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On the Similarities Between Urban Traffic Management and Communication Networks: Application of the Random Early Detection Algorithm for Self-Regulating Intersections

Communications are revolutionizing transport systems. Recently, novel technologies from the fields of telecommunications and data networks have been incorporated into both vehicle systems and road infrastructures, setting new frontiers in the design and development of Smart Cities and Smart Urban Mobility. Whereas each scenario, i.e., the vehicular one and the data communications one, has its own and well-defined particularities, there are also similarities that should be further explored. In this work, we address this confluence of research areas by investigating the applicability in vehicle traffic management of an active queue control algorithm employed in communication networks and called Random Early Detection (RED). RED provides congestion avoidance in data networks by controlling the average queue size of network buffers. It detects incipient congestion and, using probabilistic packet-marking or packet-dropping behavior, it is able to keep the data network at high throughput and low delay. In this work, we propose an adaption of RED to vehicle traffic management in signaled intersections, namely, RED for Vehicles (REDV). A complete characterization of REDV is provided, including the impact of several configuration parameters. We also conduct an extensive comparative performance evaluation via computer simulation. Results show that REDV is a light and simple algorithm able to significantly reduce the average trip time, waiting time, and CO2 emissions in a signaled intersection compared to other approaches from the related literature.