Electric vehicle tour planning

This study extends the orienteering problem with time windows by considering electric vehicles (EV) with limited range. The model can simulate the change in the battery’s state of charge at each point along the routes, and thus can be used to solve the problem of EV tourist trip design with time windows. A land-sea integrated network for Penghu low-carbon islands was tested, and then the optimal green trip designs are obtained using a heuristic method. Some unique properties of the model are also discussed, as well as a number of management or planning implications.     

Coordinated platooning with multiple speeds

In a platoon, vehicles travel one after another with small intervehicle distances; trailing vehicles in a platoon save fuel because they experience less aerodynamic drag. This work presents a coordinated platooning model with multiple speed options that integrates scheduling, routing, speed selection, and platoon formation/dissolution in a mixed-integer linear program that minimizes the total fuel consumed by a set of vehicles while traveling between their respective origins and destinations. The performance of this model is numerically tested on a grid network and the Chicago-area highway network. We find that the fuel-savings factor of a multivehicle system significantly depends on the time each vehicle is allowed to stay in the network; this time affects vehicles’ available speed choices, possible routes, and the amount of time for coordinating platoon formation. For problem instances with a large number of vehicles, we propose and test a heuristic decomposed approach that applies a clustering algorithm to partition the set of vehicles and then routes each group separately. When the set of vehicles is large and the available computational time is small, the decomposed approach finds significantly better solutions than does the full model.     

Exploration of correlation between environmental factors and mobility at signalized intersections

Characterizing the relationship between environmental factors and mobility is critical for developing a sustainable traffic signal control system. In this study, the authors investigate the correlation of the environmental impacts of transport and mobility measurements at signalized intersections. A metamodeling-based method involving experimental design, simulations, and regression analysis was developed. The simulations, involving microscopic traffic modeling and emission estimation with an emerging emission estimator, provide the flexibility of generating cases with various intersection types, vehicle types, and other parameters such as driver behavior, fuel types, and meteorological factors. A multivariate multiple linear regression (MMLR) analysis was applied to determine the relationship between environmental and mobility measurements. Given the limitations of using the built-in emissions modules within current traffic simulation and signal optimization tools, the metamodeling-based approach presented in this paper makes a methodological contribution. The findings of this study set up the base for extensive application of simulation optimization to sustainable traffic operations and management. Moreover, the comparison of outputs from an advanced estimator with those from the current tool recommend improving the emissions module for more accurate analysis (e.g., benefit-cost analysis) in practical signal retiming projects.     

An intermodal freight transport system for optimal supply chain logistics

Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results.     

Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection

In this study, some different approaches were designed, implemented, and evaluated to perform multi-criteria route planning by considering a driver’s preferences in multi-criteria route selection. At first, by using a designed neuro-fuzzy toolbox, the driver’s preferences in multi-criteria route selection such as the preferred criteria in route selection, the number of route-rating classes, and the routes with the same rate were received. Next, to learn the driver’s preferences in multi-criteria route selection and to classify any route based on these preferences, a methodology was proposed using a locally linear neuro-fuzzy model (LLNFM) trained with an incremental tree based learning algorithm. In this regard, the proposed LLNFM-based methodology reached better results for running-times, as well as root mean square error (RMSE) estimations in learning and testing processes of training/checking data-set in comparison with those of the proposed adaptive neuro-fuzzy inference system (ANFIS) based methodology. Finally, the trained LLNFM-based methodology was utilized to plan and predict a driver’s preferred routes by classifying Pareto-optimal routes obtained by running the modified invasive weed optimization (IWO) algorithm between an origin and a destination of a real urban transportation network based on the driver’s preferences in multi-criteria route selection.     

Real-time airport surface movement planning: Minimizing aircraft emissions

This paper presents a study towards the development of a real-time taxi movement planning system that seeks to optimize the timed taxiing routes of all aircraft on an airport surface, by minimizing the emissions that result from taxiing aircraft operations. To resolve this online planning problem, one of the most commonly employed operations research methods for large-scale problems has been successfully used, viz., mixed-integer linear programming (MILP). The MILP formulation implemented herein permits the planning system to update the total taxi planning every 15 s, allowing to respond to unforeseen disturbances in the traffic flow. Extensive numerical experiments involving a realistic (hub) airport environment bear out that an estimated environmental benefit of 1–3 percent per emission product can be obtained. This research effort clearly demonstrates that a surface movement planning system capable of minimizing the emissions in conjunction with the total taxiing time can be beneficial for airports that face dense surface traffic and stringent environmental requirements.     

Distributed model predictive control for railway traffic management

Every day small delays occur in almost all railway networks. Such small delays are often called “disturbances” in literature. In order to deal with disturbances dispatchers reschedule and reroute trains, or break connections. We call this the railway management problem. In this paper we describe how the railway management problem can be solved using centralized model predictive control (MPC) and we propose several distributed model predictive control (DMPC) methods to solve the railway management problem for entire (national) railway networks. Furthermore, we propose an optimization method to determine a good partitioning of the network in an arbitrary number of sub-networks that is used for the DMPC methods. The DMPC methods are extensively tested in a case study using a model of the Dutch railway network and the trains of the Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods can solve the railway traffic management problem, with the same reduction in delays, much faster than the centralized MPC method.     

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针对我国日益严重的城市道路交通拥堵问题,为缓解城市中心区交通压力,停车换乘服务作为一种有效的交通模式引入到许多城市.本文研究了在拥挤交通走廊上停车换乘系统的平衡问题.在私家车和公交车共处同一道路的综合运输系统中,出行者有三种出行模式——私家车出行模式、公交出行模式和私家车—公交换乘模式.基于这三种模式供给和需求方面的深度分析,提出了在连续走廊上出行者模式选择均衡问题,并将此问题等价转化为一个变分不等式问题.分析探讨了连续均衡模型解的性质,并通过建立公共管理模型,对停车换乘地点进行了优化.最后,通过数值算例说明了所提出模型的适用性.     

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制定铁路线路养护维修计划既要保证线路充分维修,又要兼顾经济效益.为此,以区域路网为研究对象,建立了基于整数规划的铁路线路养护维修计划优化模型.模型考虑了路网中线路之间维修资源的约束,以维修时间和股道占用状态为决策变量,以股道占用费用、维修费用及惩罚费用总和最小化为目标,通过集中安排线路中各项养护维修活动节省维修支出,减少维修活动对股道的占用时间进而达到优化目的.基于启发式算法设计了模型求解方法,仿真实验结果表明,模型具有较好的实用性,能够为编制铁路线路年度养护维修计划提供决策支持.     

直线发动机系统开发及起动怠速实验研究

依据直线发动机-发电机系统设计思路,自主搭建了一套直线发动机系统,并对其进行起动和怠速实验.结果表明,直线发动机可顺利点火起动,首循环即可着火;可在1 450r/min稳定怠速运转;点火时刻较为提前时,燃烧瞬时放热率出现双峰现象;怠速平均指示压力的均值为0.42MPa,缸压峰值的平均值为2.86MPa;二者的循环变动率皆约为4％.