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The number of passengers wanting to use lifts to travel to and from the lobby and between floors in a building has a significant effect on the quality of lift service experienced by each passenger. The traditional assumptions of lift passenger demand in office buildings are compared to measurements taken in modern buildings. The differences between traditional and modern patterns of passenger demand in office buildings are discussed. The significance of these differences on lift system design is explored. In office buildings surveyed, the daily pattern of passenger demand repeats itself with a high degree of consistency; buildings can be described as having their own demand 'signatures'. Practical applications: Designing lift systems based on modern traffic patterns and traffic levels will result in systems with characteristics that are different from those designed using traditional traffic expectations. Applying the traffic data in this paper will result in a more accurate prediction of a lift system's performance. Control system designers can use the traffic data to design dispatcher algorithms that can better respond to modern traffic conditions.

The Internet of Things (IoT) is shaping the concept of the modern intelligent built environment. The latest developments in IoT have led to secure, energy efficient systems enabling low-cost real-time analytics. In the Vertical Transportation (VT) technologies developed by the lift industry real-time analytics are facilitating predictive maintenance which in turn decreases operational and downtime costs. Data driven predictive maintenance does not always reach an optimal performance because the quality and quantity of the data matters. Fault classification and the estimation of the remaining useful life (RUL) requires a deep understanding of failure modes and component degradation. In lift systems, most of the malfunctions are due to faults developed by the automatic power operated door systems. The most widespread Structural Health Monitoring (SHM) sensor technology used in monitoring the door mechanisms are acoustic and vibration sensors. In this paper, an automatic fault detection system using Artificial Neural Networks (ANN) is implemented using vibration signal features. Obtained results reveal that the fault classification performance is high (>70%) under low noise environmental conditions.

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Ride quality of a Passenger Transportation System (PTS) is a measure of the comfort level experienced by passengers and is intimately associated with their subjective perception and sensitivity to motion and sound. This measure is affected by the noise and vibration of an operating system. On the other hand, ride quality is the measure of the PTS product quality. Ride quality of passenger transportation systems is critical for a PTS manufacturer to determine the subjective and objective quality of the system. This is especially important in high rise (high end) systems. The paper investigates the dynamic interactions that might occur between the PTS system components and their influence on ride quality.