Distributed coordinated in-vehicle online routing using mixed-strategy congestion game

This study proposes a coordinated online in-vehicle routing mechanism for smart vehicles with real-time information exchange and portable computation capabilities. The proposed coordinated routing mechanism incorporates a discrete choice model to account for drivers’ behavior, and is implemented by a simultaneously-updating distributed algorithm. This study shows the existence of an equilibrium coordinated routing decision for the mixed-strategy routing game and the convergence of the distributed algorithm to the equilibrium routing decision, assuming individual smart vehicles are selfish players seeking to minimize their own travel time. Numerical experiments conducted based on Sioux Falls city network indicate that the proposed distributed algorithm converges quickly under different smart vehicle penetrations, thus it possesses a great potential for online applications. Moreover, the proposed coordinated routing mechanism outperforms traditional independent selfish-routing mechanism; it reduces travel time for both overall system and individual vehicles, which represents the core idea of Intelligent Transportation Systems (ITS).     

Jointly optimizing cost, service, and environmental performance in demand-responsive transit scheduling

In certain fleet systems, the environmental impacts of operation are, to some extent, a controllable function of vehicle routing and scheduling decisions. However, little prior work has considered environmental impacts in fleet vehicle routing and scheduling optimization, in particular, where the impacts were assessed systematically utilizing life-cycle impact assessment methodologies such as those described by the Society of Environmental Chemistry and Toxicology. Here a methodology is presented for the joint optimization of cost, service, and life-cycle environmental consequences in vehicle routing and scheduling, which we develop for a demand-responsive (paratransit or dial-a-ride) transit system. We demonstrate through simulation that, as a result of our methodology, it is possible to reduce environmental impacts substantially, while increasing operating costs and service delays only slightly.     

车载路径与导航系统实例研究

提出了车载路径与导航系统的框架,给出了该系统在长春试验的详细报告。在本次试验中,课题组验证了该系统的几个关键部分的可用性,如行程时间预测,最优路径规划和引导,GPS/DR/MM组合定位和诱导方式等。试验结果是有益的效果与问题并存。最后,分析了这些问题的原因与解决方法以及车载路径与导航系统在中国的发展前景。     

使用浮动车检测路网可达性及交通管理策略的研究

合理的城市道路网结构以及相应的合理的交通管理策略，是缓解城市交通拥挤状况，促进城市健康发展的重要条件，合理、正确、有效地评价和分析道路网结构和现行交通管制方案，对城市道路改进和综合管理具有重要意义。本文在回顾以往评价路网性能的指标和方法的基础上，引入基于浮动车GPS/GIS数据的动态绕行效率的概念，提出了新的利用浮动车数据检测路网可达性、出行机动性，路网切换便捷度和交通管理策略合理性的方法，从浮动车数据的全新角度解释路网重要节点的连接方法和快速路封闭方法对出行的影响。最后利用西直门局部区域进行了示范，通过理论研究和实际应用，说明了利用浮动车GPS/GIS检测数据评估路网切换便捷度、交通管理合理性这一新方法是有效和具有发展前景的。     

论GSM-R移动交换网GT路由组织问题

目前中国铁路GSM-R网络正处于快速建设期内,移动网的数据规划和分析中,GT数据分析占据了极其重要的地位。站在GSM-R全路核心网的角度,就GSM-R网络建设和运维过程中存在的GT分析数据制作和路由优化等问题,提出见解,以供参考。     

TDCS/CTC系统路局中心网络防火墙桥接模式

TDCS/CTC系统作为铁路运输指挥的重要行车设备,系统网络安全是保障其正常运行的因素之一,防火墙是重要的网络安全设备。简单介绍防火墙在TDCS/CTC系统中应用情况及接入模式,并根据TDCS/CTC系统的特点详细分析中心网络的2种防火墙接入模式(路由模式和桥接模式)及透明桥接模式的优势。     

"一路一线"公交布线方法分析

论文首先对“一路一线”公交布线方法提出的国内外研究背景进行了分析论述,提出了“一路一线”布设的基本原则、基本原理以及根本目的,归纳总结了以经纬走向为主、环线为辅的主要布线方法,和开设公交专用道的“一路一线”辅助布线方法,在此基础上详细地分析了“一路一线”布设方法所存在的优势和弊端,并加以总结,以便于对国内公交布线起到一定的理论支持作用。     

The Hybrid Vehicle Routing Problem

In this paper the Hybrid Vehicle Routing Problem (HVRP) is introduced and formalized. This problem is an extension of the classical VRP in which vehicles can work both electrically and with traditional fuel. The vehicle may change propulsion mode at any point of time. The unitary travel cost is much lower for distances covered in the electric mode. An electric battery has a limited capacity and may be recharged at a recharging station (RS). A limited number of RS are available. Once a battery has been completely discharged, the vehicle automatically shifts to traditional fuel propulsion mode. Furthermore, a maximum route duration is imposed according to contracts regulations established with the driver. In this paper, a Mixed Integer Linear Programming formulation is presented and a Large Neighborhood Search based Matheuristic is proposed. The algorithm starts from a feasible solution and consists into destroying, at each iteration, a small number of routes, letting unvaried the other ones, and reconstructing a new feasible solution running the model on only the subset of customers involved in the destroyed routes. This procedure allows to completely explore a large neighborhood within very short computational time. Computational tests that show the performance of the matheuristic are presented. The method has also been tested on a simplified version of the HVRP already presented in the literature, the Green Vehicle Routing Problem (GVRP), and competitive results have been obtained.     

A bi-objective approach to routing and scheduling maritime transportation of crude oil

Maritime transportation, the primary mode for intercontinental movement of crude oil, accounts for 1.7 billion tons annually – bulk of which are carried via a fleet of large crude oil tankers. Although spectacular episodes such as Exxon Valdez underline the significant risk and tremendous cost associated with marine shipments of hazardous materials, maritime literature has focused only on the cost-effective scheduling of these tankers. It is important that oil transport companies consider risk, since the insurance premiums is contingent on the expected claim. Hence through this work, we present a mixed-integer optimization program – with operating cost and transport risk objectives, which could be used to prepare routes and schedules for a heterogeneous fleet of crude oil tankers. The bi-objective model was tested on a number of problem instances of realistic size, which were further analyzed to conclude that the cheapest route may not necessarily yield the lowest insurance premiums, and that larger vessels should be used if risk is more important as it enables better exploitation of the risk structure.