TRA-956: IMPROVING INTERSECTION THROUGHPUT USING CONNECTED VEHICLES

This paper proposes a connected vehicle based approach to improve the throughput at signalized intersections and ultimately increase the mobility of a transportation system. Connected vehicle technology demonstrates tremendous potential for improving safety and mobility, as it enables the real-time sharing of vehicle data, including position, speed, acceleration, etc., not only among vehicles but also between vehicles and infrastructure. The proposed approach takes advantage of such real-time data to develop a strategy that maximizes throughput of an isolated intersection locally. Accordingly, the problem is formulated as a two-step centralized optimization. There are two main processes in this method: optimization for vehicles in motion, and optimization for stopped vehicles. The first step maximizes the intersection throughput of vehicles in motion using advisory acceleration. The second one minimizes the total delay of the stopped vehicles by adjusting the positions at which vehicles stop. A case study is also presented to show the efficiency of the proposed approach, which improves the traffic flow throughput of an isolated signalized intersection and reduces the total delay of all vehicles

Vehicle Localization Kalman Filtering for Traffic Light Advisor Application in Urban Scenarios

The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving

A Microscopic Simulation Study of Applications of Signal Phasing and Timing Information in a Connected Vehicle Environment

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queue

Impacts of Connected and Autonomous Vehicles on the Performance of Signalized Networks: A Network Fundamental Diagram Approach

USDOT Grant 69A3551747109Many eco-driving strategies through speed control using constrained optimization algorithms have proven effective on signalized roads. However, heuristic speed limit control strategies and understanding of their overall performance across congestion levels remain an unexplored topic. In this work, we systematically study the performance of an eco-driving strategy based on Vehicle-to-Infrastructure (V2I) communication via the advisory speed limit (ASL), a speed limit designed for individual vehicles based on the idea of making vehicles enter signalized intersections at saturated headway intervals. The theoretical performance of our algorithm to vehicle trajectories is analyzed across different congestion levels. By simulating with the BA Newell\u2019s car-following model, the simplified Gipps model, and the Krauss model, calculated network fundamental diagrams (NFDs) and results of the Virginia Tech Microscopic Energy and Emission (VT-micro) model reveal an improvement in system mobility by nearly 10% and a reduction in fuel consumption by up to about 45% in the saturated condition. We further consider different market penetration rates (MPRs) and ASL implementation areas and show our algorithm can lead to about 35% fuel consumption reduction even with a 10% MPR. We recommend an ASL implementation area of about 100 meters, which can well balance the algorithm efficacy and computation cost