Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach

This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are g...

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Main Author: JI, Mengyu
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/etd_coll/512
https://ink.library.smu.edu.sg/context/etd_coll/article/1510/viewcontent/GPIS_AY2017_PhD_MengyuJI.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.etd_coll-15102023-10-03T06:12:16Z Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach JI, Mengyu This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are gained into drivers’ choices regarding optimal routes, timing, and areas with high demand. Integrating real-time information sources, such as GPS data and passenger updates, allows drivers to adapt their strategies dynamically to changing traffic conditions and emerging demand patterns. Predictive analytics models, includ- ing ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and operational effi- ciency. The incorporation of decision support systems, integrating predicted passenger flow with a Markov Decision Process (MDP) model, provides intelli- gent recommendations for resource allocation and performance optimization. Behavioral analysis is conducted to understand driver preferences, influencing the design of incentive mechanisms that motivate desirable behaviors. Continu- ous learning and adaptation through iterative population learning techniques ensure responsiveness to evolving passenger preferences and market dynamics. By implementing these data-driven strategies, taxi drivers can make informed decisions, optimize their performance, and provide enhanced services in the dynamic transportation industry. 2023-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/etd_coll/512 https://ink.library.smu.edu.sg/context/etd_coll/article/1510/viewcontent/GPIS_AY2017_PhD_MengyuJI.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Dissertations and Theses Collection (Open Access) eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
JI, Mengyu
Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
description This thesis focuses on analyzing the decision-making process of taxi drivers and providing data-driven strategies to enhance their performance. By examin- ing comprehensive historical data encompassing passenger demand patterns, drivers’ spatial dynamics, and fare structures, valuable insights are gained into drivers’ choices regarding optimal routes, timing, and areas with high demand. Integrating real-time information sources, such as GPS data and passenger updates, allows drivers to adapt their strategies dynamically to changing traffic conditions and emerging demand patterns. Predictive analytics models, includ- ing ARIMA, XGBoost, and Linear Regression, are utilized to forecast demand flow at key locations, enabling proactive decision-making and operational effi- ciency. The incorporation of decision support systems, integrating predicted passenger flow with a Markov Decision Process (MDP) model, provides intelli- gent recommendations for resource allocation and performance optimization. Behavioral analysis is conducted to understand driver preferences, influencing the design of incentive mechanisms that motivate desirable behaviors. Continu- ous learning and adaptation through iterative population learning techniques ensure responsiveness to evolving passenger preferences and market dynamics. By implementing these data-driven strategies, taxi drivers can make informed decisions, optimize their performance, and provide enhanced services in the dynamic transportation industry.
format text
author JI, Mengyu
author_facet JI, Mengyu
author_sort JI, Mengyu
title Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
title_short Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
title_full Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
title_fullStr Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
title_full_unstemmed Analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: A data-driven approach
title_sort analyzing taxi drivers’ decision-making and recommending strategies for enhanced performance: a data-driven approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/etd_coll/512
https://ink.library.smu.edu.sg/context/etd_coll/article/1510/viewcontent/GPIS_AY2017_PhD_MengyuJI.pdf
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