Passenger Demand Prediction with Cellular Footprints Jing Chu1, Kun Qian1, Xu Wang1, Lina Yao2, Fu Xiao3, Jianbo Li4, Xin Miao1 and Zheng Yang1 1School of Software, Tsinghua University 2School of Computer Science and Engineering, The University of New South Wales 3School of Computer Science, Nanjing University of Posts and Telecommunications 4College of Computer Science and Techonology, Qingdao University Presenter: Jing Chu

Motivation Imbalance Passenger Demand Prediction Passenger Waiting Time Driver Profit

Motivation Cellular Data Can get crowd related data compared with User Location Mobility Modelling Cellular Data Data Traffic Engineering User Portrait Research Can get crowd related data compared with the data from online car-hailing platform

Related Work Data sparsity problem Lack of crowd flow analysis Target to the number of pick-ups, ignoring potential passengers who eventually give up taking taxis Lack of crowd flow analysis Reflect potential passenger demand Improper handling of spatial relations Fixed grid region Fail to capture the complex spatial dependency Using deep learning model to predict citywide passenger demand with cellular data on flexible region partition accurately

Challenges Challenge 1 How to identify region characteristics? How to realize the reasonable region partition? Challenge 3 How to capture spatial and temporal correlation?

System Overview Database server module Pre-processing module Deep learning module Visualization and evaluation module

System Overview Database server module: Stores the big cellular data and provides retrival and aggregation services for fast preprocessing Pre-processing module Deep learning module Visualization and evaluation module

290 Billion Cellular Records Data Source Cellular data: Collected by a major cellular carrier of China Record: Unique anonymized user ID, create time, cell tower ID, App ID, URL Dec 5th, 2016-Feb 4th 2017 290 Billion Cellular Records 8000 Cell Towers 1.5 Million Covered Users Shenyang City, China Weather data: From Dark Sky API Weather States Temperature Wind Speed Visibility

System Overview Database server module Pre-processing module: Passenger information extraction and flexible region partition Deep learning module Visualization and evaluation module

Passenger Demand Extraction Intercepting analysis of network data packets: Using HTTP proxy tool to understand the meaning behind URLs of DiDi Chuxing Extract passenger demand from cellular data

Region Partition Partition the city by primary road network from OpenStreetMap Finer-grained partition: Partition the city by the main secondary roads if the passenger demand of any block is too high

Crowd Outflow Extraction Crowd outflow: The number of people leaving the region Outflow Start Other regions A region

System Overview Database server module Pre-processing module Deep learning module: Deep learning architecture FlowFlexDP to model and predict the passenger demand for each region Visualization and evaluation module

Deep Learning Architecture FlowFlexDP Predict the passenger demand for each region

Graph Convolutional Neural Network Model spatial dependency: Graph Convolutional Neural Network Spectral Graph Theory The normalized graph Laplacian Convolution theorem Polynomial filter: Consider k-order neighbors

GCNN with Residual Learning Capture the long-distance spatial dependency: Apply residual learning to GCNN Residual learning The residual unit of GCNN

Sequences Fusion Passenger demand = Hourly + Daily Closeness Periodicity Crowd Outflow Sequences Fusion Cross Correlation Coefficient Office Region Residential Region

Parametric-matrix Based Fusion External Factors Fusion Predicted Passenger Demand Weather Σ Regression Passenger Demand Crowd Outflow Weather & Time Metadata Parametric-matrix Based Fusion Time Metadata Holiday Snowy Day

System Overview Database server module Pre-processing module Deep learning module Visualization and evaluation module

Experimental Setting Training dataset The first 65% for training data, the second 10% for validation set and the rest 25% for testing Spatio-temporal sequence: Min-Max normalization to [0,1] External factors: One-hot coding for day-of-week, time-of-day, holidays, weather state; Min-Max normalization for the temperature, wind speed and visibility The length of hourly sequence: {3, 4, 5, 6, 7, 8} The length of daily sequence: {1, 2, 3, 4, 5, 6, 7, 8}

Evaluation Baselines HA: The Historical Average model ARIMA: The Autoregressive Integrated Moving Average SARIMA: The Seasonal Autoregressive Integrated Moving Average model VAR: Vector Auto-Regressive LSTM: Long-Short Term Memory is a Recurrent Neural Network architecture ANN: The Artificial Neural Network Metric: RMSE

Results Performance Evaluation ResGCNN: Combines GCNN and residual learning ResGCNN-D: Further adds the passenger demand daily sequence ResGCNN-DO: Further fuses the crowd outflow hourly and daily sequences FlowFlexDP: Our final model All variants achieve better performance than the baseline by at least 12.99%

Visualization Our prediction results are very close to the real state

Conclusion We demonstrate cellular data as a rich data source for passenger demand prediction, which has been largely overlooked and unexplored previously It is the first work that uses crowd flow information from cellular data for passenger demand prediction We propose a deep learning model FlowFlexDP, which uses GCNN and residual learning for predicting passenger demand on flexible region partition Evaluation results on a large-scale real data set show that our model outperforms existing models

Thanks Q&A