Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

基于多旅行商问题的应急设施服务区划分模型

基于多旅行商问题，增设集散中心需求及应急服务设施资源容量约束条件，以最小化遍历区域内全部集散中心的综合旅行时间成本为优化目标，构建一种应急设施服务区划分模型，确定各应急设施的服务区范围.设计一种复合算法求解模型，首先基于P-中值选址模型的优化理念，形成初始方案；继而加入禁忌搜索算法，结合LKH求解器对模型进行迭代优化求得最优解.基于宁波市北仑区实际拓扑网络进行案例分析，验证了模型和求解方法的有效性.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput

In this research, we present a data-splitting algorithm to optimally solve the aircraft sequencing problem (ASP) on a single runway under both segregated and mixed-mode of operation. This problem is formulated as a 0–1 mixed-integer program (MIP), taking into account several realistic constraints, including safety separation standards, wide time-windows, and constrained position shifting, with the objective of maximizing the total throughput. Varied scenarios of large scale realistic instances of this problem, which is NP-hard in general, are computationally difficult to solve with the direct use of commercial solver as well as existing state-of-the-art dynamic programming method. The design of the algorithm is based on a recently introduced data-splitting algorithm which uses the divide-and-conquer paradigm, wherein the given set of flights is divided into several disjoint subsets, each of which is optimized using 0–1 MIP while ensuring the optimality of the entire set. Computational results show that the difficult instances can be solved in real-time and the solution is efficient in comparison to the commercial solver and dynamic programming, using both sequential, as well as parallel, implementation of this pleasingly parallel algorithm.     

基于虚拟化云技术的路局中心地震预警监测系统方案及联动研究

通过研究铁路地震预警监测系统现状和建设趋势,从资源共享、重复性建设、地震联动触发信号系统停车、牵引供电系统断电等多角度分析存在的问题,结合实际需求,提出铁路局中心系统采用虚拟化云技术的建设思路,并提出地震预警监测系统通过路局中心系统与信号RBC、牵引供电中心接口的联动触发策略。文中论述了采用虚拟化云技术搭建的地震预警监测铁路局中心系统功能、系统构成、硬件配置,从而解决各条铁路接入路局中心增加大量服务器等所带来的资源浪费严重、重复性建设等问题,为建设项目节省投资;阐述了通过路局中心系统与信号RBC、牵引供电中心接口的联动触发方案,为安全提供保障的同时尽可能节省建设投资和维护成本,更好地为铁路安全保驾护航。     

道路环境对交通安全的影响及对策

交通安全,涉及到人、车以及道路和环境等很多因素。近年来,因为各种原因使得有关单位以及人员将更多的将精力放在人、车、道路这几个因素上,对道路交通环境没有过多的考虑和研究。深入研究道路交通环境,有利于道路交通的安全,文章通过对其的研究和分析,得出有关道路交通环境对交通安全的影响和解决措施,对于防止道路交通安全事故具有重要意义。同时为人们的生命财产的安全提供保障、对构建社会主义和谐社会具有很大帮助。     

谈港口国船舶污染物接收设施问题报告的处置

文中探讨了一起国际航行船舶关于港口国船舶污染物接收设施问题报告的调查情况,介绍了此次船舶污染物接收设施问题报告的原因分析及采取的措施,并向接收设施的提供者和使用者给出了优化建议,明确了各有关部门在港口国船舶污染物接收设施履约工作上的重点。     

铁路沿线城市空间物业开发研究——以宝鸡市南客站核心区站前广场工程为例

为了提升铁路沿线城市空间物业开发的设计手法,结合宝鸡市南客站核心区站前广场工程的设计实践,从系统性设计观、工程性质解析、消防设计思路、空间建构方法、无缝接驳、技术要素等方面进行研究分析,展示将铁路与城市系统进行一体化设计、利用自然资源解决消防和投资问题、平层空间中对多种交通体系的有机组织、不同坐标和高程系统背景下分区域构建柱网、各板块交接处的问题解决等技术成果,总结出同类型建筑的设计思路,力求为该领域的建筑设计发展提供有益的借鉴。     

考虑移步需求的无桩型共享单车动态调度研究

共享单车系统自发的不均衡性导致单车数量分布与用户需求分布之间产生偏移，降低了系统服务能力，需要调度实现再平衡. 现有动态调度算法缺乏考虑起点车辆供给不足，用户在出行过程中“再次”取车的移步需求，难以准确识别用户真实的出行需求分布，降低了调度效果. 本文提出以用户出行选择行为为下层，以调度车辆路径规划为上层的双层规划模型框架，设计结合仿真系统的启发式求解算法. 算例场景基于上海市虹口、杨浦区共享单车历史出行数据搭建，并进行网格化处理. 算例结果表明，模型能有效识别移步需求，提高共享单车的供需匹配能力. 针对各类调度资源配置情况，共享单车的供需匹配率提升18.07%~ 19.89%，提高了共享单车系统的管理效率.     

共享自动驾驶汽车经营策略优化分析

研究共享自动驾驶汽车(SAV)与普通汽车共存背景下，SAV公司如何根据运营目标优化经营策略并影响通勤者出行方式选择. 假设高速公路上存在一定数量的独驾通勤者，无车通勤者在SAV和地铁之间进行权衡. 分析固定需求和弹性需求下，SAV公司追求系统总成本最小或系统净收益最大和利润最大时的最优经营策略，即SAV票价和容量，进而得到均衡时模式划分情况，SAV最优发车数，系统总成本或系统净收益，以及公司利润等指标. 算例对均衡结果进一步验证发现，SAV公司垄断经营会收取较高的票价，提供容量较小的车型. 在系统最优情形下，SAV公司无法获得正利润，需要政府补贴运营.