skip to main content
10.1145/3286978.3286992acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobiquitousConference Proceedingsconference-collections
research-article

Dynamic Price Prediction in Ride-on-demand Service with Multi-source Urban Data

Published: 05 November 2018 Publication History

Abstract

Ride-on-demand (RoD) services such as Uber and Didi (in China) are becoming increasingly popular, and in these services dynamic price plays an important role in balancing the supply (i.e., the number of cars) and demand (i.e., the number of passenger requests) to benefit both drivers and passengers. However, the dynamic price also creates concerns for passengers: the "unpredictable" prices sometimes prevent them from making quick decisions at ease. One may wonder if it is possible to get a lower price if s/he chooses to wait a while. Giving passengers more information helps to tackle this concern, and predicting the prices is a possible solution.
In this paper we perform dynamic price prediction based on multi-source urban data. Price prediction helps passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is based on urban data from multiple sources, including the RoD service itself, taxi service, public transportation, weather, the map of a city, etc. The rationale behind using multi-source urban data is that the dynamic price in RoD may be influenced by different factors found in different data sources. We train a neural network to perform the prediction, and evaluate the prediction accuracy of using different combinations of multi-source urban data. Our results show that using multi-source urban data indeed helps improve the prediction accuracy, and different datasets may have varying influences on the dynamic prices.

References

[1]
Aimee Picchi. 2016. Uber vs. Taxi: Which Is Cheaper? Retrieved Jan 25, 2018 from http://bit.ly/2DMgrMc
[2]
AMap. 2017. API of AMap Service. http://bit.ly/2n8YRbZ
[3]
Omar Besbes and Assaf Zeevi. 2009. Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms. Operations Research 57, 6 (2009), 1407--1420.
[4]
Yong Cao, Thomas S. Gruca, and Bruce R. Klemz. 2003. Internet Pricing, Price Satisfaction, and Customer Satisfaction. International Journal of Electronic Commerce 8, 2 (2003), 31--50.
[5]
Chao Chen, Shuhai Jiao, Shu Zhang, Weichen Liu, Liang Feng, and Yasha Wang. 2018. TripImputor: Real-time Imputing Taxi Trip Purpose Leveraging Multi-sourced Urban Data. IEEE Transactions on Intelligent Transportation Systems 19, 10 (2018), 3292--3304.
[6]
Chao Chen, Daqing Zhang, Xiaojuan Ma, Bin Guo, Leye Wang, Yasha Wang, and Edwin Sha. 2017. CrowdDeliver: Planning Citywide Package Delivery Paths Leveraging the Crowds of Taxis. IEEE Transactions on Intelligent Transportation Systems 18, 6 (2017), 1478--1496.
[7]
Le Chen, Alan Mislove, and Christo Wilson. 2015. Peeking Beneath the Hood of Uber. In Proceedings of the 2015 ACM Conference on Internet Measurement Conference (IMC '15). ACM, New York, NY, USA, 495--508.
[8]
M. Keith Chen. 2016. Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform. In Proceedings of the 2016 ACM Conference on Economics and Computation (EC '16). ACM, New York, NY, USA, 455--455.
[9]
Peter Cohen, Robert Hahn, Jonathan Hall, Steven Levitt, and Robert Metcalfe. 2016. Using Big Data to Estimate Consumer Surplus: The Case of Uber. Retrieved Jan 25, 2018 from http://bit.ly/2pqXiWo
[10]
Jerome H. Friedman. 2001. Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics 29, 5 (2001), 1189--1232. http://www.jstor.org/stable/2699986
[11]
Suiming Guo, Chao Chen, Yaxiao Liu, Ke Xu, and Dah Ming Chiu. 2017. It Can be Cheaper: Using Price Prediction to Obtain Better Prices from Dynamic Pricing in Ride-on-demand Services. In Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '17). ACM, 1--10.
[12]
Suiming Guo, Chao Chen, Yaxiao Liu, Ke Xu, and Dah Ming Chiu. 2017. Modelling Passengers' Reaction to Dynamic Prices in Ride-on-demand Services: A Search for the Best Fare. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4 (2017), 136:1--136:23.
[13]
Suiming Guo, Chao Chen, Jingyuan Wang, Yaxiao Liu, Ke Xu, Daqing Zhang, and Dah Ming Chiu. 2018. A Simple but Quantifiable Approach to Dynamic Price Prediction in Ride-on-demand Services Leveraging Multi-source Urban Data. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3 (2018), 112:1--112:24.
[14]
Suiming Guo, Yaxiao Liu, Ke Xu, and Dah Ming Chiu. 2017. Understanding Passenger Reaction to Dynamic Prices in Ride-on-demand Service. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2017 IEEE International Conference on. IEEE, 42--45.
[15]
Suiming Guo, Yaxiao Liu, Ke Xu, and Dah Ming Chiu. 2017. Understanding Ride-on-demand Service: Demand and Dynamic Pricing. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2017 IEEE International Conference on. IEEE, 509--514.
[16]
Jonathan Hall, Cory Kendrick, and Chris Nosko. 2015. The effects of Uber's surge pricing: a case study. Retrieved Jan 25, 2018 from http://bit.ly/2kayk9O
[17]
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. 2014. Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (ADKDD'14). ACM, Article 5, 5:1--5:9 pages.
[18]
G. E. Hinton and R. R. Salakhutdinov. 2006. Reducing the Dimensionality of Data with Neural Networks. Science 313, 5786 (2006), 504--507.
[19]
Jacob Davidson. 2014. Uber Has Pretty Much Destroyed Regular Taxis in San Francisco. Retrieved Jan 25, 2018 from http://ti.me/1vegHWv
[20]
Michael L. Kasavana and A. J. Singh. 2001. Online Auctions. Journal of Hospitality & Leisure Marketing 9, 3--4 (2001), 127--140.
[21]
Bin Li, Daqing Zhang, Chao Chen, Shijian Li, Guande Qi, and Qiang Yang. 2011. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2011 IEEE International Conference on. IEEE, 63--68.
[22]
Xiaolong Li, Gang Pan, Zhaohui Wu, et al. 2012. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science 6, 1 (2012), 111--121.
[23]
Lloyd Alter. 2017. Is Uber Killing Transit? Retrieved Jan 25, 2018 from http://bit.ly/2DMf1S3
[24]
R Preston McAfee and Vera Te Velde. 2006. Dynamic pricing in the airline industry. forthcoming in Handbook on Economics and Information Systems, Ed: TJ Hendershott, Elsevier (2006). http://bit.ly/2ChavdL
[25]
H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, and Jeremy Kubica. 2013. Ad Click Prediction: A View from the Trenches. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '13). ACM, 1222--1230.
[26]
Shenzhou UCar. 2015. Annual results for the year ended 31 Dec 2015. http://bit.ly/2cFdL6U
[27]
Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. Deep-Transport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI '16). 2618--2624.
[28]
Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, and Jieping Ye. 2017. The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands on Large-Scale Online Platforms. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '17). ACM, 1653--1662.
[29]
Wikipedia. 2017. Symmetric mean absolute percentage error. http://bit.ly/2umuNKT
[30]
Kai Zhao, Denis Khryashchev, Juliana Freire, Claudio Silva, and Huy Vo. 2016. Predicting Taxi Demand at High Spatial Resolution: Approaching the Limit of Predictability. In Proceedings of the 2016 IEEE International Conference on Big Data (Big Data '16). 833--842.
[31]
Kai Zhao, Sasu Tarkoma, Siyuan Liu, and Huy Vo. 2016. Urban Human Mobility Data Mining: An Overview. In Proceedings of the 2016 IEEE International Conference on Big Data (Big Data '16). 1911--1920.

Cited By

View all
  • (2024)Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.340711911:18(29890-29910)Online publication date: 15-Sep-2024
  • (2023)Ridesharing Price Prediction: Exploring the strategies of Dynamic PricingHighlights in Business, Economics and Management10.54097/hbem.v10i.796310(106-110)Online publication date: 9-May-2023
  • (2022)RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing ServicesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517464(1-18)Online publication date: 29-Apr-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
November 2018
490 pages
ISBN:9781450360937
DOI:10.1145/3286978
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • EAI: The European Alliance for Innovation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Price prediction
  2. dynamic pricing
  3. multi-source urban data
  4. ride-on-demand service

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Conference

MobiQuitous '18
MobiQuitous '18: Computing, Networking and Services
November 5 - 7, 2018
NY, New York, USA

Acceptance Rates

Overall Acceptance Rate 26 of 87 submissions, 30%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)12
  • Downloads (Last 6 weeks)3
Reflects downloads up to 12 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.340711911:18(29890-29910)Online publication date: 15-Sep-2024
  • (2023)Ridesharing Price Prediction: Exploring the strategies of Dynamic PricingHighlights in Business, Economics and Management10.54097/hbem.v10i.796310(106-110)Online publication date: 9-May-2023
  • (2022)RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing ServicesProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517464(1-18)Online publication date: 29-Apr-2022
  • (2019)Fine-grained Dynamic Price Prediction in Ride-on-demand Services: Models and EvaluationsMobile Networks and Applications10.1007/s11036-019-01308-5Online publication date: 26-Jun-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media