出行者特征要素对公交服务模式的影响分析

公共交通是面向大众的服务,公共交通服务模式的制定与完善需要重点考虑出行者的出行需求,才能从根本上提高公共交通的竞争力。借鉴服务经济学的理论将公共交通服务模式分为公交服务标准、出行者界面、公交服务环境和技术4个维度。基于攀枝花市居民出行调查与公交专项调查数据,应用SPSS定量分析出行者特征要素与公交服务选择的相关性,以考察研究出行者对于公交服务的需求,并提出改善策略。通过研究表明,出行目的、出行时间、职业和年龄与公交服务模式的相关性较大。     

灰色模型在中长期用水量预测中的应用

为实现供水管网经济、可靠、科学的规划改扩建,给出一种基于记录时间较短、历史数据较少的用水量序列的GM(1,1)预测方法。该预测方法把原始用水量序列累加处理生成新序列后,用指数关系式拟合,通过构造参数矩阵,确定辩识参数,建立灰色模型的微分方程。通过对灰色预测方法建模机理的研究建立城市用水量预测GM(1,1)模型,并以东北某大型城市用水量为原始数据进行实际预测,模型精度检验的结果表明该模型的预测等级为高精度预测。该预测方法应用于S市的中长期用水量预测,为S市供水规划改扩建提供有效依据。     

不同类型交通需求管理实践与探索——以深圳市为例

交通需求管理是大城市交通政策的重要组成部分,提出了基于实施难易影响因素的交通需求管理措施分类,总结了深圳从20世纪90年代中期开始相继实施的一系列相对较易实施的交通需求管理措施情况,分析展望需进一步考虑的交通需求管理措施。针对交通拥挤收费这一较难及重大的需求管理措施,回顾了深圳探索性研究拥挤收费方案,包括交通拥挤收费总体调控目标、收费区域范围、收费模式与时段、收费系统技术选型等,分析了实施面临的困难,并提出了实施突破与替代的建议。     

美国城市停车政策解析

为借鉴美国城市停车政策从充分满足需求到停车需求管理转变的成功经验,回顾了美国城市停车政策的发展历史,并对21世纪以来美国城市停车政策发展的新趋势进行解析.指出城市停车政策对出行方式选择和城市空间环境规划设计具有重大影响,进而对城市交通拥堵、空气质量、土地开发密度、街道活力、步行交通环境等产生影响.最后,针对当前中国城市...     

城市交通需求管理创新：按照行驶里程定价与计费的车险

城市交通导致的一系列外部性问题日益严重,交通需求管理的作用尚未充分发挥。机动车辆保险创新可能是交通需求管理的一个有效方法。按照行驶里程定价与计费的车险是对传统车险的重大改进,能够有效减少交通量和缓解由此而导致的外部性,当前在我国大城市进行这一类型的车险业务创新可以创造良好的经济和社会价值。     

城市轨道交通需求预测方法比较

针对常用的"四阶段"法在轨道交通需求预测中存在的不足,提出轨道交通客流分配应将全人口全方式出行量在综合交通网络中进行分配的观点,并应用赋权有向图相关理论构造"综合交通网络",提出轨道交通需求预测的"三阶段"方法。     

我国供应链管理研究综述

20世纪末以来,供应链管理日益成为企业竞争的核心竞争力,是提高企业核心竞争力和国家综合国力的重要途径.通过多年的发展与实践,供应链管理理论与实践都取得了很好的充实与发展.回顾和整理了国内供应链管理文献,对供应链管理内涵和研究现状进行了归纳与总结,并提出了供应链管理未来的发展趋势.     

城市新区道路横断面规划设计研究

道路系统的建设是引导城市新区开发、改善投资环境的重要措施。城市道路横断面作为道路交通功能实现的重要载体,其合理规划设计有利于保障道路安全畅通,提高资源利用效率,实现城市社会经济的良性发展。     

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.