为研究决策惯性在出行者路径选择行为中的影响，将出行者分为高敏感型和低敏感型。在决策惯性影响与超出决策惯性影响的基础上，分析出行者在路径选择中的实际行为。从真实的决策结果出发，明确了惯性决策临界值的计算方法，通过量化对决策惯性的影响范围进行描述；分析了不同情境下的惯性行为及影响因素，采用因果图和贝叶斯网络寻求变量间关系；构建不同情境下的多元路径选择模型，从而因地制宜地计算路径选择概率。通过验证数据集，对构建模型进行验证。研究结果表明，决策惯性在出行行为中真实存在，对不同情境下的路径选择概率有不同影响，在考虑路径选择时考虑决策惯性将得到更精准的结果；同时，惯性临界值对模型的选取有较大的影响。

Lane-changing trajectory planning (LTP) is an effective concept to control automated vehicles (AVs) in mixed traffic, which can reduce traffic conflicts and improve overall traffic efficiency. To enhance the lane change safety for AVs, a co-evolutionary lane-changing trajectory planning (CLTP) method is proposed to describe the risk minimization process that co-evolves with the dynamic traffic environment in the limited literature. Firstly, the natural driving data of vehicle trajectory on the expressway provided by the High dataset are used to construct the lane-changing samples. To obtain the future traffic environment information, a deep learning neural network is adopted to capture trajectory dynamics in mobility of surrounding vehicles around a lanechanging vehicle. Secondly, the safe interaction between the subject vehicle and the surrounding vehicles is considered to establish a mathematical model for the temporal and spatial risk identification of a lane change event based on the fault tree analysis method. Subsequently, the risk minimization of lane change is considered as the objective. Based on the acceleration and deceleration overtaking rules and the trapezoidal acceleration method, the longitudinal and lateral displacement schemes during a lane change are designed. Finally, the motion parameters of longitudinal and lateral displacement are acquired to form an ideal lane change trajectory using a genetic algorithm. The results show that this method can effectively achieve higher safety of the lanechanging process, and reduce the traffic conflicts and traffic turbulence caused by dangerous lane-changing behaviors. The findings can provide theoretical support for lane change trajectory planning algorithm design of intelligent vehicles.

With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities.

为了完善雨雪天气下轨道交通客流预测模型,对哈尔滨市地铁1号线2017年12月份至2019年1月份的全线客流数据进行研究,引入客流基准值和客流偏差率的指标来量化轨道交通客流波动情况,研究雨雪天气下轨道交通客流波动规律,提出一种基于雨雪

Turning on the heating, ventilation and air-conditioning (HVAC) system is one effective measure to improve the thermal comfort of passengers in an electric bus (EB). However, it will also increase the consumption of the battery power. For an EB with limited battery capacity and that can only be charged at night, it is impossible to keep the HVAC on for all-day trips. This study aims to develop a daily HVAC operation planning model for an EB to maximize the thermal comfort of the passengers under the constraint of battery capacity. Firstly, we developed a cabin temperature estimation model to quantify the thermal comfort of the passengers. Secondly, considering the stochastic volatility of trip energy consumption, the chance-constrained programming based HVAC planning model is established, to determine the optimal HVAC gear of each trip. Finally, a real EB is taken as an example and field operational data collected in three different seasons are used in the case study. Results show that passenger's thermal comfort can be improved in different seasons after applying the proposed optimization method while avoiding the service interruption due to running down of battery power.

In order to identify the scope of active traffic control regions and improve the effect of active traffic control, this paper proposes a dynamic partitioning method of area boundaries based on benchmark intersections, taking into account the saturation, homogeneity, and correlation of intersections in the region. First, a boundary indicator correlation model was established. Next, benchmark intersections were selected based on evaluation indicators, such as traffic speed and queue length. Then, the boundary of the control region is initially defined based on the selected reference intersection, through a combination of the improved Newman algorithm. Subsequently, a spectral clustering algorithm is used to obtain the boundaries of the optimal active control subregions. Finally, a city road network is used as the study object for analysis and verification under the premise of implementing active traffic control. The results show that compared with the intersection clustering algorithm method and the boundary control subdivision method, the control effect indicators, such as the average delay and the average number of stops, have a great optimization improvement. Thus, the proposed method of regional borders combines the actual traffic flow characteristics efficiently to make a more accurate real-time dynamic division of the road network sub-areas.

Overlapping bus lines are ubiquitous in bus networks, particularly in metropolitan areas. The overlapping of bus lines can provide convenience for passengers who wish to transfer. However, it also tends to cause bus bunching at overlapping segment stops. Moreover, overlapping of bus lines introduces additional complexity to the operation of bus systems. This study aimed to dispatch bus vehicles entering overlapping segments dynamically by adopting speed control and green light extension strategies. This ensures that transferring passengers experience less transfer waiting time and reduced bus bunching at overlapping segments. The proposed model considers environmental constraints on vehicle speed and the stochastic factors of passenger arrivals at a stop. Synchronization is maximized by controlling the speed of vehicles along a roadway and determining whether a green light extension strategy is enabled. The effectiveness of the proposed model was verified by applying it to a real overlapping segment in Harbin, China. The results demonstrate that the proposed model can more than double the opportunity for synchronization in overlapping segments while reducing bus bunching at the stops in overlapping segments.

为提高重叠公交线路间协同性、减少公交运力资源浪费,提出了考虑乘客换乘的重叠线路公交调度优化方法。根据乘客起讫点与重叠区段的位置关系将乘客划分为三类,深入研究了重叠区站点各类乘客公交出行过程;针对重叠公交线路上乘客换乘行为

为提高城市道路车辆掉头安全与通行效率,以传统四相位信号控制交叉口为研究对象,提出路段提前掉头设置条件与方法。同时以车辆通行安全和效率为目标,构建了车辆提前掉头开口位置与上下游交叉口距离的判别模型、掉头车流排队长度以及掉