Radial Basis Function Neural Network for Work Zone Capacity and Queue Estimation
- 1 September 2003
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Transportation Engineering
- Vol. 129 (5), 494-503
- https://doi.org/10.1061/(asce)0733-947x(2003)129:5(494)
Abstract
An adaptive computational model is presented for estimating the work zone capacity and queue length and delay, taking into account the following factors: number of lanes, number of open lanes, work zone layout, length, lane width, percentage trucks, grade, speed, work intensity, darkness factor, and proximity of ramps. The model integrates judiciously the mathematical rigor of traffic flow theory with the adaptability of neural network analysis. A radial-basis function neural network model is developed to learn the mapping from quantifiable and nonquantifiable factors describing the work zone traffic control problem to the associated work zone capacity. This model exhibits good generalization properties from a small set of training data, a specially attractive feature for estimating the work zone capacity where only limited data is available. Queue delays and lengths are computed using a deterministic traffic flow model based on the estimated work zone capacity. The result of this research is being used to develop an intelligent decision support system to help work zone engineers perform scenario analysis and create traffic management plans consistently, reliably, and efficiently.Keywords
This publication has 14 references indexed in Scilit:
- Optimal Work Zone Lengths for Four-Lane HighwaysJournal of Transportation Engineering, 2001
- Neural Networks in Civil Engineering: 1989–2000Computer-Aided Civil and Infrastructure Engineering, 2001
- Empirical Freeway Queuing Duration ModelJournal of Transportation Engineering, 2001
- An observed traffic pattern in long freeway queuesTransportation Research Part A: Policy and Practice, 2001
- Fuzzy-Wavelet RBFNN Model for Freeway Incident DetectionJournal of Transportation Engineering, 2000
- Some traffic features at freeway bottlenecksTransportation Research Part B: Methodological, 1999
- Traffic Capacity, Speed, and Queue-Discharge Rate of Indiana’s Four-Lane Freeway Work ZonesTransportation Research Record: Journal of the Transportation Research Board, 1999
- Regularization Neural Network for Construction Cost EstimationJournal of Construction Engineering and Management, 1998
- Networks for approximation and learningProceedings of the IEEE, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989