Abstract
With the vehicular ad hoc network (VANET) technology which support vehicle-to-vehicle (V2V) and vehicle to road side unit (V2R/R2V) communications, vehicles can preview the intersection signal plan such as signal countdown message. In this paper, an ecodriving advisory system (EDAS) is proposed to reduce CO2 emissions and energy consumption by letting the vehicle continuously pass through multiple intersections with the minimum possibilities of stops. We extend the isolated intersection model to multiple continuous intersections scenario. A hybrid method combining three strategies including maximized throughput model (MTM), smooth speed model (SSM), and minimized acceleration and deceleration (MinADM) is designed, and it is compared with related works maximized throughput model (MaxTM), open traffic light control model (OTLCM), and predictive cruise control (PCC) models. Some issues for the practical application including safe car following, queue clearing, and gliding mode are discussed and conquered. Simulation results show that the proposed model outperforms OTLCM 25.1%~81.2% in the isolated intersection scenario for the CO2 emissions and 20.5%~84.3% in averaged travel time. It also performs better than the compared PCC model in CO2 emissions (19.9%~31.2%) as well as travel time (24.5%~35.9%) in the multiple intersections scenario.
Highlights
Greenhouse gases (GHG) are recognized as the main cause of the global warming, with CO2 being the primary GHG emitted through human activities
The results show that in the case of road lengths 200 m, 400 m, and 600 m, the CO2 emission of maximized throughput model (MTM) is less than maximized throughput model (MaxTM) [5] and open traffic light control model (OTLCM) [6] models on the cases from sparse traffic condition (50 vph) to congested condition (800 vph)
The results show that the MTM outperforms MaxTM [5] and OTLCM [6] models in CO2 emissions and average travel time on all the 10 cases
Summary
Greenhouse gases (GHG) are recognized as the main cause of the global warming, with CO2 being the primary GHG emitted through human activities. Our previous work [5] proposed two decision tree based eco-driving suggestion models for an isolated signalized intersection, using OBU to calculate the best eco-driving speed based on real-time information including the traffic signal countdown messages and waiting queue length broadcasted by RSU These two models are called MaxTM (maximize throughput model) and MinADM (minimize acceleration and deceleration model); simulation results show that MaxTM outperforms MinADM and Open Traffic Light Control Model (OTLCM) [6], being 5% to 102% better than MinADM and 13% to 209% better than OTLCM with regard to CO2 emissions in the simulation cases, and 8% to 14% better than MinADM and 15% to 231% better than OTLCM in the real traffic cases [5].
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