城市土地利用特征对居民出行方式的影响——以深圳市为例

为缓解日益严重的城市交通问题、优化居民出行方式,有必要研究城市土地利用与交通系统的协调问题.以深圳市为例,应用多元Logit模型(MNL模型),纳入城市土地利用变量与出行者的家庭、社会、经济等变量,分析其对居民工作与非工作出行方式的影响.研究结果表明,出行起点的城市区位是影响居民出行方式的重要因素之一.相对于组团中心和...     

基于WebGIS的铁路旅客辅助决策系统的研究与开发

以旅客出行和换乘中转站的选择及优化作为实现目标,研究了针对旅客不同出行需求的查询算法,开发出基于WebGIS的铁路旅客出行辅助决策系统.系统为旅客出行决策提供综合查询信息,将最优线路以直观的方式显示出来,实现乘车线路的交互式查询和地理信息系统的实时衔接,后台决策软件综合考虑线路状况,实时更新铁路客运时刻表数据库,给出最优乘车路径选择.     

区域运输通道内影响旅客出行方式选择的因素

首先给出运输通道的概念,阐述我国现有的旅客出行方式。从我国现有的这些交通方式入手,探讨影响旅客出行方式选择的因素,即影响人们选择出行方式的因素可分为宏观因素和微观因素。宏观因素如社会经济发展水平、城市化水平、交通政策等,决定着出行方式的总结构;微观因素如出行目的和出行时间,则决定交通方式的选择。     

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。     

承台-群桩结构波浪力理论分析

目前桥梁基础的波浪力计算大多采用数值模拟的方式进行研究,但数值模拟存在计算成本高、耗时长等缺点。因此基于线性势流理论首次推导了承台-群桩结构的波浪绕射作用计算公式,求解得到了承台波浪力的半解析解。首先将承台-群桩结构简化为上层穿出水面、下层嵌入水底的双层多柱体结构,其中上层单柱体代表承台,下层多柱体代表群桩。然后将计算域划分为承台外侧和下侧2个子域,子域间的交界面函数通过傅里叶级数处理,通过匹配特征函数展开法对每一个子域的速度势函数进行求解,最终得到承台表面波浪力。在进行解的收敛性分析和与边界元软件进行大量的对比验证后,分析了桩半径和承台高度对承台表面波浪力的影响。研究发现：在小波数范围内,承台表面的量纲一的波浪力会随着群桩的存在而增大,并随着桩半径的增加而进一步增大;同时承台高度的增加会首先对波浪力的增加有促进作用,但在承台高度达到某一临界值后,承台量纲一的波浪力将会减小。首次基于势流理论推导的双层多柱体波浪作用的理论公式,为承台-群桩结构表面波浪力的求解提出了一种新的半解析方法,相较于数值模拟,其能在保证结果准确性的同时,也能使得计算更加方便快捷、成本低廉,为之后波浪作用理论的进一步完善提供有力支撑。     

考虑行驶时间不确定性的合乘路径鲁棒优化方法

为应对实际合乘过程中时间不确定性带来的负面影响,本文研究不确定行驶时间下的合乘问题。采用预算不确定集合描述时间变量,引入不确定性水平可调节的预算系数,构建以车辆总里程最短和车辆数最少为目标的合乘路径鲁棒优化模型。并设计两阶段算法求解,第1阶段以两乘客间的可行合乘路径为基础,从车辆总里程节省率和乘客时间窗匹配灵活性两方面设计公式量化合乘匹配机会,以匹配机会为权重构建乘客图网络并聚类乘客需求;第2阶段设计以顺序插入启发式方法构造初始解的禁忌搜索算法求解。案例数据实验结果表明：本文聚类方法能保证优化质量并提高85%以上的计算效率,同时能缩减乘客等车时间和绕行距离;增大预算系数时解的鲁棒性逐渐提高,但会增加10%~40%的车辆数并降低1%~10%的里程节省率;大规模乘客案例和窄时间窗案例的合乘路径对不确定时间的敏感性更高,宽时间窗案例无需增加过多额外车辆和总里程就能达到较高水平的路径鲁棒性。     

Optimal locations and travel time display for variable message signs

This paper first develops a network equilibrium model with the travel time information displayed via variable message signs (VMS). Specifically, the equilibrium considers the impact of the displayed travel time information on travelers’ route choices under the recurrent congestion, with the endogenous utilization rates of displayed information by travelers. The existence of the equilibrium is proved and an iterative solution procedure is provided. Then, we conduct the sensitivity analyses of the network equilibrium and further propose a paradox, i.e., providing travel time information via VMS to travelers may degrade the network performance under some poor designs. Therefore, we investigate the problem of designing the VMS locations and travel time display within a given budget, and formulate it as a mixed integer nonlinear program, solved by an active-set algorithm. Lastly, numerical examples are presented to offer insights on the equilibrium results and optimal designs of VMS.     

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.     

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.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.