Comparative Analysis of Techniques used for Traffic Prediction
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Er. Navreet Kaur, Er. Meenakshi Sharma
|DOI : 10.14445/22315381/IJETT-V50P239|
Er. Navreet Kaur, Er. Meenakshi Sharma "Comparative Analysis of Techniques used for Traffic Prediction", International Journal of Engineering Trends and Technology (IJETT), V50(4),238-242 August 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Traffic Management causes drivers` disappointment and costs billions of dollars yearly in lost time and fuel utilization. So as to beat such issues, this paper exhibits a component for Intelligent Transport Systems, which expects to identify and oversee activity clog. Movement clog on street systems is only slower speeds, expanded outing time and expanded lining of the vehicles. At the point when the quantity of vehicles surpasses the limit of the street, movement blockage happens. In the metropolitan urban communities of India movement blockage is a noteworthy issue. Activity blockage is brought about when the request surpasses the accessible street limit. In this way, the concentration is to diminish an opportunity to prepare, reroute and inform vehicles. Traffic flow forecast is the key purpose of Intelligent transportation frameworks investigate and in addition the vital condition for movement administration, control and direction. Presently conventional figure strategies and models incorporate nonparametric relapse demonstrate, exponential smoothing, time arrangement examination, counterfeit neural system, Kalman separating, movement re-enactment, Euclidean distance, dynamic activity task.
1. Vadde R, Sun D, Sai JO, Faruqi MA, Leelani PT. A simulation study of using active traffic management strategies on congested freeways. J Mod Transp [Internet]. 2011;19(3):178–84. Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-84870831507&partnerID=40&md5=3d60e1b60e335be4d9c5aeefd3f8d1ae
2. Melnikov VR, Krzhizhanovskaya V V., Boukhanovsky A V., Sloot PMA. Data-driven Modeling of Transportation Systems and Traffic Data Analysis During a Major Power Outage in the Netherlands. Procedia Comput Sci. Elsevier; 2015;66:336–45.
3. Bolshinsky E, Freidman R. Traffic Flow Forecast Survey. 2012;1–15.
4. Potuzak T. Utilization of a Genetic Algorithm in Division of Road Traffic Network for Distributed Simulation. 2011 Second East Eur Reg Conf Eng Comput Based Syst. 2011;151–2.
5. Manlises CO, Martinez JM, Belenzo JL, Perez CK, Postrero MKTA. Proc. Real-time integrated CCTV using face and pedestrian detection image processing algorithm for automatic traffic light transitions. Int Conf Humanoid, Nanotechnology, Inf Technol Control Environ Manag. 2015;(December):1–4.
6. Althoff M, Magdici S. Set-Based Prediction of Traffic Participants on Arbitrary Road Networks. IEEE Trans Intell Veh [Internet]. 2016;1(X):pp 187–202. Available from: http://dx.doi.org/10.1109/TIV.2016.2622920 https://trid.trb.org/view/1439806
7. Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J [Internet]. 2014;1(1):1. Available from: http://www.robomechjournal.com/content/1/1/1
8. Lv Y, Duan Y, Kang W, Li Z, Wang F-Y. Traffic Flow Prediction With Big Data : A Deep Learning Approach. Intell Transp Syst IEEE Trans [Internet]. 2014;16(2):1–9. Available from: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6894591.
Traffic, forecast, transportation, Euclidean distance.