Inter-urban Short-term Traffic Congestion Prediction |
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Page 26
... field data of the A13 motorway on a typical February afternoon peak . They found out that neural network models fed with data with failure rates of up to 60 % were still able to produce reasonable good results . The same models ...
... field data of the A13 motorway on a typical February afternoon peak . They found out that neural network models fed with data with failure rates of up to 60 % were still able to produce reasonable good results . The same models ...
Page 33
... field data at the same site . The MLF was found to have the highest potential , among the three ANNs , to achieve a better incident detection performance . The MLF was also tested with limited field data collected from three other ...
... field data at the same site . The MLF was found to have the highest potential , among the three ANNs , to achieve a better incident detection performance . The MLF was also tested with limited field data collected from three other ...
Page 180
... field testing a fuzzy signal controller , European Journal of Operational Research , 131 ( 2 ) , pp . 273281 . Noland , R.B. ( 2001 ) . Relationships between highway capacity and induced vehicle travel , Transportation Research Part A ...
... field testing a fuzzy signal controller , European Journal of Operational Research , 131 ( 2 ) , pp . 273281 . Noland , R.B. ( 2001 ) . Relationships between highway capacity and induced vehicle travel , Transportation Research Part A ...
Common terms and phrases
0-minute prediction horizon 30 min prediction a.m. peak aggregation level algorithm Amersfoort ANN models architecture ARMA models artificial neural networks Beekbergen AM peak Beekbergen morning peak Beekbergen PM bottleneck detector calibrated chapter cloverleaf junction congestion indicator congestion prediction data set data sub-sets described developed DTM measures error false alarm evaluation false alarm percentages feed-forward forecasting freeway function Fuzzy Logic hidden neurons Hoevelaken PM peak HR RW Huisken hypothesis induction loop infrastructure input data input variables Intelligent Transportation Systems Kalman filtering learning epochs Linear Regression loop detectors mean speed min 10 min minutes MLF ANNs MLR models modellen morning peak period motorway naïve method naïve models neurons non-linear occupancy optimum outperformed output parameters patterns performance PM peak period prediction horizon Figure R-L RSW sensors series analysis significance supervised learning target data target detector test set traffic congestion traffic management travel time prediction upstream values zijn