基于兴趣点监测的顺风感知网约车市场在不确定出行需求下的鲁棒识别
Robust identification of drive-by sensing ride-hailing market with Points of Interest monitoring under uncertain ride demand
- Southwest Jiaotong University（西南交通大学）
- Lanzhou Jiaotong University（兰州交通大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对网约车感知中被动优先策略的低效，提出基于分布鲁棒优化的主动感知框架，结合增强A*路由与车辆-任务匹配，在不确定需求下提升感知覆盖率、降低成本并增强鲁棒性。
AI中文摘要：
基于出租车的移动感知已成为大规模城市环境监测的一种经济高效的范式。在实践中，网约车平台采用被动和主动两种感知策略。被动感知在载客行程中进行，受随机且空间不平衡的出行需求限制，导致覆盖范围有限且不均匀。主动感知由空驶出租车执行，对感知操作具有更强的控制力，但会产生额外的运营成本，因此通常被视为补充策略。为解决传统“被动优先、主动其次”范式的低效问题（该范式可能延迟关键兴趣点（POI）的监测并增加系统范围成本），我们提出了一种基于分布鲁棒优化（DRO）的主动移动感知框架。首先，我们开发了一种增强的基于A*的顺风感知路由策略，该策略整合了车辆-任务匹配，同时捕捉全局路由效率和局部感知机会。其次，我们将主动感知问题建模为DRO模型，该模型通过概率分布的模糊集显式考虑出行需求的不确定性，从而实现对空驶出租车路由和车辆-任务匹配的鲁棒决策。所提出的框架在静态和动态移动感知设置下均使用真实世界数据进行了评估。计算结果表明，与基准策略相比，我们的方法在感知覆盖率、运营成本和鲁棒性方面取得了更好的性能，凸显了将分布鲁棒性整合到基于出租车的感知操作中的价值。
英文摘要：
Taxi-based mobile sensing has emerged as a cost-efficient paradigm for large-scale urban environmental monitoring. In practice, both passive and active sensing strategies are adopted by ride-hailing platforms. Passive sensing, conducted during passenger-serving trips, is constrained by stochastic and spatially imbalanced ride demand, leading to limited and uneven coverage. Active sensing, executed by vacant taxis, provides greater control over sensing operations but incurs additional operational costs, and is thus typically treated as a supplementary strategy. To address the inefficiencies of the conventional "passive-first, active-second" paradigm, which may delay the monitoring of critical Points of Interest (POIs) and increase system-wide costs, we propose a Distributionally Robust Optimization (DRO)-based framework for active mobile sensing. First, we develop an enhanced A*-based drive-by sensing routing policy that integrates vehicle-task matching while capturing both global routing efficiency and local sensing opportunities. Second, we formulate the active sensing problem as a DRO model that explicitly accounts for uncertainty in ride demand through an ambiguity set of probability distributions, enabling robust decision-making for vacant taxi routing and vehicle-task matching. The proposed framework is evaluated on both static and dynamic mobile sensing settings using real-world data. Computational results demonstrate that our approach achieves better performance in terms of sensing coverage, operational cost, and robustness compared to benchmark strategies, highlighting the value of integrating distributional robustness into taxi-based sensing operations.
