Spatiotemporal Congestion-Aware Path Planning Toward Intelligent Transportation Systems in Software-Defined Smart City IoT

Abstract

In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software-defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) we propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; and 3) we utilize the time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.