main.bib

@inproceedings{1709.04875,
  doi = {10.24963/ijcai.2018/505},
  url = {https://doi.org/10.24963/ijcai.2018/505},
  year = {2018},
  month = jul,
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  author = {Bing Yu and Haoteng Yin and Zhanxing Zhu},
  title = {Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence}
}

@incollection{Vlahogianni2015,
  doi = {10.1007/978-3-319-18320-6_7},
  url = {https://doi.org/10.1007/978-3-319-18320-6_7},
  year = {2015},
  publisher = {Springer International Publishing},
  pages = {107--128},
  author = {Eleni I. Vlahogianni},
  title = {Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities},
  booktitle = {Computational Methods in Applied Sciences}
}

@article{Williams2003,
  doi = {10.1061/(asce)0733-947x(2003)129:6(664)},
  url = {https://doi.org/10.1061/(asce)0733-947x(2003)129:6(664)},
  year = {2003},
  month = nov,
  publisher = {American Society of Civil Engineers ({ASCE})},
  volume = {129},
  number = {6},
  pages = {664--672},
  author = {Billy M. Williams and Lester A. Hoel},
  title = {Modeling and Forecasting Vehicular Traffic Flow as a Seasonal {ARIMA} Process: Theoretical Basis and Empirical Results},
  journal = {Journal of Transportation Engineering}
}

@inproceedings{YuhanJia2016,
  doi = {10.1109/itsc.2016.7795712},
  url = {https://doi.org/10.1109/itsc.2016.7795712},
  year = {2016},
  month = nov,
  publisher = {{IEEE}},
  author = {Yuhan Jia and  Jianping Wu and  Yiman Du},
  title = {Traffic speed prediction using deep learning method},
  booktitle = {2016 {IEEE} 19th International Conference on Intelligent Transportation Systems ({ITSC})}
}

@article{Chen_Song_Yamada_Shibasaki_2016,
  title={Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference},
  volume={30}, url={https://ojs.aaai.org/index.php/AAAI/article/view/10011},
  number={1},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  author={Chen, Quanjun and Song, Xuan and Yamada, Harutoshi and Shibasaki, Ryosuke},
  year={2016},
  month={Feb.}
}

@inproceedings{1506.04214,
  author = {Shi, Xingjian and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-kin and Woo, Wang-chun},
  title = {Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting},
  year = {2015},
  publisher = {MIT Press},
  address = {Cambridge, MA, USA},
  abstract = {The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.},
  booktitle = {Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1},
  pages = {802–810},
  numpages = {9},
  location = {Montreal, Canada},
  series = {NIPS'15}
}

@inproceedings{1409.3215,
  author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.},
  title = {Sequence to Sequence Learning with Neural Networks},
  year = {2014},
  publisher = {MIT Press},
  address = {Cambridge, MA, USA},
  abstract = {Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous state of the art. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.},
  booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
  pages = {3104–3112},
  numpages = {9},
  location = {Montreal, Canada},
  series = {NIPS'14}
}

@inproceedings{1312.6203,
  title = "Spectral networks and locally connected networks on graphs",
  author = "Joan Bruna and Wojciech Zaremba and Arthur Szlam and Yann Lecun",
  year = "2014",
  language = "English (US)",
  booktitle = "International Conference on Learning Representations (ICLR2014), CBLS, April 2014",
}

@article{2004.08555,
  author={Yin, Xueyan and Wu, Genze and Wei, Jinze and Shen, Yanming and Qi, Heng and Yin, Baocai},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  title={Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions},
  year={2021},
  volume={},
  number={},
  pages={1-17},
  doi={10.1109/TITS.2021.3054840}
}

@article{Rabiner1986,
  doi = {10.1109/massp.1986.1165342},
  url = {https://doi.org/10.1109/massp.1986.1165342},
  year = {1986},
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  volume = {3},
  number = {1},
  pages = {4--16},
  author = {L. Rabiner and B. Juang},
  title = {An introduction to hidden Markov models},
  journal = {{IEEE} {ASSP} Magazine}
}

@article{1612.01022,
  title={Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework},
  author={Yuankai Wu and Huachun Tan},
  journal={ArXiv},
  year={2016},
  volume={abs/1612.01022}
}

@incollection{Seo2018,
  doi = {10.1007/978-3-030-04167-0_33},
  url = {https://doi.org/10.1007/978-3-030-04167-0_33},
  year = {2018},
  publisher = {Springer International Publishing},
  pages = {362--373},
  author = {Youngjoo Seo and Michaël Defferrard and Pierre Vandergheynst and Xavier Bresson},
  title = {Structured Sequence Modeling with Graph Convolutional Recurrent Networks},
  booktitle = {Neural Information Processing}
}

@article{1707.01926,
  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
  author={Yaguang Li and Rose Yu and Cyrus Shahabi and Yan Liu},
  journal={arXiv: Learning},
  year={2018}
}

@article{10.1145/3293317,
author = {Wang, Hongjian and Tang, Xianfeng and Kuo, Yu-Hsuan and Kifer, Daniel and Li, Zhenhui},
title = {A Simple Baseline for Travel Time Estimation Using Large-Scale Trip Data},
year = {2019},
issue_date = {March 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {10},
number = {2},
issn = {2157-6904},
url = {https://doi.org/10.1145/3293317},
doi = {10.1145/3293317},
abstract = {The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi 8 Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [29]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this article, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big-data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by big data and these approaches could serve as new baselines for some traditional computational problems.},
journal = {ACM Trans. Intell. Syst. Technol.},
month = {jan},
articleno = {19},
numpages = {22},
keywords = {Travel time estimation, baseline, trajectory data, big data}
}

@inproceedings{10.1145/2623330.2623656,
author = {Wang, Yilun and Zheng, Yu and Xue, Yexiang},
title = {Travel Time Estimation of a Path Using Sparse Trajectories},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623656},
doi = {10.1145/2623330.2623656},
abstract = {In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {25–34},
numpages = {10},
keywords = {travel time estimation, spatial trajectories, spatio-temporal data mining, tensor decomposition, urban computing},
location = {New York, New York, USA},
series = {KDD '14}
}