Probabilistic tools for traffic management

Tomas Singliar (Pitt/CS)

PhD Proposal Defense

Friday, December 14th, 2007
2:00pm - SENSQ 5317

Abstract

As road capacity expansion is hitting the wall of cost, environmental regulations and engineering challenges, traffic management practitioners are starting to recognize the value of quantitative decision making for improvements in utilization and performance of existing road networks.
Civil engineering manuals have characteristically boiled their guidance down to a single number or recommendation.

However, traffic behaves stochastically and often cannot be well characterized with such simplification. We design generative models that describe the joint  distribution of observed traffic patterns. The primary use for such models is to impute data missing due to sensor malfunctions that are frequent in practice.

Computational decision-making machinery such as Markov decision processes offers a principled way to aid traffic managers to absorb more complex traffic descriptions into their decisions. Quantitative decision-making paradigms fundamentally require probabilistic descriptions of traffic state. Again, the standard models of traffic flow are deterministic and fully observable and therefore unfit to provide the requisite probabilities in a stochastic network. I propose to build a model of traffic flow inspired by time-tested deterministic flow models, but one that deals with uncertainty of measurement and unobservability of certain important quantities.

A set of optimization algorithms for common tasks such as vehicle routing will be designed, using the traffic flow model as their probabilistic underpinning. The optimizations focus on sparsely explored criteria such as ``minimize the probability of being late'', rather than on the well-understood minimal expected travel time.

Dissertation Adviser

Prof. Milos Hauskrecht, Department of Computer Science

Committee Members

Prof. Rebecca Hwa, Department of Computer Science
Prof. Gregory Cooper, Department of Biomedical Informatics
Dr. Geoff Gordon, CMU