City traffic congestion is a major bottleneck due to issues such as average commute times, increasing pollution, and economic challenges. This research proposes a traffic control system to enhance city transport utilizing the NexusFlow technology. The research bundles together various instruments and techniques to analyze GPS data and forecast the volume of vehicle traffic depending on factors like time of day of week, weather, holidays, etc. Thus, it is possible to characterize traffic trends and relationships based on various parameters. As the system operates algorithmically, the route optimization with NexusFlow builds an adaptive model to change lights, lanes, and routes over time to minimize congestion and travel duration proactively in real time. Data exchange is carried out for vehicles and infrastructure, and the system needs to take proactive steps. Cellular Vehicle to Everything (CV2X) gives an edge in traffic communication with other vehicles and different on-road elements. LiDAR offers optimal connectivity, providing passengers and roadway monitoring with real-time, instantly assessable data to ensure smoother flow in a traffic environment. Edge computing reduces delays in data transmission, which can be regarded as a force multiplier. Traffic control operations improve with faster models to arrive at a decision edge. Tests and trials show excellent results with real-life outcomes while measuring the scale and ensuring the system’s smooth operation. One concern, however, of this technology is the issue of privacy and security; it is necessary to implement a stringent privacy policy on stored data. The study presents a dynamic traffic control system with NexusFlow that is compliant with regulations. The results presented elevated communication accuracy between ${5 m}$ and ${3 m}$ GPS systems and reduced congestion for projected flow between 1200 vehicles $/ \mathrm{min}$ and 950 vehicles $/ \mathrm{min}$.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Dynamic Traffic Orchestration for Seamless Urban Mobility using Nexus Flow


    Contributors:
    Sheeba, G. (author) / Jana, S. (author)


    Publication date :

    2024-11-06


    Size :

    675247 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamic Traffic Flow to Promote Sustainable Mobility

    Rahman, Adlyn Nazurah Abdul / Sufahani, Suliadi Firdaus | Springer Verlag | 2022


    AI Based Traffic Flow Prediction for Smart Urban Mobility

    Bhosale, Ashwini / Shaikh, Arshiya / Kamble, Arpita et al. | IEEE | 2024


    Multimodal Mobility Framework: Towards Seamless Mobility Experience

    Vennelakanti, Ravigopal / Sankaranarayanasamy, Malarvizhi / Saeedi, Ramyar et al. | British Library Conference Proceedings | 2021


    The challenge of seamless mobility

    Meyer, Wolfgang | IuD Bahn | 2003


    Seamless Traffic Information in Dynamic Navigation Up to Date

    Glas, Regina / Belzner, Heidrun / Lange, Tim et al. | Tema Archive | 2012