DYNAMIC TRAVEL TIME PREDICTION WITH REAL-TIME AND HISTORICAL DATA

By S. Chien, Cahndra Mouly Kuchipudi. 2003

348 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a5dc66e8be74fcb3df0de99defc10f11afae0a25

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. Weibin Zhang
4. John C. Golias
5. Gabriel-Miro Muntean
6. John Murphy
7. Soufiene Djahel
8. Ronan Doolan

In Control and Systems Engineering

1. Eleni I. Vlahogianni
2. Yinhai Wang
3. John C. Golias
4. Weibin Zhang
5. Gabriel-Miro Muntean
6. John Murphy
7. Soufiene Djahel
8. Ronan Doolan

In Transportation

1. Yinhai Wang
2. Eleni I. Vlahogianni
3. John C. Golias
4. Weibin Zhang
5. Gabriel-Miro Muntean
6. John Murphy
7. Soufiene Djahel
8. Ronan Doolan

Citations in the corpus

• Short‐term traffic forecasting: Overview of objectives and methods (2004, methodology): “…techniques (ITS advanced probe vehicle techniques, automatic vehicle identification (AVI) systems, etc.) that provide a way of direct travel time data collection, travel time forecasting is made directly from these travel time measurements (Park et al. , 1998, Chien and Kuchipudi, 2002).”
• Travel time prediction with LSTM neural network (2016, neutral): “The Kalman filtering algorithm [9] estimates the predicted travel time and updates the prediction continually as a new observation becomes available.”
• A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches (2015, methodology): “Finally, the third factor that affects the forecasting accuracy is the technique used to model the traffic data, such as statistical and time series analysis models [107], [108], the well-known autoregressive-integratedmoving-average model [103], [106], neural networks [105], pattern recognition tec”
• Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting (2019, methodology): “Early traffic forecasting approaches, e.g., Linear Regression based approach (Nikovski et al., 2005), Kalman Filtering based approach (Chien et al., 2003), and Auto-Regressive Integrated Moving Average (ARIMA) based approach (Lippi et al., 2013), mainly employ shallow machine learning for a single…”
• An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “Some researchers applied this method in traffic flow [25] and travel time perdition [26] and they also discussed the effect of starting network parameters to the prediction performance.”