Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting

By Xiaomo Jiang, Hojjat Adeli. 2005

367 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/545bef80804ac73aea7e170165a2f270bec8c321

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ashif Panakkat
2. Matthew G. Karlaftis
3. Eleni I. Vlahogianni
4. John C. Golias
5. Hojjat Adeli
6. Billy M. Williams
7. Yunlong Zhang
8. Zhirui Ye

In Building and Construction

1. Eleni I. Vlahogianni
2. John C. Golias
3. Jianhua Guo
4. Wei Huang
5. Billy M. Williams
6. Matthew G. Karlaftis
7. Fiona Crawford
8. David P. Watling

In Control and Systems Engineering

1. Eleni I. Vlahogianni
2. Billy M. Williams
3. Jianhua Guo
4. Wei Huang
5. Fiona Crawford
6. Richard D. Connors
7. David P. Watling
8. Yunlong Zhang

Citations in the corpus

• Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification (2014, methodology): “Second, non-parametric approaches have been applied, including neural network and its variations (Clark et al., 1992; Smith and Demetsky, 1994; Park et al., 1998; Zhang, 2000; Dia, 2001; Chen and Grant-Muller, 2001; Yin et al., 2002; Jiang and Adeli, 2005; Vlahogianni et al., 2005; Dunne and Ghosh, ”
• Automated pavement crack detection and segmentation based on two‐step convolutional neural network (2020, methodology): “Machine learning (ML) has been well used in solving civil engineering issues like work zone capacity estimation (Adeli & Jiang, 2003), traffic incident detection (Adeli & Samant, 2000), traffic flow forecasting (Jiang & Adeli, 2005), and earthquake location prediction (Panakkat & Adeli, 2009).”
• Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network (2017, methodology): “…learning techniques were reported in the field of transportation engineering, such as traffic incident detection (Adeli and Samant, 2000), work zone capacity estimation (Adeli and Jiang, 2003), traffic flow forecasting (Jiang and Adeli, 2005), and traffic sign classification (Ciresan et al., 2012).”
• Short-term traffic forecasting: Where we are and where we’re going (2014, background): “, 2012), wavelets (Jiang and Adeli, 2005), genetic algorithms (Hong et al.”
• A probabilistic neural network for earthquake magnitude prediction (2009, positive): “…& Adeli, 2001), structural engineering (Adeli & Karim, 1997a, 1997b; Adeli & Kim, 2001; Adeli & Park, 1995a, 1995b, 1995c, 1996; Hung & Adeli, 1994b; Jiang & Adeli, 2005a, 2007, 2008a, 2008b; Sirca & Adeli, 2001; Tashakori & Adeli, 2002), transportation engineering (Adeli & Jiang, 2003; Adeli &…”
• A statistical method for estimating predictable differences between daily traffic flow profiles (2017, background): “Other time series based approaches include Jiang and Adeli (2005) who present a ‘time-delay recurrent wavelet neural network model’ which they propose could be used for predicting traffic flow profiles on future days. In this approach, all past data is considered as one long tim e series and therefo”
• Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction (2013, background): “Several short-term prediction methods for traffic flow, including artificial intelligence and optimization-based methods, have been proposed during the last several decades [1], [8], [9], [16], [17], [31], [32], [37].”
• Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, methodology): “Based on WNN-LM, better performance in function learning [51], including short-term traffic flow forecasting [23], can be obtained than those of the conventional feedforward NNs.”
• Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, methodology): “Regarding short-term prediction of traffic flow only, a dynamic wavelet NNet model was used to predict hourly traffic flow, including time of the day and day of the week as variables in the model (Jiang & Adeli, 2000).”
• Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007, methodology): “, 2004), dynamic wavelet neural networks (Jiang and Adeli, 2005), nonparametric regression (Davis and Nihan, 1991; Smith and Demetsky, 1997), time series models (Ahmed and Cook, 1979; Nihan and Holmesland, 1980; Hamed et al.”