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

Authors:

Hua Wei,

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

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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.

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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
Huang LLi PGao QLiu GLuo ZLi T(2024)Diffusion probabilistic model for bike-sharing demand recovery with factual knowledge fusionNeural Networks10.1016/j.neunet.2024.106538179(106538)Online publication date: Nov-2024
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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
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Index Terms

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

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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).

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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]

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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

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https://dl.acm.org/doi/10.1145/3649141
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Recommendations

ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service

Passenger Trip Planning using Ride-Sharing Services

Predicting ride-hailing passenger demand: A POI-based adaptive clustering federated learning approach

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