Development of hybrid optimization of train schedules model for N-track rail corridors

From a capacity perspective, efficient utilization of a railway corridor has two main objectives; avoidance of schedule conflicts, and finding a proper balance between capacity utilization and level of service (LOS). There are several timetable tools and commercial rail simulation packages available to assist in reaching these objectives, but few of them offer both automatic train conflict resolution and automatic timetable management features for the different types of corridor configurations. This research presents a new rescheduling model to address some of the current limitations. The multi-objective linear programming (LP) model is called “Hybrid Optimization of Train Schedules” (HOTS), and it works together with commercial rail simulation tools to improve capacity utilization or LOS metrics. The HOTS model uses both conflict resolution and timetable compression techniques and is applicable to single-, double-, and multiple-track corridors (N-track networks), using both directional and bi-directional operations. This paper presents the approach, formulation and data requirements for the HOTS model. Single and multi-track case studies test and demonstrate the model’s train conflict resolution and timetable compression capabilities, and the model’s results are validated by using RailSys simulation package. The HOTS model performs well in each tested scenario, providing comparable results (either improved or similar) to the commercial packages.     

Robust single-track train dispatching model under a dynamic and stochastic environment: A scenario-based rolling horizon solution approach

After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10-30%.     

Use of combinatorial auctions in the railway industry: Can the “invisible hand” draw the railway timetable?

Rail capacity is currently administratively allocated in Europe, whereas the economic literature has often contemplated the opportunity of introducing market mechanisms, auctions in particular, into this industry. This article tries to fill the gap between practice and theory. It first describes the properties of rail capacity (rigidity and non-homogeneity) and shows that because of its very nature, this capacity must be allocated through combinatorial auctions. As identified by the economic literature, using combinatorial auctions introduces a lot of complexity (winner determination and information burden) into the allocation process. To deal with this complexity, some form of centralized planning is necessary to design the right market mechanisms and to allocate capacity. This could have strong consequences on the current deregulation process.     

基于指定到达追踪间隔的高铁车站接车进路长度研究

确定合理的高铁车站接车进路长度对压缩到达追踪间隔时间有重要意义。本文首先通过构建满足到达追踪间隔时间的高铁车站接车进路长度计算模型,提出了接车进路长度的主要影响因素为由线路限制速度、站前坡坡度、制动力使用系数三因素(简称三因素)所确定的车载设备监控制动距离内列车运行时间。然后,通过对常见的线路限制速度、站前坡坡度、制动力使用系数取值下的车载设备监控制动距离内列车运行时间进行牵引计算仿真,并运用三因素方差分析法分析了三因素的影响显著度,得到了线路限制速度、站前坡坡度对高铁车站接车进路长度影响显著的结论。最后,基于高铁车站接车进路长度计算模型,得到了一组指定到达追踪间隔下的高铁车站接车进路长度表,为高铁车站设计提供思路。     

西江黄金水道智能交通调度系统

文章通过分析西江调度的背景,对航道水位和船舶吃水量进行研究,用精确的数学语言做出假设,利用综合调度的内在规律和恰当的数学工具,构造各个量(常量和变量)之间的不等式关系,求解西江黄金水道各港点的船舶签证数和船舶吃水控制,以及各梯级枢纽上下航船舶控制数值,以便对西江黄金水道进行港口、航道、船闸、船舶四位一体的综合调度。     

复线桥施工对既有桥梁的安全控制分析

在既有桥梁下新建复线桥时,新建桥梁施工过程必然对既有桥梁产生影响。文章以重庆高家花园复线桥主桥为研究对象,分析复线桥主桥基础施工、桥墩施工和主梁施工各个阶段的风险源,并采用风险矩阵分析,合理评估每一风险事态对既有桥梁造成的风险等级,提出风险应对措施,以减少总体风险损失。通过风险评估,可为复线桥施工提供科学依据,杜绝安全隐患与风险,加快施工进度。     

Optimizing train stopping patterns and schedules for high-speed passenger rail corridors

High-speed railway (HSR) systems have been developing rapidly in China and various other countries throughout the past decade; as a result, the question of how to efficiently operate such large-scale systems is posing a new challenge to the railway industry. A high-quality train timetable should take full advantage of the system’s capacity to meet transportation demands. This paper presents a mathematical model for optimizing a train timetable for an HSR system. We propose an innovative methodology using a column-generation-based heuristic algorithm to simultaneously account for both passenger service demands and train scheduling. First, we transform a mathematical model into a simple linear programming problem using a Lagrangian relaxation method. Second, we search for the optimal solution by updating the restricted master problem (RMP) and the sub-problems in an iterative process using the column-generation-based algorithm. Finally, we consider the Beijing–Shanghai HSR line as a real-world application of the methodology; the results show that the optimization model and algorithm can improve the defined profit function by approximately 30% and increase the line capacity by approximately 27%. This methodology has the potential to improve the service level and capacity of HSR lines with no additional high-cost capital investment (e.g., the addition of new tracks, bridges and tunnels on the mainline and/or at stations).     

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.