Signal Phase And Timing Research Articles

Global energy management optimization for a group of 4WD hybrid electric vehicles in connected vehicle environment

A hierarchical energy management control strategy was proposed to realize the global optimal energy management for a group of four-wheel-drive hybrid electric vehicles (4WD HEVs) in connected vehicle environment. The higher level controller utilized signal phase and timing (SPAT) for the evaluation of the target velocities and optimal target velocity prediction objective function model was established based on model predictive control (MPC). The lower level controller adopted dynamic programming (DP) for the energy management of the vehicles. dSPACE-based simulation results validate that the higher level controller can avoid the 4WD HEVs from red light stopping and avoid collisions of the vehicles. Compared with the Gipps car following model, the fuel economy is increased by 21.4%, 21.5% and 20.6% when the lower level controller is based on DP, ECMS and the rule-based strategy, respectively. In addition, the DP-based lower level controller can achieve good velocity tracking and SOC balance. Compared with ECMS and rule-based control strategies, the average fuel economy for the group of 4WD HEVs with DP-based strategy is improved by 7.8% and 15.9% , respectively.

Crowdsourcing Phase and Timing of Pre-Timed Traffic Signals in the Presence of Queues: Algorithms and Back-End System Architecture

This paper describes a crowdsourcing-based system for phase and timing estimation of pre-timed traffic signals. The input crowd is a real-time feed of sparse and low-frequency probe vehicle data, and the output is an estimated collection of Signal Phase and Timing (SPaT) information. The estimations could be ultimately fed into a connected vehicle's driver assistant application. Different from the authors' previous work, the approach described in this paper ensures the accuracy of the SPaT estimations even in the presence of queues. This was achieved by investigating the probe data influenced by the heavy traffic and the delay in queues. This paper is also a sequel to the authors' previous work as it provides an in-depth overview of the crowdsourcing algorithms and their back-end implementation. The accuracy of the crowdsourcing algorithm is also experimentally evaluated for a selection of pre-timed traffic lights in San Francisco, CA, USA, by utilizing a real-time data feed of San Francisco's public buses as an example data source.

Leveraging Connected Vehicle Technology and Telematics to Enhance Vehicle Fuel Efficiency in the Vicinity of Signalized Intersections

Driving on highways and arterial roadways involves vehicle acceleration, braking, cruising, coasting, and idling episodes. As the vehicle speed deviates from its “fuel optimum speed,” additional fuel is consumed, thus reducing the vehicle fuel efficiency. The research presented in this article develops a connected vehicle application entitled Eco-Cooperative Adaptive Cruise Control (ECACC) that uses infrastructure-to-vehicle (I2V) communication to receive signal phasing and timing (SPaT) data, predict future constraints on a vehicle's trajectory, and optimize its trajectory to minimize the vehicle's fuel consumption level. The trajectory optimization is made using a moving horizon dynamic programming (DP) approach. A modified A-star algorithm is developed to enhance the computational efficiency of the DP for use in real-time implementations. The model is calibrated and tested on 30 top-sold vehicles in the United States and is demonstrated to provide fuel savings within the vicinity of signalized intersections in the range of 5 to 30%.

Dynamic Eco-Driving for Signalized Arterial Corridors and Its Indirect Network-Wide Energy/Emissions Benefits

There are various strategies being considered to reduce fuel consumption and emissions from the transportation sector. From the transportation operations perspective, one strategy that has gained interest worldwide is eco-driving. Eco-driving typically consists of changing a person's driving behavior based on providing general advice to the driver, such as accelerating slowly, driving smoothly, and reducing high speeds. More advanced dynamic eco-driving provides real-time advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. The concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. This article considers dynamic eco-driving in an arterial corridor with traffic control signals, where signal phase and timing (SPaT) information of traffic lights is provided to the vehicle as it drives down the corridor. The vehicle can then adjust its velocity while traveling through the corridor with the goal of minimizing fuel consumption and emissions. A dynamic eco-driving velocity planning methodology has been developed and is described herein. This algorithm has then been extensively tested in simulation, showing individual vehicle fuel consumption and CO2 reductions of around 10–15%, depending on corridor parameters including traffic volume, traffic speed, and other factors. This 10–15% improvement is realized directly by the vehicle that is equipped with this dynamic eco-driving technology and can be accomplished with very little time loss. We have also carried out an extensive analysis of the entire traffic stream under different traffic volumes and different penetration rates of the dynamic eco-driving technology. It is found that there are also significant indirect network-wide energy and emissions benefits on the overall traffic, even at low penetration rates of the technology-equipped vehicles. Based on the simulation results, the maximum fuel saving and emission reduction occur during medium traffic volumes (corresponding to traffic volume of 300 vehicles/hr/link) and with low penetration rates (5%, 10%, and 20%). Under these conditions, the total traffic energy/emissions savings typically triple what is saved from the technology-equipped vehicles alone (e.g., total 4% savings compared to 1.3% savings). This is due primarily to the eco-driving velocity planning affecting nonequipped vehicles that follow behind.