Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

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.

Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure.Thus,this paper develops a Markov chain-based model to recognize platoons.A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability.The videos,recorded with a high-definition camera,contain field driving data from three Tesla vehicles,which can achieve Level 2 autonomous driving.The simulation results show that the recognition rate exceeds 80%when the connected and autonomous vehicle penetration rate is higher than 0.7.Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition.The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.

Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

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.

Theoretical and Experimental Study on The Speed-up of Freight Train with Mixed Marshaling of Light and Heavy Vehicle

To study the influence of the speed-up of a freight train with mixed marshaling of light and heavy vehicles on the dynamic behavior,a dynamic model of the freight train was established based on the modular method of cyclic variables,and the dynamic behavior of the freight train was simulated and analyzed under different marshaling patterns,speeds and line conditions.On-site speed-up test with different marshaling freight trains was carried out,and the stability and ride-index of the train before and after the speed-up were compared and analyzed.The feasibility of increasing the speed of freight trains with mixed marshaling of light and heavy cars was demonstrated theoretically and experimentally.The results show that the theory is in good agreement with the test,which can effectively reflect the dynamic behavior of the vehicle.The dynamic behavior of the freight train in the study meets the requirements of increasing speed to 90 km/h.This paper provides a theoretical basis and method for railway freight transportation and the speed-up of freight vehicles.

Edge-assisted indexing for highly dynamic and static data in mixed reality connected autonomous vehicles

The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger comfort.However,the substantial and varied data generated by MR-Connected AVs(MR-CAVs),encompassing both highly dynamic and static information,presents formidable challenges for efficient data management and retrieval.In this paper,we formulate our indexing problem as a constrained optimization problem,with the aim of maximizing the utility function that represents the overall performance of our indexing system.This optimization problem encompasses multiple decision variables and constraints,rendering it mathematically infeasible to solve directly.Therefore,we propose a heuristic algorithm to address the combinatorial complexity of the problem.Our heuristic indexing algorithm efficiently divides data into highly dynamic and static categories,distributing the index across Roadside Units(RSUs)and optimizing query processing.Our approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations,thereby shifting the burden away from the vehicles themselves.Our algorithm strategically places data in the cache,optimizing cache hit rate and space utilization while reducing latency.The quantitative evaluation demonstrates the superiority of our proposed scheme,with significant reductions in latency(averaging 27%-49.25%),a 30.75%improvement in throughput,a 22.50%enhancement in cache hit rate,and a 32%-50.75%improvement in space utilization compared to baseline schemes.

Investigation and Optimization of Priority for Non-motor Vehicles in the Old Urban Area of Nanchang

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.

Development of an Intelligent Vehicle Experiment System

Existing vehicle experiment systems tend to focus on the research of vehicle dynamics by conducting performance tests on every system or some parts of the vehicle so as to improve the entire performance of the vehicle. Virtual technology is widely utilized in various vehicle test-beds. These test-beds are mainly used to simulate the driving training, conduct the research on drivers＇ behaviors, or give virtual demonstrations of the transportation environment. However, the study on the active safety of the running vehicle in the virtual environment is still insufficient. A virtual scene including roads and vehicles is developed by using the software Creator and Vega, and radars and cameras are also simulated in the scene. Based on dSPACE＇s rapid prototyping simulation and its single board DS1103, a simulation model including vehicle control signals is set up in MATLAB/Simulink, the model is then built into C code, and the system defined file（SDF） is downloaded to the DS1103 board through the experiment debug software ControlDesk and is kept running. Programming is made by mixing Visual C＋＋ 6.0, MATLAB API and Vega API. Control signals are read out by invoking library function MLIB/MTRACE of dSPACE. All the input, output, and system state values are acquired by arithmetic and are dynamically associated with the running status of the virtual vehicle. An intelligent vehicle experiment system is thus developed by virtue of program and integration. The system has not only the demonstration function, such as general driving, cruise control, active avoiding collision, but also the function of virtual experiment. Parameters of the system can be set according to needs, and the virtual test results can be analyzed and studied and used for the comparison with the existing models. The system reflects the running of the intelligent vehicle in the virtual traffic environment, at the same time, the system is a new attempt performed on the intelligent vehicle travel research and provides also a new research method for the development of intelligent vehicles.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Excellent complete conversion activity for methane and CO of Pd/TiO_2-Zr_(0.5)Al_(0.5)O_(1.75) catalyst used in lean-burn natural gas vehicles

Palladium catalysts are supported on TiO2, ZrO2, A12O3, Zro.sAlo.501.75 and TiO2-Zro.sAlo.501.75 prepared by co-precipitation method, re- spectively. Catalytic activities for methane and CO oxidation are evaluated in a gas mixture that simulated the exhaust from lean-burn natural gas vehicles （NGVs）. Pd/TiO2-Zro.sAlo.501.75 performs the best catalytic activity among the tested five catalysts. For CH4, the light-off temperature （Tso） is 254 ℃, and the complete conversion temperature （Tgo） is 280 ℃; for CO, Tso is 84 ℃, and Tgo was 96 ℃. Various techniques, including N2 adsorption-desorption, X-ray diffraction （XRD）, H2-temperature-programmed reduction （H2-TPR）, X-ray photoelec- tron spectroscopy （XPS）, and scanning electron microscopy （SEM） are employed to characterize the effect of supports on the physicochemical properties of prepared catalysts. N2 adsorption-desorption and SEM show that TiO2-Zro.5Al0.501.75 expresses uniform nano-particles and large meso-pore diameters of 26 nm. H2-TPR and XRD indicate that PdO is well dispersed on the supports and strongly interacted with each other. The results of XPS show that the electron density around PdO and the proportion of active oxygen on TiO2-Zro.sAl0.501.75 are maxima among the five supports.

Delays caused by motorized vehicles unable to clear intersections in China: Graphical analysis

In many Chinese cities,motorized vehicles （M-vehicles） move slowly at intersections due to the interference of a large number of non-motorized vehicles （NM-vehicles）.The slow movement makes a part of M-vehicles fail to leave intersections timely after the traffic signal tums red,and thereby conflicts between vehicles from two directions occur.The phenomenon was analyzed graphically by using the cumulative vehicle curve.Delays in three cases were modeled and compared：NM-vehicle priorities and M-vehicle priorities with all-red intervals unable to release all vehicles,and longer all-red intervals ensuring release all vehicles.Marginal delays caused by two illegal behaviors that occasionally happened in mixed traffic intersections were also investigated.It is concluded that increasing the speed of M-vehicles leaving intersections and postponing the entering of NM-vehicles are the keys in mathematics,although they are uneasy in disordered mixed traffic intersections due to a dilemma between efficiency and orders in reality.The results could provide implications for the traffic management in the cities maintaining a large number of M-and NM-vehicles.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

Impact Analysis of EV Charging with Mixed Control Strategy

EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.

A Bayesian Approach with Prior Mixed Strategy Nash Equilibrium for Vehicle Intention Prediction

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years,where connected and automated vehicles have to interact with human-driven vehicles.In this context,it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles,the possible maneuvers and the interactions between traffic participants within the seconds to come.This article presents a Bayesian approach for vehicle intention forecasting,utilizing a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium(MSNE)as a prior estimate to model the reciprocal influence between traffic participants.The likelihood is then computed based on the Kullback-Leibler divergence.The game is modeled as a static nonzero-sum polymatrix game with individual preferences,a well known strategic game.Finding the MSNE for these games is in the PPAD∩PLS complexity class,with polynomial-time tractability.The approach shows good results in simulations in the long term horizon(10s),with its computational complexity allowing for online applications.

Evaluation of platooning configurations for connected and automated vehicles at an isolated roundabout in a mixed traffic environment

Platooning has emerged to be one of the most promising applications for connected and automated vehicles(CAVs).However,there is still limited research on the effect of platooning configurations.This study sets out to investigate the effect of CAV platoon configurations at a typical isolated roundabout in a mixed traffic environment.Investigated platoon configurations include maximum platoon size,platoon willingness,and platoon type.Extensive simulation experiments are carried out in simulation of urban mobility(SUMO),considering various traffic conditions,including different penetration rates,traffic flows,and turning percentages.Results show that:(1)increasing the maximum platoon size and platoon willingness generally improves the throughput increment and delay reduction;and(2)heterogeneous platoons outperform homogeneous platoons in all traffic conditions.

A Multi-Vehicle, Multi-Factory Assignment Problem: A Case of Coca-Cola Bottling Company at Ahinsan and Spintex-Ghana

Determining the type of vehicles to transport goods between multiple factories and numerous distributors with different demands is one of the major logistic decisions that have to be made by industry players to reduce the cost of operations. A Mixed-Integer Quadratic Programming (MIQP) model was used to optimally distribute goods to 105 distributors from two factories across Ghana. The formulated model and analysis show that the existence of multiple vehicles in a fleet purposely for long hauling of goods also renders an optimal minimum cost as compared to a single-vehicle fleet. This optimum minimum cost accounts for 0.2066 of the total cost incurred by the two factories. This resulted in a 25% reduction in transportation cost. Again, a single-vehicle fleet with loading capacity within the mean value of all individual demands gave a minimum cost next to the optimal minimum.

混合工质电动汽车空调全生命周期气候性能分析

空调作为整车系统中环境调控的关键子系统,其全生命周期的碳排放对于电动汽车的环保和排放要求至关重要。本文结合汽车空调系统全生命周期气候性能LCCP模型及相关数据,对全国各省份电动汽车空调系统的LCCP情况进行分析,同时对两种不同制热方案以及不同电力碳强度下的LCCP进行了比较分析。结果表明,RE170/R134a低GWP混合工质(二甲醚与R134a质量分数比90∶10)替换后电动汽车空调LCCP值降低了11.2%~28.1%,而采用热泵替换PTC电加热器后电动汽车空调LCCP值降低了0~33.1%,此外,随着未来我国电力碳强度与电动汽车保有量的变化,至2035年单辆车的LCCP值将会下降31.7%~39.3%,而全国整体电动汽车LCCP值将大幅上升。

融入智能网联汽车的混行交通流混沌特性

为了研究混行交通流混沌特性、辨析影响混行车队混沌程度的因素,在传统交通流理论基础上,利用Cao方法和改进的Cao方法确定混行交通流延迟时间和嵌入维数,对混行交通流序列进行相空间重构并通过计算最大Lyapunov指数判定其混沌特性.对混行交通流中智能网联汽车(intelligent connected vehicle,ICV)协同自适应巡航(cooperative adaptive cruise control,CACC)车辆比例及延迟时间关键参数进行影响分析.结果表明:在跟驰过程中车头间距序列的最大Lyapunov指数小于0时,混行交通流存在混沌;CACC车辆比例增加能够减弱混沌的时间区域,比如当CACC车辆比例达到0.6时,跟驰系统趋于稳定;CACC车辆的延迟时间对混沌的影响显著,保持低通信延迟才能发挥CACC车辆的作用,从而有效抑制混沌.

智能网联环境下信号交叉口车辆轨迹重构模型

车辆轨迹数据提供了大量的时空交通流信息,可用于各类交通研究.传统车辆轨迹模型多以人工驾驶环境为研究对象,普遍未考虑由常规车(RV)、网联人工驾驶车(CV)以及智能网联车(CAV)组成的混合交通流的影响.为解决该问题,构建智能网联环境下信号交叉口全样本车辆轨迹重构模型.首先,介绍并分析智能网联环境下城市道路交叉口处车辆组成及排队通过情况;然后,构建城市道路混合交通流轨迹数量估计模型,并针对前后车的排队情况提出虚拟车的概念,用于估计不同车辆的交通状态;最后,设计数值仿真实验分析交通流密度和网联车渗透率对模型的影响,并基于NGSIM数据进行实例验证.结果表明:轨迹重构模型的数量误差和位置误差均随着交通流密度和网联车渗透率的增大而减小,如交通流密度由20 veh/km增大至50 veh/km的过程中,模型数量误差和位置误差均呈现下降趋势,且最大误差分别不超过6.88%和8.02 m;与网联人工驾驶车渗透率相比,智能网联车的渗透率对模型结果影响更大.

考虑CAV自主停车行为的混合交通均衡配流模型

为了评估网联自动驾驶汽车(CAV)的自主停车行为对交通系统效率的影响,建立了CAV通勤流、人工驾驶汽车(HDV)通勤流、CAV自主停车流3类交通流混行下的交通均衡配流模型.通过引入CAV停车需求内生变量,考虑CAV对路段通行能力的提升效应,从而定量描述CAV停车需求分布以及3类交通流在路段上混行的拥挤效应.分别采用用户均衡、随机用户均衡原则描述CAV、HDV出行者的路径选择行为,采用Logit离散选择模型描述自主停车CAV的停车场选择行为,由此建立多用户混合交通均衡条件以及等价的变分不等式(VI)模型.由于模型中CAV自主停车需求为未知的内生变量,提出一种改进的相继加权平均法求解该模型.最后,通过算例验证了混合交通均衡配流模型及求解算法的有效性.