Road Traffic Monitoring System Based on Mobile Devices and Bluetooth Low Energy Beacons

The paper proposes a method, which utilizes mobile devices (smartphones) and Bluetooth beacons, to detect passing vehicles and recognize their classes. The traffic monitoring tasks are performed by analyzing strength of radio signal received by mobile devices from beacons that are placed on opposite sides of a road. This approach is suitable for crowd sourcing applications aimed at reducing travel time, congestion, and emissions. Advantages of the introduced method were demonstrated during experimental evaluation in real-traffic conditions. Results of the experimental evaluation confirm that the proposed solution is effective in detecting three classes of vehicles (personal cars, semitrucks, and trucks). Extensive experiments were conducted to test different classification approaches and data aggregation methods. In comparison with state-of-the-art RSSI-based vehicle detection methods, higher accuracy was achieved by introducing a dedicated ensemble of random forest classifiers with majority voting.

[1]  Wazir Zada Khan,et al.  Mobile Phone Sensing Systems: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[2]  Vinny Cahill,et al.  Sensor networks for smart roads , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06).

[3]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[4]  Jian Wu,et al.  A new method and verification of vehicles detection based on RSSI variation , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[5]  Emilio Corchado,et al.  A survey of multiple classifier systems as hybrid systems , 2014, Inf. Fusion.

[6]  Bettina Schnor,et al.  Indoor Positioning: A Comparison of WiFi and Bluetooth Low Energy for Region Monitoring , 2016, HEALTHINF.

[7]  Andrew Campbell,et al.  The Rise of People-Centric Sensing , 2008, IEEE Internet Computing.

[8]  Bartlomiej Placzek,et al.  Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[9]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[10]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[11]  Christian Wietfeld,et al.  Radio-Based Traffic Flow Detection and Vehicle Classification for Future Smart Cities , 2017, 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).

[12]  P.A. Ioannou,et al.  The Use of Microscopic Traffic Simulation Model for Traffic Control Systems , 2007, 2007 International Symposium on Information Technology Convergence (ISITC 2007).

[13]  Dimitrios Gunopulos,et al.  SmartTrace: Finding similar trajectories in smartphone networks without disclosing the traces , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[14]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[15]  Moustafa Youssef,et al.  Robust and ubiquitous smartphone-based lane detection , 2016, Pervasive Mob. Comput..

[16]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[17]  Germán Montoro,et al.  Using Smartphones to Assist People with Down Syndrome in Their Labour Training and Integration: A Case Study , 2017, Wirel. Commun. Mob. Comput..

[18]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[19]  Bartlomiej Placzek,et al.  A self-organizing system for urban traffic control based on predictive interval microscopic model , 2014, Eng. Appl. Artif. Intell..

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Drago Zagar,et al.  Using radio irregularity for vehicle detection in adaptive roadway lighting , 2012, 2012 Proceedings of the 35th International Convention MIPRO.

[22]  Athanasios V. Vasilakos,et al.  Characterizing the role of vehicular cloud computing in road traffic management , 2017, Int. J. Distributed Sens. Networks.

[23]  Yang Song,et al.  Channel Access and Power Control for Mobile Crowdsourcing in Device-to-Device Underlaid Cellular Networks , 2018, Wirel. Commun. Mob. Comput..

[24]  Margaret Martonosi,et al.  SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory , 2011, MobiSys '11.

[25]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[26]  Maria E. Niessen,et al.  NoiseTube: Measuring and mapping noise pollution with mobile phones , 2009, ITEE.

[27]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[28]  Christoph Schlieder,et al.  Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer , 2008, ACE '08.

[29]  Bartlomiej Placzek,et al.  Wireless Network with Bluetooth Low Energy Beacons for Vehicle Detection and Classification , 2018, CN.

[30]  Tomasz Orczyk,et al.  Human activity detection based on the iBeacon technology , 2016 .

[31]  Gian Luca Marcialis,et al.  Fusion of Face Recognition Algorithms for Video-Based Surveillance Systems , 2003 .

[32]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[33]  Sang Hyuk Son,et al.  WiTraffic: Low-Cost and Non-Intrusive Traffic Monitoring System Using WiFi , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[34]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[35]  R. Prabha,et al.  KNODET: A Framework to Mine GPS Data for Intelligent Transportation Systems at Traffic Signals , 2017, 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT).

[36]  Marcin Bernaś,et al.  Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction , 2015 .

[37]  Yunhao Liu,et al.  Smartphones Based Crowdsourcing for Indoor Localization , 2015, IEEE Transactions on Mobile Computing.

[38]  Gavin Brown,et al.  Ensemble Learning , 2010, Encyclopedia of Machine Learning and Data Mining.

[39]  Moustafa Youssef,et al.  RF-Based Vehicle Detection and Speed Estimation , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[40]  C. Serodio,et al.  Vehicle Detection for Outdoor Car Parks using IEEE 802 . 15 . 4 , 2013 .

[41]  Purushottam Kulkarni,et al.  Wireless across road: RF based road traffic congestion detection , 2011, 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011).