Previous studies on the collaborative control of signals and vehicles—including both connected autonomous vehicles and connected autonomous buses(CABs)—in networked intersections predominantly emphasized enhancing t...Previous studies on the collaborative control of signals and vehicles—including both connected autonomous vehicles and connected autonomous buses(CABs)—in networked intersections predominantly emphasized enhancing the stability of heterogeneous flows to augment intersection efficiency.However,these studies often overlooked the crucial criterion of CAB punctuality.Addressing this significant gap,the present paper introduces a CAB travel time estimation model and a punctuality evaluation index into optimization problems to ensure the adherence to bus schedules.A mixed integer linear programming model,incorporating binary signal state variables,is established with the goals of heterogeneous platoon operation stability,bus arrival punctuality,and intersection efficiency.To ensure efficient model resolution,binary auxiliary logic variables are employed to linearize the relationship between signal transitions and the operational state of the heterogeneous flow.An evaluation is conducted using a standard four-arm intersection,wherein parameters like CAB proportions and overall traffic volume are varied for comprehensive testing.Simulation outcomes compellingly show that the proposed approach markedly improves CAB punctuality and diminishes energy consumption by enhancing heterogeneous flow stability.Specifically,there is an average increase of 22.3%in punctuality and a reduction of at least 15.1%in energy consumption.展开更多
Environmental sustainability is a crucial issue for all human beings,and vehicle emissions significantly contribute to climate change.This has prompted many countries,including China,Norway,and Germany,to focus on ele...Environmental sustainability is a crucial issue for all human beings,and vehicle emissions significantly contribute to climate change.This has prompted many countries,including China,Norway,and Germany,to focus on electrifying transportation.This study quantifies the life cycle carbon dioxide(CO_(2))emissions of electric buses(EBs)in Guangzhou,China,via a life cycle analysis methodology,revealing an average life cycle emission of 1,097.07 g CO_(2)·km−1·vehicle−1.The operation and charging stage contributes the most to the lifespan of CO_(2)emissions at 69.6%,driven by carbon-intensive power grid.Compared with conventional internal combustion engine buses,EBs result in significant emission reductions,but regional grid carbon intensity variations across China mean that their benefits depend on nationwide green energy adoption.By 2030,emissions are projected to decline by 15.28%,aligning with carbon peak goals.The findings emphasize that transitioning to renewable energy grids and hybrid technologies is critical for sustainable transportation.展开更多
In urban settings,fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration,deceleration,and idling in vehicles.These maneuvers contribute to elevated energy us...In urban settings,fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration,deceleration,and idling in vehicles.These maneuvers contribute to elevated energy use and emissions.Advances in vehicle-to-vehicle and vehicleto-infrastructure communication technologies allow autonomous vehicles(AVs)to perceive signals over long distances and coordinate with other vehicles,thereby mitigating environmentally harmful maneuvers.This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation.The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges.Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed.A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits.The proposed algorithm results in a 12.2%reduction in energy consumption relative to standard driving practices,without a significant extension in travel time.展开更多
基金supported by the key project of National Natural Science Foundation of China(No.52432011)Jiangsu Province Science Fund for Distinguished Young Scholars(No.BK20200014),and VINNOVA(No.2024-00810).
文摘Previous studies on the collaborative control of signals and vehicles—including both connected autonomous vehicles and connected autonomous buses(CABs)—in networked intersections predominantly emphasized enhancing the stability of heterogeneous flows to augment intersection efficiency.However,these studies often overlooked the crucial criterion of CAB punctuality.Addressing this significant gap,the present paper introduces a CAB travel time estimation model and a punctuality evaluation index into optimization problems to ensure the adherence to bus schedules.A mixed integer linear programming model,incorporating binary signal state variables,is established with the goals of heterogeneous platoon operation stability,bus arrival punctuality,and intersection efficiency.To ensure efficient model resolution,binary auxiliary logic variables are employed to linearize the relationship between signal transitions and the operational state of the heterogeneous flow.An evaluation is conducted using a standard four-arm intersection,wherein parameters like CAB proportions and overall traffic volume are varied for comprehensive testing.Simulation outcomes compellingly show that the proposed approach markedly improves CAB punctuality and diminishes energy consumption by enhancing heterogeneous flow stability.Specifically,there is an average increase of 22.3%in punctuality and a reduction of at least 15.1%in energy consumption.
基金supported by the National Natural Science Foundation of China(No.52372313)JPI Urban Europe and Energimyndigheten(No.e-MATS,P2023-00029).
文摘Environmental sustainability is a crucial issue for all human beings,and vehicle emissions significantly contribute to climate change.This has prompted many countries,including China,Norway,and Germany,to focus on electrifying transportation.This study quantifies the life cycle carbon dioxide(CO_(2))emissions of electric buses(EBs)in Guangzhou,China,via a life cycle analysis methodology,revealing an average life cycle emission of 1,097.07 g CO_(2)·km−1·vehicle−1.The operation and charging stage contributes the most to the lifespan of CO_(2)emissions at 69.6%,driven by carbon-intensive power grid.Compared with conventional internal combustion engine buses,EBs result in significant emission reductions,but regional grid carbon intensity variations across China mean that their benefits depend on nationwide green energy adoption.By 2030,emissions are projected to decline by 15.28%,aligning with carbon peak goals.The findings emphasize that transitioning to renewable energy grids and hybrid technologies is critical for sustainable transportation.
基金supported by VINNOVA(ICV-safe,2019-03418)Energimyndigheten and JPI Urban Europe through e-MATS project(P2023-00029)AI Center(CHAIR)at Chalmers University of Technology(CHAIR-CO-EAIVMS-2021-009).
文摘In urban settings,fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration,deceleration,and idling in vehicles.These maneuvers contribute to elevated energy use and emissions.Advances in vehicle-to-vehicle and vehicleto-infrastructure communication technologies allow autonomous vehicles(AVs)to perceive signals over long distances and coordinate with other vehicles,thereby mitigating environmentally harmful maneuvers.This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation.The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges.Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed.A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits.The proposed algorithm results in a 12.2%reduction in energy consumption relative to standard driving practices,without a significant extension in travel time.
