Freight Operational Characteristics Mined from Anonymous Mobile Sensor Data

Abstract

Effective transportation performance measurement (TPM) benefits from ubiquitous transportation system monitoring both spatially and temporally. In the context of freight-oriented TPM, traditional devices such as inductive loops, cameras, manual counts, and so forth, may fail to provide comprehensive and high-resolution coverage, providing, for example, only volume counts for a small subset of links across a large network with no indication of trip linkages. New sources of big data from mobile sensors including on-board global positioning system (GPS) devices allow more comprehensive network coverage and insights into trip chaining behaviors. However, to gain actionable insights into system performance from large and noisy streams of mobile sensor data, it is necessary to mine it for relevant operational characteristics of the trucks it represents. Such characteristics include stop locations, stop duration, stop time of day, trip length, and trip duration. To address this methodological need, this paper presents three heuristic algorithms: “stop identification,”“path identification,” and “trip identification.” To address the issue of determining relevant operational characteristics, a multinomial logit (MNL) model approach is applied to determine the commodity carried based on the outputs of the heuristic algorithms. The MNL model is novel in that it relates operational characteristics to commodity carried thus filling a critical data gap that currently limits the development of advanced freight forecasting models. The set of models developed in this paper allow large-scale GPS data to be used for freight planning while maintaining levels of data anonymity that allow such data to be shared with public agencies.