FreeSim_Mobile: A novel approach to real-time traffic gathering using the apple iPhone™

In this paper, we present a preliminary application for the iPhone™ [2] that uses the built-in GPS receiver along with the web capabilities utilizing a V2I architecture to send a continuous flow of data to a central server where FreeSim [13–15], a real-time traffic simulator, applies the proportional model algorithm [18] to determine the time to traverse a roadway in order to report in real-time the current flow of traffic. At the University of Alaska, Anchorage, we currently have vehicle tracking devices installed in 80 probe vehicles that traverse the Anchorage area. The high cost associated with vehicle tracking devices makes it difficult to penetrate a large vehicular network on limited funds, so we must look towards other available technologies, such as the constantly-expanding cellular network. In this paper we look at the iPhone™ 3G capability of reporting accurate and reliable locations by describing our sample application and comparing its reported GPS accuracy to the existing vehicle probes we have. We will then present a study of its performance of calculating an accurate traffic flow where a chosen section of roadway was driven. Drivers equipped with an iPhone™ 3G cellular phone and a vehicle tracking device manually timed how long it took to travel along the test road section. The vehicle tracking devices report speed and location every 10 seconds whereas the iPhone™ is capable of reporting the location every second, though we were receiving it every eight seconds. From this data, we calculated the amount of time to traverse the test roadway section using the proportional model algorithm and compared it to the actual amount of time it took to traverse the test roadway section. We found that the vehicle tracking device had an average error factor of 4.43% from the actual time to traverse the roadway section (as determined by the stopwatch), whereas the iPhone™ was found to have an error factor of 4.18%. The outcome of the case study is used to determine that the iPhone™ is relatively as accurate as a vehicle tracking device, though it is important to note that the iPhone™ is more limited than a device attached to a vehicle in the data it can obtain to only reporting its location.

[1]  Hiroshi Shigeno,et al.  Implementation and experiment of multi-modal transmission system for stable communication , 2010, 2010 IEEE Vehicular Networking Conference.

[2]  Tao Zhang,et al.  Non-interactive malicious behavior detection in vehicular networks , 2010, 2010 IEEE Vehicular Networking Conference.

[3]  Azim Eskandarian,et al.  A Reliable Link-Layer Protocol for Robust and Scalable Intervehicle Communications , 2007, IEEE Transactions on Intelligent Transportation Systems.

[4]  Christian Poellabauer,et al.  Balancing broadcast reliability and transmission range in VANETs , 2010, VNC.

[5]  J. Miller,et al.  Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[6]  Andreas Pitsillides,et al.  Adaptive probabilistic flooding for Information Hovering in VANETs , 2010, 2010 IEEE Vehicular Networking Conference.

[7]  M.A. Labrador,et al.  Dynamic Management of Real-Time Location Data on GPS-Enabled Mobile Phones , 2008, 2008 The Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies.

[8]  Lachlan L. H. Andrew,et al.  Effect of retransmissions on the performance of the IEEE 802.11 MAC protocol for DSRC , 2010, 2010 IEEE Vehicular Networking Conference.

[9]  Jenn-Hwan Tarng,et al.  Investigation of Vehicle-to-Infrastructure Communications based on IPv6-Based Automotive Telematics , 2007, 2007 7th International Conference on ITS Telecommunications.

[10]  Ellis Horowitz,et al.  Algorithms for real-time gathering and analysis of continuous-flow traffic data , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[11]  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).

[12]  Ellis Horowitz,et al.  FreeSim – A V2V and V2R Freeway Traffic Simulator , 2007 .

[13]  Véronique Vèque,et al.  Quantitative model for evaluate routing protocols in a vehicular ad hoc networks on highway , 2010, 2010 IEEE Vehicular Networking Conference.

[14]  Eleni I. Vlahogianni,et al.  Pattern-Based Short-Term Urban Traffic Predictor , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[15]  Timothy Menard,et al.  Determining time to traverse road sections based on mapping discrete GPS vehicle data to continuous flows , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  Ling Yu,et al.  Providing enhanced cellular coverage in public transportation with smart relay systems , 2010, 2010 IEEE Vehicular Networking Conference.

[17]  Nader M. Rabadi Revised self-certified implicit certificate scheme for anonymous communications in vehicular networks , 2010, 2010 IEEE Vehicular Networking Conference.

[18]  Máire O'Neill,et al.  MONET Special Issue on Next Generation Hardware Architectures for Secure Mobile Computing , 2007, Mob. Networks Appl..

[19]  Michel Pasquier,et al.  POP-TRAFFIC: a novel fuzzy neural approach to road traffic analysis and prediction , 2006, IEEE Transactions on Intelligent Transportation Systems.

[20]  Ellis Horowitz,et al.  FreeSim - a free real-time freeway traffic simulator , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[21]  Tiago Fioreze,et al.  Extending DNS to support geocasting towards VANETs: A proposal , 2010, 2010 IEEE Vehicular Networking Conference.

[22]  Klaus Bogenberger,et al.  Reliable Pretrip Multipath Planning and Dynamic Adaptation for a Centralized Road Navigation System , 2007, IEEE Transactions on Intelligent Transportation Systems.