Passengers spend much time on elevator journeys in high-rise buildings every day, in which unnecessary stops caused by lack of cab capacity take up a certain proportion of the journey time. This study proposes real-time occupancy-aware smart dispatching to avoid pick-up failure by introducing the occupancy information that reflects elevator capacity into the optimization model, thus improving dispatching performance. Occupancy awareness is firstly implemented with deep learning-based object detection to provide estimated capacity. Traffic pattern recognition is implemented with time series analysis and fuzzy logic. Case-based reasoning is applied to recognize the current usage pattern and to deploy specific dispatching strategies. A prioritized A* search model is built to solve dispatching optimization with occupancy information. Discrete event simulation is conducted with Simio and MATLAB to validate the proposed dispatching model.

乘客每天在高层建筑的电梯旅行中花费大量时间，在这种情况下，由于驾驶室容量不足而引起的不必要的停留会占用一定的旅行时间。这项研究提出了一种实时的可感知占用的智能调度，通过将反映电梯容量的占用信息引入优化模型中，从而避免了接机失败，从而提高了调度性能。首先通过基于深度学习的对象检测来实现占用意识，以提供估计的容量。交通模式识别是通过时间序列分析和模糊逻辑来实现的。基于案例的推理可用于识别当前的使用模式并部署特定的调度策略。建立了优先级A *搜索模型，以解决占用信息的调度优化问题。