Paper Details

TRB 91st Annual Meeting (January 22-26, 2012)

Event Number:	493
Event Title:	Research in Traffic Flow Theory and Characteristics, Part 4 (Part 1, Session 403; Part 2, Session 433; Part 4, Session 493)
Event Date:	Jan 24 2012 8:30AM- 10:15AM
Event Location:	Marriott, Salon 2
Event Description:
Event Agenda:

Macroscopic Traffic Modeling for Large-Scale Evacuation Planning (12-3579)

Quite a few Simulation-Based Planning Models have been developed over the past 10 years and are being used with increasing frequency. The majority of the simulation models employed in such large scale software systems are either microscopic or mesoscopic. These simulation models provide an acceptable level of “fidelity” in support of Dynamic Traffic Assignment (DTA) models, at significant computational cost. To reduce this cost, macroscopic simulation models such as those based on the Cell Transmission Model (CTM) or on the General Link Transmission Model (GLTM) have been proposed for use in analyzing large scale networks comprised of thousands of network links. This paper describes a new macroscopic traffic flow model used in a large scale evacuation planning software named DYNEV II. This new model is designed for generalized networks with link lengths ranging from 100 feet to several miles, being represented as a single processing unit. To gain computation speed, the model is designed to accommodate a simulation time step of one minute or greater, which is considerably longer than is generally used for other models. The model accommodates up to four turn movements from each link, a wide range of traffic control at junctions and properly represents intense congested conditions and its spillback mechanisms. The simulation model supports and interfaces with a DTA model. Experience with this simulation model reveals a computation time of less than one minute for a network of some 3000 links servicing 300,000 vehicles over an area of 700 square miles and a simulated time of about 8 hours, using a standard PC. The model design and implementation are described and representative results are presented.

Authors
     Lieberman, Edward , KLD Associates, Inc.
     Xin, Wuping , KLD Associates, Inc.