Showing papers by "Xiaolei Ma published in 2011"

DRIVE Net: E-Science Transportation Platform for Data Sharing, Visualization, Modeling, and Analysis

Abstract: 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 regionwide 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.

Processing Commercial Global Positioning System Data to Develop a Web-Based Truck Performance Measures Program

Abstract: Although trucks move larger volumes of goods than other modes of transportation, public agencies know little about their travel patterns and how the roadway network performs for trucks. Trucking companies use data from the Global Positioning System (GPS) provided by commercial vendors to dispatch and track their equipment. This research collected GPS data from approximately 2,500 trucks in the Puget Sound, Washington, region and evaluated the feasibility of processing these data to support a statewide network performance measures program. The program monitors truck travel time and system reliability and will guide freight investment decisions by public agencies. While other studies have used a limited number of project-specific GPS devices to collect frequent location readings, which permit a fine-grained analysis of specific roadway segments, this study used data that involved less frequent readings but that were collected from a larger number of trucks for more than a year. Automated processing was used...

Development of Web-Based Analysis System for Real-Time Decision Support on Arterial Networks:

Abstract: With increasing amounts of data being collected for intelligent transportation systems on arterial networks, the archival, management, and analysis of complex network traffic data have become a challenge. Challenges include inconsistent data connections, data quality control, query performance, traffic prediction, and computational limitations. The web-based RADAR Net system is presented to address these challenges. This system adopts a relational database with link, intersection, and detector entities. Relational data demonstrate its query performance and scalability. The system contains four layers: offline server, online server (middleware), online server (Java Servlet), and online client. This four-layer design successfully distributes the computational burden on the server. To monitor arterial performance, link speeds are calculated directly from loop detector data retrieved in Bellevue, Washington. The system can dynamically predict and smooth real-time loop spot speeds by using an alpha-beta filter...

Real-time Travel Time Prediction on Urban Arterial Network

Abstract: Travel time is one of the most desired operational variables serving as a key measure of effectiveness for evaluating the system performance of freeways and urban arterials. With accurate travel time information, decision makers, road users, and traffic engineers can make informed decisions. However, retrieving network-level travel time information has several challenges, such as traffic data collection and travel time estimation and prediction. This research addresses these challenges by developing innovative methodologies and computer applications. First, the authors developed a two-step empirical approach to effectively estimating link journey speeds using merely advance single-loop detector outputs. Second, an α–β filter is adopted to dynamically predict and smooth real-time spot speeds resulted from loop measurements. In addition to travel time estimation and prediction, a dynamic shortest path algorithm is also developed to determine the shortest travel time route based on real-time traffic condition. Furthermore, the developed algorithms are implemented in a web-based system called Real-time Analysis and Decision-making for ARterial Networks (RADAR Net). For real-time operations of RADAR Net, sensor and signal control databases are carefully designed to ensure fast query performance in a growing network-wide traffic dataset. Also, the data visualization and statistical analysis modules are added to RADAR Net to facilitate user applications. Currently, the RADAR Net system is part of the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net) (www.uwdrive.net) developed by the STAR Lab of the University of Washington. RADAR Net is currently being operated in real-time for arterial traveler information, performance evaluation, and analysis.