Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized tr...Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.展开更多
With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportati...With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future development of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.展开更多
Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor...Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor vehicle riders to a certain extent.According to the current situation of priority for non-motor vehicles in the old urban area of Nanchang,through field investigation,questionnaire investigation and interview,this study summarized the existing problems,and put forward optimization suggestions for these problems,in order to provide reference for areas with similar conditions.展开更多
The types and quantities of volatile organic compounds （VOCs） inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spec...The types and quantities of volatile organic compounds （VOCs） inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer （TD-GC/MS）. Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions （temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration）. Static vehicle testing demonstrated that although the amount of total volatile organic compounds （TVOC） detected within each vehicle was relatively distinct （4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C）, toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle＇s air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.展开更多
Recent reports from World Health Organization(WHO)show the impact of human negligence as a serious concern for road accidents and casualties worldwide.There are number of reasons which led to this negligence;hence,nee...Recent reports from World Health Organization(WHO)show the impact of human negligence as a serious concern for road accidents and casualties worldwide.There are number of reasons which led to this negligence;hence,need of intelligent transportation system(ITS)gains more attention from researchers worldwide.For achieving such autonomy different sensors are involved in autonomous vehicles which can sense road conditions and warn the control system about possible hazards.This work is focused on designing one such sensor system which can detect and range multiple targets under the impact of adverse atmospheric conditions.A high-speed Linear Frequency Modulated Continuous Wave(LFMCW)based Photonic Radar is proposed to detect multiple targets by integrating Mode division multiplexing(MDM).Reported results in terms of range frequency,Doppler frequency and range resolution are demonstrated using numerical simulations with the bandwidths of 1 and 4 GHz and under adverse atmospheric conditions carrying 75 dB/km of attenuation.To prove the effectiveness of the proposed photonic radar,moving targets are also demonstrated with different speed.System reported substantial range resolution of 15 cm using 1 GHz of bandwidth and 3 cm using 4 GHz of bandwidth.展开更多
Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the ...Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.展开更多
文摘Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.
文摘With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future development of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.
文摘Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor vehicle riders to a certain extent.According to the current situation of priority for non-motor vehicles in the old urban area of Nanchang,through field investigation,questionnaire investigation and interview,this study summarized the existing problems,and put forward optimization suggestions for these problems,in order to provide reference for areas with similar conditions.
文摘The types and quantities of volatile organic compounds （VOCs） inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer （TD-GC/MS）. Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions （temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration）. Static vehicle testing demonstrated that although the amount of total volatile organic compounds （TVOC） detected within each vehicle was relatively distinct （4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C）, toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle＇s air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.
基金This research project is supported by the Second Century Fund(C2F)Chulalongkorn University,Thailand.This research work is also funded by TSRI Fund(CU_FRB640001_01_21_8)+1 种基金The authors also would like to thank Taif University Researchers supporting project number(TURSP-2020/228)Taif University,Taif,Saudi Arabia.
文摘Recent reports from World Health Organization(WHO)show the impact of human negligence as a serious concern for road accidents and casualties worldwide.There are number of reasons which led to this negligence;hence,need of intelligent transportation system(ITS)gains more attention from researchers worldwide.For achieving such autonomy different sensors are involved in autonomous vehicles which can sense road conditions and warn the control system about possible hazards.This work is focused on designing one such sensor system which can detect and range multiple targets under the impact of adverse atmospheric conditions.A high-speed Linear Frequency Modulated Continuous Wave(LFMCW)based Photonic Radar is proposed to detect multiple targets by integrating Mode division multiplexing(MDM).Reported results in terms of range frequency,Doppler frequency and range resolution are demonstrated using numerical simulations with the bandwidths of 1 and 4 GHz and under adverse atmospheric conditions carrying 75 dB/km of attenuation.To prove the effectiveness of the proposed photonic radar,moving targets are also demonstrated with different speed.System reported substantial range resolution of 15 cm using 1 GHz of bandwidth and 3 cm using 4 GHz of bandwidth.
基金supported by the National Key R&D Program of China(2022YFE0100100).
文摘Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.
