Development of a behaviorally induced system optimal travel demand management system

The basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This “passive” way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.     

基于海明距离-TOPSIS法的山区公路隧道扩建方案直觉模糊优选

为了解决山区高速公路隧道改扩建方案选取决策困难而又缺乏理论依据的问题,基于直觉模糊数的海明距离,结合TOPSIS方法,增加专家权向量修正,进而得到处理具有优先级别关系的多属性问题的决策方法。分析山区高速公路隧道改扩建的影响因素,建立隧道扩建方案影响指标体系。从依托工程实际出发,邀请指定的专家对指标优先级别排序,然后利用已建的数学模型实现扩建方案寻优。该方法在厦蓉高速公路隧道改扩建施工方案优选中得到有效运用,说明该方法实用可行,对隧道改扩建施工方案以及类似工程方案优选问题具有重要的指导意义。     

基于网格交通状态分类的行程时间规律挖掘与计算

在出租车轨迹数据挖掘的基础上，本文提出基于网格交通状态的行程时间计算方法。在区域网格化的基础上，利用出租车全球定位系统(Global Positioning System, GPS)数据构建区域网格宏观基本图，并对宏观基本图的流量-密度关系进行拟合；进而使用高斯混合聚类法，将区域交通状态分类为畅通、轻度拥堵和重度拥堵。对不同交通状态网格的行程时间进行挖掘分析，发现3类交通状态下网格行程时间表现出不同的分布特征，畅通、轻度拥堵和重度拥堵的最佳行程时间分布分别为Gamma分布、Weibull分布和对数正态分布；通过不同状态网格行驶时间联合概率密度分布的近似拟合推导出路径网格行程时间概率密度模型。本文提出的方法可以快速计算一 定可靠度条件下的行程时间，对不同线路和时间内的案例分析结果表明，该方法对路径行程时间估计的平均绝对误差在1%~16%，可以为交通诱导与未来导航提供技术方法支撑。     

社会偏好视野下旅游出行服务链定价博弈模型

为综合分析社会偏好理论视野下的服务链定价策略博弈问题，本文构建了服务提供商和出行即服务(Mobility as a Service, MaaS)平台组成的旅游出行服务链联盟。基于社会偏好理论建立不平等厌恶决策模式、利他决策模式和社会福利综合决策模式下的Stackelberg博弈模型，与分散决策和合作决策两种服务链基本决策模式下的博弈结果进行比较。探究MaaS环境下考虑决策者社会偏好特征的旅游出行服务产品的定价策略及服务链收益，并通过数值分析验证了模型的有效性。结果表明，决策权的分散导致旅游出行服务产品的销售价格和服务链整体最优收益降低。不平等厌恶决策模式下，产品的最优销售价格不受影响，服务链整体利润不变；利他决策模式下，服务链整体最优收益不断上升；社会福利综合决策模式下，各最优决策变量受服务提供商利他偏好的影响更大。关注服务链决策者的不平等厌恶偏好并实施利他行为，可以提高旅游出行服务产品的需求和总体收益，有利于旅游出行服务链的协调运营状态，实现交旅融合发展。     

基于出行方式决策过程的价格调控策略辅助措施研究

从提高个体在价格调控策略下的绿色出行意向入手，制定价格调控策略的辅助措施是提升价格调控策略有效性的重要手段.本文基于出行决策行为变化自调节阶段模型，构建了价格调控策略下的居民出行方式决策过程概念模型.利用拥堵收费和奖励策略下的出行方式决策心理调查数据，基于结构方程模型，对比分析了拥堵收费和奖励策略下不同机动化出行习惯强度个体通勤与非通勤出行方式决策心理的差异化特征.最后，针对出行方式决策过程中动机形成阶段，计划行为阶段，以及实施计划阶段的心理特征，分别提出了价格调控策略的辅助措施，为价格调控策略实施提供了更好的辅助决策支持.     

基于改进两步移动搜索法的轨道交通接驳可达性分析

研究城市轨道交通接驳可达性对提升轨道交通服务水平具有重要意义。本文从居民出发地角度，运用基于距离衰减的改进两步移动搜索法，计算并对比分析从居民出发地分别采取步行、 共享单车和公交这3种接驳方式到达轨道交通站点的接驳可达性。以成都三环外地铁2号线和6号线沿线区域为例，运用ArcGIS工具分析区域居民出发地的可达性空间分布差异和不同接驳方式的可达性差异，并结合人口分布梳理轨道交通出行重点区域，挖掘出行盲区。计算结果表明：越靠近轨道交通站和越靠近城市中心的区域，3种接驳方式的平均可达性越高；总体上，步行接驳可达性最小，空间分布差异最明显；步行、共享单车和公交接驳可达性高的区域分别集中在轨道站 0.8，1.5，3.0 km服务半径范围内；公交沿线接驳可达性高于非沿线接驳可达性；通过对比可达性和人口空间分布，有效识别区域轨道交通出行的重点盲区、次要盲区、一般盲区和隐性盲区。     

基于单目视觉车距测量方法综述

车距测量是高级驾驶辅助系统(ADAS)的关键技术,是前碰撞预警和自适应巡航等功能的基础,单目摄像头相比于毫米波雷达等传感器价格低廉,获取图像信息量丰富,在ADAS系统中应用广泛,很多学者对单目测距技术进行了深入研究。论文主要介绍了四种基于单目视觉的车距测量方法,详细阐述了几何关系法、成像模型法、数据回归建模法和逆透视变换法的原理及测距模型,通过分析基本测距方法的不足,逐步介绍其改进形式。最后给出了每一种方法的特点,并对发展趋势进行展望。     

考虑行程时间可靠性的城市冷链末端配送站选址研究

城市冷链“最后一公里”的配送时效性对冷链产品货损率和客户满意度影响很大，因此提升城市冷链末端配送时效性具有重要意义. 恶劣的城市交通环境及高度分散的客户常导致配送时效性难以提升. 从行程时间可靠性角度出发，基于复杂网络理论提出一种城市冷链末端配送站选址方法. 先利用复杂网络理论构建城市冷链末端需求点网络模型，充分考虑行程时间可靠性；再利用社团检测方法，对冷链末端客户进行阶段性聚类；然后，利用度中心性和强度中心性进行选址决策；最后，对选址方案进行配送时效性评估. 通过模拟构建城市冷链末端物流需求点网络并进行选址决策，表明所提方法准确有效，能充分保证配送时效性.     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

Performance measure for reliable travel time of emergency vehicles

Travel time is very critical for emergency response and emergency vehicle (EV) operations. Compared to ordinary vehicles (OVs), EVs are permitted to break conventional road rules to reach the destination within shorter time. However, very few previous studies address the travel time performance of EVs. This study obtained nearly 4-year EV travel time data in Northern Virginia (NOVA) region using 76,000 preemption records at signalized intersections. First, the special characteristics of EV travel time are explored in mean, median, standard deviation and also the distribution, which display largely different characteristics from that of OVs in previous studies. Second, a utility-based model is proposed to quantify the travel time performance of EVs. Third, this paper further investigates two important components of the utility model: benchmark travel time and standardized travel time. The mode of the distribution is chosen as benchmark travel time, and its nonlinear decreasing relationship with the link length is revealed. At the same time, the distribution of standardized travel time is fitted with different candidate distributions and Inv. Gaussian distribution is proved to be the most suitable one. Finally, to validate the proposed model, we implement the model in case studies to estimate link and route travel time performance. The results of route comparisons also show that the proposed model can support EV route choice and eventually improve EV service and operations.