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Intellevator: Enhancing Elevator System Efficiency by Proactive Computing on The Traffic Flow

Due to the momentary increment of traffic load and the limited capacity, elevator system faces the bottleneck of peak-time traffic congestion problem in tall commercial buildings. This paper presents our proposal Intellevator: an adaptive elevator system enabled to manage the traffic load distribution for efficiency optimization. The proposal that leveraged the Internet of things (IoT) and data-driven adaptive intelligence has been experimented on an existing elevator system. Markov Decision Process (MDP) was performed for real-time decision-making and feedback the decision on the interface of the target floor. The key novelty is that our proposal enables the passively working elevator system to be capable to compute and adaptive control the traffic load distribution for time efficiency. We conducted a user study to collect user preferences for policy-making. The system efficiency also has been evaluated by a further simulation based on the collected user preferences. Besides, the simulation also revealed the correlations between the performance improvement and elevator environment parameter setting.