A Unified Framework for Predicting KPIs of On-Demand Transport Services

Having a better understanding of the key performance indicators (KPIs, e.g., demand and unmet demand) in the next time slot (e.g., next hour) is important for on-demand transport services, such as Uber and DiDi, to improve the service quality. In addition to the spatio-temporal dynamics, KPIs of on-demand transport services are also affected by many exogenous factors from different domains, e.g., the traffic condition from transportation domain and the weather condition from meteorology domain. Therefore, this paper proposes a unified framework to fuse the data collected from different domains to predict multiple KPIs for on-demand transport services. As demonstrated by the experiments, the proposed framework can capture both long-term regularity and short-term dynamics, thus achieving a better performance than the existing solutions in predicting KPIs.

[1]  Michel Ferreira,et al.  On Predicting the Taxi-Passenger Demand: A Real-Time Approach , 2013, EPIA.

[2]  Michal Maciejewski,et al.  Analysis of Berlin's taxi services by exploring GPS traces , 2015, 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[3]  Padhraic Smyth,et al.  Adaptive event detection with time-varying poisson processes , 2006, KDD '06.

[4]  Daniela Rus,et al.  ChangiNOW: A mobile application for efficient taxi allocation at airports , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

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

[6]  Zhaohui Wu,et al.  Prediction of urban human mobility using large-scale taxi traces and its applications , 2012, Frontiers of Computer Science.

[7]  Torbjörn Ekman,et al.  HMM-Based Decision Fusion in Wireless Sensor Networks With Noncoherent Multiple Access , 2015, IEEE Communications Letters.

[8]  Yan Huang,et al.  Detecting regions of disequilibrium in taxi services under uncertainty , 2012, SIGSPATIAL/GIS.

[9]  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).

[10]  João Gama,et al.  A predictive model for the passenger demand on a taxi network , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[11]  Jie Xu,et al.  A Method for Private Car Transportation Dispatching Based on a Passenger Demand Model , 2015, IOV.

[12]  Kian Hsiang Low,et al.  Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems , 2015, IEEE Transactions on Automation Science and Engineering.

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

[14]  Wei Wu,et al.  Estimating Taxi Demand-Supply Level Using Taxi Trajectory Data Stream , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[15]  Yong Yu,et al.  Inferring gas consumption and pollution emission of vehicles throughout a city , 2014, KDD.

[16]  Wei Wu,et al.  Taxi Queue, Passenger Queue or No Queue? - A Queue Detection and Analysis System using Taxi State Transition , 2015, EDBT.

[17]  Naoto Mukai,et al.  Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network , 2012, IIMSS.

[18]  Jane Yung-jen Hsu,et al.  Context-aware taxi demand hotspots prediction , 2010, Int. J. Bus. Intell. Data Min..

[19]  Kai Zhang,et al.  A Framework for Passengers Demand Prediction and Recommendation , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[20]  Pierluigi Salvo Rossi,et al.  Distributed detection of a non-cooperative target via generalized locally-optimum approaches , 2016, Inf. Fusion.

[21]  Shih-Fen Cheng,et al.  Density Peaks Clustering Approach for Discovering Demand Hot Spots in City-scale Taxi Fleet Dataset , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[22]  Gyung-Leen Park,et al.  Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[23]  Markus Lienkamp,et al.  Analyzing and Modeling a City’s Spatiotemporal Taxi Supply and Demand: A Case Study for Munich , 2016 .