In Japan's logistics sector, stricter regulations on driver working hours have led to redesigning long-haul transportation operations. Relay transport, in which transportation tasks are shared by multiple drivers via relay hubs, has been proposed as an effective solution. However, relay transport involves complex interactions among trucks, drivers, cargo, and hub operations, and waiting and delays may arise depending on processing and traffic conditions, making operational planning and decision-making difficult. Related studies mainly rely on mathematical optimizationand macroscopic simulation models with aggregated vehicle representations, which have limited capability to capture the propagation of waiting and delays at relay hubs. This study develops an agent-based digital twin simulator for logistics infrastructure that explicitly integrates trucks, drivers, and cargo as individual agents. Transportation processes are modeled through state transitions under operational constraints in a time-stepped manner. The simulator incorporates time-dependent travel times, allowing transportation durations to vary according to departure time slots so as to reflect traffic congestion and uncertainties. This enables the digital twin to capture interaction between traffic conditions and relay hub processing that influence waiting time and overall lead time. Within the simulator, subsequent transportation can depart only after the arrival of preceding transportation and the completion of transfer processing at the relay hub. This framework enables the analysis of how variations in relay hub processing and arrival times propagate into waiting time and lead times. Comparative simulation experiments are conducted based on actual operational data under multiple scenarios with different departure time slots and relay hub processing. The results show that waiting and delays in relay transport emerge from the interaction between traffic-dependent travel times and relay hub processing. The proposed digital twin simulator provides a quantitative decision support framework for identifying relay transport strategies that are robust to traffic uncertainty.