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Traditional network models encapsulate travel behavior among allorigin-destination pairs based on a simplified and generic utility function.Typically, the utility function consists of travel time solely and itscoefficients are equated to estimates obtained from stated preference data.While this modeling strategy is reasonable, the inherent sampling bias inindividual-level data may be further amplified over network flow aggregation,leading to inaccurate flow estimates. This data must be collected from surveysor travel diaries, which may be labor intensive, costly and limited to a smalltime period. To address these limitations, this study extends classicalbi-level formulations to estimate travelers' utility functions with multipleattributes using system-level data. We formulate a methodology grounded onnon-linear least squares to statistically infer travelers' utility function inthe network context using traffic counts, traffic speeds, traffic incidents andsociodemographic information, among other attributes. The analysis of themathematical properties of the optimization problem and of its pseudo-convexitymotivate the use of normalized gradient descent. We also develop a hypothesistest framework to examine statistical properties of the utility functioncoefficients and to perform attributes selection. Experiments on synthetic datashow that the coefficients are consistently recovered and that hypothesis testsare a reliable statistic to identify which attributes are determinants oftravelers' route choices. Besides, a series of Monte-Carlo experiments suggestthat statistical inference is robust to noise in the Origin-Destination matrixand in the traffic counts, and to various levels of sensor coverage. Themethodology is also deployed at a large scale using real-world multi-sourcedata in Fresno, CA collected before and during the COVID-19 outbreak.

Statistical inference of travelers' route choice preferences with system-level data