An object-oriented neural network approach to short-term traffic forecasting

By Hussein Dia. European Journal of Operational Research, 2001

420 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a230bf493775954f0fe2ba50eb47b6faea7b00c9

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. Eleni I. Vlahogianni
3. Mu-Chen Chen
4. Yu Wei
5. Ruimin Ke
6. Zhiyong Cui
7. Matthew G. Karlaftis
8. John C. Golias

In Control and Systems Engineering

1. Mu-Chen Chen
2. Yu Wei
3. Eleni I. Vlahogianni
4. Yinhai Wang
5. Javier del Ser
6. Ibai Lana
7. M. Vélez
8. Billy M. Williams

In Transportation

1. Mu-Chen Chen
2. Yu Wei
3. Eleni I. Vlahogianni
4. Yinhai Wang
5. John C. Golias
6. Yisheng Lv
7. Feiyue Wang
8. Yanjie Duan

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, ”
• Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, background): “Digital Object Identifier 10.1109/TITS.2011.2174051 2) The models generated by these methods may not be able to capture some strongly nonlinear characteristics of short-term traffic flow data.”
• Accurate freeway travel time prediction with state-space neural networks under missing data (2005, background): “…(Innamaa, 2001; Rilett and Park, 2001; Park and Rilett, 1999; Cheu, 1998; Huisken and Van Berkum, 2003) and recurrent (Abdulhai et al., 1999; Dia, 2001; Ishak et al., 2003; Van Lint et al., 2002) neural networks and various hybrid approaches (Chen et al., 2001; Park and Rilett, 1998; Ishak…”
• An Aggregation Approach to Short-Term Traffic Flow Prediction (2009, background): “series analysis [6], [7], Bayesian networks [8], neural networks (NNs) [9]–[12], fuzzy NNs [13], [14], nonparametric regression”
• Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, background): “…Van Arem et al., 1997; Lee et al., 2006), Kalman filter based multilayer perceptron (Vythoulkas, 1993), time-delay neural networks (Zhang, 2000; Dia, 2001), radial basis function neural networks (Zheng et al., 2006), dynamic neural networks (Chen and Grant-Muller, 2001), state-space neural…”
• Travel time prediction with LSTM neural network (2016, methodology): “The objected-oriented neural network approach [12] uses the current observed speed and flow data of the upstream and downstream stations on the freeway section as the network input, and predicts the future travel time on that section at the network output.”
• Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks (2002, positive): “The temporal aspect of traffic prediction has been taken into account by using recurrent and time-delayed neural networks ( 19, 21 ).”
• Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “Again, simulation models are the best suited to address this issue, parametrizing preceding inputs; notwithstanding, some researchers have concentrated on this matter by incorporating spatial information to time series [28], neural networks [37]–[39] or other non-parametric approaches [14], [40].”
• Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions (2009, background): “…but also other traffic parameters including speed (Ishak & Alecsandru, 2004; Ishak, Kotha, & Alecsandru, 2003; Xiao, Sun, Ran, & Oh, 2003; Xiao, Sun, Ran, & Oh, 2004), travel time (Dia, 2001; Vanajakshi & Rilett, 2004), and occupancy (Zhang, 2000), have been predicted in real-time by NNet models.”
• Short‐term traffic forecasting: Overview of objectives and methods (2004, methodology): “Chen et al. (2001) predicted traffic conditions 12 steps ahead at 15-min intervals, and Dia (2001) predicted travel time 45 steps ahead at 20-s interval.”