ew 18: e9

Research Article

RoadSpeedSense: Context-Aware Speed Profiling from Smart-phone Sensors

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  • @ARTICLE{10.4108/eai.7-1-2020.162802,
        author={Ratna Mandal and Pallav Sonowal and Manish Kumar and Sujoy Saha and Subrata Nandi},
        title={RoadSpeedSense: Context-Aware Speed Profiling from Smart-phone Sensors},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={1},
        keywords={Speed Profiling, Honk, WiFi, Road Condition, Intersection Density, Interpretable Machine Learning},
        doi={10.4108/eai.7-1-2020.162802}
    }
    
  • Ratna Mandal
    Pallav Sonowal
    Manish Kumar
    Sujoy Saha
    Subrata Nandi
    Year: 2020
    RoadSpeedSense: Context-Aware Speed Profiling from Smart-phone Sensors
    EW
    EAI
    DOI: 10.4108/eai.7-1-2020.162802
Ratna Mandal1,*, Pallav Sonowal1, Manish Kumar1, Sujoy Saha1, Subrata Nandi1
  • 1: National Institute of Technology, Durgapur, India
*Contact email: ratna.mandal.iem@gmail.com

Abstract

INTRODUCTION: There are several online mapping systems like Google Maps, Waze, Here, Apple Maps, Bing Maps, etc. which are developed to visualize real-time traffic conditions which rely on crowdsourced GPS trails; obtained from worldwide smartphone users. Such systems still suffer from some limitations like a) inadequate traffic information in suburban cities and rural zones, b) system failure to infer the proper reasons for slow traffic state, c) difficulties in the extraction of raw traffic data for further development of any customized application. Significant spatio-temporal similarity patterns are observed in city traffic behavior unless there are some exceptional events like any disaster, VIP visit, international cricket match or bad weather condition, etc.

OBJECTIVES: Designing a framework and developing a system which enables collection of raw sensor information from users and to identify a model to generate a speed profile of city roads using historical logs as well as to infer the context of slow traffic based on ambient subjective road features and to provide map visualization.

METHODS: We have used road surface quality, density of vehicles, type of neighborhood and road geometry for developing speed profile for a particular road segment. We have carried out the experiments on different classification algorithms like, K-nearest Neighbor(KNN), Decision Tree(DT), Random Forest(RF) and Gradient Boost(GB) with necessary tuning of parameters.

RESULTS: GB outperforms other classification algorithms in estimating the speed class of road segments among all classifier algorithms with highest F1- Score of 0.8345. A fair driver rating system which can be derived from our results.

CONCLUSION: The results obtained from the proposed novel framework provide a proof of concept that speed profiles may be successfully derived from ambient road features even when sample space is sparse.