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Passenger processing space estimations described in textbooks and guidebooks are based solely on peak passenger demand and personal space standards describing perceived passenger comfort. The economic aspects of facility sizing are disregarded by those methods. In this article, an analytical tool based on deterministic queuing theory is presented for determining optimal personal space, which minimizes total cost. Total cost is defined as the sum of the cost of constructing, operating, and maintaining (COM) the facility and the cost of passenger (user) inconvenience. While COM cost is derived as a function of the expected maximum queue length and personal space, passenger cost is defined as a function of average queue length and personal space. Application of the tool is demonstrated using a hypothetical data from a typical passenger check-in lobby. It is shown that optimal personal space is influenced less by unit user cost than by unit construction cost, even when scale economies exit. Moreover, the degree of elasticity and the optimal personal space are dependent on the cost functional form. CE Database keywords: Terminal facilities; Passengers; Cost effectiveness; Optimization; Spacing; Estimation.

Continually changing shipping industry practices, cargo composition, world markets, and competition among ports are making revenue estimates uncertain. This paper demonstrates the application of Monte Carlo simulation for scrutinizing sources of uncertainty and their impact on investment risk. Strengths and weaknesses of Monte Carlo simulation are discussed using the Port of Colombo development project in Sri Lanka as a case example.

Although parking revenue is a principal source of income, supply of parking infrastructure at airports is based largely on expected needs. Although that is a rational basis, high investment costs and management fees are requiring developers and financiers to carefully analyze investment risks. This paper focuses on sources of investment risk in airport parking infrastructure development and discusses the application of Monte Carlo simulation to estimate and understand the impacts of cash flow uncertainties on project feasibility. It is shown that cost overruns, which are common in construction project development, have the most significant impact on return risk.

Accurate estimates of demand are critical for planning, designing, and operating public transit systems. Previous research has demonstrated that the expected demand in rural areas is a function of both demographic and transit system variables. Numerous models have been proposed to describe the relationship between the aforementioned variables. However, most of them are site specific and their validity over time and space is not reported or perhaps has not been tested. Moreover, input variables in some cases are extremely difficult to quantify. In this article, the estimation of demand using the generalized linear modeling technique is discussed. Two separate models, one for fixed-route and another for demand-responsive services, are presented. These models, calibrated with data from systems in nine different states, are used to demonstrate the appropriateness and validity of generalized linear models compared to the regression models. They explain over 70% of the variation in expected demand for fixed-route services and 60% of the variation in expected demand for demand-responsive services. It was found that the models are spatially transferable and that data for calibration are easily obtainable.

The appropriateness of the current space standards (occupancy rates) for airline check-in area design is discussed. Based on a study of passenger walking patterns at the two international airports in Montreal, it is suggested that the design standards recommended in the International Air Transport Association guidelines result in an oversupply of space, sometimes in excess of twice the required minimum. This is attributed partly to the definition of capacity and the lack of a formal procedure for treating items such as luggage, carts, and stationary passengers that impede movement. It is proposed that the design occupancy rate be determined by adjusting the maximum occupancy rate to account for the impedance. Maximum occupancy rate is derived according to the relationship between occupancy rate and walking speed of passengers without baggage or carts, whereas adjustment factors are computed along the same principles as the passenger-car equivalency factors used in highway capacity analysis. The standard equivalent passenger in the present case is defined as a stationary passenger without a cart, and the space required by moving passengers with and without carts is estimated relative to the standard passenger. Application of the technique is explained using a numerical example.

A model based on an expected total-cost minimization approach is developed for determining the optimal-degree curvature of simple horizontal curves on undivided two-lane highways. Expected total cost is defined as the sum of the construction costs and the expected cost of accidents. Construction cost is treated as a deterministic function of curvature, and the expected cost of accidents is determined in relation to the amount by which required (demand) curvature differs from design (supply) curvature. It is shown that the optimal degree of curvature is highly sensitive to the skewness of the probability distribution of the required curvature. This distribution is derived from the fundamental relationship between speed and degree of curvature, treating speed to be a random variable. The optimal value is shown to change in direct proportion to skewness of the speed distribution. User cost, in terms of travel time, is treated as a special case when examining the sensitivity of optimal design curvature to construction- and accident-cost functions. Model applications in curve design are illustrated by a numerical example.