浅析未来客运交通方式发展趋势

本文分析了客运交通结构现状不够合理之处，指出公共交通系统的不足。通过研究国内外城市交通发展模式，对我国城市未来客运交通发展趋势作出了预测，认为在私人小汽车发展的浪潮前，优先发展公共交通，建立以公共交通为主体，其他交通方式为补充的客运交通系统，给小汽车以适度的发展空间。     

车流密度变化条件下的跟驰模型研究

把车流密度的跳跃性变化作为车流波现象,在高峰小时或者异常情况下(如雨、雪、雾等异常天气),快速路的瓶颈地段经常出现堵塞以及由堵塞引起的车流波。车流波不仅影响瓶颈点下游车辆的运行,还会降低瓶颈点上游路段的有效通过能力。通过大量的交通数据采集,构建基于模糊推理的驾驶员感知——预测行为模型和车流波传播条件下驾驶员微观跟驰模型。结果表明,模型可以有效、准确地模拟车流波传播过程中驾驶员行为。     

基于运量的国民经济效益网络计算法

通过对现行国民经济效益计算方法的研究．运用交通分配理论论证了现行计算方法中运量分析存在的问题。依据交通分配理论和消费者剩余理论，将交通建设项目吸引区内整个路网作为研究对象，建立了基于运量的国民经济效益网络计算法。实例计算表明该方法的有效性。     

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交通地理信息系统(GIS-T)，来源于地理信息系统和交通信息系统的集成．本文在工程实践的基础上，系统地阐述了基于GIS-T的交通管理综合信息平台的架构设计．本系统的建设目标是，研究开发为交通管理综合信息平台服务的智能交通地理信息系统(GIS-T)，建成一个基于网络环境的、实时的、可视化的交通管理地理信息服务平台．基于此，分别从交通地理信息系统的功能上、系统的体系结构以及系统的软件、硬件设计等方面进行了较为全面深入的研究，为进一步深入开发与研究交通管理综合信息平台从一定程度上奠定基础．     

弹性需求下多类型用户拥挤收费模型

基于时间价值不同的多类型用户特性,提出了弹性需求下多类型用户的拥挤收费模型,并以一个简化的路网实例比较了多类型用户收费模型与单一类型用户收费模型的区别。简化实例分析结果表明:当费率水平较低时,单一类型用户收费模型高估了路网的流量、社会效益,当费率水平较高时,则低估了路网的流量和社会效益;同时单一类型用户收费模型的路网社会效益和利润区域对应的道路通行能力区间较宽,费率水平区间窄,而多类型用户收费模型的路网社会效益和利润区域对应的道路通行能力区间相对较窄,费率水平区间较宽。     

Deriving macroscopic fundamental diagrams from probe data: Issues and proposed solutions

Well-defined relationships between flow and density averaged spatially across urban traffic networks, more commonly known as Macroscopic Fundamental Diagrams (MFDs), have been recently verified to exist in reality. Researchers have proposed using MFDs to monitor the status of urban traffic networks and to inform the design of network-wide traffic control strategies. However, it is also well known that empirical MFDs are not easy to estimate in practice due to difficulties in obtaining the requisite data needed to construct them. Recent works have devised ways to estimate a network’s MFD using limited trajectory data that can be obtained from GPS-equipped mobile probe vehicles. These methods assume that the market penetration level of mobile probe vehicles is uniform across the entire set of OD pairs in the network; however, in reality the probe vehicle market penetration rate varies regionally within a network. When this variation is combined with the imbalance of probe trip lengths and travel times, the compound effects will further complicate the estimation of the MFD.To overcome this deficit, we propose a method to estimate a network’s MFD using mobile probe data when the market penetration rates are not necessarily the same across an entire network. This method relies on the determination of appropriate average probe penetration rates, which are weighted harmonic means using individual probe vehicle travel times and distances as the weights. The accuracy of this method is tested using synthetic data generated in the INTEGRATION micro-simulation environment by comparing the estimated MFDs to the ground truth MFD obtained using a 100% market penetration of probe vehicles. The results show that the weighted harmonic mean probe penetration rates outperform simple (arithmetic) average probe penetration rates, as expected. This especially holds true as the imbalance of demand and penetration level increases. Furthermore, as the probe penetration rates are generally not known, an algorithm to estimate the probe penetration rates of regional OD pairs is proposed. This algorithm links count data from sporadic fixed detectors in the network to information from probe vehicles that pass the detectors. The simulation results indicate that the proposed algorithm is very effective. Since the data needed to apply this algorithm are readily available and easy to collect, the proposed algorithm is practically feasible and offers a better approach for the estimation of the MFD using mobile probe data, which are becoming increasingly available in urban environments.     

Using high-resolution event-based data for traffic modeling and control: An overview

Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.     

Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.     

Border crossing delay prediction using transient multi-server queueing models

As a result of the continued increase in travel demand coupled with the need for tighter security and inspection procedures after September 11, border crossing delay has recently become a critical issue with tremendous economic and social costs. The current paper develops multi-server queuing models to estimate border crossing delay in support of a predictive traveler information system for the crossings. Two classes of multi-server models are considered: (1) models with exponential inter-arrival times and Erlang service times; and (2) a more generic model with a Batch Markovian Arrival Process (BMAP) and phase types (PH) services. As a case study, the models are developed based on real-time traffic volume and inspection time data collected at one of the major US–Canada border crossings, the Peace Bridge, and their transient solution is obtained using heuristic methods. For validation, the queueing models’ estimates are compared to the results from a detailed microscopic traffic simulation model of the Peace Bridge border crossing. The comparison shows that the transient queueing model, along its heuristic solution algorithm, is capable of predicting border crossing delay. Finally, a set of sensitivity analysis tests are conducted, and the developed models are incorporated within an optimization framework to help inform border crossing management strategies.     

Time-varying effects of influential factors on incident clearance time using a non-proportional hazard-based model

Incident clearance time is a major performance measure of the traffic emergency management. A clear understanding of the contributing factors and their effects on incident clearance time is essential for optimal incident management resource allocations. Most previous studies simply considered the average effects of the influential factors. Although the time-varying effects are also important for incident management agencies, they were not sufficiently investigated. To fill up the gap, this study develops a non-proportional hazard-based duration model for analyzing the time-varying effects of influential factors on incident clearance time. This study follows a systematic approach incorporating the following three procedures: proportionality test, model development/estimation, and effectiveness test. Applying the proposed model to the 2009 Washington State Incident Tracking System data, five factors were found to have significant but constant (or time independent) effects on the clearance time, which is similar to the findings from previous studies. However, our model also discovered thirteen variables that have significant time-varying impacts on clearance hazard. These factors cannot be identified through the conventional methods used in most previous studies. The influential factors are investigated from both macroscopic and microscopic perspectives. The population average effect evaluation provides the macroscopic insight and benefits long-term incident management, and the time-dependent pattern identification offers microscopic and time-sequential insight and benefits the specific incident clearance process.