ABSTRACT
Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.
Supplemental Material
- HERE Traffic API. 2019. https://developer.here.com/.Google Scholar
- K. Boriboonsomsin et al. 2012. Eco-routing navigation system based on multisource historical and real-time traffic information. IEEE Transactions on Intelligent Transportation Systems, Vol. 13, 4 (2012), 1694--1704.Google ScholarDigital Library
- J. Bruna et al. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations (ICLR' 14).Google Scholar
- M. Castro-Neto et al. 2009. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, Vol. 36, 3 (2009), 6164--6173.Google ScholarDigital Library
- J. Contreras et al. 2003. ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, Vol. 18, 3 (2003), 1014--1020.Google ScholarCross Ref
- M. Defferrard et al. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the 29th Neural Information Processing Systems. (NIPS '16).Google Scholar
- D. Deng et al. 2016. Latent space model for road networks to predict time-varying traffic. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16).Google ScholarDigital Library
- I. S. Dhillon et al. 2004. Kernel K-means: spectral clustering and normalized cuts. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Dining (KDD '04).Google ScholarDigital Library
- R. Gao et al. 2019. Aggressive driving saves more time? multi-task learning for customized travel time estimation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI '19).Google ScholarCross Ref
- Y. Gu et al. 2016. From twitter to detector: real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies., Vol. 67 (2016), 321--342.Google ScholarCross Ref
- Y. He et al. 2019. Traffic influence degree of urban traffic accident based on speed ratio. Journal of Highway and Transportation Research and Development (English Edition), Vol. 13, 3 (2019), 96--102.Google ScholarCross Ref
- S. Hochreiter and J. Schmidhuber. 1997. Long Short-term Memory. Neural Computation, Vol. 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- R. J. Javid and R. J. Javid. 2018. A framework for travel time variability analysis using urban traffic incident data. IATSS Research, Vol. 42, 1 (2018), 30--38.Google ScholarCross Ref
- I. Johnson et al. 2017. Beautiful but at what cost?: an examination of externalities in geographic vehicle routing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 1, 2 (2017), 1--21.Google ScholarDigital Library
- T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of 5th International Conference on Learning Representations (ICLR '17).Google Scholar
- Y. Li et al. 2018. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In Proceedings of the 6th International Conference on Learning Representations. (ICLR '18).Google Scholar
- Z. Li et al. 2017. Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, 11 (2017), 3204--3217.Google ScholarDigital Library
- L. Lin et al. 2015. Modeling the impacts of inclement weather on freeway traffic speed: exploratory study with social media data. Transportation Research Record, Vol. 2482, 1 (2015), 82--89.Google ScholarCross Ref
- L. Lin et al. 2017. Road traffic speed prediction: a probabilistic model fusing multi-source data. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, 7 (2017), 1310--1323.Google ScholarCross Ref
- L. I. Lin. 1989. A concordance correlation coefficient to evaluate reproducibility. Biometrics., Vol. 67 (1989), 255--268.Google ScholarCross Ref
- Y. Lv et al. 2014. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, 2 (2014), 865--873.Google Scholar
- Z. Lv et al. 2018. LC-RNN: a deep learning model for traffic speed prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI '18).Google ScholarCross Ref
- X. Ma et al. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, Vol. 54 (2015), 187--197.Google ScholarCross Ref
- X. Ma et al. 2017. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors, Vol. 17, 4 (2017), 818.Google ScholarCross Ref
- T. Mikolov et al. 2010. Recurrent neural network based language model. In Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH'10).Google ScholarCross Ref
- M. Miller and C. Gupta. 2012. Mining traffic incidents to forecast impact. In Proceedings of the 1st ACM SIGKDD International Workshop on Urban Computing (Urbcomp '12).Google Scholar
- B. Pan et al. 2012. Utilizing real-world transportation data for accurate traffic prediction. In Proceedings of the IEEE 12th International Conference on Data Mining (ICDM '12).Google ScholarDigital Library
- M. M. Rathore et al. 2016. Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, Vol. 101 (2016), 63--80.Google ScholarDigital Library
- D. J. Rumsey. 2015. U can: statistics for dummies. (2015).Google Scholar
- X. Shi et al. 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In Proceedings of Neural Information Processing Systems (NIPS' 15). 802--810.Google Scholar
- A. J. Smola and B. Schölkopf. 2004. A tutorial on support vector regression., Vol. 14, 3 (2004), 199--222.Google Scholar
- Y. Tong et al. 2017. The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD' 17).Google ScholarDigital Library
- N. Viovy et al. 1992. The best index slope extraction (BISE): a method for reducing noise in NDVI time-series. International Journal of Remote Sensing, Vol. 13, 8 (1992), 1585--1590.Google ScholarCross Ref
- R. Xie et al. 2018. We know your preferences in new cities: mining and modeling the behavior of travelers. IEEE Communications Magazine, Vol. 56, 11 (2018), 28--35.Google ScholarCross Ref
- Yahoo. 2019. https://developer.yahoo.com/weather/.Google Scholar
- H. Yao et al. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI' 18).Google ScholarCross Ref
- H. Yao et al. 2019. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI' 19).Google ScholarDigital Library
- B. Yu et al. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI' 17).Google Scholar
- S. X. Yu and J. Shi. 2003. Multiclass spectral clustering. In Proceedings the 9th IEEE International Conference on Computer Vision (ICCV '03).Google Scholar
- Z. Yuan et al. 2018. Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '18).Google ScholarDigital Library
- H. Zhang et al. 2018. Detecting urban anomalies using multiple Spatio-temporal data sources. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 2, 1 (2018), 54:1--54:18.Google ScholarDigital Library
- J. Zhang et al. 2016. DNN-based prediction model for spatial-temporal data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. (SIGSPATIAL '16).Google ScholarDigital Library
- J. Zhang et al. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI '17).Google ScholarCross Ref
- C. Zheng et al. 2020. Gman: a graph multi-attention network for traffic prediction. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI' 20).Google ScholarCross Ref
- J. Zheng and L. M. Ni. 2013. Time-dependent trajectory regression on road networks via multi-task learning. In Proceedings of 27th AAAI Conference on Artificial Intelligence (AAAI' 13).Google Scholar
Index Terms
- Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction
Recommendations
Markov Chain Model Approach for Traffic Incident Length Prediction
ICSET 2017: Proceedings of the 2017 International Conference on E-Society, E-Education and E-TechnologyOne of the most challenging parts of traffic modeling is how to model traffic behavior during traffic incidents. One of the possible approaches to this problem is to use historical data to identify typical incidents and use this knowledge to classify ...
Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction
SummaryThrough accurate network‐wide traffic prediction, network operators can agilely manage resources and improve robustness by proactively adapting to new traffic patterns, especially for traffic engineering, capacity planning and quality of service ...
This paper proposes a novel spatiotemporal graph convolutional recurrent network (SGCRN) for short‐term traffic matrix (TM) prediction in the large‐scale IP backbone networks. By learning network‐wide traffic as graph‐structured TM time‐series, SGCRN ...
Time-adaptive graph convolutional network for traffic prediction
ICDLT '21: Proceedings of the 2021 5th International Conference on Deep Learning TechnologiesTraffic prediction is of great significance to route planning and transportation management. Due to the complex nonlinear spatiotemporal dependence between traffic data and various unpredictable traffic conditions, traffic prediction has been considered ...
Comments