A Strategy for Emergency Vehicle Preemption and Route Selection

Emergency vehicle preemption (EVP) aims to give right of way to emergency vehicles (EV) heading toward the incident location through a network of signalized intersections by creating a green wave en-route. The design goals of EVP systems are two folds: first, to avoid any hindrance to the passage of EV along the road and at the intersections and second, to reduce the negative impact of preemption on general traffic. The negative impact of EVP on normal traffic can be minimized by selecting appropriate preemption strategy. The EVP schemes proposed earlier aim to minimize the travel time of the EV with no or little consideration to the negative impact of EVP on the normal traffic. In this study, a joint strategy for optimal path selection and EV preemption is developed. The proposed scheme selects the optimal path for the EV before it departs from its origin and then activates the preemption on each intersection en-route at the right time to clear the intersection before the EV reaches. The proposed EVP scheme also aims to minimize the impact of EVP over normal traffic at both stages (i.e., path selection phase and preemption phase). A major advantage of the proposed method is that once the optimal path is selected, the emergency information can be disseminated to other vehicles using vehicle-to-vehicle and vehicle-to-infrastructure communication in the EV path to clear the entire route or the approaching lane. The strategy was tested using a microscopic simulation environment for a real traffic network. The findings indicated a major reduction in the travel time of the EV while minimizing the impact of preemption on the normal traffic. The proposed strategy and evaluation procedure can be helpful for corresponding agencies and practitioners to assess the impact of implementing preemption on existing or proposed arterials. - 2019, The Author(s).Open access funding provided by the Qatar National Library.Scopu

The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality

Traffic signal control (TSC) is a high-stakes domain that is growing in importance as traffic volume grows globally. An increasing number of works are applying reinforcement learning (RL) to TSC; RL can draw on an abundance of traffic data to improve signalling efficiency. However, RL-based signal controllers have never been deployed. In this work, we provide the first review of challenges that must be addressed before RL can be deployed for TSC. We focus on four challenges involving (1) uncertainty in detection, (2) reliability of communications, (3) compliance and interpretability, and (4) heterogeneous road users. We show that the literature on RL-based TSC has made some progress towards addressing each challenge. However, more work should take a systems thinking approach that considers the impacts of other pipeline components on RL.Comment: 26 pages; accepted version, with shortened version published at the 12th International Workshop on Agents in Traffic and Transportation (ATT '22) at IJCAI 202

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.Comment: 19 figures, 10 tables. Manuscript extended on previous work arXiv:2109.05429, arXiv:2111.0027

IoT-based emergency vehicle services in intelligent transportation system

Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs’ travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%

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

Path Clearance for Emergency Vehicles Through the Use of Vehicle-to-Vehicle Communication

The study described in this paper evaluated and tested a new strategy to enable emergency response vehicles (EVs) to navigate through congestion at signalized intersections more efficiently. The proposed strategy involves the use of vehicle-to-vehicle communication to send messages to alert vehicles to the approach of the EV and to provide specific instructions on maneuvering to allow the EV to proceed through congested signalized intersections as quickly as possible. This movement is achieved by creation of a split in the vehicle queue in one lane at a critical location to allow the EV to proceed at its desired speed but minimize the disruption to the rest of the traffic. The proposed method uses kinematic wave theory (i.e., shock wave theory) to determine the critical point in the vehicle queue. The proposed method is simulated in a microscopic traffic simulator for evaluation. The results show that this strategy can significantly shorten the travel time for EVs through congested signalized intersections

Deployment of ITS: A Summary of the 2010 National Survey Results

This report presents summary results of the 2010 ITS Deployment Tracking survey, the most recent survey conducted by the ITS Deployment Tracking Project. The U.S. Department of Transportation and its member agencies, including the Research and Innovative Technology Administration, have pursued a research and development agenda, the Intelligent Transportation System (ITS) Program, designed to integrate the latest in information technologies to improve the safety, mobility, and reliability of surface transportation modes. Within metropolitan areas, implementation of these advanced technologies has been accomplished by a variety of state and local transportation and emergency management agencies as well as the private sector. In order to measure the rate of ITS deployment within the nation s largest metropolitan areas, the ITS Deployment Tracking Project has conducted a nationwide survey of state and local transportation and emergency management agencies nearly every year since 1997. The results presented in this report are intended to be a summary of the entire database from the 2010 survey. Access to the complete survey results and previous national surveys are available on-line at http://www.itsdeployment.its.dot.gov. The website also provides access to survey results in the form of downloadable reports, including a survey summary for each survey type and fact sheets. Nearly 1,600 surveys were distributed to state and local transportation agencies in 2010. A total of seven (7) survey types were distributed including: Freeway Management, Arterial Management, Transit Management, Transportation Management Center (TMC), Electronic Toll Collection (ETC), Public Safety Law Enforcement, and Public Safety Fire/Rescue. Among other things, the data collection results indicate that ITS has moved from being experimental to mainstream and interest in continuing investments in ITS continues to be very strong. When asked about future deployment plans, one-third to three-fourths of the different agency types report they will expand current deployments and about half are planning to invest in new technologies over the next three years

Estimating Effects of Multipath Propagation on GPS Signals

Multipath Simulator Taking into Account Reflection and Diffraction (MUSTARD) is a computer program that simulates effects of multipath propagation on received Global Positioning System (GPS) signals. MUSTARD is a very efficient means of estimating multipath-induced position and phase errors as functions of time, given the positions and orientations of GPS satellites, the GPS receiver, and any structures near the receiver as functions of time. MUSTARD traces each signal from a GPS satellite to the receiver, accounting for all possible paths the signal can take, including all paths that include reflection and/or diffraction from surfaces of structures near the receiver and on the satellite. Reflection and diffraction are modeled by use of the geometrical theory of diffraction. The multipath signals are added to the direct signal after accounting for the gain of the receiving antenna. Then, in a simulation of a delay-lock tracking loop in the receiver, the multipath-induced range and phase errors as measured by the receiver are estimated. All of these computations are performed for both right circular polarization and left circular polarization of both the L1 (1.57542-GHz) and L2 (1.2276-GHz) GPS signals