University Researchers Examined the Mobility Benefits of Connected and Automated Vehicle (CAV) Technologies at Varied Levels of Market Penetration.
Connected and Automated Vehicles (CAV) technology has a great potential for improving road capacity and reducing traffic incidents, congestion, energy consumption as well as emissions. Given that the transportation network constitutes a vital part of supply chain systems, CAVs are expected to impact many aspects of a supply chain system. The goal of this study was to develop a simulation framework that quantitatively assessed the direct and indirect effects of CAV on supply chain system performance and used a hypothetical fresh potato supply chain system as an illustrative example, in which all potatoes were produced in Washington State and delivered to two other states by varying the levels of CAV market penetration and self-driven truck adoption rates. Specifically, five different levels; zero, 25, 50, 75, and 100 percent, of CAV market penetration rates and self-driven truck adoption rates, respectively, were considered. A complete Human Driven Vehicle (HDV) traffic environment was considered as the baseline scenario.
Using a conceptual framework, causes and effects were identified to develop a simulation for evaluating CAV impacts on supply chain performance. Independent variables included CAV market penetration and self-driven truck adoption rates, which influenced key parameters such as traffic incidents, roadway capacity, travel time, driver wage, fuel economy, and emissions. These parameters affected dependent variables like fresh food loss, cost components, and total emissions. Data was gathered from existing literature, and transportation analysis results were incorporated into the supply chain evaluation.
In a hypothetical fresh potato supply chain case study, county-level production data from Washington was sourced from the USDA. Two systems transporting potatoes to California and Mississippi were examined, with a one-week replenishment cycle. The supply chain consisted of 17 production sites, eight distribution centers, and 100 ultimate destinations, connected by transportation edges.
- Results indicated that CAV could result in up to 53.04 percent decrease in the total transportation time over the replenishment cycle, comparing 299.49 days of transportation time with zero percent CAV and self-driven truck penetrations to 140.63 days of transportation time with 100 percent CAV and self-driven truck penetrations.
- In a fully HDV traffic environment with zero percent CAV penetration rate, adopting self-driven trucks could decrease transportation time by up to 16.67 percent, comparing 299.49 days of transportation time without CAV or self-driven trucks to 249.58 days of transportation time with 100 percent self-driven trucks and no CAVs.
- The total transportation time could be reduced by up to 43.65 percent in a fully CAV traffic environment (100 percent CAV penetration) without adopting any self-driven trucks.
- Results suggest that a CAV traffic environment is more effective in reducing travel time than adopting self-driven trucks in supply chain networks, primarily due to the additional travel time needed for a HDV that was estimated at 20 percent of the CAV-involved travel time in this case study.
- Findings also show that replacing human-driven trucks with self-driven trucks for shipments could reduce GHG emissions by up to 18.77 percent, making them an eco-friendly alternative in supply chain systems.