{'fraction_non_alphanumeric': 0.013083048919226393, 'fraction_numerical': 0.004550625711035267, 'mean_word_length': 6.06425702811245, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A study of the environmental impacts of intelligent automated vehicle control at intersections via V2V and V2I communications

Abstract This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO2 relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO2 emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.