Multi-vehicle Route Planning for Efficient Urban Freight Transport

The urban parking spaces for loading/unloading are typically over-occupied, which shifts delivery operations to traffic lanes and pavements, increases traffic, generates noise, and causes pollution. We present a data analytics based routing optimization that improves the circulation of vehicles and utilization of parking spaces. We formalize this new problem and develop a novel multivehicle route planner that avoids congestions at loading/unloading areas and minimizes the total duration. We present the developed tool with an illustration and analysis for the urban freight in the city of Barcelona, which monitors tens of thousands of deliveries every day. Our system includes an effective evaluation of candidate routes by considering the waiting times and further delays of other deliverers as a first class citizen in the optimization. A two-layer local search is proposed with a greedy randomized adaptive method for variable neighborhood search. Our approach is applied and validated over data collected across Barcelona's urban freight transport network, which contains 3,704,034 parking activities. Our solution is shown to significantly improve the use of available parking spaces and the circulation of vehicles, as evidenced by the results. The analysis also provides useful insights on how to manage delivery routes and parking spaces for sustainable urban freight transport and city logistics.