Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach

By Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias. 2005

631 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/83d0feb4374e59bec6aa8b167cbd04bc48ee093a

How researchers see this paper

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

In Building and Construction

1. Yinhai Wang
2. Ruimin Ke
3. Zhiyong Cui
4. Yunpeng Wang
5. Xiaolei Ma
6. Li Pan
7. Yongxue Tian
8. Eleni I. Vlahogianni

In Control and Systems Engineering

1. Yinhai Wang
2. Li Pan
3. Yongxue Tian
4. Ruimin Ke
5. Zhiyong Cui
6. Mu-Chen Chen
7. Yu Wei
8. Eleni I. Vlahogianni

In Transportation

1. Yinhai Wang
2. Li Pan
3. Yongxue Tian
4. Mu-Chen Chen
5. Yu Wei
6. Yunpeng Wang
7. Xiaolei Ma
8. Eleni I. Vlahogianni

Citations in the corpus

• A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “[27] proposed a genetic algorithm approach to optimally tune the network.”
• Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, background): “…and Demetsky (1997), Chang (1999), Chen and Grant-Muller (2001), Dougherty and Cobett (1997), Kirby, Watson, and Dougherty (1997), Kwon and Stephanedes (1994), Vlahogianni, Karlaftis, and Golias (2005), Yasdi (1999), Yun, Namkoong, Rho, Shin, and Choi (1988) and Zhong, Sharma, and Lingras (2005).”
• Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “Various ANN models were developed for predicting traffic flow [25]–[34].”
• An Aggregation Approach to Short-Term Traffic Flow Prediction (2009, background): “(NP) [15], [16], and intelligence computation [17]–[19].”
• Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, methodology): “…previous studies have proposed hybrid MLP models by incorporating the ARIMA model (Zhang, 2003; Zeng et al., 2008), seasonal ARIMA model (Tseng et al., 2002; Zhang and Qi, 2005), genetic algorithms (Vlahogianni et al., 2005), fuzzy logic rules (Rojas et al., 2008; Zhang and Ye, 2008), and so on.”
• LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (2020, positive): “In general, there are two kinds of traffic prediction tasks according to the length of prediction time, namely the short-term (5∼30 minutes) and long-term (30∼60 minutes) [Ostring and Sirisena, 2001; Vlahogianni et al., 2005].”
• Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “As a data modeling tool, it has also been adopted in ITS such as traffic flow prediction [66], travel time prediction [67], traffic accident”
• 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, ”
• Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “The authors contribute a methodological workflow to select and tune the model parameters that has been extensively referred [10], [12]–[17].”
• Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network (2015, methodology): “Vlahogianni et al. optimized the neural network with genetic approach and applied the model to short-term traffic flow prediction [5].”