Algorithms to Quantify Impact of Congestion on Time-Dependent Real-World Urban Freight Distribution Networks

Abstract

Urban congestion presents considerable challenges to time-definite transportation service providers. Package, courier, and less than truckload operations and costs are severely affected by growing congestion levels. With congestion increasing at peak morning and afternoon periods, public policies and logistics strategies that avoid or minimize deliveries during congested periods have become crucial for many operators and public agencies. However, in many cases these strategies or policies can introduce unintended side effects, such as higher labor costs, shorter working hours, and tighter customer time windows. Research efforts to analyze and quantify the impact of congestion are hindered by the complexities of vehicle routing problems with time-dependent travel times and the lack of networkwide congestion data. Research used real-world road network data to estimate travel distance and time matrices, land use and customer data to localize and characterize demand patterns, congestion data from an extensive archive of freeway and arterial street traffic sensor data to estimate time-dependent travel times, and an efficient time-dependent vehicle routing (TDVRP) solution method to design routes. Novel algorithms were developed to integrate real-world road network and travel data to TDVRP solution methods. Results show the impact of congestion on depot location, fleet size, and distance traveled.