Enabling Demand Modeling from Privately Held Mobility Data

Alexei Pozdnoukhov, Madeleine Sheehan and Mogeng Yin

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

Abstract: This papers presents the design of the travel mode detection component within a generic architecture of processing individual mobility data. It approaches mode detection in two steps, each aiming at a particular objective. The first step develops a discriminative classifier that detects the mode of the observed trips or a sequence of modes in a multiple leg journey. It requires a considerable amount of ground truth data with known modes to be available for training. It also relies on a k-shortest path algorithm that generates plausible alternatives routes for the journey. The second step utilizes the discriminative recognition step of the observed mode in order to build a behaviorally grounded model that predicts the chosen mode within a set of available alternatives as a function of user characteristics and transportation system variables. It is based on the discrete choice modelling paradigm and results in a set of parameters calibrated for distinct neighborhoods and/or segments of population. The overall framework therefore enables travel mode choice modeling and a consequent policy analysis and transportation planning scenario evaluation by leveraging privacy-sensitive individual mobility data possibly held in a secure private repository. It provides a set of algorithms to drastically reduce the latency and costs of obtaining a crucial information for models used in transportation planning practices. The performance and accuracy of the algorithms is evaluated experimentally within a large metropolitan region of the San Francisco Bay Area.

Keywords: Engineering; Modeling; demand; scenario evaluation; privacy (search for similar items in EconPapers)
Date: 2018-09-11
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