Predicting real-time surge pricing of ride-sourcing companies

Abstract Ride-sourcing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. The prediction of surge prices in the next few minutes to hours encapsulates the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the efficiency and reliability of transportation networks. This paper explores the spatio-temporal correlations between the urban environment, traffic flow characteristics, and surge multipliers. We propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a log-linear model with L 1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in Pittsburgh up to two hours in advance using data from the previous hour out-performing the overall mean and the historical average in all but 3 of the 49 locations in Pittsburgh and outperforming three non-linear methods in 28 of the 49 locations. The model is able to out-perform the overall mean, historical mean, and non-linear methods on Lyft surge multipliers in Pittsburgh up to 20 min in advance. Cross-correlation of Uber and Lyft surge multipliers is also explored.

[1]  M. Burris,et al.  Impact of Variable Pricing on Temporal Distribution of Travel Demand , 2001 .

[2]  M. Keith Chen,et al.  Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform , 2016, EC.

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

[4]  Ghim Ping Ong,et al.  Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data , 2018, Transportation Research Part C: Emerging Technologies.

[5]  Pinchao Zhang,et al.  User-centric interdependent urban systems: Using time-of-day electricity usage data to predict morning roadway congestion , 2017, Transportation Research Part C: Emerging Technologies.

[6]  Chung-Cheng Lu,et al.  A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction , 2011 .

[7]  Hai Yang,et al.  Economic Analysis of Ride-sourcing Markets , 2016 .

[8]  Xidong Pi,et al.  A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources , 2019, Transportation Research Part C: Emerging Technologies.

[9]  Zuo-Jun Max Shen,et al.  Modeling taxi services with smartphone-based e-hailing applications , 2015 .

[10]  Dennis L. Sun,et al.  Exact post-selection inference, with application to the lasso , 2013, 1311.6238.

[11]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[12]  Laura A. Dabbish,et al.  Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers , 2015, CHI.

[13]  Shuguan Yang,et al.  Understanding and Predicting Travel Time with Spatio-Temporal Features of Network Traffic Flow, Weather and Incidents , 2019, IEEE Intelligent Transportation Systems Magazine.

[14]  Peyman Noursalehi,et al.  Real time transit demand prediction capturing station interactions and impact of special events , 2018, Transportation Research Part C: Emerging Technologies.

[15]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[16]  Yang Li,et al.  Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks ☆ , 2017 .

[17]  R. Tibshirani,et al.  A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.

[18]  Mee Young Park,et al.  L1‐regularization path algorithm for generalized linear models , 2007 .

[19]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[20]  Emilio Frazzoli,et al.  On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment , 2017, Proceedings of the National Academy of Sciences.

[21]  Mu-Chen Chen,et al.  Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks , 2012 .

[22]  Christo Wilson,et al.  Peeking Beneath the Hood of Uber , 2015, Internet Measurement Conference.

[23]  Wanli Min,et al.  Real-time road traffic prediction with spatio-temporal correlations , 2011 .

[24]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[25]  Zhixi Wan,et al.  Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity , 2019, Transportation Research Part B: Methodological.

[26]  Hai Yang,et al.  Pricing strategies for a taxi-hailing platform , 2016 .

[27]  Kostas Bimpikis,et al.  Spatial Pricing in Ride-Sharing Networks , 2019, Oper. Res..

[28]  Steven D. Levitt,et al.  Using Big Data to Estimate Consumer Surplus: The Case of Uber , 2016 .

[29]  Hai Yang,et al.  Equilibrium properties of taxi markets with search frictions , 2011 .

[30]  Yafeng Yin,et al.  Geometric matching and spatial pricing in ride-sourcing markets , 2018, Transportation Research Part C: Emerging Technologies.

[31]  Alex Rosenblat,et al.  Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers , 2016 .

[32]  L. Vanajakshi,et al.  A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed , 2004, IEEE Intelligent Vehicles Symposium, 2004.