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论文编号：	15685
作者编号：	2320234009
上传时间：	2025/12/9 11:21:46
中文题目：	J市T网约车平台运力协同管理与优化策略研究
英文题目：	Research on Collaborative Management and Optimization Strategy of Transportation Capacity on T Network Ride hailing Platform in J City
指导老师：	李勇建教授
中文关键字：	网约车平台；运力协同管理；资源配置；优化策略
英文关键字：	Online car Hailing platform; Capacity management; Resource allocation; Optimization strategy
中文摘要：	近年来，网约车作为“互联网+出行”的典型代表，已深刻改变城市出行方式与交通结构。随着平台注册司机数量和订单量的快速增长，行业进入规范化与精细化运营阶段。然而，随着监管趋严、市场竞争加剧以及平台盈利压力增大，如何在保障合规与安全的前提下，实现运力高效配置、成本控制和司机留存，成为行业和平台亟待解决的核心问题。J市网约车平台在渠道管理效率、激励机制有效性、不同运力模式协调以及司机黏性提升等方面仍存在明显挑战，这对平台的运营绩效和行业的高质量发展形成制约。 针对上述问题，本文以J市T网约车平台为研究对象，采用理论分析与实证分析相结合的方法，系统研究其运力协同管理现状、问题成因及优化策略。研究首先构建运力协同管理理论框架，结合资源优化配置理论、期望理论和渠道管理理论，分析平台运力结构、渠道协同、激励体系及司机行为特点；随后，通过数据收集与指标设计，对平台运力规模、订单完成率、空驶率、司机留存率及投诉率等关键指标进行量化分析，识别问题并诊断成因；最后，提出多渠道一体化引流、分层激励体系、灵活运力组合、动态调度及职业发展通道等优化方案，并通过试点应用对方案效果进行评估。结果显示，优化方案在提升运力效率、降低运营成本、增强司机黏性和提升服务质量等方面取得显著成效。 本文研究不仅丰富了网约车运力协同管理理论，尤其是在渠道协同、激励设计及灵活运力模式应用方面提供了新的实证依据，也为平台实践提供了可操作的优化路径。研究成果可为其他城市网约车平台在精细化运力协同管理、成本控制及司机激励体系设计方面提供参考，同时对行业高质量发展、智慧出行体系建设及城市交通优化具有重要实践价值。
英文摘要：	In recent years, as a typical representative of "Internet plus travel", online car hailing has profoundly changed the urban travel mode and traffic structure. With the rapid growth of the number of registered drivers and orders on the platform, the industry has entered a stage of standardized and refined operation. However, with stricter regulation, intensified market competition, and increased pressure on platform profitability, how to achieve efficient allocation of transportation capacity, cost control, and driver retention while ensuring compliance and safety has become a core issue that the industry and platforms urgently need to address. The J city ride hailing platform still faces significant challenges in terms of channel management efficiency, incentive mechanism effectiveness, coordination of different transportation modes, and driver stickiness improvement, which constrains the platform's operational performance and the high-quality development of the industry. In response to the above issues, this thesis takes the T ride hailing platform in J city as the research object, and adopts a combination of theoretical analysis and empirical analysis to systematically study the current situation, causes of problems, and optimization strategies of its transportation capacity collaborative management. The study first constructs a theoretical framework for collaborative management of transportation capacity, and combines resource optimization allocation theory, expectation theory, and channel management theory to analyze the platform's transportation capacity structure, channel collaboration, incentive system, and driver behavior characteristics; Subsequently, through data collection and indicator design, key indicators such as platform capacity scale, order completion rate, empty driving rate, driver retention rate, and complaint rate were quantitatively analyzed to identify problems and diagnose their causes; Finally, optimization schemes such as multi-channel integrated drainage, hierarchical incentive system, flexible capacity combination, dynamic scheduling, and career development channels are proposed, and the effectiveness of the schemes is evaluated through pilot applications. The results show that the optimization plan has achieved significant results in improving transportation efficiency, reducing operating costs, enhancing driver stickiness, and improving service quality. This study not only enriches the theory of collaborative management of ride hailing transportation capacity, but also provides new empirical evidence in channel collaboration, incentive design, and flexible transportation mode application. It also provides an operable optimization path for platform practice. The research results can provide reference for other city ride hailing platforms in terms of refined capacity coordination management, cost control, and driver incentive system design. At the same time, it has important practical value for the high-quality development of the industry, the construction of smart travel systems, and the optimization of urban transportation.
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