Improving Supply Chain Logistics with Agent-Based Spatiotemporal Mechanistic Enumeration and Probe Vehicle Data

Traditional methods of transportation performance and land use valuation rely on metrics that emphasize daily traffic volume and ideal travel speeds of transportation systems; however, enterprise logistics are better informed through data-driven models that identify reliable transportation routes between origin and destination. Enterprise and personal logistics are prone to disruptions from the variability of travel times across hours and days of the week. There is a critical need to assess and monitor the performance of global supply chains from the perspective of freight operations. In this work, a data-driven agent-based spatiotemporal mechanistic enumeration model was developed to evaluate the variability of completed round trips based on departure time, seasonality, and freight transaction times. The mechanistic enumeration methods utilize probe-vehicle travel time data that have been collected from devices equipped with Global Positioning System (GPS) receivers. Based on the success criteria of completed round trips, the results were evaluated to explore how operating conditions for an origin site (e.g., distribution center) and destination (e.g., maritime port) are influenced by the inherent variability of highway traffic performance and freight handling times.