On Centralized and Decentralized Architectures for Traffic Applications

The role of smartphones in traffic applications is typically limited to front-end interface. Although smartphones have significant computational resources, which are most likely to increase further in the near future, most of the computations are still performed on servers. In this paper, we study the computational performance of centralized, decentralized, and hybrid architectures for intelligent transportation system applications. We test these architectures on various Android devices. For implementation, we consider Android Software Development Kit (SDK) and Android Native Development Kit (NDK). Numerical results show that recent smartphones take less than 1 s to estimate the speed for each road segment in a network of 10 000 links from speed measurements at 1000 links. The proposed decentralized architecture significantly reduces the overhead of the communication network and paves the way for new cooperative traffic applications and operations.

[1]  Ram Dantu,et al.  Safe Driving Using Mobile Phones , 2012, IEEE Transactions on Intelligent Transportation Systems.

[2]  Andreas Möller,et al.  DriveAssist - A V2X-Based Driver Assistance System for Android , 2012, MuC Workshopband.

[3]  Yang Zhang,et al.  Traffic forecasting using least squares support vector machines , 2009 .

[4]  Luis Miguel Bergasa,et al.  DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[5]  Mun Choon Chan,et al.  Using mobile phone barometer for low-power transportation context detection , 2014, SenSys.

[6]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[7]  Imad Aad,et al.  From big smartphone data to worldwide research: The Mobile Data Challenge , 2013, Pervasive Mob. Comput..

[8]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[9]  Eric Horvitz,et al.  Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service , 2005, UAI.

[10]  Fabien Moutarde,et al.  Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization , 2012, ArXiv.

[11]  Reto Meier.  Professional Android Application Development , 2008 .

[12]  M. Ganesh Kumar,et al.  Mobile Century Using GPS Mobile Phones as Traffic Sensors : A Field Experiment , 2008 .

[13]  Carol L Schweiger,et al.  Use and Deployment of Mobile Device Technology for Real-Time Transit Information , 2011 .

[14]  Peter Nijkamp,et al.  Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities , 2011, GeoJournal.

[15]  Muhammad Tayyab Asif,et al.  Low-dimensional models for missing data imputation in road networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Soondal Park,et al.  Shortest paths in a network with time-dependent flow speeds , 1998, Eur. J. Oper. Res..

[17]  S. Muthukrishnan,et al.  Relative-Error CUR Matrix Decompositions , 2007, SIAM J. Matrix Anal. Appl..

[18]  Ranveer Chandra,et al.  Empowering developers to estimate app energy consumption , 2012, Mobicom '12.

[19]  Alexandre M. Bayen,et al.  Trade-offs Between Inductive Loops and GPS Probe Vehicles for Travel Time Estimation: Mobile Century Case Study , 2012 .

[20]  Seung-Hyun Yoon,et al.  Benchmarking Java application using JNI and native C application on Android , 2012, 2012 12th International Conference on Control, Automation and Systems.

[21]  Jae Wook Jeon,et al.  Evaluating performance of Android platform using native C for embedded systems , 2010, ICCAS 2010.

[22]  Muhammad Tayyab Asif,et al.  CUR decomposition for compression and compressed sensing of large-scale traffic data , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[23]  Rick Rogers,et al.  Android Application Development - Programming with the Google SDK , 2009 .

[24]  Tomer Toledo,et al.  Evaluation of the Potential Benefits of Advanced Traveler Information Systems , 2006, J. Intell. Transp. Syst..

[25]  Tamara G. Kolda,et al.  Scalable Tensor Factorizations for Incomplete Data , 2010, ArXiv.

[26]  Steve Hanna,et al.  A survey of mobile malware in the wild , 2011, SPSM '11.

[27]  Carlo Ratti,et al.  Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome , 2011, IEEE Transactions on Intelligent Transportation Systems.

[28]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[29]  Muhammad Tayyab Asif,et al.  Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[30]  Wei-Chiang Hong,et al.  Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm , 2011, Neurocomputing.

[31]  A. Bayen,et al.  Mobile Transit Trip Planning with Real-Time Data , 2011 .