Short-term traffic forecasting: Where we are and where we’re going

By Eleni I. Vlahogianni, Matthew G. Karlaftis, John C. Golias. 2014

1,130 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/36561538892e4f814b7bb63dd32d2c8fccbdfbfd

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. Ruimin Ke
2. Zhiyong Cui
3. Yinhai Wang
4. Zhifeng Bao
5. A. K. Qin
6. Farhana Murtaza Choudhury
7. Baihua Zheng
8. David Alexander Tedjopurnomo

In Control and Systems Engineering

1. David P. Watling
2. Zhiyong Cui
3. Ruimin Ke
4. Yanru Zhang
5. Ali Haghani
6. Yinhai Wang
7. Kristian C. Henrickson
8. Javier del Ser

In Transportation

1. Yinhai Wang
2. Zhiyong Cui
3. Ruimin Ke
4. David P. Watling
5. Zhifeng Bao
6. A. K. Qin
7. Farhana Murtaza Choudhury
8. Baihua Zheng

Citations in the corpus

• Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “In the early 1980s, some traffic prediction methods started to use the past few hours’ traffic data to forecast the traffic states in the next few minutes (mostly no more than 5 minutes) by parameterized models [14].”
• LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “Therefore, how to promote the forecast accuracy by making use of the massive traffic data has grown into a hotspot in recent years [1–3].”
• Forecasting: theory and practice (2020, background): “(see, for example, Whittaker et al., 1997; Vlahogianni et al., 2004; Kamarianakis and Prastacos, 2005; Vlahogianni et al., 2014).”
• STREETS: A Novel Camera Network Dataset for Traffic Flow (2019, background): “There are of course simpler, classical baseline models for traffic forecasting and we refer the interested reader to [30] to learn more about these methods.”
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, background): “Previous work [2][3][4][5] on this topic roughly categorizes existing models into two categories: classical statistical methods and machine learning models.”
• Capturing Car-Following Behaviors by Deep Learning (2018, methodology): “For example, we had checked all the traffic flow prediction models mentioned in a recent survey [56].”
• Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, methodology): “[2] reviewed the methods and applications from 2004 to 2013, and discussed ten challenges that were significant at the time.”
• An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, background): “[3] updated the literature from 2004 to 2013.”
• Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, background): “Short-term traffic forecasting based on data-driven models for ITS applications has great influ-ence on the overall performance of modern transportation systems Vlahogianni et al. (2014).”
• Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management (2019, background): “During the last decade, the technological landscape of transportation has gradually integrated disruptive technology paradigms into current transportation management systems, leading to Intelligent Transportation Systems (ITS) [1], [2].”
• High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, background): “Previous work [2][3][4][5] on this topic roughly categorizes existing models into two categories: classical statistical methods and machine learning models.”
• A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, background): “in their 2014 survey paper [9] and discuss only the challenges that have been solved or partially solved using deep neural network.”
• A statistical method for estimating predictable differences between daily traffic flow profiles (2017, methodology): “The majority of the research aimed at predicting traffic flow profiles focuses on short term forecasting for real-time applications (see Vlahogianni et al. (2004) and Vlahogianni et al. (2014) for a review of methods applied).”
• Road Traffic Forecasting: Recent Advances and New Challenges (2018, methodology): “Prediction Horizon Prediction horizon [1], [7], [10], [11], [13], [25], [29], [30]”
• A gradient boosting method to improve travel time prediction (2015, methodology): “The ML method can also capture the underlying structure of data even when they are not apparent (Vlahogianni et al., 2014).”