A retrospective and prospective survey of time-use research

The central basis of the activity-based approach to travel demand modeling is that individuals' activity-travel patterns are a result of their time-use decisions within a continuous time domain. This paper reviews earlier theoretical and empirical research in the time-use area, emphasizing the need to examine activities in the context or setting in which they occur. The review indicates the substantial progress made in the past five years and identifies some possible reasons for this sudden spurt and rejuvenation in the field. The paper concludes that the field of time-use and its relevance to activity-travel modeling has gone substantially past the "tip of the iceberg", though it certainly still has a good part of the "iceberg" to uncover. Important future areas of research are identified and discussed.     

A cooperative waiting strategy based on elliptical areas for the Dynamic Pickup and Delivery Problem with Time Windows

The purpose of this paper to present a cooperative scheduling algorithm for solving the Dynamic Pickup and Delivery Problem with Time Windows (DPDPTW). The idea behind cooperative waiting strategies is to calculate simultaneously the waiting times for all nodes in the solution. Classical non‐cooperative scheduling algorithms perform the scheduling for each route independently of the scheduling of the other routes. We present the Cooperative Scheduling Problem (CSP) based on the elliptical areas generated by vehicles waiting at their nodes. The CSP is solved by means of a genetic algorithm and is evaluated by using a set of benchmarks based on real‐life data found in the literature. Initially, two waiting strategies are presented: Wait‐Early‐Time scheduling and Balanced‐Departure scheduling. Extensive empirical simulations have been carried out by analyzing the degree of dynamism and the average waiting time, a new concept defined to take into account the gap between the time windows of pickup and delivery nodes. Copyright © 2016 John Wiley & Sons, Ltd.     

A case study of Beijing bus crew scheduling: a variable neighborhood‐based approach

Crew scheduling for bus drivers in large bus agencies is known to be a time‐consuming and cumbersome problem in transit operations planning. This paper investigates a new meta‐heuristics approach for solving real‐world bus‐driver scheduling problems. The drivers' work is represented as a series of successive pieces of work with time windows, and a variable neighborhood search (VNS) algorithm is employed to solve the problem of driver scheduling. Examination of the modeling procedure developed is performed by a case study of two depots of the Beijing Public Transport Group, one of the largest transit companies in the world. The results show that a VNS‐based algorithm can reduce total driver costs by up to 18.1%, implying that the VNS algorithm may be regarded as a good optimization technique to solve the bus‐driver scheduling problem. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling behavioural rules for daily activity scheduling using fuzzy logic

The paper focuses on how trip time variability affects re-scheduling of daily activities. A delay in a trip or an early arrival can contribute to changes in the timing, location of the next activity, and to the deletion/addition of some activities. We propose the idea of using fuzzy logic rules to explain the effect of variability in travel time on the benefits perceived by an individual with the changes, and to model different actions that the individuals take in order to re-establish the steadiness of the existing timetable. The fuzzy model is used to handle the imprecision of the data which is unstructured text. The results show that large deviations in trip duration are more likely to induce significant changes in the timetable whereas small deviations are either ignored or translated into modified timing of the next activity. In choosing an action, greater importance is assigned to the flexibility of the following activity, to the magnitude of the trip time saving/delay, and to the duration of the next activity. Time savings are not favoured unless they can be readily transferred into additional activity time allocated to the next activity or to a new activity. The fuzzy rules based system is capable of predicting satisfactorily the strategy of coping with uncertainty in travel times and the satisfaction sensed with the change.     

网络化运营条件下的地铁乘务计划优化方法

随着城市轨道交通网络化运营程度的不断深入,乘务管理也需要不断适应线路结构、客流特征、列车方案、管理制度等因素的变化需求。乘务计划优化是一类综合考虑各类因素的多目标组合优化问题。对计划列车运行图、线路条件、乘务班制、劳动时间等乘务计划编制的关键因素进行分析,从编制条件和编制目标等方面研究了网络化运营条件下乘务计划的编制难点,从乘务班制、任务划分、网络统筹、编制的智能化和信息化等方面提出网络化运营条件下乘务计划编制优化的建议与方法。     

客运专线动车组运用计划优化模型与算法

在不固定动车组运用区段的前提下,以全部列车形成的环形排列为动车组交路,在环形排列中以日常检修和一级检修的时间间隔和里程间隔为约束,列车接续费用最少为优化目标,建立动车组运用计划和检修计划的一体化优化模型。环形排列状的动车组交路有效描述动车组运用的均匀性,日常检修和一级检修约束使得动车组运用计划优化中融合检修计划的优化。通过引入罚值函数和三交换邻域结构,设计求解模型的模拟退火算法。最后对144列列车的运用计划和检修计划进行实例分析,并分析各项优化指标。     

技术站配流与调机运用综合问题的拉格朗日松弛算法

技术站调度问题中配流与调机运用是关系密切的两个基础问题。基于单机器调度和资源分配理论,构建单解单编技术站配流与调机运用综合问题的混合整数线性规划模型,同时确定出发列车的编组内容和调机的解编任务,使得出发列车满足列车编组要求,且车辆在站加权总停留时间最小。设计拉格朗日松弛算法松弛掉连接约束,将原问题分解为解体子问题、配流子问题和编组子问题,对偶问题采用传统次梯度优化算法求解。最后,采用既有文献算例对该算法的有效性进行测试,结果表明:与分支定界算法相比,该算法能够实现计算质量和时间的较好折中。     

地铁乘务计划计算机编制要素分析

乘务计划编制要素的分析是乘务计划编制的基础。在借鉴已有文献研究的基础上,结合北京地铁各线路实际运营情况,总结乘务计划编制的基本流程,确定编制过程中值乘班制、轮换车站用餐地点及出、退勤地点四大影响因素;对比分析常用轮转制度特点,在计算轮转班组数后确定主值乘制度;轮换车站的设置是乘务任务配对工作的基础,通过计算先确定单一交路和复杂交路的轮换点数量,再根据折返条件、列车交路、线路长度与车辆段位置等选择轮换车站的位置;最后依据运营管理实际情况确定用餐和出退勤地点。其中,轮转制度和轮换车站的设置直接影响着乘务员的工作强度,在乘务计划编制时需要着重考虑。     

BIM融合通信技术在轨行区施工管理信息化平台的应用

针对地铁工程管理里程跨度大、行车调度难、安全监控视频缺失、安全生产预警预控难、全过程管理易缺位等突出问题,提出采用基于BIM的融合通信技术对轨行区进行信息化施工管理。阐述BIM技术与通信技术特点,提出基于BIM融合通信技术的轨行区施工管理信息化平台的目标、设计及开发方式。该平台在广州地铁4号线南延段进行实践应用,提升了隧道内无线通信的稳定性,解决了隧道内设备的防振、防水、防潮以及网络传输无中断等问题,可实现BIM平台派工信息的实时同步以及平台移动端办公。     

基于工作负荷的地铁行车调度人员的配置方案

通过对地铁行车调度岗位工作负荷的建模与测算,基于层次分析法确定工作负荷影响因子,采用逐次回归分析法构建调度工作负荷预测模型,在整合DORATASK方法与零基定员法的基础上建立行车调度人员配置预测模型。利用该模型,可以定量分析现有地铁行车调度配置的工作负荷程度,并可对行车调度岗位人力资源进行合理配置。经过上海地铁实际验证,证明该模型可以有效降低行车调度工作负荷,对于提高调度作业稳定性、降低作业风险具有较高的指导价值。