Abstract
Discovering hidden knowledge patterns in trajectory data can help to hunt passengers for taxi drivers. And it is an important issue in the intelligent transportation domain. However, the existing approaches are inaccurate in real applications. Hence in this paper, by using the GPS trajectory big data of taxis, we innovatively present an efficient and effective recommendation system (TRec) for hunting passengers with deep neural structures. This proposed recommendation system is mainly based on the wide & deep model, which is trained wide linear frameworks and deep neural networks together and can simultaneously have the benefits of memorization and generalization to hunt passengers. Meanwhile, in order to improve the accuracy of hunt passengers, our proposed recommendation system uses experienced taxi drivers as learning objects, while considering the prediction of hunting passengers, the prediction of road condition and the evaluation of earnings simultaneously. A performance study using the real GPS trajectory dataset is conducted to evaluate our proposed recommendation system. The experimental evaluation shows that the proposed recommendation system is both efficient and effective. This work strides forward a first step toward building a recommendation system for hunting passengers based on the wide & deep model.
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References
Qin G, Li T, Yu B et al (2017) Mining factors affecting taxi drivers’ incomes using GPS trajectories. Transp Res Part C Emerg Technol 79:103–118
Leng B, Du H, Wang J et al (2016) Analysis of taxi drivers’ behaviors within a battle between two taxi apps. IEEE Trans Intell Transp Syst 17(1):296–300
Majumder N, Poria S, Gelbukh A et al (2017) Deep learning-based document modeling for personality detection from text. IEEE Intell Syst 32(2):74–79
Yuan S, Wu X, Xiang Y (2017) SNE: signed network embedding. In: The Pacific-Asia conference on knowledge discovery and data mining, 2017, pp 183–195
Wang Z, Li Z, Ding X et al (2016) Overlapping community detection based on node location analysis. Knowl-Based Syst 105:225–235
Yuan S, Wu X, Xiang Y (2018) Task-specific word identification from short texts using a convolutional neural network. Intell Data Anal 22(3):533–550
Zhang XY, Yin F, Zhang YM et al (2018) Drawing and recognizing chinese characters with recurrent neural network. IEEE Trans Pattern Anal Mach Intell 40(4):849–862
Cheng HT, Koc L, Harmsen J et al (2016) Wide & deep learning for recommender systems. In: The 1st workshop on deep learning for recommender systems, 2016, pp 7–10
Tao CC (2007) Dynamic taxi-sharing service using intelligent transportation system technologies. In International conference on wireless communications, networking and mobile computing, 2007, pp 3209–3212
Asadi B, Vahidi A (2011) Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Trans Control Syst Technol 19(3):707–714
Wennberg K, Wiklund J, DeTienne DR et al (2010) Reconceptualizing entrepreneurial exit: divergent exit routes and their drivers. J Bus Ventur 25(4):361–375
Cheng L, Tian K, Yao DD (2017) POSTER: detection of CPS program anomalies by enforcing cyber-physical execution semantics. In: ACM SIGSAC conference on computer and communications security, 2017, pp 2483–2485
Nguyen HH, Chan CW (2004) Multiple neural networks for a long term time series forecast. Neural Comput Appl 13(1):90–98
Yu J, Hong C, Rui Y et al (2018) Multitask autoencoder model for recovering human poses. IEEE Trans Ind Electron 65(6):5060–5068
Huang Z, Shijia E, Zhang J et al (2016) Pairwise learning to recommend with both users’ and items’ contextual information. IET Commun 10(16):2084–2090
Gisbrecht A, Schulz A, Hammer B (2015) Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing 147:71–82
Pan M, Jiang J, Kong Q et al (2017) Radar HRRP target recognition based on t-SNE segmentation and discriminant deep belief network. IEEE Geosci Remote Sens Lett 14(9):1609–1613
Zhang K, Zuo W, Chen Y et al (2017) Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142–3155
Fu X, Huang J, Lu H et al (2017) Top-k taxi recommendation in realtime social-aware ridesharing services. In: International Symposium on Spatial and Temporal Databases, 2017, pp 221–241
Dong H, Zhang X, Dong Y, et al (2014) Recommend a profitable cruising route for taxi drivers. In: 17th International IEEE conference on intelligent transportation systems, 2014, pp 2003–2008
Zhao K, Tarkoma S, Liu S et al (2016) Urban human mobility data mining: an overview. In: IEEE international conference on big data, 2016, pp 1911–1920
Wongsuphasawat K, Smilkov D, Wexler J et al (2018) Visualizing dataflow graphs of deep learning models in tensorflow. IEEE Trans Vis Comput Gr 24(1):1–12
Zeiler MD, Ranzato M, Monga R et al (2013) On rectified linear units for speech processing. In: IEEE international conference on acoustics, speech and signal processing, 2013, pp 3517–3521
Feng M, Xiang B, Zhou B (2016) Distributed deep learning for question answering. In: The 25th ACM international on conference on information and knowledge management, 2016, pp 2413–2416
Nakata K, Orihara R, Mizuoka Y et al (2017) A comprehensive big-data-based monitoring system for yield enhancement in semiconductor manufacturing. IEEE Trans Semicond Manuf 30(4):339–344
Anthimopoulos M, Christodoulidis S, Ebner L et al (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216
Vahidi A, Eskandarian A (2003) Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans Intell Transp Syst 4(3):143–153
Wang R, Chow CY, Lyu Y et al (2018) Taxirec: recommending road clusters to taxi drivers using ranking-based extreme learning machines. IEEE Trans Knowl Data Eng 30(3):585–598
Zhu QY, Qin AK, Suganthan PN et al (2005) Evolutionary extreme learning machine. Pattern Recognit 38(10):1759–1763
Wong RCP, Szeto WY, Wong SC (2014) A cell-based logit-opportunity taxi customer-search model. Transp Res Part C Emerg Technol 48:84–96
Yuan NJ, Zheng Y, Zhang L et al (2013) T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans Knowl Data Eng 25(10):2390–2403
Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, 2014, pp 25–34
Ding Y, Liu S, Pu J et al (2013) Hunts: a trajectory recommendation system for effective and efficient hunting of taxi passengers. In: 14th International conference on mobile data management, 2013, pp 107–116
Xu X, Zhou J, Liu Y et al (2015) Taxi-rs: taxi-hunting recommendation system based on taxi gps data. IEEE Trans Intell Transp Syst 16(4):1716–1727
Yin C, Lin Y, Yang C (2017) A classification and predication framework for taxi-hailing based on big data. In: International conference on intelligent computing, 2017, pp 747–758
Kong X, Xia F, Wang J et al (2017) Time-location-relationship combined service recommendation based on taxi trajectory data. IEEE Trans Ind Inf 13(3):1202–1212
De Brébisson A, Simon É, Auvolat A et al (2015) Artificial neural networks applied to taxi destination prediction. arXiv preprint arXiv:1508.00021
Zhang J, Zheng Y, Qi D et al (2016) DNN-based prediction model for spatio-temporal data. In: The 24th ACM SIGSPATIAL international conference on advances in geographic information systems, 2016, p 92
Ma X, Yu H, Wang Y et al (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE 10(3):e0119044
Zhao R, Wang D, Yan R et al (2018) Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Trans Ind Electrons 65(2):1539–1548
Wang Z, Li Z, Yuan G et al (2018) Tracking the evolution of overlapping communities in dynamic social networks. Knowl-Based Syst 157:81–97
Mohammadi M, Al-Fuqaha A, Guizani M et al (2018) Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet Things J 5(2):624–635
Jiang T, Gao S, Wang D et al (2017) A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders. IEEJ Trans Electr Electron Eng 12(1):105–115
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This work is supported by the National Natural Science Foundation of China (61772366) and the Natural Science Foundation of Shanghai (No. 17ZR1445900).
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Huang, Z., Shan, G., Cheng, J. et al. TRec: an efficient recommendation system for hunting passengers with deep neural networks. Neural Comput & Applic 31 (Suppl 1), 209–222 (2019). https://doi.org/10.1007/s00521-018-3728-2
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DOI: https://doi.org/10.1007/s00521-018-3728-2