10 Jun 2022

Inland Waterway Network Mapping of AIS Data for Freight Transportation Planning

Abstract: Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84·0% accuracy in detecting stops at ports and 83·5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

30 Jul 2020

PUBLICATION NOTICE: A Comparison of Frost Depth Estimates from Ground Observations and Modelling Using Measured Values and Reanalysis Data for Vehicle Mobility 

Abstract: Frozen soils can withstand heavy vehicle loads and provide major maneuver corridors in locations where the soils are otherwise too weak to support the loading conditions. Vehicle mobility models require input of the ground conditions to assess seasonal traffickability. Increasingly, measured air temperatures from weather station locations are becoming more widespread, however they lack a global gridded coverage. Similarly, ground profile measurements, such as soil temperature and moisture, are significant inputs to estimate depths of frost. New data products, such as gridded reanalysis data provides weather and soil data on a gridded global scale. This study compared frost depths determined from measured soil temperatures at stations in North Dakota and Minnesota with frost depths determined from soil temperatures from NASA’s Modern Era Retrospective Analysis for Research Application Version 2 (MERRA-2). The objectives of the study were to evaluate the usefulness of the MERRA-2 data to provide estimates of frost depth, and to determine the accuracy of estimated frost depths from modelling using either measured air temperatures or reanalysis air temperature data. To estimate the maximum frost depth a one-dimensional decoupled heat and moisture flow model was used. Differences in estimated frost depth resulted from modelling when compared to the measured soil temperatures. These differences are likely due to the influence of a snow layer. The properties of the snow layer play an important role in estimating the depth of frost. Improved material properties of the snow layer are needed to more accurately estimate the depth of ground freezing.