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MPC-458, In this study, researchers used the agent-based simulation (ABS) for modeling agricultural transportation demand. With the simulation model, geographic information systems (GIS) was utilized to collect and analyze remote sensing of agricultural crops. The researchers reviewed agent-based modeling in freight and public transportation planning to fill the gap between traditional modeling efforts and emerging needs of adopting behavioral modeling for an agricultural transportation model. The study then investigated adoption of an agent-based model for a large-scale travel demand model. The objective of the proposed model was to provide a platform to analyze grain transportation movement at the micro-level, since most industry reports lack such micro-level freight analysis while supporting regional and statewide planning.

To support agricultural logistics and energy development, road and bridge infrastructure has been in North Dakota due to the recent oil boom and the long-term importance of the agricultural industry. With the advance of simulation and data mining, the agent-based model (ABM) has emerged as a solution. Agent-based modeling techniques reflect a high level of detail for travel patterns in a region or state. This research will review state-of-the-art ABM in transportation, determine an agent’s travel behavior in rural and small urban freight movement, design a multi-agent system, and investigate applicability of the agent’s travel behavior to statewide freight demand mode. This paper outlines an agent-based freight transportation model of the grain upstream supply chain for Cass County in North Dakota. The objective is to develop a model incorporating stochastic variables to capture the uncertainties each entity faces, and consequently the effects of variables and strategies on traffic flows. This model simulates a robust level of the decision-making process at a granular level to assess the impact of cargo policy at local, state, regional, and national scales.

Due to the recent oil boom, the Fort Berthold Reservation has experienced a dramatic increase in highway and local traffic. To support energy transportation and provide safe roads, the reservation needs cost-efficient and effective transportation planning for present and future needs. A quality road network for the reservation is crucial for transportation operations and management. This study will demonstrate how to integrate multiple road networks to provide comprehensive digital roads using public sources and provide guides to perform a quality control (QC) assessment before delivering data and using these data for geospatial analysis. This paper will also provide the fundamental concepts for quality assurance (QA) and QC. Thus, tribal geographic information system (GIS) professionals working with other reservations will gain second-hand experience configuring quality checks and processes for automation and running the automated data checks. With the integrated road networks, the tribal transportation agency can develop bike line management, conduct ambulance service coverage analysis, develop a program of asset management, and plan road maintenance and rehabilitation. The authors recommend that the agency develop a linear referencing system (LRS) on the proposed road network to adopt efficient asset management and version control.