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Multi-stage deep probabilistic prediction for travel demand

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Abstract

Accurate demand prediction is an essential component of any decision support system for smart vehicle dispatching. However, predicting real time demand at the micro-geographical level is a challenging task due to its underlying complexity, and prior work has focused largely on predicting one-step-ahead demand at a macro-geographical level, often using classical times series models that do not consider exogenous factors or quantify the prediction uncertainty in a probabilistic setting. In this paper, we propose an end-to-end deep learning-based framework with a novel architecture to predict multi-step-ahead real time travel demand, along with uncertainty estimation. The model is multi-stage and captures both spatial and temporal aspects. We employ the encoder-decoder framework with variations of convolution and LSTM units, and incorporate an attention mechanism into the model to quantify the interdependence between the input and output elements. We illustrate our model using two real data sets: a taxi and a ride-sharing service (Uber) for the city of New York.

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References

  1. Ezeh A, Oyebode O, Satterthwaite D, Chen YF, Ndugwa R, Sartori J, Mberu B, Melendez-Torres G, Haregu T, Watson SI et al (2017) The history, geography, and sociology of slums and the health problems of people who live in slums. The Lancet 389(10068):547

    Article  Google Scholar 

  2. Pant P, Harrison RM (2013) Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: a review. Atmospheric Environment 77:78

    Article  Google Scholar 

  3. Yuan NJ, Zheng Y, Zhang L, Xie X (2012) T-finder: A recommender system for finding passengers and vacant taxis. IEEE Transactions on Knowledge and Data Engineering 25(10):2390

    Article  Google Scholar 

  4. Seow KT, Dang NH, Lee DH (2009) A collaborative multiagent taxi-dispatch system. IEEE Transactions on Automation Science and Engineering 7(3):607

    Article  Google Scholar 

  5. Niu K, Wang C, Zhou X, Zhou T (2019) Predicting Ride-Hailing Service Demand via RPA-LSTM. IEEE Transactions on Vehicular Technology 68(5):4213

    Article  Google Scholar 

  6. Wang D, Yang Y, Ning S (2018) DeepSTCL: A deep spatio-temporal ConvLSTM for travel demand prediction. In: 2018 International joint conference on neural networks (IJCNN). IEEE, pp 1–8

  7. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  8. Wang S, Cao J, Yu P (2020) Deep learning for spatio-temporal data mining: A survey. IEEE Trans Knowl Data Eng

  9. Kontou E, Garikapati V, Hou Y (2020) Reducing ridesourcing empty vehicle travel with future travel demand prediction. Transportation Research Part C: Emerging Technologies 121:102826

    Article  Google Scholar 

  10. Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2020) A comprehensive survey on traffic prediction. arXiv:2004.08555

  11. Chen L, Thakuriah PV, Ampountolas K (2021) Short-term prediction of demand for ride-hailing services: A deep learning approach. J Big Data Anal Transp:1–21

  12. Guo G, Zhang T (2020) A residual spatio-temporal architecture for travel demand forecasting. Transportation Research Part C: Emerging Technologies 115:102639

    Article  Google Scholar 

  13. Zhang C, Zhu F, Lv Y, Ye P, Wang FY (2021) MLRNN: Taxi demand prediction based on multi-level deep learning and regional heterogeneity analysis. IEEE Trans Intell Transp Syst

  14. Jin G, Cui Y, Zeng L, Tang H, Feng Y, Huang J (2020) Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network. Transportation Research Part C: Emerging Technologies 117:102665

    Article  Google Scholar 

  15. Ke J, Feng S, Zhu Z, Yang H, Ye J (2021) Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach. Transportation Research Part C: Emerging Technologies 127:103063

    Article  Google Scholar 

  16. Zou X, Zhang S, Zhang C, James J, Chung E (2021) Long-term origin-destination demand prediction with graph deep learning. IEEE Trans Big Data

  17. Zhang Y, Yang Y, Zhou W, Wang H, Ouyang X (2021) Multi-city traffic flow forecasting via multi-task learning. Appl Intell:1–19

  18. Mohler GO, Short MB, Brantingham PJ, Schoenberg FP, Tita GE (2011) Self-exciting point process modeling of crime. Journal of the American Statistical Association 106(493):100

    Article  MathSciNet  Google Scholar 

  19. Chan W, Jaitly N, Le Q, Vinyals O (2016) Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. In: 2016 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 4960–4964

  20. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  21. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K et al (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144

  22. Prabhavalkar R, Rao K, Sainath TN, Li B, Johnson L, Jaitly N (2017) A comparison of sequence-to-sequence models for speech recognition. In: Interspeech, pp 939–943

  23. Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059

  24. Box GE, Pierce DA (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association 65(332):1509

    Article  MathSciNet  Google Scholar 

  25. Moghimi B, Safikhani A, Kamga C, Hao W, Ma J (2018) Short-term prediction of signal cycle on an arterial with actuated-uncoordinated control using sparse time series models. IEEE Transactions on Intelligent Transportation Systems 20(8):2976

    Article  Google Scholar 

  26. Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2013) Predicting taxi-passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems 14(3):1393

    Article  Google Scholar 

  27. Cohen B, Muñoz P (2016) Sharing cities and sustainable consumption and production: towards an integrated framework. Journal of cleaner production 134:87

    Article  Google Scholar 

  28. Tong Y, Chen Y, Zhou Z, Chen L, Wang J, Yang Q, Ye J, Lv W (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, pp 1653–1662

  29. Deng D, Shahabi C, Demiryurek U, Zhu L, Yu R, Liu Y (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, pp 1525–1534

  30. Yan X, Liu X, Zhao X (2020) Using machine learning for direct demand modeling of ridesourcing services in Chicago. Journal of Transport Geography 83:102661

    Article  Google Scholar 

  31. Liu Z, Chen H, Li Y, Zhang Q (2020) Taxi demand prediction based on a combination forecasting model in hotspots. J Adv Transp:2020

  32. Hess A, Spinler S, Winkenbach M (2021) Real-time demand forecasting for an urban delivery platform. Transportation Research Part E: Logistics and Transportation Review 145:102147

    Article  Google Scholar 

  33. Cocca M, Teixeira D, Vassio L, Mellia M, Almeida JM, Couto da Silva AP (2020) On car-sharing usage prediction with open socio-demographic data. Electronics 9(1):72

    Article  Google Scholar 

  34. Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109

    Article  Google Scholar 

  35. Qian X, Ukkusuri SV, Yang C, Yan F (2020) Short-Term Demand Forecasting for on-Demand Mobility Service. IEEE Trans Intell Transp Syst

  36. Petrović A, Nikolić M, Jovanović M, Delibašić B (2019) Gaussian conditional random fields for classification. arXiv:1902.00045

  37. Xu J, Rahmatizadeh R, Bölöni L, Turgut D (2017) Real-time prediction of taxi demand using recurrent neural networks. IEEE Transactions on Intelligent Transportation Systems 19(8):2572

    Article  Google Scholar 

  38. Ke J, Zheng H, Yang H, Chen XM (2017) Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies 85:591

    Article  Google Scholar 

  39. Guo G, Yuan W (2020) Short-term traffic speed forecasting based on graph attention temporal convolutional networks. Neurocomputing 410:387

    Article  Google Scholar 

  40. Gan Z, Li C, Zhou J, Tang G (2021) Temporal convolutional networks interval prediction model for wind speed forecasting. Electric Power Systems Research 191:106865

    Article  Google Scholar 

  41. Zhao F, Wang W, Sun H, Yang H, Wu J (2021) Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network. Transportmetrica B: Transport Dynamics:1–19

  42. Nejadettehad A, Mahini H, Bahrak B (2020) Short-term demand forecasting for online car-hailing services using recurrent neural networks. Applied Artificial Intelligence 34(9):674

    Article  Google Scholar 

  43. Tang J, Liang J, Liu F, Hao J, Wang Y (2021) Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network. Transportation Research Part C: Emerging Technologies 124:102951

    Article  Google Scholar 

  44. Irie K, Prabhavalkar R, Kannan A, Bruguier A, Rybach D, Nguyen P (2019) Model unit exploration for sequence-to-sequence speech recognition. arXiv:1902.01955

  45. Badrinarayanan V, Kendall A (2017) Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(12):2481

    Article  Google Scholar 

  46. Mao X, Shen C, Yang YB, (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Advances in neural information processing systems, pp 2802–2810

  47. Paisley J, Blei D, Jordan M (2012) Variational Bayesian inference with stochastic search. arXiv:1206.6430

  48. Hernández-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International conference on machine learning, pp 1861–1869

  49. Gude V, Corns S, Long S (2020) Flood prediction and uncertainty estimation using deep learning. Water 12(3):884

    Article  Google Scholar 

  50. Liu Y, Qin H, Zhang Z, Pei S, Jiang Z, Feng Z, Zhou J (2020) Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model. Applied Energy 260:114259

    Article  Google Scholar 

  51. Dusenberry M, Jerfel G, Wen Y, Ma Y, Snoek J, Heller K, Lakshminarayanan B, Tran D (2020) Efficient and scalable bayesian neural nets with rank-1 factors. In: International conference on machine learning. PMLR, pp 2782–2792

  52. Li G, Yang L, Lee CG, Wang X, Rong M (2020) A Bayesian deep learning RUL framework integrating epistemic and aleatoric uncertainties. IEEE Trans Ind Electron

  53. Williams MR, Savitsky TD (2021) Uncertainty estimation for pseudo-bayesian inference under complex sampling. International Statistical Review 89(1):72

    Article  MathSciNet  Google Scholar 

  54. Srivastava N (2013) Improving neural networks with dropout. University of Toronto 182(566):7

    Google Scholar 

  55. Zhu L, Laptev N (2017) Deep and confident prediction for time series at uber. In: 2017 IEEE International conference on data mining workshops (ICDMW). IEEE, pp 103–110

  56. Papernot N, McDaniel P (2018) Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning. arXiv:1803.04765

  57. Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA (2019) Deep learning for time series classification: A review. Data Mining and Knowledge Discovery 33(4):917

    Article  MathSciNet  Google Scholar 

  58. Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:1408.5882

  59. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  60. Yang J, Nguyen MN, San PP, Li XL, Krishnaswamy S (2015) Deep convolutional neural networks on multichannel time series for human activity recognition. In: Twenty-fourth international joint conference on artificial intelligence

  61. Chen G (2016) A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation. arxiv: 1610.02583

  62. Sherstinsky A (2018) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. arxiv:1808.03314

  63. Miller J, Hardt M (2018) When recurrent models don’t need to be recurrent. arxiv:1805.10369

  64. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735

    Article  Google Scholar 

  65. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling

  66. Neubig G (2017) Neural machine translation and sequence-to-sequence models: A tutorial. arxiv:1703.01619

  67. Puheim M, Madarász L (2014) Normalization of inputs and outputs of neural network based robotic arm controller in role of inverse kinematic model. In: 2014 IEEE 12th International symposium on applied machine intelligence and informatics (SAMI), pp 85–89

  68. Hao S, Lee DH, Zhao D (2019) Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transportation Research Part C: Emerging Technologies 107:287

    Article  Google Scholar 

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Authors and Affiliations

  1. Industrial Engineering Department, University of Pittsburgh, 15203, Pittsburgh, PA, USA

    Dhaifallah Alghamdi, Kamal Basulaiman & Jayant Rajgopal

  2. Department of Industrial Engineering, College of Engineering, King Saud University, P.O.Box 800, Riyadh, 11421, Saudi Arabia

    Kamal Basulaiman

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  1. Dhaifallah Alghamdi
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Correspondence to Dhaifallah Alghamdi.

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Alghamdi, D., Basulaiman, K. & Rajgopal, J. Multi-stage deep probabilistic prediction for travel demand. Appl Intell 52, 11214–11231 (2022). https://doi.org/10.1007/s10489-021-03047-1

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