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.     

新冠肺炎疫情下城市公共交通非常规防疫策略——以宁波市为例

在国家重大突发疫情面前，城市公共交通具有保障出行与阻断疫情的双重责任。面对新型冠状病毒感染的肺炎（简称新冠肺炎，COVID-19）疫情，城市公共交通既要保证有效运输，又要降低疫情扩散的风险。地铁和常规公交是城市公共交通系统不可缺少的一部分，在城市公共交通中占据重要地位。针对宁波市城市公共交通系统中存在的防疫问题，提出了基于问题驱动的城市公共交通非常规防疫策略。研究结果表明：新冠肺炎疫情形势下，城市公共交通系统防疫需要启动应急响应机制，遵循分区分类防控的基本原则，在保障人们刚性出行需求的同时，必须阻断疫情传播，减少通过公共交通运输造成的交叉感染，实现疫情可防可控。除体温检测、佩戴口罩与洗手消毒等防疫措施之外，还应结合地方实际和风险评估等级采取非常规的组合防疫策略，即：①面向城市常规公交，建议采取网格化运营策略、需求响应式运营策略以及应急公交接驳策略；②面向城市轨道交通，建议采取3级组合防疫策略，即暂停运营策略、车厢隔离防疫策略以及需求响应式防疫策略。以上常规公交与轨道交通的多种组合防疫策略需根据疫情的发展情况动态调整，及时启动相应级别的公共交通应急预案，被动防御与主动防控相结合，积极发挥城市公共交通在疫情时期的交通骨干作用和应急保障作用。     

Estimating flight-level price elasticities using online airline data: A first step toward integrating pricing,demand, and revenue optimization

We estimate flight-level price elasticities using a database of online prices and seat map displays. In contrast to market-level and route-level elasticities reported in the literature, flight-level elasticities can forecast responses in demand due to day-to-day price fluctuations. Knowing how elasticities vary by flight and booking characteristics and in response to competitors’ pricing actions allows airlines to design better promotions. It also allows policy makers the ability to evaluate the impacts of proposed tax increases or time-of-day congestion pricing policies. Our elasticity results show how airlines can design optimal promotions by considering not only which departure dates should be targeted, but also which days of the week customers should be allowed to purchase. Additionally, we show how elasticities can be used by carriers to strategically match a subset of their competitors’ sale fares. Methodologically, we use an approach that corrects for price endogeneity; failure to do so results in biased estimates and incorrect pricing recommendations. Using an instrumental variable approach to address this problem we find a set of valid instruments that can be used in future studies of air travel demand. We conclude by describing how our approach contributes to the literature, by offering an approach to estimate flight-level demand elasticities that the research community needs as an input to more advanced optimization models that integrate demand forecasting, price optimization, and revenue optimization models.