Software-Defined Networking for Emergency Traffic Management in Smart Cities
2019 (English)In: 3rd International Workshop on Vehicular Ad-hoc Networks for Smart Cities, IWVSC 2019 (Vehicular Ad-hoc Networks for Smart Cities), Springer , 2019, p. 59-70Conference paper, Published paper (Refereed)
Abstract [en]
Vehicle traffic management is becoming more complex due to increased traffic density in cities. Novel solutions are necessary for emergency vehicles, which despite growing congestion must be able to quickly reach their destination. Emergency vehicles are usually equipped with transmitters to control the traffic lights on their path and warn other vehicles with sirens. Transmitters are operated manually and, like sirens, have a limited range. Smart cities can make use of novel network models to facilitate traffic management. In this paper, we design a traffic management application leveraging software-defined network controllers for traffic preemption. The proposed application leverages the logical centralization of the SDN control plane to improve traffic management. Results from evaluating the application under five different scenarios indicate that emergency vehicles can reach their destination much faster, with very little effect on the surrounding traffic.
Place, publisher, year, edition, pages
Springer , 2019. p. 59-70
Keywords [en]
Application programs, Emergency traffic control, Emergency vehicles, Signaling, Smart city, Software defined networking, Traffic congestion, Transmitters, Control planes, Emergency traffic management, Network models, Novel solutions, Traffic densities, Traffic light, Traffic management, Vehicle traffic, Vehicular ad hoc networks
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-45094DOI: 10.1007/978-981-15-3750-9_5Scopus ID: 2-s2.0-85084924409ISBN: 9789811537493 (print)OAI: oai:DiVA.org:ri-45094DiVA, id: diva2:1451136
Conference
3rd International Workshop on Vehicular Ad-hoc Networks for Smart Cities, IWVSC 2019. 13 November 2019 through 13 November 2019
Note
Funding details: Stiftelsen för Strategisk Forskning, SSF, RIT17-0035; Funding text 1: This paper was partially supported by the Swedish Foundation for Strategic Research, grant RIT17-0035, and partially supported by the Wallenberg Autonomous Systems and Software Program (WASP).
2020-07-022020-07-022020-07-02Bibliographically approved