Behavioral Models for Microscopic Traffic Simulation

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

This research aims to develop a new generation of microscopic traffic simulation with driving behavior models that capture strategic behaviors of drivers and utilize high performance computational methods. Flexible simulation tools are needed to conduct experiments of innovative Intelligent Transportation Systems (ITS) including dynamic traffic management technologies and traffic control and routing algorithms. More reliable simulation tools are also needed to study traffic impacts such as congestion, safety, energy consumption and air pollution. These applications require detailed and accurate models of drivers' behavior. Online and offline applications to large networks and increasingly complex models and traffic management systems require the use of more efficient computational methods. The most notable driving behavior models are acceleration (or car following) and lane-changing models. State-of-the-art models will be enhanced to include more realistic behaviors such as: Proactive anticipatory behavior: Drivers create opportunities to undertake their desired maneuvers by anticipating future traffic conditions and acting upon them. This is contrasted with existing models in which drivers passively react to present traffic conditions. Extended field of view: Drivers' decisions are based on traffic conditions in their extended neighborhood as opposed to simply following a leader or reacting to the adjacent vehicles. Interdependent decisions: Interdependencies exist across different decisions (e.g. the effect of lane changing on acceleration) and within one decision over time (e.g. the effect of past lane changes on future ones). These dependencies are ignored in existing models: lane-changing models assume that once a driver decides to change lanes, s/he will passively consider available gaps as they appear in the traffic stream - the driver does not adjust his/her acceleration in order to change lanes. As a result, capacities of merging, weaving and similar facilities are not captured correctly. The development of high performance computing implementations will extend the applicability of microscopic traffic simulation. Using coarse-grained parallel and distributed computing platforms will allow for the development of portable codes using public domain inter-processor communication software libraries. We will review the state-of-the-art in network decomposition methods, thus creating a basis for follow-up research. The focus of the project will be the development and calibration of innovative driving behavior models capturing the intelligent behavior principles mentioned above. Calibrated models will, in the future, be implemented in MITSIMLab, a microscopic traffic simulation laboratory developed at MIT. Simulations will be conducted to test the models and to suggest further refinements. This award is made under the Exploratory Research on Engineering the Transport Industries (ETI) program solicitation.