Virtual Sensors

Recent advances in mobile networks and an increase in the number of GPS-equipped vehicles have led to exponential growth in real-time data generation. In the past decade, several online mapping and vehicle tracking services have made their data available to third-party users. This paper explores opportunities for use of real-time traffic data provided by online services and introduces a virtual sensor methodology for collecting, storing, and processing large volumes of network-level data. To assess the validity of the collected data with the proposed methodology, this paper compares these data with data from physical loop detectors and electronic toll tag readers. Statistical analyses show a strong correlation between the travel time measurements from infrastructure-based sensors and virtual sensors. A travel time reliability analysis is then conducted with the virtual sensor data methodology. The results are promising for future research and implementation.

[1]  Giovanni Flammia,et al.  Turning Transportation Planning Data Into Effective Web Sites , 2004 .

[2]  Yao-Jan Wu,et al.  An Interactive Web-based System for Urban Traffic Data Analysis , 2009, Int. J. Web Appl..

[3]  John Rice,et al.  Accurate estimation of travel times from single-loop detectors 1 1 Funding for this research was pro , 1998 .

[4]  Linda Highfield,et al.  Interactive web-based mapping: bridging technology and data for health , 2011, International journal of health geographics.

[5]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[6]  Fumitaka Kurauchi,et al.  Using Bus Probe Data for Analysis of Travel Time Variability , 2009, J. Intell. Transp. Syst..

[7]  Fangfang Zheng,et al.  Urban link travel time estimation based on sparse probe vehicle data , 2013 .

[8]  Robert L. Bertini,et al.  Implementing a User-Oriented Web-Based Traffic Data Management and Archive System , 2007 .

[9]  Yao-Jan Wu,et al.  Field Experiments on Bluetooth-Based Travel Time Data Collection , 2010 .

[10]  Yao-Jan Wu,et al.  An Interactive Web-based System for Urban Traffi c Data Analysis , 2009, Int. J. Web Appl..

[11]  Dogru Nejdet.  Traffic Accident Detection By Using Machine Learning Methods , 2012 .

[12]  Kitae Kim,et al.  Evaluation of Technologies for Freeway Travel Time Estimation: Case Study of I-287 in New Jersey , 2011 .

[13]  Fahui Wang,et al.  Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications , 2011, Ann. GIS.

[14]  Huairui Guo,et al.  Travel Time Estimation with Correlation Analysis of Single Loop Detector Data , 2006 .

[15]  William H. Schneider,et al.  Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials , 2011 .

[16]  Darcy M. Bullock,et al.  Real-Time Measurement of Travel Time Delay in Work Zones and Evaluation Metrics Using Bluetooth Probe Tracking , 2010 .

[17]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[18]  Sumiran Shah,et al.  An Advanced Technology Selection Model using Neuro Fuzzy Algorithm for Electronic Toll Collection System , 2011 .

[19]  Kaan Ozbay,et al.  Commercial Vehicle Travel Time Estimation in Urban Networks Using GPS Data from Multiple Sources , 2013 .

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

[21]  Ali M. Shatnawi,et al.  Intelligent Traffic Light Flow Control System Using Wireless Sensors Networks , 2010, J. Inf. Sci. Eng..

[22]  S. Koonin,et al.  Big data and city living – what can it do for us? , 2012 .

[23]  Ayush Kr. Mittal,et al.  A novel approach to implement green wave system and detection of stolen vehicles , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[24]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[25]  Haris N. Koutsopoulos,et al.  Requirements and potential of GPS-based floating car data for traffic management: Stockholm case study , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[26]  D Middleton,et al.  VEHICLE DETECTOR EVALUATION , 2002 .

[27]  Chao Chen,et al.  The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[28]  Imad Aad,et al.  The Mobile Data Challenge: Big Data for Mobile Computing Research , 2012 .

[29]  Pengfei Shi,et al.  An Algorithm for License Plate Recognition Applied to Intelligent Transportation System , 2011, IEEE Transactions on Intelligent Transportation Systems.

[30]  Levente Buttyan,et al.  Traffic analysis attacks and countermeasures in wireless body area sensor networks , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[31]  Scott Peterson,et al.  Evaluation of Non-Intrusive Technologies for Traffic Detection , 2010 .

[32]  G. Broll,et al.  Microsoft Corporation , 1999 .

[33]  He Zhao,et al.  Video Image Vehicle Detection System for Signaled Traffic Intersection , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[34]  Weihua Wang,et al.  License Plate Recognition Algorithm Based on Radial Basis Function Neural Networks , 2009, 2009 International Symposium on Intelligent Ubiquitous Computing and Education.

[35]  Lin Luo,et al.  An Efficient Method of License Plate Location , 2009, 2009 First International Conference on Information Science and Engineering.

[36]  Brian L. Smith,et al.  Kalman Filter Approach to Speed Estimation Using Single Loop Detector Measurements under Congested Conditions , 2009 .

[37]  Philip J Tarnoff,et al.  Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors , 2010 .

[38]  William H. Dutton,et al.  Clouds, big data, and smart assets: Ten tech-enabled business trends to watch , 2010 .

[39]  J. Manyika,et al.  Are you ready for the era of ‘big data’? , 2010 .

[40]  Paul Schonfeld,et al.  Metamodels for estimating waterway delays through series of queues , 1998 .

[41]  José Eugenio Naranjo,et al.  Floating Car Data Augmentation Based on Infrastructure Sensors and Neural Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.