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The concept of an idealised optimal benchmark (IOB) is used in many engineering disciplines. An example of an IOB from the area of thermodynamics is the formula for evaluating the maximum possible efficiency of a heat engine. This paper explores the concept of an IOB in the area of elevator traffic analysis. It shows that the classical method of elevator traffic design by calculating the value of the round trip time is an example of an IOB; it also lists the assumptions that lie behind the formulae to illustrate this. It then extends the concept of an IOB to calculating the maximum performance of an elevator system when destination group control is applied under incoming traffic conditions. Formulae are derived for finding the minimum values of the expected number of stops (S) and the highest reversal floor (H) under destination group control during incoming traffic conditions. The assumption is that the L elevators in the group are sequenced (or rotated) to the L virtual sectors in the building, in order to equalise the handling capacities of the L sectors in the group. A numerical example is presented to illustrate the calculation of the maximum possible handling capacity and comparing it to the handling capacity that is achieved under conventional incoming traffic group control. Three numerical algorithms are also used to find the practical minimum values of H and S, the results of which are compared to the IOB using the equations derived above. Practical application: The concept and the accompanying formulae presented in this paper allow the elevator traffic designer to assess the improvement in the handling capacity of the elevator traffic system when he/she changes the group controller from a conventional group controller to a destination group controller. This improvement could be as much as 200%.

The potential for using computer vision techniques to solve several shortcomings associated with traditional road safety and behavior analysis is demonstrated. Surrogate data such as traffic conflicts provide invaluable information that can be used to understand collision-contributing factors and the collision failure mechanism better. Recent advances in computer vision techniques have encouraged the use of proactive safety surrogate measures such as detection of conflicts and violations. The objective of this study is to demonstrate the automated safety diagnosis of pedestrian crossing safety issues by using computer vision techniques. The automated safety diagnosis is applied at a major signalized intersection in downtown Vancouver, British Columbia, Canada, at which concerns had been raised regarding the high conflict rate between vehicles and pedestrians as well as the elevated number of traffic violations (i.e., jaywalking). This study is unique in its attempt to extract conflict indicators and detect violations from video sequences in a fully automated way. This line of research benefits safety experts because it provides a prompt and objective safety evaluation for intersections. The research also provides a permanent database for traffic information that can be beneficial for a sound safety diagnosis as well as for developing safety countermeasures.