The enormous amount of data produced by the parking facilities, if properly analyzed, can change the way those facilities operate. The collected data points form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as setting specific parking policies (e.g. electrical charging only parking spaces for sustainable and eco-friendly parking).
However, there lacks an intelligent tool to assess the layout design and operational performance of parking facilities with respect to all aforementioned concerns. This research proposes an agent-based framework and system for off-street parking simulation, in which the elements such as drivers, pedestrians, aisles, spots, entrances, are modeled as agents to capture their behaviors. The agents are classified based on their measures and behaviors into two categories: entities (e.g. drivers and pedestrians) and locations (e.g. aisles and spots). The state transition processes of drivers and pedestrians are modeled as state machines. The consequent choice behaviors of the entities are modeled to replicate the spot choice and route choice within the parking facility. The model and system have the flexibility to be extended to incorporate modern special types of parking spots and intelligent parking management and guidance systems. Simulation outputs and statistics, such as utilization, turnover, occupied duration can be collected and analyzed to assess the potential outcome of the design and operational decisions. To investigate the performance of the proposed simulator, this study designs a Software as a Service (SaaS) platform to visualize simulation dynamics and outputs. The methodology for data collection, processing, and user behavior extraction in the parking system is also developed.
Application of the developed simulation system to a real-world case study demonstrates its capability to support decisions in designing, maintaining, operating parking facilities. In the case study, a Long-Short Term Memory (LSTM) neural network is used to replicate the arrival and departure of the vehicles with good credibility. The simulation heatmap validates the frequency of parking spot utilization to be consistent with reality. The online simulator can also calculate the active capacity of the parking system, which is defined in this study to be an affective measure to evaluate alternative parking facility layout designs. Numerical studies under various application scenarios are further conducted with quantitative results to demonstrate that: (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots have negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of the parking maneuvers and reduce the efficiency during arrival rush hour.
In summary, the proposed parking simulator outperforms the state-of-practice simulators in the following aspects: (a) facilitates the customization of the discrete choice models and machine learning models for parking and pedestrian behavior modeling; (b) better captures low-speed mixed traffic dynamics with logic-based state machines rather than just adopting traditional road traffic modeling; and (c) offers capabilities and quantitative methods to aid the design of smart parking facility with multi-use, automated, shared spots, or electrical charging stations.
刘玥教授，1997-2004年就读于同济大学获学士与硕士学位，2009年获美国马里兰大学帕克分校土木工程博士学位。现任威斯康星大学密尔沃基分校终身正教授、博士生导师、城市交通研究中心主任、交通学科带头人、美国国家科学院TRB危机交通管理委员会、高速路运营管理及智能交通系统委员会委员，任美国国土安全部、自然科学基金、美国交通部、美国能源部评审专家、美国科学协会、土木工程师协会、电气电子工程师协会、运筹学与管理科学学会、国际交通工程师协会委员。刘玥教授主攻交通控制、运筹与管理研究10余年，近五年科研经费600万美元，发表国际知名学术期刊和会议论文200余篇（SCI收录72篇），成果被30多个国家和地区研究机构独立引用；任数十种国际权威期刊审稿人和国际学术期刊编委，刘玥教授先后获得美国土木工程师学会国际期刊全球最佳评阅人奖、国际交通工程师协会（ITE）“交通杯”奖、Daniel Fambro最佳论文奖、Martin Bruening最佳论文奖、美国智能交通系统协会最佳论文奖、马里兰州长授予的“优秀人才认可奖”、马里兰大学系统博士研究最高荣誉Ann Wylie奖等。刘玥教授负责研发的大都市危机交通管理决策支持系统被美国总统特勤办公室广泛采用以应对奥巴马总统就职典礼非常态交通管理并被美国权威媒体CNN广泛报道。其代表性研究成果“非常态城市快速路集成控制理论”发表于多种国际顶级核心杂志，并应用于美国270号州际公路，成为美国联邦政府集成运输走廊管理示范项目。刘玥教授关于过饱和控制研究已成为Web of Science统计交通控制领域他引次数最高文章之一。