法眼观小件快运

近几年一种被称为"小件快运"的运输方式悄然兴起. 其特点是:第一,客运企业运输小件物品;第二,托运人不是乘客,"人贷分离";第三,客运企业利用客运班车运输小件物品;第四,运输速度快,满足了小件货物快速流转的需求.     

打造绿色交通,看看小件快运做了哪些努力?

正依托客车底舱,凭借速度快、固定的多班次,实现快递货物"当日达",道路客运小件快运的发展正日益受到客运企业的重视,并视其为新的经济增长点。实际上,发展小件快运业务,不仅能够提升道路运输企业效益,对实现企业持续安全健康发展也具有积极意义。     

小麻雀:期待飞向更远

在道路运输行业,小件快运并不是个新鲜词,它是道路客运市场开辟出的一条新路径.虽然缺乏统一完善的标准和规范,缺乏国家政策的明确支持,但各地客运企业和小件快运业务的发展依然热火朝天.     

小件快运中的最短运输时间问题

小件快运经过多年的发展,已经成为公路客运一个新的经济增长点,越来越多的公路客运企业和汽车客运站加入到经营小件快运的行列,小件快运也逐渐由单个企业的独立运营发展到多个企业联合的网络化运营。对中转运输中多条运输线路进行分析,得到最短运输时间线路,从而提高运输效率。     

小件快运:“错位”与“融合”铺就腾飞路——访交科院现代物流研发中心主任赖平仲

小件快运利用客车底舱发展快速运输,成本低、价值高、节能环保,优势明显。目前,大部分道路客运企业和客运场站积极求索,也引起不少道路运输管理部门的重视,吸引了不少地方邮政、民航等部门的协力推进。     

小件快运变身术：小件快运：“错位”与“融合”铺就腾飞路——访交科院现代物流研发中心主任赖平仲

小件快运利用客车底舱发展快速运输,成本低、价值高、节能环保,优势明显。目前,大部分道路客运企业和客运场站积极求索,也引起不少道路运输管理部门的重视,吸引了不少地方邮政、民航等部门的协力推进。     

需求响应交通服务的法规思考

<正>需求响应交通可以为乘客提供门到门的辅助公共交通服务,提供与私人小汽车相竞争的出行服务,也是公共交通引导城市发展(TOD)理念的具体实践,这些都是推动需求响应交通服务发展的重要法理依据。"《道路运输条例》(2012年修正本)中的道路旅客运输是指用客车运送旅客、为社会公众提供的商业性服务(包括班车客运、包车客运和旅游客运)。《道路旅客运输及客运站管理规定》(交通运输部令2012年第8号)界定班车客运是在城乡道路上按照固定线路、时间、站点     

依托客运网络 发展小件快运

随着交通运输业的快速发展,广大城乡群众出行机会增多,出行方式也呈现多样化,其中集客货运于一体的出行方式成为一种比较普遍的现象。但是毕竟货随人走还是存在一些不方便的地方,因此将一些货运业务选择特定的客运班线进行快运成为一种新的运输方式。有需求就会有市场,客运企业悄悄改变几十年一贯制的客运模式,利用客运车辆进行小件快运便如雨后春笋般地冒了出来。由于高铁等其他运输方式对市场的影响,道路客运企业在相关线路上的客源有所下降,而油价却     

福建客运服务触“网”而新——市民轻松购票 企业优化经营 管理精准有据

<正>在福建,买汽车票不用到站里排长队,点几下手机屏就能轻松搞定;客运站客车安全例检、排班调度等轻点鼠标一键操作,还能在线收揽小件货物增加额外收入;管理部门做规划不用再被动地向企业要数据,运力、运量的真实情况在网上就能一览无遗……带来这些变化的,是福建省运管部门主导建设的全省道路客运联网售票系统。它不仅能卖票,还通过互联网技术改变了传统客运行业发展的滞后与被动,成为客运企业转型升级的"侦察兵"、运管部门运筹帷幄的"情报源"。     

旅游客运:路在企业脚下

从国外客运企业发展的历程来看,许多道路客运企业均从重点提供班线运输服务延伸到提供旅游客运服务,并从旅游客运企业演变为旅游企业,进而主导旅游市场,道路客运"旅游化"已成为发展趋势。     

基于高速铁路常旅客计划的全流程智慧行李服务方案构建

随着"铁路畅行"和"客运提质计划"的提出,提供高质量、个性化的旅客服务成为高速铁路客运发展的关键方向。为打造全新旅客行李服务概念,基于"铁路畅行"会员常旅客计划,构建全流程、门到门智慧行李服务方案,从旅客需求角度出发,采取"人货分离"模式,突破传统站内行李服务的模式,拓宽业务场景,延长商业链,形成一套系统完整的方案。全流程智慧行李服务作为一项新的服务模式,为培育旅客需求,提高顾客粘合度,挖掘高铁行李服务的潜在市场,优化铁路盈利结构,加快构建铁路客运服务体系,提升铁路在运输市场的竞争力提供支持。     

The load-dependent vehicle routing problem and its pick-up and delivery extension

The present paper examines a Vehicle Routing Problem (VRP) of major practical importance which is referred to as the Load-Dependent VRP (LDVRP). LDVRP is applicable for transportation activities where the weight of the transported cargo accounts for a significant part of the vehicle gross weight. Contrary to the basic VRP which calls for the minimization of the distance travelled, the LDVRP objective is aimed at minimizing the total product of the distance travelled and the gross weight carried along this distance. Thus, it is capable of producing sensible routing plans which take into account the variation of the cargo weight along the vehicle trips. The LDVRP objective is closely related to the total energy requirements of the vehicle fleet, making it a credible alternative when the environmental aspects of transportation activities are examined and optimized. A novel LDVRP extension which considers simultaneous pick-up and delivery service is introduced, formulated and solved for the first time. To deal with large-scale instances of the examined problems, we propose a local-search algorithm. Towards an efficient implementation, the local-search algorithm employs a computational scheme which calculates the complex weighted-distance objective changes in constant time. Solution results are presented for both problems on a variety of well-known test cases demonstrating the effectiveness of the proposed solution approach. The structure of the obtained LDVRP and VRP solutions is compared in pursuit of interesting conclusions on the relative suitability of the two routing models, when the decision maker must deal with the weighted distance objective. In addition, results of a branch-and-cut procedure for small-scale instances of the LDVRP with simultaneous pick-ups and deliveries are reported. Finally, extensive computational experiments have been performed to explore the managerial implications of three key problem characteristics, namely the deviation of customer demands, the cargo to tare weight ratio, as well as the size of the available vehicle fleet.     

Solving the bicriterion routing and scheduling problem for hazardous materials distribution

Hazardous materials routing and scheduling decisions involve the determination of the minimum cost and/or risk routes for servicing the demand of a given set of customers. This paper addresses the bicriterion routing and scheduling problem arising in hazardous materials distribution planning. Under the assumption that the cost and risk attributes of each arc of the underlying transportation network are time-dependent, the proposed routing and scheduling problem pertains to the determination of the non-dominated time-dependent paths for servicing a given and fixed sequence of customers (intermediate stops) within specified time windows. Due to the heavy computational burden for solving this bicriterion problem, an alternative algorithm is proposed that determines the k-shortest time-dependent paths. Moreover an algorithm is provided for solving the bicriterion problem. The proximity of the solutions of the k-shortest time-dependent path problem with the non-dominated solutions is assessed on a set of problems developed by the authors.     

Signal control optimization for automated vehicles at isolated signalized intersections

Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2–36.9% and increase throughput by 2.7–20.2%, depending on the demand scenario.     

Optimization of headways with stop‐skipping control: a case study of bus rapid transit system

Bus rapid transit system is designed to provide high‐quality and cost‐efficient passenger transportation services. In order to achieve this design objective, effective scheduling strategies are required. This research aims at improving the operation efficiency and service quality of a BRT system through integrated optimization of its service headways and stop‐skipping strategy. Based on cost analysis for both passengers and operation agencies, an optimization model is established. A genetic algorithms based algorithm and an application‐oriented solution method are developed. Beijing BRT Line 2 has been chosen as a case study, and the effectiveness of the optimal headways with stop‐skipping services under different demand levels has been analyzed. The results has shown that, at a certain demand level, the proposed operating strategy can be most advantageous for passengers with an accepted increase of operating costs, under which the optimum headway is between 3.5 and 5.5 min for stop‐skipping services during the morning peak hour depending on the demand with the provision of stop‐skipping services. The effectiveness of the optimal headways with stop‐skipping services is compared with those of existing headways and optimal headways without stop‐skipping services. The results show that operating strategies under the optimal headways with stop‐skipping services outperforms the other two operating strategies with respect to total costs and in‐vehicle time for passengers. Copyright © 2014 John Wiley & Sons, Ltd.     

A rolling horizon algorithm for auto-carrier transportation

This paper introduces a rolling horizon algorithm to plan the delivery of vehicles to automotive dealers by a heterogeneous fleet of auto-carriers. The problem consists in scheduling the deliveries over a multiple-day planning horizon during which requests for transportation arrive dynamically. In addition, the routing of the auto-carriers must take into account constraints related to the loading of the vehicles on the carriers. The objective is to minimize the sum of traveled distances, fixed costs for auto-carrier operation, service costs, and penalties for late deliveries. The problem is solved by a heuristic that first selects the vehicles to be delivered in the next few days and then optimizes the deliveries by an iterated local search procedure. A branch-and-bound search is used to check the feasibility of the loading. To handle the dynamic nature of the problem, the complete algorithm is applied repeatedly in a rolling horizon framework. Computational results on data from a major European logistics service provider show that the heuristic is fast and yields significant improvements compared to the sequential solution of independent daily problems.     

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.     

Dynamic demand estimation and microscopic traffic simulation of a university campus transportation network

Abstract

This article documents the authors' experience with the modeling, simulation, and analysis of a university transportation system, using the TRansportation ANalysis and SIMulation System (TRANSIMS). The processes of data preparation and network coding are described, followed by the algorithm developed to estimate the dynamic 24-hour demand, which includes a procedure for estimating the ‘desirability’ of the different parking lots from readily available data. The dynamic demand estimation algorithm is validated by comparing estimated and observed parking lot occupancies, where it is shown that the algorithm is capable of replicating observed results. Finally, an example is included to demonstrate how the developed model can be used in campus transportation planning. Besides serving as a first case study for using TRANSIMS to model a university campus, the study's contributions include the development of a procedure for parking lot desirability ranking and a practical procedure for estimating dynamic demand on university campuses.     

Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation

The tremendous use of hazardous materials has promoted the economic development, which also brings about a growing risk causing a widespread concern. In this work, we consider a location-scheduling problem on hazardous materials transportation under the assumption that transportation risks are time-dependent fuzzy random variables. First, we formulate a scheduling optimization model and design a fuzzy random simulation based genetic algorithm to optimize the departure time and dwell times for each depot–customer pair. Then we establish an expected value model and design a modified particle swarm optimization algorithm to minimize the en route risks and site risks. Finally, numerical examples are given to illustrate the effectiveness of the proposed models and algorithms.