Real scenario and simulations on GLOSA traffic light system for reduced CO2 emissions, waiting time and travel time

Cooperative ITS is enabling vehicles to communicate with the infrastructure to provide improvements in traffic control. A promising approach consists in anticipating the road profile and the upcoming dynamic events like traffic lights. This topic has been addressed in the French public project Co-Drive through functions developed by Valeo named Green Light Optimal Speed Advisor (GLOSA). The system advises the optimal speed to pass the next traffic light without stopping. This paper presents results of its performance in different scenarios through simulations and real driving measurements. A scaling is done in an urban area, with different penetration rates in vehicle and infrastructure equipment for vehicular communication. Our simulation results indicate that GLOSA can reduce CO2 emissions, waiting time and travel time, both in experimental conditions and in real traffic conditions.Comment: in 22nd ITS World Congress, Oct 2015, Bordeaux, France. 201

Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

Prediction of dynamic pairwise wake vortex separations for approach and landing

Design and performance of the Wake Vortex Prediction and Monitoring System WSVBS are described. The WSVBS has been developed to tactically increase airport capacity for approach and landing on single runways as well as closely-spaced parallel runways. It is thought to dynamically adjust aircraft separations dependent on weather conditions and the resulting wake vortex behavior without compro-mis>ing safety. Dedicated meteorological instrumentation and short-term numerical terminal weather prediction provide the input to the prediction of wake-vortex behavior and respective safety areas. LIDAR monitors the correctness of WSVBS predictions in the most critical gates at low altitude. The WSVBS is integrated in the arrival manager AMAN of DLR. Performance tests of the WSVBS have been accomplished at Frankfurt airport in winter 2006/07 and at Munich Airport in summer 2010. Aircraft separations for landings on single runways have been compared employing the concepts of either heavy-medium weight class combinations or dynamic pairwise separations where individual aircraft type pairings are considered. For the very conservative baseline setup of the WSVBS the potential capacity gains of dynamic pairwise operations for single runways appear to be very small. On the other hand, the consideration of individual aircraft types and their respective wake characteristics may almost double the fraction of time when radar separation could be applied

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

A Simulation-Optimization Method for Signal Synchronization with Bus Priority and Driver Speed Advisory to Connected Vehicles

The paper introduces a model-based optimization procedure for the design of a control system with signal synchronization, real-time bus priority and green light speed advisory to car drivers. The traffic model simulates car traffic as platoons and bus movements individually. An optimization routine simulates the effect of different bus priority rules, which can be actuated online through bus identification devices and applies a metaheuristic algorithm to optimize signal settings. The macroscopic model and the design method have been applied and also tested in microsimulation on a principal street in Rome with a tram line on a reserved lane. Results obtained show that offline signal optimization and online signal priority can significantly reduce both travel times of bus riders and delays for total traffic. Similarly, speed advisory to drivers, if considered in signal optimization, can improve not only drivers' delays but even transit passengers' delays because it allows more efficient use of the road

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

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