Abstract: This study evaluates the operational impact of Automated Guided Vehicles (AGVs) on port efficiency and regional transportation infrastructure, specifically focusing on the Long Beach Container Terminal (LBCT). Using a continuous approximation model, the research develops closed-form equations to analyze the relationship between AGV deployment, container dwell times, and hinterland traffic congestion. Findings indicate that deploying 100 AGVs at LBCT optimizes seaside operations, reducing priority container dwell times to as low as 1–4 minutes . however, this increased throughput creates a bottleneck at the landside, where container dwell times can escalate to 7,000 minutes. This imbalance significantly exacerbates congestion in the adjacent road network, leading to severe delays for trucks transporting cargo inland. The study concludes that while automation enhances internal port productivity, strategic deployment must account for external infrastructure limits to prevent shifting bottlenecks from the quay to the local community. These insights provide a framework for port authorities to balance technological adoption with urban logistical resilience.

Dr. Shailesh Chandra is an Associate Professor in the Department of Civil Engineering and Construction Engineering Management (CECEM) at California State University, Long Beach. A recognized expert in connected and automated vehicle (CAV) technology, freight logistics, and intermodal transportation resilience, his research integrates artificial intelligence, big data analytics, and optimization-based modeling to enhance freight transportation network. His extensive work spans critical infrastructure domains, including maritime container ports and urban freight corridors, often in collaboration with major partners like the Port of Long Beach, Caltrans, and the U.S. Department of Transportation. Dr. Chandra was honored with a Best Paper Award in 2024 for his impactful analysis of port vehicle efficiency.