Understanding commodity flow through a region is key for estimating the demand for freight transportation facilities and services, forecasting energy consumption, analyzing safety risks, and addressing environmental concerns. Transportation planners and decision makers use commodity flow data to develop and implement long-term freight plans and manage infrastructure. State-of-the-practice commodity flow estimations based on regional socioeconomic data and periodic surveys have limited spatial and temporal coverage. Moreover, no existing methods tie vehicles to commodity movements at the link level. Although intrusive inductive loop detectors can identify the industry served (or commodity carried) by trucks based on the truck’s body type, intrusive sensor performance is limited by pavement quality. Unfortunately, poor pavement conditions are common in locations with high truck volumes. This paper investigates the use of a non-intrusive traffic sensor, Lidar, for high-resolution truck body-type classification. This paper develops a proof-of-concept Lidar sensor and a truck body-type classification model capable of classifying five-axle tractor-trailers into distinct body types: van and container, platform, low-profile trailer, tank, and hopper and end dump. These body-class groups link to commodity movements and provide insight into link-level commodity flows. Data for model development and validation were collected along a major interstate corridor and a low-speed local road. The classification model achieves an 81% true positive rate (TPR) with class-specific TPR as high as 94% and average volume accuracy of 87% for the primary test location. Overall, the proposed sensor represents an adequate proof of concept to evaluate the industry served by trucks on a network link.

To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.

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.

The majority of freight is transported within the U.S. by road. However, the use of alternative modes, such as rail and barge, is associated with lower transportation and infrastructure maintenance costs, release of highway capacity, increased safety, and lower emissions. Thus, there is a latent opportunity for shippers and consumers to benefit from modal shift. In this context, strategically located freight-transfer facilities to improve rail and barge access is key. Moreover, for states with lower commodity tonnages and access to short-line rail and navigable waterways, transload facilities have significant potential to shift freight to underutilized modes. This paper develops a multi-criteria assessment framework to identify strategic locations for transload facilities at the state level. Using a statewide travel demand model (STDM) as the main data source, this framework provides a sketch-planning tool to support decision-making for state Departments of Transportation and economic development agencies. The multi-criteria quantify four measures of facility potential: (a) interaction with the transportation network, (b) amount of freight transported between major freight routes, (c) spatial aggregation, and (d) directionality aggregation. Each criterion is estimated and combined at the county level to produce a multi-criteria score, which defines a county’s potential to support transload movements. Using this score, counties are ranked, and facilities prioritized. The framework is applied to Arkansas and validated using the STDM for base (2010) and forecast (2040) years.

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