Passenger Demand Prediction with Cellular Footprints

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this paper, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of DiDi Chuxing from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

[1]  Yunhao Liu,et al.  Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis , 2019, IEEE Transactions on Mobile Computing.

[2]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[3]  Weiwei Sun,et al.  CLSTERS: A General System for Reducing Errors of Trajectories Under Challenging Localization Situations , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[4]  Depeng Jin,et al.  Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment , 2015, Internet Measurement Conference.

[5]  Xiqun Chen,et al.  Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach , 2017, ArXiv.

[6]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[7]  Xiqun Chen,et al.  Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach , 2017 .

[8]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[9]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[10]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[11]  Kai Zhao,et al.  Predicting taxi demand at high spatial resolution: Approaching the limit of predictability , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Mo Li,et al.  How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing , 2012, IEEE Transactions on Mobile Computing.

[14]  Shing Chung Josh Wong,et al.  Equlibrium of Bilateral Taxi-Customer Searching and Meeting on Networks , 2010 .

[15]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[16]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[17]  Yunhao Liu,et al.  Peer-to-Peer Indoor Navigation Using Smartphones , 2017, IEEE Journal on Selected Areas in Communications.

[18]  Laura Ferrari,et al.  Urban Sensing Using Mobile Phone Network Data: A Survey of Research , 2014, ACM Comput. Surv..

[19]  Fan Zhang,et al.  Exploring human mobility with multi-source data at extremely large metropolitan scales , 2014, MobiCom.

[20]  Lin Zhang,et al.  Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting , 2017 .

[21]  Sirajum Munir,et al.  Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network , 2014, 2014 IEEE International Congress on Big Data.

[22]  Xiuwen Yi,et al.  DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.

[23]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[24]  Amedeo R. Odoni,et al.  Inferring Unmet Demand from Taxi Probe Data , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[25]  Rade Stanojevic,et al.  From Cells to Streets: Estimating Mobile Paths with Cellular-Side Data , 2014, CoNEXT.

[26]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[27]  Victor C. S. Lee,et al.  TaxiRec: Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines , 2018, IEEE Trans. Knowl. Data Eng..

[28]  Yunhao Liu,et al.  Incentives for Mobile Crowd Sensing: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[29]  João Gama,et al.  Predicting Taxi–Passenger Demand Using Streaming Data , 2013, IEEE Transactions on Intelligent Transportation Systems.

[30]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[31]  Jie Xu,et al.  ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service , 2016, CIKM.

[32]  Albert-László Barabási,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[33]  Chenshu Wu,et al.  Automatic Radio Map Adaptation for Indoor Localization Using Smartphones , 2018, IEEE Transactions on Mobile Computing.

[34]  Carlos Sarraute,et al.  A study of age and gender seen through mobile phone usage patterns in Mexico , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[35]  Yunhao Liu,et al.  Smartphones Based Crowdsourcing for Indoor Localization , 2015, IEEE Transactions on Mobile Computing.

[36]  Victor C. S. Lee,et al.  TaxiRec: Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines , 2015, IEEE Transactions on Knowledge and Data Engineering.

[37]  Wei Cao,et al.  DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).