基于公交智能调度系统的公交换乘算法实现

通过对城市居民公交出行选择影响因素和选择逻辑的分析,利用地理信息系统（GIS）技术构建公交换乘数据模型和公交智能调度系统数据计算出行时间,以此为基础实现以最少换乘次数为第一目标,出行时间最短为第二目标的公交换乘算法。该算法考虑了步行换乘、行驶时间、换乘时间及公交线路上、下行因素对换乘查询的影响,能够较快地提供公交换乘方案。     

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。     

波浪作用下粉沙质底沙运动特性的试验研究

本文利用山东潍坊现场沙(d_(50)≈0.0813mm),通过水槽试验研究了波浪作用下粉沙质底沙的运动特性、床面形态、起动特点及输沙规律等,为分析研究粉沙质海岸的泥沙问题提供了基本数据和研究方法.     

公路运输统计工作存在的问题及改进措施

公路运输统计是整个社会经济统计的重要组成部分，也是道路运输管理的基础工作。公路运输统计为部门宏观调控、制定政策提供决策和依据。文章从统计指标、统计工作适应性、统计制度、协调运作、手段和技术及人员等方面论述了我国目前公路运输统计工作中存在的问题，并提出建议。     

Biofuel futures in road transport – A modeling analysis for Sweden

First and second generation biofuels are among few low-carbon alternatives for road transport that currently are commercially available or in an early commercialization phase. They are thus potential options for meeting climate targets in the medium term. For the case of Sweden, we investigate cost-efficient use of biofuels in road transport under system-wide CO₂ reduction targets to 2050, and the effects of implementation of targets for an almost fossil-free road transport sector to 2030. We apply the bottom-up, optimization MARKAL_Sweden model, which covers the entire Swedish energy system including the transport sector. For CO₂ reductions of 80% to 2050 in the Swedish energy system as a whole, the results of the main scenario show an annual growth rate for road transport biofuels of about 6% from 2010 to 2050, with biofuels accounting for 78% of road transport final energy use in 2050. The preferred biofuel choices are methanol and biomethane. When introducing additional fossil fuel phase-out policies in road transport (−80% to 2030), a doubling of the growth rate to 2030 is required and system CO₂ abatement costs increases by 6% for the main scenario. Results imply that second generation biofuels, along with energy-efficient vehicle technologies such as plug-in hybrids, can be an important part of optimized system solutions meeting stringent medium-term climate targets.     

Valuing crowding in public transport: Implications for cost-benefit analysis

This paper investigates the valuation of crowding in public transport trips and its implications in demand estimation and cost-benefit analysis. We use a choice-based stated preference survey where crowding levels are represented by means of specially designed pictures, and use these data to estimate flexible discrete choice models. We assume that the disutility associated with travelling under crowded conditions is proportional to travel time. Our results are consistent with and extend previous findings in the literature: passenger density has a significant effect on the utility of travelling by public transport; in fact, the marginal disutility of travel time in a crowded vehicle (6 standing-passengers/m²) is 2.5 times higher than in a vehicle with available seats. We also compare the effects of different policies for improving bus operations, and the effect of adding crowding valuation in cost-benefit analysis. In doing that, we endogenise the crowding level as the result of the equilibrium between demand and supplied bus capacity. Our results indicate that important benefits may be accrued from policies designed to reduce crowding, and that ignoring crowding effects significantly overestimate the bus travel demand the benefits associated with pure travel time reductions.     

楚雄市公共交通现状及对策研究

针对楚雄市城市交通的发展,通过实地调查、问卷调查、跟车调查等方法,在分析楚雄市公交现状概况、居民出行方式结构基本特征、公交线路网基本特征、公交客流基本特征的基础上,分析楚雄市公共交通发展存在的问题,并提出相应的对策.     

Editorial Comment

This article examines fundamental questions concerning the origins and evolution of academic transport studies and the dynamics of its historical development. These fundamental questions are generally little discussed, yet are vital in gaining a better understanding of how the subject is driven forward. This article therefore attempts to answer these questions by taking the UK as a case study, with the principal method being evidence from semi-structured interviews with 20 senior figures integrally involved in the development of the subject. The characterization of transport studies provided by Allsop is employed as an analytical framework, enabling the construction of a historical narrative that can provide salient insights. Thus, it is argued that for academic transport studies the degree of early cross-fertilization with public arenas has meant that subsequent developments have been shaped by the subject's contextual environment. Most prominent among these developments has been the trend towards wider disciplinary breadth. This article examines the evolution of these processes, while the concluding section discusses their implications for consequent strengths and limitations of the subject.     

面向传染病疫情防控的公共交通运行管理决策支持研究

近年来全球新发重大传染病疫情不断出现，已成为人类社会必须防范应对的重大风险。公共交通在传染病疫情防控过程中承担着阻断病毒传播和保障复工复产的功能，疫情期间公共交通运行管理的决策需求和技术支撑体系与日常情况有显著差异。现有研究多针对公交日常运行决策需求展开，虽有少量针对突发公共事件的应急管理决策支持的研究，但多针对自然灾害和事故灾难场景，无法迁移应用于传染病疫情防控。基于此，以新型冠状病毒肺炎（COVID-19）疫情为例，综合考虑突发公共卫生事件应急管理流程和疫情防控实际情况，系统梳理疫情不同阶段的防控目标和决策需求，提出一种面向传染病疫情防控的公共交通运行管理决策支持系统框架，建立基于公共卫生事件案例库、多源数据融合库、公交数据分析技术库和公交防疫策略库的功能架构，并设计不同功能模块的算法模型。研究以厦门为例，对提出的决策支持系统的功能架构和算法模型进行验证。研究结果表明，构建公交乘客出行链的成功率为89.7%，并可应用于疫情不同阶段的关联客流分析、感染者同乘人员的追溯分析、医护人员等防疫人员的通勤出行识别、公交运行满载率监控等方面。研究成果不仅对传染病疫情防控有实用价值，而且对突发公共事件应急管理决策支持方法亦有理论贡献。     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.