October 20, 2017


Stochastic Travel Demand Estimation: Improving Network Identifiability using Multi-day Observation Sets


1:40 p.m. – 3:00 p.m.


1605 Tilia, Room 1103, West Village


Stochastic travel demand estimation is essential to support many resilience and reliability based transportation network analy-ses. The problem of estimating travel demand based on sensor data often results in an ill-posed inverse problem, where solution uniqueness cannot be ensured. To overcome this challenge, effective utilization of more information/data, preferably from re-liable sources, becomes critical. Conventional demand estimation methods often sacrifice system structural information during the process of compressing sensor data into its statistics. Loss of structural information, which captures critical relation between observed and estimated parameters, inevitably causes more dependence on unrealistic assumptions and unreliable data. In this presentation, I will discuss a new method that is designed to preserve all structural information contained from different observation sets. The proposed hierarchical framework integrates two traditionally distinctive identification problems, mean demand estimation and trip table reconstruction. Through mathematical analyses and numerical experiments, I will show that the proposed framework improves parameter identifiability and leads to better estimation quality compared to conventional methods. This framework flexible to accommodate a wide variety of travel behavior assumptions and estimation principles. As an example among many possible alternatives, Wardrop equilibrium based traffic assignment and generalized least square are implemented in the case studies reported here.

Biographical Sketch

Yueyue Fan is a professor in Civil and Environmental Engineering at UC Davis. She is also a faculty member affiliated with graduate programs of Transportation Technology and Policy, Applied Mathematics, and Business Analytics at UC Davis. Dr. Fan’s research aim to improving the sustainability and resilience of transportation and energy infrastructure systems, with special interest in integrating applied mathematics, data science, and engineering domain knowledge to address challenges brought by system uncertainty, dynamics, and indeterminacy issues.

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