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原文传递 Mesoscopic Car-Truck Flow Modeling and Simulation: Theory and Applications.

题名：	Mesoscopic Car-Truck Flow Modeling and Simulation: Theory and Applications.
作者：	Ma, W.; Pi, X.; Qian, Z.
关键词：	Mesoscopic traffic flow, Pollutants, Traffic delays, Traffic simulation, Travel time, Truck traffic, Vehicle miles of travel, Vehicle mix, Freight transportation, Highways, Operations and traffic management, Planning and forecasting, Carnegie Mellon University
摘要：	Traffic flow modeling and simulation is central to transportation system analysis. Existing research has been primarily focusing on cars, while trucks are overlooked or modeled separately from cars. Unfortunately, characteristics of freight demand, such as when and how trucks travel, and how truck flow interacts with car flow, are unclear. This becomes the main hurdle for improving truck mobility. This research aims at developing a holistic framework for mesoscopic traffic simulation that mixes both cars and trucks, by considering their interrelations simultaneously. The result includes the prediction of travel time, travel delay, vehicle-mile-traveled and emissions for both cars and trucks, at each road segment and intersection by time of day. Thus, potential traffic management strategies for both passenger cars and freight transportation can be evaluated and deployed. This project is a continuation of the research from the Mobility21 project Data-driven Network Models for Analyzing Multi-modal Transportation Systems in FY 2018 led by PI Qian. It further extends the data-driven multi-modal modeling on passenger transportation (cars and buses) to the one that integrates both passenger and freight transportation. While the former model is still being improved, the latter model is the focus of FY 2019 that will bring more potential deployment partners from governmental agencies and private trucking companies. The expected outcome of this research is a framework of car-truck modeling in the regional transportation network, followed by a prototype web application that implements it using data of cars and trucks collected over many years in the state. The application also provides user interfaces to manage various scenarios of road closures/extensions and visualize the resultant system metrics for both cars and trucks. The simulation models and web application will be integrated into an open-source dynamic network analysis toolkit to test its effectiveness in the Philadelphia Metro network.
报告类型：	科技报告