[Objective] Overhaul operations for urban rail transit vehicles pose challenges and bottlenecks in the organization of maintenance work. Currently, there is a lack of systematic exploration of the overhaul contents and process design tailored for maintenance. [Method] Analyzing and refining the process design for the advanced overhaul schedule of urban rail transit vehicles is essential to improving the rationality of maintenance work arrangement, and effectively address the increased maintenance work demands resulting from the expansion of line network mileage and passenger flow volume. Based on existing research achievements regarding maintenance facility layout and maintenance task sequence optimization, the current urban rail transit vehicle overhaul situation is analyzed. Features such as shift repairs between lines, concentrated component-specific repairs, and diverse maintenance strategies in the overhaul operations are summarized. Subsequently, the process characteristics of the overhaul are outlined from the perspectives of process flow, maintenance strategies, and allocation of maintenance resources. Furthermore, a flexible process for overhaul is proposed, including the introduction of parallel operations in the process flow, adjustment of maintenance strategies based on the operational capacity of the maintenance line, and the separation of maintenance equipment and personnel in the allocation of maintenance resources. A computational example analysis is carried out based on the actual overhaul process of a 6-car formation urban rail transit train. [Result & Conclusion] Under a fixed process flow, the overhaul time required for synchronous maintenance strategy is 25 days. By applying flexible process, the required maintenance time is reduced to only 17.5 days, resulting in a 7.5-day reduction in maintenance time compared to the fixed process. The reduction in maintenance time stems from the introduction of parallel operations and the avoidance of executing synchronous overhaul strategy for the entire vehicle when the maintenance facility capacity is insufficient. The computational example results indicate that applying flexible process can effectively shorten the overhaul time of trains, thereby enhancing the maintenance efficiency of urban rail transit vehicles.

为了量化城市轨道交通（以下简称为“城轨”）车辆基地规模对列车运营的影响，在建设规划阶段合理决策车辆基地规模，研究了单一线路下多车辆基地、多交路、多折返站条件下的车辆基地规模分配问题。首先，以列车空驶里程为优化目标，综合考虑不同首发模式，并面向列车运行的不同阶段构建混合整数线性规划模型。其次，以广州13号线远期规划为例验证所提出方法的有效性。最后，探讨了不同首发模式下车辆基地收发车方向、发车能力与折返站开启方案对空驶里程及规模分配结果的影响。研究结果表明：所提出的模型可快速获得经济节约型的车辆基地规模分配结果；此外，车辆基地收发车方向与发车能力对规模分配存在一定影响，不同首发模式下影响程度存在差别，折返站开启方案仅在均匀首发条件下可影响规模分配结果。研究结果为车辆基地规模分配决策提供了一种新的方法，可作为城轨车辆基地规模实际决策的依据。

为了量化城市轨道交通(以下简称为"城轨")车辆基地规模对列车运营的影响,在建设规划阶段合理决策车辆基地规模,研究了单一线路下多车辆基地、多交路、多折返站条件下的车辆基地规模分配问题.首先,以列车空驶里程为优化目标,综合考虑不同首发模式,并面向列车运行的不同阶段构建混合整数线性规划模型.其次,以广州13号线远期规划为例验证所提出方法的有效性.最后,探讨了不同首发模式下车辆基地收发车方向、发车能力与折返站开启方案对空驶里程及规模分配结果的影响.研究结果表明:所提出的模型可快速获得经济节约型的车辆基地规模分配结果;此外,车辆基地收发车方向与发车能力对规模分配存在一定影响,不同首发模式下影响程度存在差别,折返站开启方案仅在均匀首发条件下可影响规模分配结果.研究结果为车辆基地规模分配决策提供了一种新的方法,可作为城轨车辆基地规模实际决策的依据.

城轨网络化运营下的多车辆基地规模分配成为线网规划阶段亟待解决的实际应用问题.从全网运营前集中发车、早高峰加车及收车回场3个阶段出发,在考虑全网车辆基地规模与出入线能力约束以及高峰加车对正线运营车辆影响的基础上,构建了以全网车辆总空驶里程最短为优化目标的混合整数线性规划模型.将此方法应用于某个多线路跨线运营且存在多交路运营模式的实际案例中,分别计算了各车辆基地规模在无约束条件、规模均匀分配条件及实际规模约束条件下3种场景的模型计算结果.结果表明:无约束条件下的空驶里程最短,实际规模约束条件下的最短空驶里程较无规模约束条件场景增加2.32％,而规模均匀分配下的空驶里程较无规模约束条件场景增加164.2％.

地铁车辆基地是城市轨道交通建设的重要组成之一,具有占地面积大、范围广的特点,在城市轨道交通的前期研究中,如何控制好车辆基地的占地面积,缓解城市土地资源紧张的现状是值得研究的课题.文章以地铁A、B车型为例,通过对车辆基地占地面积影响因素的分析,建立线性回归模型对车辆基地占地面积指标进行优化,再以实例验证了模型的合理性.

针对有轨电车车辆基地不同的功能需求,明确各类车辆基地对应的维修任务;基于检修人员配置与检修工艺流程,分析了车辆的检修效率;根据有轨电车车辆基本参数特征、车辆基地不同的总平面布置形式及列位排列方式,通过作图法统计出各检修设施4、6模块下的单位模块用地面积;结合国内其它有轨电车车辆基地的设计情况,总结了其它附属设施、道路、绿化面积指标;组合相应的设施面积,提出了用地面积控制指标,实际案例分析结论表明研究结论具有较好的可应用性.

从技术标准入手,分析了智能轨道快运(ART)、有轨电车和地铁3种轨道交通系统车辆基地总平面布置的区别.通过实例分析与计算,ART、有轨电车及地铁的车辆基地占地指标比为2∶3∶6.可见,ART车辆基地占地指标最小,其总平面布置最为紧凑、灵活.

Most existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.Implications: Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

With the installation of median exclusive bus lanes (XBLs) on urban expressways, understanding of the specific weaving behavior and estimation of the capacity of weaving areas are essential for developing effective engineering measures and operational strategies for expressway management. This study developed an analytical model for estimating the capacity of weaving, merge, and diverge sections with the median XBL installed on an urban expressway by using a microsimulation approach. An in-depth analysis of weaving behaviors was conducted and factors that influenced the weaving section capacity were identified. Specifically, an off-ramp weaving ratio and a newly defined variable called the bus ratio in the total weaving volume were used. A Vissim simulation model was developed and calibrated on the basis of field data collected in Beijing. From the simulation results, a regression model on the capacity estimation was proposed; the model was further validated with a statistical approach and field capacity data. The results demonstrate that the proposed capacity model is applicable and easy to use with an acceptable accuracy. The model can be used not only to estimate the weaving section capacity with a median XBL but also to estimate the capacity of merge or diverge sections with a median XBL under appropriate constraints. Moreover, the proposed model can be applied to examine different bus operational strategies. Hence, a comparative analysis was conducted of the effectiveness of different measures to improve traffic flows in the weaving section with the installation of a median XBL. A recommendation is provided.

This paper develops an application-oriented model to estimate waiting times as a function of bus departure time intervals. Bus stops are classified into Type A and B depending on whether they are connected with urban rail transit systems. Distributions of passenger arrival rates are analyzed based on field data for Beijing. The results indicate that the best fits for the distribution of passenger arrival rates for Type A and B bus stops are the lognormal distribution and gamma distribution, respectively. By analyzing relationships between passenger arrival rates and bus departure time intervals, it is demonstrated that parameters of the passenger arrival rate distribution can be expressed by the average and coefficient of variation of bus departure time intervals in functional relationships. The validation shows that the model provides a reliable estimation of the average passenger waiting time based on readily available bus departure time intervals.

The degree of crowding on board is one of the crucial factors affecting the attraction of a bus system. In order to provide an overview of the on-board crowding degree of a bus route or network for both managers and passengers, Crowding Index is proposed in this paper. The Crowding Index can quantify the degree of crowding in a single number. Firstly, the load factors index between adjacent bus stops is calculated based on the passenger volume and the number of buses at a certain interval. Then the load factor index is converted to Crowding Index through linear transform based on the relationship between them. The Crowding Index is based on a scale of ‘0’ to ‘10’ and ‘0’ is considered the ‘best’. Thirdly, the passenger turnover volume on each segment is chosen as the weight to calculate the Crowding Index of the bus route or network. Finally, case studies are conducted to verify the calculation method. The results show that the Crowding Index can reflect the degree of crowding of a bus route or network properly in a visual way. The research can provide a support for evaluating the degree of crowding on board.

Developing a reliable and practical method to estimate passenger waiting time becomes a key issue in the evaluation of service quality for public transit systems. However, existing methods lack of applicability in practices due to the need of the costly data collection. This paper develops an application-oriented model to estimate the waiting time as a function of bus departure time intervals. First, distributions of passenger arrival rates for two types of bus stops are analyzed based on field data collected in Beijing. Bus stops are classified into Type A and B, depending on whether they are connected with urban rail transit systems. The results show that the lognormal distribution has the best fit for Type A bus stop, and gamma distribution provides the best fit for Type B bus stop. Second, considering the convenience to extract the data of bus departure times from existing intelligent transit systems, the relationship between passenger arrival rates and bus departure time intervals is analyzed. It is demonstrated that parameters of the passenger arrival rate distribution for both two types of stops can be expressed by the average and CV (Coefficient of Variation) of bus departure time intervals in functional relationships. Then, an application-oriented waiting time model is proposed. Finally, a model validation is conducted, resulting in the NMSE (Normalized Mean Square Error) of 0.0854 and 0.0126 for Type A and B stops, respectively. Thus, the proposed model is shown to provide a reliable estimation of the average passenger waiting time based on only readily available bus departure time intervals.

基于北京市城市道路上小汽车与公交车的实测行程速度数据,对不同空间和时间维度下小汽车与公交车的速度特性进行了对比分析,从而为交通运行管理提供数据支持和参考.提出了定量表征小汽车与公交车速度变化趋势一致性的关联度指标,并对其计算方法进行了比选和论证.同时借助表征小汽车与公交车各自速度离散性的方差指标,以及表征二者速度大小差异性的绝对速度差指标,从不同道路类型、不同时段,以及有无公交专用道3个角度对小汽车与公交车各自的速度离散性、二者的速度差异性及关联性进行了对比分析.结果表明,快速路上小汽车与公交车的关联度最弱,q值为0.100;高峰时段的关联度较平峰时段强,q值分别为0.031和0.051;无公交专用道主干路上的关联度较有公交专用道主干路强,q值分别为0.101和0.083.基于大量数据论证得到的研究结论不仅具有普遍意义,而且量化了小汽车与公交车的速度特性.

The installation of the exclusive bus lane (XBL) on an urban expressway will likely cause multiple turbulences of traffic flow in weaving sections near the exit/entrance, which exhibits more complexities than the configuration without an exclusive bus lane. However, an analytical procedure for estimating the capacity of weaving sections for exclusive bus lanes on an urban expressway is not existent. This paper attempts to present a micro-simulation approach for developing the analytical procedure for the capacity of weaving sections with an exclusive bus lane. First, with a thorough analysis of the operation of weaving sections, three factors that influence the capacity are identified. Then, a VISSIM based micro-simulation framework is developed. The regression analysis of factors influencing the capacity is conducted for the median XBL. Further, simulation experiments are designed. Finally, the analytical model on the Capacity Reduction Factor (CRF ) is established, which considers the impact of both roadway allocation from XBL and more complex weaving behavior due to the installation of XBL. A randomly chosen weaving section along the third ring road in Beijing was used to validate the proposed model. Because there has not been an XBL installed on the expressway ring road, a simulation model is developed for this chosen weaving section, in which the median XBL is installed and the capacity is calculated by the proposed model. The results are compared with those from the simulation model, demonstrating that capacities obtained from the proposed model are close to the simulated ones with small relative errors. Thus, the proposed model is shown to provide a reliable capacity estimation for weaving sections of the median exclusive bus lanes on an urban expressway.

An integrated BRT vehicle travel-time prediction model was proposed that used the support vector machine (SVM) to predict the initial travel time and applied the Kalman filter algorithm to dynamically adjust the results of the predicted travel time. Based on the GPS data, a case study of the BRT line 2 in Chaoyang district, Beijing, was conducted with the help of the proposed prediction model. BRT vehicle travel time during the morning peak hour and the off-peak hour was predicted by both the proposed model and the Kalman filter model. The results prove that the proposed model is more suitable for predicting the BRT vehicle travel time with a high prediction accuracy, and the accuracy for the off-peak hour is higher than the one for the peak hours.