Collective shortest paths for minimizing congestion on temporal load-aware road networks

Shortest path queries over graphs are usually considered as isolated tasks, where the goal is to return the shortest path for each individual query. In practice, however, such queries are typically part of a system (e.g., a road network) and their execution dynamically affects other queries and network parameters, such as the loads on edges, which in turn affects the shortest paths. We study the problem of collectively processing shortest path queries, where the objective is to optimize a collective objective, such as minimizing the overall cost. We define a temporal load-aware network that dynamically tracks expected loads while satisfying the desirable 'first in, first out' property. We develop temporal load-aware extensions of widely used shortest path algorithms, and a scalable collective routing solution that seeks to reduce system-wide congestion through dynamic path reassignment. Experiments illustrate that our collective approach to this NP-hard problem achieves improvements in a variety of performance measures, such as, i) reducing average travel times by up to 63%, ii) producing fairer suggestions across queries, and iii) distributing load across up to 97% of a city's road network capacity. The proposed approach is generalizable, which allows it to be adapted for other concurrent query processing tasks over networks