混有CACC车辆和ACC车辆的混合交通流驾驶舒适性

为了探索协同自适应巡航控制(cooperative adaptive cruise control,CACC)车辆对交通系统的潜在影响,分析了CACC车辆市场普及过程中存在的CACC车辆、自适应巡航控制(adaptive cruise control,ACC)车辆与人工驾驶车辆混合交通流驾驶舒适性.应用加州伯克利PATH实车验证的ACC模型和CACC模型进行数值仿真实验,采用国际ISO-2631-1标准评价混合交通流舒适性,并对ACC和CACC期望车间时距进行参数敏感性分析.最后,从交通流稳定性的角度,对舒适性仿真结果进行了讨论.结果表明:随着CACC市场率的增加,舒适性呈现先恶化、再逐渐提升的趋势.较大的ACC车间时距有利于抑制舒适性的恶化程度,CACC车辆对舒适性的提升作用不受其车间时距取值的影响.混合交通流稳定性定性地决定了舒适性的变化趋势,人工车辆安装车车通信设备,有助于舒适性的逐渐提升.     

基于航模辅助观测的车辆轨迹提取方法

为了克服交通流定点视频观测方法在观测范围上的局限性,提出了一种基于飞行航模辅助视频观测的地面道路车辆轨迹提取方法．首先应用飞行航模在空中拍摄获得道路交通流视频,并将视频分解为连续逐帧图片;其次应用针孔成像模型和空间坐标转换算法,对逐帧航拍图片内的车辆坐标进行提取和转换,以获得车辆的运行时空轨迹数据;最后进行了该方法的3类误差分析.结果表明,该方法在标准棋盘格试验下提取坐标的相对误差小于5％,实际道路交通目标坐标提取的精度达到90％以上．航模辅助视频观测法可实现对交通目标的大范围低成本观测,能够满足交通工程的观测需求．     

On the Uniqueness of User Equilibrium Flow with Speed Limit

This technical note focuses on the link flow uniqueness of user equilibrium (UE) with speed limits. Under a mild assumption on the monotonicity of link travel time function, the UE link flow solutions are well recognized to be unique. However, the incorporation of speed limits in the network has undermined the strict monotonicity of link travel time functions, thus the UE flows on the links with speed limits may not be unique. This note addresses the uniqueness problem with two major contributions. First, a polyhedron defined on links is provided, and it is proven that the UE link flow is unique if and only if the polyhedron only contains one value. Second, two concise methods are proposed to mathematically check whether the polyhedron is a singleton, which can be easily solved and convenient for practical use.     

Evaluation of the predictability of real-time crash risk models

The primary objective of the present study was to investigate the predictability of crash risk models that were developed using high-resolution real-time traffic data. More specifically the present study sought answers to the following questions: (a) how to evaluate the predictability of a real-time crash risk model; and (b) how to improve the predictability of a real-time crash risk model. The predictability is defined as the crash probability given the crash precursor identified by the crash risk model. An equation was derived based on the Bayes’ theorem for estimating approximately the predictability of crash risk models. The estimated predictability was then used to quantitatively evaluate the effects of the threshold of crash precursors, the matched and unmatched case-control design, and the control-to-case ratio on the predictability of crash risk models. It was found that: (a) the predictability of a crash risk model can be measured as the product of prior crash probability and the ratio between sensitivity and false alarm rate; (b) there is a trade-off between the predictability and sensitivity of a real-time crash risk model; (c) for a given level of sensitivity, the predictability of the crash risk model that is developed using the unmatched case-controlled sample is always better than that of the model developed using the matched case-controlled sample; and (d) when the control-to-case ratio is beyond 4:1, the increase in control-to-case ratio does not lead to clear improvements in predictability.     

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.     

Investigation of work zone crash casualty patterns using association rules

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40 mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties.     

Establishing collision zones for obstacles moving with uncertainty

This paper presents a methodology to include obstacles moving with uncertainty in path planning algorithms. Around each moving obstacle, a collision zone is defined indicating a high collision likelihood space. These zones are treated as stationary obstacles providing the input to a path planning algorithm. Samples of moving obstacles' positions are assumed to be available. Three models of motion for moving obstacles are considered: (1) obstacles moving randomly, (2) obstacles whose motion is structured but consists of random parameters, (3) obstacles whose motion is predictable as a function of time. Simulation examples yielding collision zones are presented.     

A cellular automata model for circular movements of pedestrians during Tawaf

Masjid Al-Haram in Saudi Arabia is one of the most crowded pilgrimage locations in the world. More than two million pilgrims gather in Saudi Arabia annually during the Hajj season, and it is compulsory for them to perform a series of actions in the mosque. In the court area, pilgrims perform one of the most important rituals of Hajj, called Tawaf, which consists of seven laps of circular movement around the Kaabah, which is situated in the centre of the court. After the Tawaf, pilgrims pray in the court and leave from one of several doors. In this paper, we present a cellular automata model for the simulation of the pilgrims’ circular Tawaf movement. We also use a discrete-event model to simulate the actions and behaviours of the pilgrims. The proposed models are used in a software platform to simulate the actions and movements of pilgrims in the area. We then present an example application of the model in predicting whether specific changes to the architecture could increase the throughput of the system.     

Comparative analysis of the spatial analysis methods for hotspot identification

Spatial analysis technique has been introduced as an innovative approach for hazardous road segments identification (HRSI). In this study, the performance of two spatial analysis methods and four conventional methods for HRSI was compared against three quantitative evaluation criteria. The spatial analysis methods considered in this study include the local spatial autocorrelation method and the kernel density estimation (KDE) method. It was found that the empirical Bayesian (EB) method and the KDE method outperformed other HRSI approaches. By transferring the kernel density function into a form that was analogous to the form of the EB function, we further proved that the KDE method can eventually be considered a simplified version of the EB method in which crashes reported at neighboring spatial units are used as the reference population for estimating the EB-adjusted crashes. Theoretically, the KDE method may outperform the EB method in HRSI when the neighboring spatial units provide more useful information on the expected crash frequency than a safety performance function does.     

多分类支持向量机在公交换乘识别中的应用

为获取居民公交出行的换乘信息,设计了一套基于多分类支持向量机(multi-class support vector machine)的公交换乘识别方法.通过融合GPS数据和公交IC卡数据获取训练样本,利用多分类支持向量机进行样本训练,选取最佳训练样本量,并采用网格搜索法结合粒子优化算法对模型参数进行标定,以获取最优SVM分类模型.测试结果显示模型分类精度可达90%.以佛山市公交车GPS数据和IC卡数据对算法进行验证,并获取公交换乘量、公交换乘比例等基本换乘数据.结果表明:算法可在少样本条件下完成公交换乘识别,且分类识别精度高,尤其适用于公交线网复杂的大城市公交换乘识别,有助于在公交前期规划时进行线路布设和枢纽选址.