Next Article in Journal
Multi-Lidar System Localization and Mapping with Online Calibration
Next Article in Special Issue
Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models
Previous Article in Journal
A Multi-Story Expandable Systematic Hierarchical Construction Information Classification System for Implementing Information Processing in Highway Construction
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 18
10.3390/app131810192
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation

by
Ka Seng Chou
1,2,*,
Kei Long Wong
1,2,
Boliang Zhang
1,
Davide Aguiari
3,
Sio Kei Im
4,
Chan Tong Lam
1,
Rita Tse
1,
Su-Kit Tang
1 and
Giovanni Pau
1,2,3,5
1
Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
2
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
3
Autonomous Robotics Research Center, Technology Innovation Institute (TII), Abu Dhabi P.O. Box 9639, United Arab Emirates
4
Macao Polytechnic University, Macao SAR 999078, China
5
Samueli Computer Science Department, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(18), 10192; https://doi.org/10.3390/app131810192
Submission received: 23 August 2023 / Revised: 6 September 2023 / Accepted: 8 September 2023 / Published: 11 September 2023
(This article belongs to the Special Issue Revolutionary Innovation in Artificial Intelligence: Modern Application and Its Impact)
Browse Figures
Versions Notes

Abstract

An essential part of a city’s transportation infrastructure, taxis allow for regular encounters between drivers and customers. Nevertheless, there are issues with efficiency since there is an imbalance in the supply and demand for taxis. This study describes the creation of a platform that serves both customers and taxi drivers by offering immediate forecasts of demand and fare. Root mean squared error (RMSE) of 3.31 and a negative log-likelihood of −3.84, the long short-term memory recurrent neural network (LSTM-RNN) with the mixture density network (MDN) is employed to forecast taxi demand. The best RMSE of 3.24 is obtained for fare prediction via an ensemble learning model that integrates linear regression (LR), ridge regression (RR), and multilayer perceptron (MLP). To ensure peak performance, the models are systematically created, implemented, trained, and improved. By integrating these models into a web application interface, the taxi service system offers a better overall user experience, which improves urban mobility.
Keywords: taxi demand; taxi fare; LSTM-MDN; ensemble learning; intelligent transportation taxi demand; taxi fare; LSTM-MDN; ensemble learning; intelligent transportation

Share and Cite

MDPI and ACS Style

Chou, K.S.; Wong, K.L.; Zhang, B.; Aguiari, D.; Im, S.K.; Lam, C.T.; Tse, R.; Tang, S.-K.; Pau, G. Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation. Appl. Sci. 2023, 13, 10192. https://doi.org/10.3390/app131810192

AMA Style

Chou KS, Wong KL, Zhang B, Aguiari D, Im SK, Lam CT, Tse R, Tang S-K, Pau G. Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation. Applied Sciences. 2023; 13(18):10192. https://doi.org/10.3390/app131810192

Chicago/Turabian Style

Chou, Ka Seng, Kei Long Wong, Boliang Zhang, Davide Aguiari, Sio Kei Im, Chan Tong Lam, Rita Tse, Su-Kit Tang, and Giovanni Pau. 2023. "Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation" Applied Sciences 13, no. 18: 10192. https://doi.org/10.3390/app131810192

APA Style

Chou, K. S., Wong, K. L., Zhang, B., Aguiari, D., Im, S. K., Lam, C. T., Tse, R., Tang, S.-K., & Pau, G. (2023). Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation. Applied Sciences, 13(18), 10192. https://doi.org/10.3390/app131810192

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop