Real-time road traffic prediction with spatio-temporal correlations

By Wanli Min, Laura Wynter. 2011

687 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/2b535a6bdc9329f89d09e0ca38b3c3a563617978

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. Weibin Zhang
2. Nizar Bouguila
3. Saeed Rahmani
4. Asiye Baghbani
5. Zachary Patterson
6. Zhengbing He
7. Yinghao Yu
8. Jingmeng Liu

In Transportation

1. Yinghao Yu
2. Weibin Zhang
3. Jingmeng Liu
4. Zheng Zhao
5. Srinivas Peeta
6. F. Karray
7. Nizar Bouguila
8. Feng Shu

Citations in the corpus

• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “They compared the performance of their model with baselines, including the latest observation, historical average, support vector regression, Kalman filter, MSTARMA [176], LASSO, and LSTM, and showed that their framework outperforms all baselines by a significant margin.”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, background): “Therefore, the study of both spatial and temporal traffic data has attracted increasing amounts of attention (Wu et al. 2016; Min and Wynter 2011).”
• LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “Over the past few decades, many data analysis models have been proposed to solve the short-term traffic forecast, including historical average and smoothing [4, 5], statistical and regression methods [6, 7], traffic flow theory-based methods [8, 9] and machine learning techniques [10, 11].”
• Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach (2016, methodology): “The main techniques used in the first class are autoregressive integrated moving average (ARIMA)-based models [6]–[9] and Kalman filtering [10]–[13].”
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting (2020, methodology): “For statistic-based model, such as autoregressive integrated moving average (ARIMA)[11] and Bayesian networks [12], spatial dependencies are modeled from a probabilistic view.”
• Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach (2017, background): “, 2017; Zhang and Lin, 2017) and short-term traffic prediction in a road network (Min and Wynter, 2011; Vlahogianni et al., 2007).”