Short-term traffic speed prediction plays a key importance in urban traffic management and operation. The literature review indicates that lane-based short-term urban traffic forecasting is still rare. In this study, genetic algorithms (GAs) are used to optimize the input data sets for artificial neural network (ANN) models and locally weighted regression (LWR) models to achieve optimal prediction under various input and traffic settings. The GA designed ANN (GA-ANN) and GA designed LWR (GA-LWR) models are used to predict 5-minute short-term traffic speed for four lanes of an urban road. For the peak and off-peak time traffic speed prediction, both aggregate and disaggregate GA-ANN and GA-LWR models are developed and tested, with the former forecasting traffic Speed of every 5-min with a 30-min period (e.g., 7:25–7:55 am morning peak) and the latter for forecasting traffic speed of a particular 5-min of each weekday (7:25–7:30 am of Monday to Friday). In addition, for peak and off-peak traffic speed prediction, the GA-ANN disaggregate model results in most of average errors of 3–4% and most of the 95th percentile errors are lower than 8% for each of the four lanes. Meanwhile, for the peak and off-peak time traffic speed prediction, the GA-LWR disaggregate model results in most of average errors of 1–2% and most of the 95th percentile errors are lower than 4% for each of the four lanes. When compared to previous models that usually offer average errors more than 6–15%, such empirical findings should be interesting and instrumental for transportation authorities to put through their city-or state-wide ATIS.
Hybrid lane-based short-term urban traffic speed forecasting: A genetic approach
01.08.2017
462557 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Taylor & Francis Verlag | 2018
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