary measure of this within-subjects research is saccade speeds over time, analyzed with time series analysis. Other metrics recorded included fixation duration, and lane deviations. A majority of participants did not glance at the sUAS longer than two seconds and fixation durations were reduced as a function of altitude. Some of the participants did display increased complexity of saccade movements during encounters with sUAS; however, the majority did not display increased complexity. Participants did not significantly deviate in the driving lanes when encountering a sUAS operation. The findings outlined in the current study suggest that commercial sUAS operations near or over roadways will cause no more risk of distraction than current external distractors present while driving

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

Transport Systems: Safety Modeling, Visions and Strategies

This reprint includes papers describing the synthesis of current theory and practice of planning, design, operation, and safety of modern transport, with special focus on future visions and strategies of transport sustainability, which will be of interest to scientists dealing with transport problems and generally involved in traffic engineering as well as design, traffic networks, and maintenance engineers