This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Modeling and Simulation of Car-Following Scenario Based on Historical Memory
Technical Paper
2020-01-5224
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
In order to study the problem of short-term traffic prediction more effectively, experts and scholars have put forward various car-following models. Among data-driven algorithms, k-nearest neighbor algorithm is the most widely used due to its simplicity, flexibility and high accuracy. In this paper, the three-dimensional model based on improved knn algorithm is constructed. It transforms the input vector of basic knn algorithm into a three-row matrix. Data of three dimensions are considered, including only the previous moment data, short period of history data and long period of history data. Three-dimensional matrixes are constructed for prediction and similarity is measured by sum of 2-norm of 3 row vectors. Besides, weighted distance calculation method based on temporal distance is introduced to differentiate impacts data of different dimensions have on the results. Furthermore, time ranges of three dimensions of the input matrix are expanded (including the data of the past 1, 5, and 10 moments respectively). Results show that the model with larger time ranges performs better with 1~5 percentages lower three error evaluation indicators. After experimenting on a single vehicle, this paper examines the prediction results of a group of vehicles following a vehicle using basic kNN model and the three-dimensional one. By comparison, the proposed three-dimensional model reduces prediction error evidently and keeps the mean absolute percentage error under 20% instead of 30% of basic kNN model for most vehicles. In the end, model considering both the preceding car as well as the following car is examined to discover whether taking more cars into account leads to better performance. The results are discussed and analysis is drawn that model considering less vehicles has a slight advantage of reducing prediction error by less than 5% for most vehicles over the other one due to existing redundancy.
Authors
Citation
Liu, Z., Zhang, J., Jiang, X., Zheng, L. et al., "Modeling and Simulation of Car-Following Scenario Based on Historical Memory," SAE Technical Paper 2020-01-5224, 2020, https://doi.org/10.4271/2020-01-5224.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Pipes , L.A. An Operational Analysis of Traffic Dynamics Journal of Applied Physics 24 274 281 1953
- Gipps , P. A Behavioural Car-Following Model for Computer Simulation Transportation Research Part B 15 105 111 1981
- Treiber , M. , Hennecke , A. , and Helbing , D. Congested Traffic States in Empirical Observations and Microscopic Simulations Physical Review E 62 1805 1824 2000
- Davis , G. , and Nihan , N. Nonparametric Regression and Short-Term Freeway Traffic Forecasting Journal of Transportation Engineering 117 178 188 1991
- Williams , B.M. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results Journal of Transportation Engineering 6 6 664 672 2003
- Takaba , K. , Iiguni , Y. , and Tokumaru , H. An Improved Tracking Kalman Filter Using a Multilayered Neural Network Mathematical and Computer Modelling 23 119 128 1996
- Guo , J. , Huang , W. , and Williams , B.M. Adaptive Kalman Filter Approach for Stochastic Short-Term Traffic Flow Rate Prediction and Uncertainty Quantification Transportation Research Part C: Emerging Technologies 43 50 64 2014
- Smith , B.L. , Williams , B.M. , and Oswald , R.K. Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting Transportation Research Part C 10 4 303 321 2002
- Smith , B.L. , and Oswald , R.K. Meeting Real Time Traffic Flow Forecasting Requirements with Imprecise Computations Computer-Aided Civil and Infrastructure Engineering 18 3 201 213 2003
- Clark , S. Traffic Prediction Using Multivariate Nonparametric Regression Journal of Transportation Engineering 129 2 161 168 2003
- Dougherty , M.S. , and Cobbett , M.R. Short-Term Inter-Urban Traffic Forecasts Using Neural Networks International Journal of Forecasting 13 21 31 1997
- Yu , H.-Y. , and Bang , S.-Y. An Improved Time Series Prediction by Applying the Layer-By-Layer Learning Method to FIR Neural Networks Neural Networks 10 1717 1729 1997
- Beliaev , I. , and Kozma , R. Time Series Prediction Using Chaotic Neural Networks on the CATS Benchmark Neurocomputing 70 2426 2439 2007
- Yang , Z. , Jin , L. , and Wang , M. Forecasting Baltic Panamax Index with Support Vector Machine Journal of Transportation Systems Engineering and Information Technology 11 50 57 2011
- Yu , R. , Wang , G. , Zheng , J. , and Wang , H. Urban Road Traffic Condition Pattern Recognition Based on Support Vector Machine Journal of Transportation Systems Engineering and Information Technology 13 130 136 2013
- Ševčíková , H. , Raftery , A.E. , and Waddell , P.A. Assessing Uncertainty in Urban Simulations Using Bayesian Melding Transportation Research Part B: Methodological 41 652 669 2007
- Yakowitz , S. Nearest Neighbor Method for Time Series Analysis Journal of Time Series Analysis 8 235 247 1987
- Zhang , T. , Hu , L. , Liu , Z. , and Zhang , Y. Nonparametric Regression for the Short-Term Traffic Flow Forecasting IEEE, International Conference on Mechanic Automation and Control Engineering 2010
- Zhang , L. , Liu , Q. , Yang , W. , Wei , N. et al. An Improved K-nearest Neighbor Model for Short-Term Traffic Flow Prediction Procedia - Social and Behavioral Sciences, 13th COTA International Conference of Transportation Professionals (CICTP 2013) 2013 96 653 662
- He , Z. , Zheng , L. , and Guan , W. A Simple Nonparametric Car-Following Model Driven by Field Data Transportation Research Part B 80 185 201 2015
- Guo , F. , Krishnan , R. , and Polak , J.W. Short-Term Traffic Prediction under Normal and Incident Conditions Using Singular Spectrum Analysis and the K-Nearest Neighbour Method IET IET and ITS Conference on Road Transport Information and Control (RTIC) 2012
- Habtemichael , F.G. , and Cetin , M. Short-Term Traffic Flow Rate Forecasting Based on Identifying Similar Traffic Patterns Transportation Research Part C 66 61 78 2016
- Dang , T.T. , Ngan , H.Y.T. , and Liu , W. Distance-Based K-Nearest Neighbors Outlier Detection Method in Large-Scale Traffic Data IEEE IEEE International Conference on Digital Signal Processing (DSP) 2015
- Ma , M. , Liang , S. , and Qin , Y. A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach Symmetry 11 6 815 2019
- Peng , Y. , Liu , S. , and Dennis , Z.Y. An Improved Car-Following Model With Consideration of Multiple Preceding and Following Vehicles in a Driver’s View Physica A 471 436 444 2020
- Wu , S. , Yang , Z. , and Zhu , X. Improved k-nn for Short-Term Traffic Forecasting Using Temporal and Spatial Information Journal of Transportation Engineering 140 2014
- Cai , P. , Wang , Y. , Lu , G. , Chen , P. et al. A Spatiotemporal Correlative K-Nearest Neighbor Model for Short-Term Traffic Multistep Forecasting Transportation Research Part C 62 21 34 2016
- Gong , X. and Wang , F. Three Improvements on KNN-NPR for Traffic Flow Forecasting IEEE The IEEE 5th International Conference on Intelligent Transportation Systems 2002
- Liang , Z. , Huang , H. , Zhu , C. , and Zhang , K. A Tensor-Based K-Nearest Neighbors Method for Traffic Speed Prediction under Data Missing Transportmetrica B Second Round 182 199 2019
- Li , L. , Sheng , X. , Du , B. , Wang , Y. et al. A Deep Fusion Model Based on Restricted Boltzmann Machines for Traffic Accident Duration Prediction Engineering Applications of Artificial Intelligence 93 2020