A Low-Cost Automatic Vehicle Identification Sensor for Traffic Networks Analysis

In recent years, different techniques to address the problem of observability in traffic networks have been proposed in multiple research projects, being the technique based on the installation of automatic vehicle identification sensors (AVI), one of the most successful in terms of theoretical results, but complex in terms of its practical application to real studies. Indeed, a very limited number of studies consider the possibility of installing a series of non-definitive plate scanning sensors in the elements of a network, which allow technicians to obtain a better conclusions when they deal with traffic network analysis such as urbans mobility plans that involve the estimation of traffic flows for different scenarios. With these antecedents, the contributions of this paper are (1) an architecture to deploy low-cost sensors network able to be temporarily installed on the city streets as an alternative of rubber hoses commonly used in the elaboration of urban mobility plans; (2) a design of the low-cost, low energy sensor itself, and (3) a sensor location model able to establish the best set of links of a network given both the study objectives and of the sensor needs of installation. A case of study with the installation of as set of proposed devices is presented, to demonstrate its viability.

[1]  Edna Barros,et al.  An embedded automatic license plate recognition system using deep learning , 2018, 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC).

[2]  Alex Doboli,et al.  Stochastic Model-Based Heuristics for Fast Field of View Loss Recovery in Urban Traffic Management Through Networks of Video Cameras , 2011, IEEE Transactions on Intelligent Transportation Systems.

[3]  Rong Du,et al.  On Maximizing Sensor Network Lifetime by Energy Balancing , 2017, IEEE Transactions on Control of Network Systems.

[4]  Khalid Mahmood Awan,et al.  Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition , 2019, Sensors.

[5]  Badal Soni,et al.  Survey on Automatic Number Plate Recognition (ANR) , 2015 .

[6]  方华 google,我,萨娜 , 2006 .

[7]  Raffaele Cerulli,et al.  Maximizing lifetime in wireless sensor networks with multiple sensor families , 2015, Comput. Oper. Res..

[8]  Raffaele Cerulli,et al.  Vehicle-ID sensor location for route flow recognition: Models and algorithms , 2015, Eur. J. Oper. Res..

[9]  Enrique Castillo,et al.  Observability in traffic networks. Plate scanning added by counting information , 2012 .

[10]  Monica Gentili,et al.  Review of optimal sensor location models for travel time estimation , 2018 .

[11]  HongQing Yu,et al.  Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System , 2019, Sensors.

[12]  Khaled F. Hussain,et al.  Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach , 2019, Operations Research Perspectives.

[13]  Enrique Castillo,et al.  Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks , 2010 .

[14]  Enrique F. Castillo,et al.  The Observability Problem in Traffic Network Models , 2008, Comput. Aided Civ. Infrastructure Eng..

[15]  Inmaculada Gallego,et al.  Plate scanning tools to obtain travel times in traffic networks , 2017, J. Intell. Transp. Syst..

[16]  Zhu Han,et al.  Internet of Vehicles: Sensing-Aided Transportation Information Collection and Diffusion , 2018, IEEE Transactions on Vehicular Technology.

[17]  Enrique Castillo,et al.  Dealing with Error Recovery in Traffic Flow Prediction Using Bayesian Networks Based on License Plate Scanning Data , 2011 .

[18]  Yanfeng Ouyang,et al.  Reliable sensor deployment for network traffic surveillance , 2011 .

[19]  G A Morgan,et al.  Data collection techniques. , 2001, Journal of the American Academy of Child and Adolescent Psychiatry.

[20]  Enrique F. Castillo,et al.  Optimal Use of Plate-Scanning Resources for Route Flow Estimation in Traffic Networks , 2010, IEEE Transactions on Intelligent Transportation Systems.

[21]  Yousef Shafahi,et al.  Vehicle identification sensors location problem for large networks , 2018, J. Intell. Transp. Syst..

[22]  Lajos Hanzo,et al.  Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications , 2018, IEEE Wireless Communications.

[23]  Bartlomiej Placzek,et al.  A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring , 2018, Sensors.

[24]  Inmaculada Gallego,et al.  A New Model for Locating Plate Recognition Devices to Minimize the Impact of the Uncertain Knowledge of the Routes on Traffic Estimation Results , 2020, Journal of Advanced Transportation.

[25]  Atul Patel,et al.  Automatic Number Plate Recognition System (ANPR): A Survey , 2013 .

[26]  Monica Gentili,et al.  Locating sensors on traffic networks: Models, challenges and research opportunities , 2012 .

[27]  A. Çapar,et al.  License Plate Recognition From Still Images and Video Sequences: A Survey , 2008, IEEE Transactions on Intelligent Transportation Systems.

[28]  J. Y. Yen,et al.  Finding the K Shortest Loopless Paths in a Network , 2007 .

[29]  Sherali Zeadally,et al.  Sensor Technologies for Intelligent Transportation Systems , 2018, Sensors.

[30]  Enrique Castillo,et al.  Observability of traffic networks. Optimal location of counting and scanning devices , 2013 .