时间：2013.12.25 14：00
地点：新主楼D218

报告1： Intersection Safety Evaluation Using High-Resolution Traffic Signal Data
报告人：吴新开
简介：This research proposes an intersection safety evaluation system, which is able to quantify the safety performance of signalized intersections and identify the emerging and impending hazardous situations using high-resolution traffic signal data collected from existing loop detection systems. Such a system is much needed for the implementation of a real-time intersection collision avoidance system. Also, the intersection safety information collected from a network-wide system can be used to rank the safety severity for all intersections in the network, therefore helps agencies identify the intersections which need most improvement. In detail, the system fulfills the following two major functions: 1) Estimate potential traffic conflicts based on real traffic conditions. This function will focus on estimating both rear-end and crossing (i.e. right-angle) traffic conflicts; and 2) Predict red-light violations. This function is to identify possible red-light violations, which is a major factor that leads to traffic conflicts. The overall intersection safety can be evaluated based on a combination of the potential traffic conflicts and red-light-running cases. The proposed system has been applied to a corridor with 3 intersections located at Minneapolis, MN. This research is expected to contribute to the improvement of intersection safety.

报告2： DRIVE Net: An E-Science Transportation Platform for Big Data, Big Discovery, and Big Decision
报告人：马晓磊
简介：In past decades, transportation research has been driven by mathematical equations and has relied on scarce data. With increasing amounts of data being collected from intelligent transportation system sensors, data-driven or data-based research is expected to expand soon. Most online systems are designed to handle one type of data, such as from freeway or arterial sensors. Even if transportation data are ubiquitous, data usability is difficult to improve. A framework is proposed for a region-wide web-based transportation decision system that adopts digital roadway maps as the base and provides data layers for integrating multiple data sources (e.g., traffic sensor, incident, accident, and travel time). This system, called the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net), provides a practical method for facilitating data retrieval and integration and enhances data usability. Moreover, DRIVE Net offers a platform for optimizing transportation decisions that also serves as an ideal tool for visualizing historical observations spatially and temporally. Not only can DRIVE Net be used as a practical tool for various transportation analyses, with the use of its online computation engine, DRIVE Net can also help evaluate the benefit of a specific transportation solution. In its current implementation, DRIVE Net demonstrates potential to be used soon as a standard tool to incorporate more data sets from different fields (e.g., health and household data) and offer a platform for real-time decision making.

报告3： The potentials of using Big data in revolutionizing urban planning and transport analysis and modeling in China
报告人：刘凤
简介：The continuing urban population growth and economic development in China have led to the reshaping of metropolitan space layout among residential, employment and shopping locations, generating growing mismatch between mobility demand and transport services. Although a variety of public policies have been introduced to ease the situation of the transport network, the measures are still lagging behind the pace of urban growth. A reliable model to accurately analyze the conditions of the current land use and road network system as well as to identify serious problems between them has been lacking. The investigation will provide important information in addressing critical problems in the urban transport system and seeking solutions that best match the mobility demand. With the recent advancement of wireless communication technologies, such as Global Positioning System (GPS), the potentials of using the Big data to develop such a method are demonstrated. The established new model can be easily transferable to many cities across China, thus paving a way for the development of a new, effective and cheaply realized land use and transport analysis system that supports the city growth and transport network development into a long sustainable future.