research-article

A Unified Framework with Multi-source Data for Predicting Passenger Demands of Ride Services

Authors:

Hua Wei,

Yong Zhang, and

Jie XuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 6

Article No.: 56, Pages 1 - 24

https://doi.org/10.1145/3355563

Published: 15 October 2019 Publication History

Get Access

Abstract

Ride-hailing applications have been offering convenient ride services for people in need. However, such applications still suffer from the issue of supply-demand disequilibrium, which is a typical problem for traditional taxi services. With effective predictions on passenger demands, we can alleviate the disequilibrium by pre-dispatching, dynamic pricing or avoiding dispatching cars to zero-demand areas. Existing studies of demand predictions mainly utilize limited data sources, trajectory data, or orders of ride services or both of them, which also lacks a multi-perspective consideration. In this article, we present a unified framework with a new combined model and a road-network-based spatial partition to leverage multi-source data and model the passenger demands from temporal, spatial, and zero-demand-area perspectives. In addition, our framework realizes offline training and online predicting, which can satisfy the real-time requirement more easily. We analyze and evaluate the performance of our combined model using the actual operational data from UCAR. The experimental results indicate that our model outperforms baselines on both Mean Absolute Error and Root Mean Square Error on average.

Supplementary Material

wang (wang.zip)

Supplemental movie and image files for, A Unified Framework with Multi-source Data for Predicting Passenger Demands of Ride Services

Download
1.52 MB

References

[1]

2016. Scalable and Flexible Gradient Boosting. Retrieved from https://xgboost.readthedocs.io/en/latest/.

Google Scholar

[2]

2017. BIRCH. Retrieved from http://scikit-learn.org/stable/modules/clustering.html.

Google Scholar

[3]

2017. Didi Chuxing. Retrieved from https://en.wikipedia.org/wiki/Didi_Chuxing.

Google Scholar

[4]

2017. Uber. Retrieved from https://en.wikipedia.org/wiki/Uber_(company).

Google Scholar

[5]

2017. UCAR. Retrieved from https://www.crunchbase.com/organization/ucar.

Google Scholar

[6]

G. E. P. Box and G. M. Jenkins. 1971. Time series analysis, forecasting and control [J]. Journal of the American Statistical Association 134, 3 (1971).

Google Scholar

[7]

Ye Ding, Siyuan Liu, Jiansu Pu, and Lionel M. Ni. 2013. HUNTS: A trajectory recommendation system for effective and efficient hunting of taxi passengers. In 14th International Conference on Mobile Data Management. 107--116.

Google Scholar

[8]

T. Hastie, J. Friedman, and R. Tibshirani. 2010. The elements of statistical learning [J]. Technometrics 45, 3 (2010), 267--268.

Google Scholar

[9]

Hector Gonzalez, Jiawei Han, Xiaolei Li, Margaret Myslinska, and John Paul Sondag. 2007. Adaptive fastest path computation on a road network: A traffic mining approach. In 33rd International Conference on Very Large Data Bases. 794--805.

Google Scholar

[10]

S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780.

Digital Library

Google Scholar

[11]

Yan Huang and Jason W. Powell. 2012. Detecting regions of disequilibrium in taxi services under uncertainty. In 20th International Conference on Advances in Geographic Information Systems. 139--148.

Google Scholar

[12]

Ren-Hung Hwang, Yu-Ling Hsueh, and Yu-Ting Chen. 2015. An effective taxi recommender system based on a spatio-temporal factor analysis model [J]. Information Sciences 314 (2015), 28--40.

Digital Library

Google Scholar

[13]

Wenbo Jiang, Tianyu Wo, Mingming Zhang, Renyu Yang, and Jie Xu. 2015. A method for private car transportation dispatching based on a passenger demand model. In 2nd International Conference on Internet of Vehicles - Safe and Intelligent Mobility.

Digital Library

Google Scholar

[14]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436.

Google Scholar

[15]

Bin Li, Daqing Zhang, Lin Sun, 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 2011 IEEE International Conference on Pervasive Computing and Communications Workshops. 63--68.

Crossref

Google Scholar

[16]

Xiaolong Li, Gang Pan, Zhaohui Wu, Guande Qi, Shijian Li, Daqing Zhang, Wangsheng Zhang, and Zonghui Wang. 2012. Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science 6, 1 (2012), 111--121.

Crossref

Google Scholar

[17]

Yangdong Liu, Ye Tian, Bo Yuan, Chang Wu, Weishuo Qian, and Wei Mao. 2014. Providing useful information for passengers based on TTF model. In 2014 IEEE International Conference on Internet of Things (iThings), and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom). 39--44.

Digital Library

Google Scholar

[18]

Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, and David S. Rosenblum. 2016. Urban water quality prediction based on multi-task multi-view learning. In 25th International Joint Conference on Artificial Intelligence. 2576--2581.

Google Scholar

[19]

Qi Luo, Junming Zhang, Zhihan Liu, and Jinglin Li. 2014. On discovering regional taxi service disequilibrium with geographical collaborative filtering. In International Conference on Informative and Cybernetics for Computational Social Systems. 51--56.

Google Scholar

[20]

Luis Moreira-Matias, João Gama, Michel Ferreira, and Luís Damas. 2012. A predictive model for the passenger demand on a taxi network. In 15th International IEEE Conference on Intelligent Transportation Systems. 1014--1019.

Crossref

Google Scholar

[21]

Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, and Luís Damas. 2012. Online predictive model for taxi services. In 11th international conference on Advances in Intelligent Data Analysis. 230--240.

Digital Library

Google Scholar

[22]

Luis Moreira-Matias, Joao Gama, Michel Ferreira, Joao Mendes-Moreira, and Luis Damas. 2013. On predicting the taxi-passenger demand: A real-time approach. In Portuguese Conference on Artificial Intelligence. 54--65.

Crossref

Google Scholar

[23]

Luis Moreira-Matias, Joao Gama, Michel Ferreira, João Mendes-Moreira, and Luis Damas. 2013. Predicting taxi--passenger demand using streaming data. IEEE Transactions on Intelligent Transportation Systems 14, 3 (2013), 1393--1402.

Digital Library

Google Scholar

[24]

Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, and Luis Damas. 2016. Time-evolving O-D matrix estimation using high-speed GPS data streams [J]. Expert Systems with Applications 44, C (2016), 275--288.

Google Scholar

[25]

Santi Phithakkitnukoon, Marco Veloso, Carlos Bento, Assaf Biderman, and Carlo Ratti. 2010. Taxi-aware map: Identifying and predicting vacant taxis in the city. In International Joint Conference on Ambient Intelligence (2010). 86--95.

Crossref

Google Scholar

[26]

Guande Qi, Gang Pan, Shijian Li, Zhaohui Wu, Daqing Zhang, Lin Sun, and Laurence Tianruo Yang. 2013. How long a passenger waits for a vacant taxi--large-scale taxi trace mining for smart cities. In IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. 1029--1036.

Digital Library

Google Scholar

[27]

Haochen Tang, Michael Kerber, Qixing Huang, and Leonidas Guibas. 2013. Locating lucrative passengers for taxicab drivers. In 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 504--507.

Digital Library

Google Scholar

[28]

Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological) 58, 1 (1996), 267--288.

Crossref

Google Scholar

[29]

Hua Wei, Yuandong Wang, Tianyu Wo, Yaxiao Liu, and Jie Xu. 2016. ZEST: A hybrid model on predicting passenger demand for chauffeured car service. In 25th ACM International on Conference on Information and Knowledge Management. 2203--2208.

Digital Library

Google Scholar

[30]

Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie, and Guangzhong Sun. 2011. Where to find my next passenger. In 13th International Conference on Ubiquitous Computing. 109--118.

Digital Library

Google Scholar

[31]

Nicholas Jing Yuan, Yu Zheng, and Xing Xie. 2012. Segmentation of Urban Areas Using Road Networks [J]. Microsoft Technical Report.

Google Scholar

[32]

Desheng Zhang, Tian He, Shan Lin, Sirajum Munir, and John Stankovic. 2014. Dmodel: Online taxicab demand model from big sensor data in a roving sensor network. In IEEE International Congress on Big Data. 152--159.

Digital Library

Google Scholar

[33]

Desheng Zhang, Tian He, Shan Lin, Sirajum Munir, and John A. Stankovic. 2015. Online cruising mile reduction in large-scale taxicab networks. IEEE Transactions on Parallel and Distributed Systems 26, 11 (2015), 3122--3135.

Digital Library

Google Scholar

[34]

Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In 31st AAAI Conference on Artificial Intelligence. 1655--1661.

Google Scholar

[35]

Kai Zhang, Zhiyong Feng, Shizhan Chen, Keman Huang, and Guiling Wang. 2016. A framework for passengers demand prediction and recommendation. In IEEE International Conference on Services Computing. 340--347.

Crossref

Google Scholar

[36]

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 IEEE International Conference on Big Data. 833--842.

Crossref

Google Scholar

[37]

Xudong Zheng, Xiao Liang, and Ke Xu. 2012. Where to wait for a taxi? In ACM SIGKDD International Workshop on Urban Computing. 149--156.

Digital Library

Google Scholar

[38]

Yu Zheng, Furui Liu, and Hsun Ping Hsieh. 2013. U-Air: When urban air quality inference meets big data. In 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1436--1444.

Digital Library

Google Scholar

[39]

Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li. 2015. Forecasting fine-grained air quality based on big data. In 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2267--2276.

Digital Library

Google Scholar

[40]

Qi Zhou, Junming Zhang, Jinglin Li, and Shangguang Wang. 2014. Discovering regional taxicab demand based on distribution modeling from trajectory data. In IEEE 17th International Conference on Computational Science and Engineering. 1605--1610.

Digital Library

Google Scholar

Cited By

View all

Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Sun XYe QHu HWang YHuang KWo TXu J(2023)Synthesizing Realistic Trajectory Data With Differential PrivacyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324129024:5(5502-5515)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3241290
Teusch JGremmel JKoetsier CJohora FSester MWoisetschläger DMüller J(2023)A Systematic Literature Review on Machine Learning in Shared MobilityIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33343934(870-899)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3334393
Show More Cited By

Index Terms

A Unified Framework with Multi-source Data for Predicting Passenger Demands of Ride Services
1. Information systems
  1. Information systems applications

Recommendations

Passenger Trip Planning using Ride-Sharing Services
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Ride-sharing can potentially address transportation challenges such as traffic congestion and air pollution by letting drivers share their cars unused capacity with a number of passengers. However, even though multiple ride-sharing services exist and ...
Read More
Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Predicting passenger pickup/dropoff demands based on historical mobility trips has been of great importance towards better vehicle distribution for the emerging mobility-on-demand (MOD) services. Prior works focused on predicting next-step passenger ...
Read More
Predicting the Dynamic Demand of Bike-Sharing System in Chicago with Divvy Operation Data: A Data-Driven approach for bike-sharing demand forecasting
ICEEG '21: Proceedings of the 5th International Conference on E-Commerce, E-Business and E-Government

During the recent decade, bike-sharing programs have increasingly become an irreplaceable part of the public transportation system. However, at the same time when people start to recognize its convenience, the ever-growing demand of the sharing bikes ...
Read More

Reviews

Reviewer: Amos O Olagunju

Ride-sharing service customers look for and deserve fair fares. However, with the use of the Internet to access competing fares when booking shared rides, how should ride-sharing providers forecast passenger demands in order to remain competitive Wang et al. offer a new framework for predicting ride-sharing demands originating from different data sources. The authors concisely review literature on fair prediction algorithms. The existing algorithms-ride-finding, passenger-finding, and global dispatching-are deficient because they (a) undersupply rides that satisfy the timely demands of passengers, and (b) oversupply scheduled rides that increase the delay times for drivers to pick up new passengers. Consequently, the authors present a framework for investigating "the bias between the trajectory data and the real ground truth." The authors offer unique contributions for effectively studying the ride-sharing patterns: (1) a parameter for differentiating ride time rates in alternative areas, and (2) a machine learning model, predicated on a variety of global positioning system (GPS) meteorological datasets, to effectively target fare-sharing riders and drivers based on big data analyses. Numerous experiments were performed with real-life datasets to ascertain the effectiveness of the proposed model. The experimental results compare favorably with the well-known results of statistical and machine learning algorithms in the literature. Even though the experimental dataset is limited to one region, there is no doubt that researchers can replicate the experiments given the continued challenges and research opportunities related to the Internet of Things (IoT).

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 6

December 2019

282 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3366748

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2019

Accepted: 01 August 2019

Revised: 01 June 2019

Received: 01 November 2017

Published in TKDD Volume 13, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Key R&D Program of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
490
Total Downloads

Downloads (Last 12 months)37
Downloads (Last 6 weeks)6

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Sun XYe QHu HWang YHuang KWo TXu J(2023)Synthesizing Realistic Trajectory Data With Differential PrivacyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324129024:5(5502-5515)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3241290
Teusch JGremmel JKoetsier CJohora FSester MWoisetschläger DMüller J(2023)A Systematic Literature Review on Machine Learning in Shared MobilityIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33343934(870-899)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3334393
Sun XWo T(2023)A Privacy-Preserving and Research-Utilizable Trajectory Generator via Deep Generative Approach2023 6th International Conference on Electronics Technology (ICET)10.1109/ICET58434.2023.10211675(1017-1021)Online publication date: 12-May-2023
https://doi.org/10.1109/ICET58434.2023.10211675
Papanastasiou EGiovanidis A(2023)Constrained expectation-maximisation for inference of social graphs explaining online user–user interactionsSocial Network Analysis and Mining10.1007/s13278-023-01037-413:1Online publication date: 2-Mar-2023
https://doi.org/10.1007/s13278-023-01037-4
Huang WSun GWang MZhao WYang J(2023)Influence Embedding from Incomplete Observations in Sina WeiboWeb Information Systems Engineering – WISE 202310.1007/978-981-99-7254-8_9(111-121)Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-99-7254-8_9
Liu CXiao ZWang DWang LJiang HChen HYu J(2022)Exploiting Spatiotemporal Correlations of Arrive-Stay-Leave Behaviors for Private Car Flow PredictionIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31373819:2(834-847)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TNSE.2021.3137381
Wang YYin HWu LChen TLiu C(2022)Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver PairsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3112739(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2021.3112739
Gao JLi XWang CHuang X(2022)BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.310624323:8(11666-11676)Online publication date: Aug-2022
https://doi.org/10.1109/TITS.2021.3106243
Pan XDang YWang HHong DLi YDeng H(2022)Resilience model and recovery strategy of transportation network based on travel OD-grid analysisReliability Engineering & System Safety10.1016/j.ress.2022.108483223(108483)Online publication date: Jul-2022
https://doi.org/10.1016/j.ress.2022.108483
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

Supplementary Material

References

Cited By

Index Terms

Recommendations

Passenger Trip Planning using Ride-Sharing Services

Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks

Predicting the Dynamic Demand of Bike-Sharing System in Chicago with Divvy Operation Data: A Data-Driven approach for bike-sharing demand forecasting

Reviews

Access critical reviews of Computing literature here

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

HTML Format

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations