Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-de ...

[1]  A. Arabi Yazdi,et al.  Flood flow forecasting using ANN, ANFIS and regression models , 2013, Neural Computing and Applications.

[2]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[3]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[4]  Yi Lu Murphey,et al.  Real time vehicle speed prediction using a Neural Network Traffic Model , 2011, The 2011 International Joint Conference on Neural Networks.

[5]  Hojjat Adeli,et al.  TOWARD INTELLIGENT VARIABLE MESSAGE SIGNS IN FREEWAY WORK ZONES: NEURAL NETWORK MODEL , 2004 .

[6]  Hussein Dia,et al.  An object-oriented neural network approach to short-term traffic forecasting , 2001, Eur. J. Oper. Res..

[7]  Antony Stathopoulos,et al.  Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow , 2008 .

[8]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .

[9]  L K Walter,et al.  Effectiveness of speed indicator devices on reducing vehicle speeds in London , 2008 .

[10]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[11]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[12]  Mahmoud Omid,et al.  Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs , 2014 .

[13]  Nazrin Ullah,et al.  Flood Flow Modeling in a River System Using Adaptive Neuro-Fuzzy Inference System , 2013 .

[14]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Jonathan R. M. Hosking,et al.  Partitioning Nominal Attributes in Decision Trees , 1999, Data Mining and Knowledge Discovery.

[17]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[18]  Amir Etemad-Shahidi,et al.  An alternative approach for the prediction of significant wave heights based on classification and regression trees , 2008 .

[19]  Adel W. Sadek,et al.  A prototype case-based reasoning system for real-time freeway traffic routing , 2001 .

[20]  S. A. Smulders,et al.  Variable speed control: state-of-the-art and synthesis , 1998 .

[21]  Chiu Liu,et al.  Kalman filtering estimation of traffic counts for two network links in tandem , 2003 .

[22]  Susan Grant-Muller,et al.  Use of sequential learning for short-term traffic flow forecasting , 2001 .

[23]  Byungkyu Park,et al.  Hybrid Neuro-Fuzzy Application in Short-Term Freeway Traffic Volume Forecasting , 2002 .

[24]  Yorgos J. Stephanedes,et al.  COMPARATIVE EVALUATION OF ADAPTIVE AND NEURAL-NETWORK EXIT DEMAND PREDICTION FOR FREEWAY CONTROL , 1994 .

[25]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

[26]  Mark Dougherty,et al.  Effectiveness of trigger speed of vehicle-activated signs on mean and standard deviation of speed , 2016 .

[27]  Khaled Almejalli Intelligent real-time decision support systems for road traffic management : multi-agent based fuzzy neural networks with a GA learning approach in managing control actions of road traffic centres , 2010 .

[28]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[29]  Virginia P Sisiopiku,et al.  Implementing Active Traffic Management Strategies in the U.S. , 2009 .

[30]  Lin Yan,et al.  Urban Expressway Control Based on Fuzzy Logic and Variable Speed Limit , 2007 .

[31]  Mark Dougherty,et al.  A REVIEW OF NEURAL NETWORKS APPLIED TO TRANSPORT , 1995 .

[32]  Stef Smulders,et al.  Control of freeway traffic flow by variable speed signs , 1990 .

[33]  Ashraf Fahmy,et al.  Neuro-fuzzy modelling and control of robotic manipulators , 2005 .

[34]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[35]  P. Coiffet Modelling and Control , 1983 .

[36]  Zhaosheng Yang,et al.  A new algorithm of incident detection on freeways , 2001, IVEC2001. Proceedings of the IEEE International Vehicle Electronics Conference 2001. IVEC 2001 (Cat. No.01EX522).

[37]  Zhirui Ye,et al.  Short-Term Traffic Flow Forecasting Using Fuzzy Logic System Methods , 2008, J. Intell. Transp. Syst..

[38]  Tao Li,et al.  Adaptive dynamic neuro-fuzzy system for traffic signal control , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[39]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[40]  Lawrence W. Lan,et al.  A Novel Method to Predict Traffic Features Based on Rolling Self-Structured Traffic Patterns , 2014, J. Intell. Transp. Syst..

[41]  Eleni I. Vlahogianni,et al.  Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach , 2005 .

[42]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .