Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

Emergence of cooperation in congested road networks using ICT and future and emerging technologies: A game-based review

Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.     

Economic Valuation of Coastal and Marine Resources: Bohol Marine Triangle,Philippines

This article provides results on the net benefits generated from the natural resources in the Bohol Marine Triangle (BMT) in the Philippines. The BMT spans over 112,000 ha and its coastal ecosystems are rich in biodiversity and provide economic opportunities to the coastal communities. With a 10% discount rate, the accumulated total net benefits for the BMT resources over a 10-year period is US$11.54 million. Tourism and the municipal fisheries are the most important direct use values of the coastal and marine resources of the BMT accounting for 44% and 39% of the total net benefits. Annual revenues attributed to ecosystems were as follows: coral reefs, US$1.26 million; beach/intertidal area, US$1.12 million; marine waters, US$646,501; mangrove, US$239,561; and seagrass, US$105,990. The large market values indicate the dependence of the local community on the BMT coastal and marine resources. In the same way, non-market values show the important life-support functions of coastal and marine ecosystems. The net benefits reflect the magnitude of potential losses due to improper management of coastal and marine resources in the BMT. This valuation highlights the importance of the coastal services to the BMT economy and draws attention to the benefits the local stakeholders derive from BMT coastal resources. Policy measures can now take into account these values to justify a sufficient investment in coastal management efforts to sustain the flow of coastal services in the interest of current and future generations.     

基于相对变形的大变形隧道地应力反算

针对复杂地质条件下高地应力大变形隧道地应力复杂多变的特点，为满足该类隧道动态设计对地应力资料的要求，提出基于上台阶开挖后拱腰水平收敛与拱顶下沉的相对比值并结合开挖揭示的围岩情况，建立二维数值模型反算地应力的方法。以丽江至香格里拉铁路中义隧道为例，对该方法进行详细说明。研究表明所述方法具有无需地应力测试、计算方便的优点，施工期间可用于地应力分析、预测。中义隧道典型区段地应力反算结果表明： 1）隧道出口及2号横洞工区的地应力分布特点并不相同； 2）围岩及埋深差别不大的区段，隧道水平收敛变形出现较大差异主要是由于地应力侧压力系数不同引起的。     

铁路小半径曲线刚构-连续梁桥设计研究

为研究小半径曲线刚构-连续梁桥的受力特点,以某(36+60+36)m铁路刚构-连续梁桥为背景进行分析。采用BSAS、MIDAS、ANSYS建立有限元模型,针对该桥小半径、施工复杂等特点,从弯扭耦合效应、刚度、变形、稳定性等方面进行详细分析,结果表明:该桥具有足够的强度、刚度和稳定性。     

洋山深水港区工程码头及接岸结构选型

洋山深水港工程依托外海岛礁地形通过填海造地形成港区,其关键技术之一就是采用何种码头及接岸结构型式才能阻挡软土地基上平均23～26 m高回填土的作用。文章重点介绍一期工程码头及接岸结构在复杂的自然条件、较高的使用要求、恶劣的施工条件以及较短施工周期等条件下,按照有利于工程快速施工、有利于陆域形成以及有利于控制质量的原则,提出采用高桩码头结构 斜顶桩板桩承台驳岸结构型式。实施效果表明:该结构型式适合洋山港区的地质条件和建设条件,有利于港区的陆域形成及后续工程的推进,技术风险小;后方高回填土对码头结构没有影响。     

水工模型试验的流速测量方法与数据处理程序

在水工模型试验中,基础数据的准确度与精确度直接关系到科研成果的质量。流速是模型试验中观测的主要数据之一。模型流速的测量方法有许多种,如示踪物法、光电法、声学多普勒法等。光电法是目前水工模型试验中应用最多的流速测量方法。文章所述的模型测杆流速处理程序实现的功能就是计算流速测杆采集流速的平均值并直观地以流速过程线图显示。通过应用于上海国际航运中心洋山港区整体物理模型试验的实践表明,程序采用微调方法,解决了判断涨落潮时存在的问题,提高了水工模型试验工作的精度和效率,为科学决策提供了依据。     

斗轮堆取料机的发展趋势

斗轮堆取料机普遍采用PLC和变频器调速、液压驱动、网络控制等技术。其生产能力显著提升,造型得到了改进,产品向大型化、无人化、多样化、环保和配套国际化的方向发展。国际市场对斗轮堆取料机的需求大。     

有移动热源的海上大空间通风技术研究

由于有某种热源在海上大空间内移动,使得大空间通风系统也与其他一般大舱空间的通风系统不同.目前国内外对海上大空间通风系统尚无明确的规范及要求.在对大空间通风进行模型试验研究和相应的计算机模拟计算基础上,确定大空间通风系统初步方案,为大空间通风系统设计提供科学合理的试验和理论依据.