Collaborative Research: Modeling and Analysis of Advanced Parking Management for Traffic Congestion Mitigation

在人口密集的城市地区，停车是一个日益严重的问题。对许多人来说，在这些地区找到停车位是一种充满不确定性和挫折感的不愉快经历。为停车而巡航使本已拥堵的城市街道上的交通变得更加糟糕，并导致大量时间和燃料的浪费。在交通领域，基于智能手机的停车管理应用程序已经出现。这些应用程序帮助司机找到停车位，允许他们使用智能手机查看停车位的实时可用性和价格，并引导他们开放停车位，无论是预留的还是其他的。该奖项为分析这些新兴的停车管理服务提供了理论基础和方法。这项研究的结果有助于更好地了解先进的停车管理服务对停车竞争和出行模式的影响。这项研究制定了在人口密集的城市地区减少交通拥堵和排放的政策。该奖项通过提供新材料和案例研究以及让代表不足的学生群体参与研究，对工程教育产生了积极的影响。该项目使用博弈论、动态和随机规划方法，通过先进的停车管理来研究时间和空间出行模式，生成一套分析工具，解释先进的停车管理服务的潜在工作机制，并评估其缓解交通拥堵的潜力。通过基于智能体的仿真，验证了理论的有效性和适用性，揭示了现实停车搜索行为下停车竞争的复杂结果。本文的工作加深了人们对停车管理的认识和分析，丰富了早间通勤模型和车辆路径模型的文献。

Parking is a growing problem in dense urban areas. To many, finding a parking space in these areas is an unpleasant experience of uncertainty and frustration. Cruising for parking makes traffic on already-congested urban streets even worse and leads to significant waste in time and fuel. In transportation, smartphone-based parking management applications have emerged. These applications help drivers find parking spaces by allowing them to use smartphones to view real-time availability and prices of parking spaces and guide them to open parking spaces, reserved or otherwise. This award develops theoretical foundations and methodologies for analyzing these emerging parking management services. Results from this research provide a better understanding of the impacts of advanced parking management services on parking competition and travel patterns. The research develops policies to reduce traffic congestion and emissions in dense urban areas. This award positively impacts engineering education by offering new materials and case studies and engaging underrepresented student groups in research.Using game-theoretic, dynamic and stochastic programming approaches to investigate both temporal and spatial travel patterns with advanced parking management, this project generates a set of analytical tools that explain the underlying working mechanisms of advanced parking management services and gauge their potential for reducing traffic congestion. The theoretical efforts in this research are complemented by an agent-based simulation, which tests the validity and applicability of the theories, and unveils complex outcomes of parking competition under realistic parking search behaviors. This work advances the knowledge and analysis of parking management and enriches the literature of modeling morning commute and vehicle routing.