Xi Lin
ABSTRACT
Estimation of path travel time provides great value to applications like bus line designs and route plannings. Existing approaches are mainly based on single-source trajectory datasets that are usually large in size to ensure a satisfactory performance. This leads to two limitations: 1) Large-scale data may not always be attainable, e.g. city-scale public bus data is usually small compared to taxi data due to relative fewer bus trips in a day. 2) Considering only single-source trajectory data neglects the potential estimation-improving insights of external data, e.g. trajectory dataset of other vehicle sources obtained from the same geographical region. A challenge is how to effectively utilize such other trajectory sources. Moreover, existing work does not attend the important attributes of a trajectory including vehicle ID, day of week, rainfall level etc., which are important for estimating the path travel time. Motivated by these and the recent successes of neural network models, we propose Attribute-related Hybrid Trajectories Network~(AtHy-TNet), a neural model that effectively utilizes the attribute correlations, as well as the spatial and temporal relationships across hybrid trajectory data. We apply this to a novel problem of estimating path travel time of a type of vehicles using a hybrid trajectory dataset that includes trajectories from other vehicle types. We demonstrate in our experiments the benefits of considering hybrid data for travel time estimation, and show that AtHy-TNet significantly outperforms state-of-the-art methods on real-world trajectory datasets.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- Rich Caruana. 1997. Multitask learning. Machine learning , Vol. 28, 1 (1997), 41--75.Google Scholar
- Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, and Dinesh Manocha. 2018. TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions. arXiv preprint arXiv:1812.04767 (2018).Google Scholar
- Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015).Google Scholar
- Jian Dai, Bin Yang, Chenjuan Guo, Christian S Jensen, and Jilin Hu. 2016. Path cost distribution estimation using trajectory data. Proceedings of the VLDB Endowment , Vol. 10, 3 (2016), 85--96.Google ScholarDigital Library
- Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics . 315--323.Google Scholar
- Google. 2019. Google Maps Platform. https://developers.google.com/maps/documentation/Google Scholar
- Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi. 2018. Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2255--2264.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation , Vol. 9, 8 (1997), 1735--1780.Google Scholar
- Jilin Hu, Chenjuan Guo, Bin Yang, Christian S Jensen, and Lu Chen. 2018. Recurrent Multi-Graph Neural Networks for Travel Cost Prediction. arXiv preprint arXiv:1811.05157 (2018).Google Scholar
- Erik Jenelius and Haris N Koutsopoulos. 2018. Urban network travel time prediction based on a probabilistic principal component analysis model of probe data. IEEE Transactions on Intelligent Transportation Systems , Vol. 19, 2 (2018), 436--445.Google ScholarDigital Library
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. 3146--3154.Google ScholarDigital Library
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).Google Scholar
- Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, and Yan Liu. 2018. Multi-task representation learning for travel time estimation. In International Conference on Knowledge Discovery and Data Mining,(KDD) .Google ScholarDigital Library
- Yang Li, Dimitrios Gunopulos, Cewu Lu, and Leonidas Guibas. 2017. Urban Travel Time Prediction using a Small Number of GPS Floating Cars. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 3.Google ScholarDigital Library
- Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha. 2018. TrafficPredict: Trajectory prediction for heterogeneous traffic-agents. arXiv preprint arXiv:1811.02146 (2018).Google Scholar
- Bei Pan, Ugur Demiryurek, and Cyrus Shahabi. 2012. Utilizing real-world transportation data for accurate traffic prediction. In 2012 IEEE 12th International Conference on Data Mining. IEEE, 595--604.Google ScholarDigital Library
- Mahmood Rahmani, Haris N Koutsopoulos, and Erik Jenelius. 2017. Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach. Transportation Research Part C: Emerging Technologies , Vol. 85 (2017), 628--643.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level.. In IJCAI, Vol. 16. 2618--2624.Google ScholarDigital Library
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1--9.Google ScholarCross Ref
- Youze Tang, Andy Diwen Zhu, and Xiaokui Xiao. 2012. An efficient algorithm for mapping vehicle trajectories onto road networks. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 601--604.Google ScholarDigital Library
- Anirudh Vemula, Katharina Muelling, and Jean Oh. 2018. Social attention: Modeling attention in human crowds. In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 1--7.Google ScholarDigital Library
- Dong Wang, Wei Cao, Mengwen Xu, and Jian Li. 2016a. Etcps: An effective and scalable traffic condition prediction system. In International Conference on Database Systems for Advanced Applications. Springer, 419--436.Google ScholarDigital Library
- Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. 2018. When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks. AAAI.Google Scholar
- Hongjian Wang, Xianfeng Tang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. 2019. A simple baseline for travel time estimation using large-scale trip data. ACM Transactions on Intelligent Systems and Technology (TIST) , Vol. 10, 2 (2019), 19.Google ScholarDigital Library
- Jingyuan Wang, Qian Gu, Junjie Wu, Guannan Liu, and Zhang Xiong. 2016b. Traffic speed prediction and congestion source exploration: A deep learning method. In Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 499--508.Google ScholarCross Ref
- Yequan Wang, Minlie Huang, Li Zhao, et almbox. 2016c. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 conference on empirical methods in natural language processing . 606--615.Google ScholarCross Ref
- Yilun Wang, Yu Zheng, and Yexiang Xue. 2014. Travel time estimation of a path using sparse trajectories. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 25--34.Google ScholarDigital Library
- Bin Yang, Chenjuan Guo, and Christian S Jensen. 2013. Travel cost inference from sparse, spatio temporally correlated time series using Markov models. Proceedings of the VLDB Endowment , Vol. 6, 9 (2013), 769--780.Google ScholarDigital Library
- Jing Yuan, Yu Zheng, Xing Xie, and Guangzhong Sun. 2013. T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence. IEEE Trans. Knowl. Data Eng. , Vol. 25, 1 (2013), 220--232.Google ScholarDigital Library
- Hanyuan Zhang, Hao Wu, Weiwei Sun, and Baihua Zheng. 2018. DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision. arXiv preprint arXiv:1802.02147 (2018).Google Scholar
- Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction.. In AAAI . 1655--1661.Google Scholar
- Jing Zhao, Jiajie Xu, Rui Zhou, Pengpeng Zhao, Chengfei Liu, and Feng Zhu. 2018. On Prediction of User Destination by Sub-Trajectory Understanding: A Deep Learning based Approach. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 1413--1422.Google ScholarDigital Library
Index Terms
- Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network
Recommendations
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter
SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of DataIn this paper, we study the problem of origin-destination (OD) travel time estimation where the OD input consists of an OD pair and a departure time. We propose a novel neural network based prediction model that fully exploits an important fact ...
Travel time estimation of a path using sparse trajectories
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningIn 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 ...
Encoding network-constrained travel trajectories using routing algorithms
This study proposes a generic encoder for network-constrained travel trajectories, and it implements two encoders by combining the proposed generic encoder with two routing algorithms, which reduce the size of a travel trajectory's path along a road ...
Comments