Providing Real-time Driver Advisories in Connected Vehicles: A Data-Driven Approach Supported by Field Experimentation

Approximately 94\% of the annual transportation crashes in the U.S. involve driver errors and violations contributing to the $1 Trillion losses in the economy. Recent V2X communication technologies enabled by Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) can provide cost-effective solutions for many of these transportation safety applications and help reduce crashes up to 85%. This research aims towards two primary goals. First, understanding the feasibility of deploying V2V-based safety critical applications under the constraints of limited communication ranges and adverse roadway conditions. Second, to develop a prototype application for providing real-time advisories for hazardous driving behaviors and to notify neighboring vehicles using available wireless communication platform. Towards accomplishing the first goal, we have developed a mathematical model to quantify V2V communication parameters and constraints pertaining to a DSRC-based “Safe pass advisory” application and validated the theoretical model using field experiments by measuring the communication ranges between two oncoming vehicles. We also investigated the impacts of varying altitudes, vehicle-interior obstacles, and OBU placement on V2V communication reliability and its implications. Along the direction of the second goal, we derived a data-driven model to characterize the acceleration/deceleration profile of a regular passenger vehicle with respect to speed and throttle position. As a proof of concept demonstration, we implemented an IoT-based communication architecture for disseminating the hazardous driving alerts to vulnerable drivers through cellular and/or V2X communication infrastructure

Real-Time Work Zone Traffic Management via Unmanned Air Vehicles

Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in realtime traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Connected Vehicle Data-Based Tools for Work Zone Active Traffic Management

Work zones present challenges to safety and mobility that require agencies to balance limited resources with vital traffic management activities. It is important to obtain operational feedback for successful active traffic management in work zones. Extensive literature exists regarding the impact of congestion and recommendations for work zone design to provide safe and efficient traffic operations. However, it is often infeasible or unsafe to inspect every work zone within an agency’s jurisdiction. This dissertation outlines the use of connected vehicle data, crash data, and geometric data from mobile light detection and ranging (LiDAR) technology for active traffic management in work zones. Back-of-queue crashes on high-speed roads are often severe and present an early opportunity for leveraging connected vehicle data to mitigate queueing. The connected vehicle data presented in this dissertation provides compelling evidence that there are significant opportunities to reduce back-of-queue crashes by warning drivers of unexpected congestion ahead. In 2014 and 2015, approximately 1% of the total mile-hours of Indiana interstates were operating below 45 MPH and were considered congested. Congested conditions were observable in the connected vehicle data prior to 18.5% of all interstate crashes. The congested crash rate was found to be 20.6-24.0 times greater than the uncongested crash rate. A real-time queue alert system was developed to detect queues and notify INDOT personnel via email. When average speeds drop below 45 MPH, queue monitoring algorithms are triggered, and an alert is sent to selected individuals. Still camera images, work schedules, and crash reports were used to ground-truth the alert system. The notification model could be easily extended to in-car notification. A weekly work zone report was developed for use by the Indiana Department of Transportation (INDOT) for the purpose of assessing and improving both mobility and safety in work zones. The report includes a number of graphs, figures, and statistics to present a comprehensive picture of performance. This weekly report provided a mechanism for INDOT staff to maintain situational awareness of which work zones were most challenging for queues and during what periods those were likely to occur. These weekly reports provided the foundation for objective dialog with contractors and project managers to identify mechanisms to minimize queueing and allocate public safety resources. Lastly, this dissertation discusses the integration of LiDAR-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. The advantages and disadvantages of the technology are discussed. A number of case studies demonstrate versatility of the technology in transportation applications

Geographic Information Systems, Evacuation Planning and Execution

Evacuation planning has for decades relied on the results derived from mathematical modeling and scenario development. While there exist many mathematical and simulation models dealing with evacuation planning most lack one or more critical components needed by the individuals or agencies responsible for removing people from harm’s way. Those critical components are real-time access to and representation of data to establish appropriate evacuation strategies. All the pieces for a real-time centralized evacuation system exist but have yet to be integrated as a single point system. The focus of this chapter is the underutilization of geographic information systems (GIS)

Multi-Purpose ESS/ITS Data Collection Sites, SPR72-00-0003-042, 2014

This document presents the results of a state-of-practice survey of transportation agencies that are installing intelligent transportation sensors (ITS) and other devices along with their environmental sensing stations (ESS) also referred to as roadway weather information system (RWIS) assets