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Reducing the cost of operations is one of the most important considerations for urban transport systems. Deadhead kilometer costs are an important aspect of operating costs and are associated with buses traveling empty to and from depots; minimizing these costs is referred to as the bus-to-depot allocation problem and addressing it may be important in reducing overall costs. We present a model and an associated decision support system (DSS) for optimally allocating buses to depots while minimizing deadhead costs and keeping depot occupancy at 'ideal' operational levels. The model and DSS are tested in the Athens, Greece, large urban transit system and results indicate that considerable cost savings can be realized compared to current allocation schemes. DOI: 10.1061/(ASCE)TE.1943-5436.0000129 CE Database subject headings: Public transportation; Buses; Decision support systems; Greece. Author keywords: Transit, bus allocations.

Understanding and predicting individual mode choice decisions can help address issues ranging from forecasting demand for new modes of transport to understanding the underlying traveler behavior and characteristics. Early research in mode choice modeling revolved, almost exclusively, around the family of logit models. But a number of researchers have recently argued that these models place restrictions on their parameters that compromise their performance and have thus experimented with a number of newly developed, flexible mathematical techniques. The present paper extends prior research by developing a methodology for predicting individual mode choice based on a nonparametric classification methodology that imposes very few constraining assumptions in yielding mode choice predictions. Preliminary results, using data from three vastly different international settings, are promising, especially when considering that the models are successful while using only a limited number of independent variables to achieve these predictions. CE Database subject headings: Transportation models; Predictions; Transportation planning; Travel demand.

Research on short-term traffic conditions prediction has been largely concerned with parameters such as flow, occupancy, and speed, ignoring at the same time predictions during congestion, a period when predictions are needed the most. Stemming from the practical need to predict traffic parameters during congested periods, this paper uses the principles of duration modeling to address an important question: given the onset of congestion, how long will it last? As such, the goal of this paper is to propose an approach for estimating the duration of congestion on a given road section and the probability that, given its onset, congestion will end during the following time period. The results indicate that the Loglogistic functional form best describes congestion duration, and that the probability of congestion ending within a specified time period is likely if it has lasted up to approximately 12 min (with a peak at 6 min). Further, it was found that if congestion lasted over 21 min it was probably caused by something external to the traffic system events. DOI: 10.1061/(ASCE)0733-947X(2002)128:6(587) CE Database keywords: Traffic congestion; Urban areas; Models.

Knowledge of the cost and production structure of transit systems can be instrumental in uncovering an array of important information regarding their operation. Optimal fleet size and pricing, average and marginal cost, and the potential for success of deregulation and privatization efforts can be addressed successfully when the cost structure of transit systems is known. Research on the cost structure of mass transit systems using panel data frequently shows conflicting results with respect to issues such as economies of scale and density, and factor substitution elasticities. In this analysis we extend prior research in two significant directions. First, we control for cross-sectional heterogeneity that may otherwise seriously bias the resulting parameter estimates. Second, we make an explicit effort to capture differences in the economies of scale between large, medium, and small transit systems. based on panel data for fixed-route public transit systems, the results indicate that transit services are characterized by a nonhomothetic production function, exhibiting returns to density and scale that depend on the size of the transit firm.

An effective public transportation management system (PTMS) requires accurate and efficient models for the prediction of rolling-stock conditions. If the state of any given rolling-stock unit is known, its future condition can be predicted from the corresponding deterioration curves. The purpose of this study was twofold: first, to evaluate and model the relative importance of factors causing deterioration of rolling stock and, second, to provide projections of future condition to be used in transit capital programming. A methodology was developed for the estimation of rolling-stock deterioration models from condition rating data. Using a rolling-stock inspection data set from Indiana, the capabilities of the proposed methodology are empirically demonstrated. This ordered probit-based methodology provides models that are intuitively appealing, fundamentally sound, and a useful and easy-to-use tool in projecting future rolling-stock condition. The models presented in this paper are a part of the public transit management system being developed in Indiana for determining optimal rolling-stock maintenance, repair, and replacement strategies.

Air-transport demand forecasting constitutes an important determinant of airport planning, design, and operations. Errors in forecasting can be very costly. Underestimating demand may lead to increased congestion, delay, and inadequate airport facilities. Overestimating demand may also create serious economic problems for airport authorities. It is, therefore, very important for airport planners to develop reliable forecasting models and to understand possible limitations in the forecasting accuracy of these models. The objective of this paper is to examine the predicting ability and forecasting accuracy of air-travel demand models. In particular, an analytical framework for developing econometric models is presented and postfact analysis is used to test the accuracy of the models. Statistical data describing air-travel demand patterns for two major international airports, the Frankfurt and the Miami International Airports, are used to demonstrate the effectiveness of the proposed models. In addition, the effect of external factors such as the deregulation of the air-transport industry is examined. The results suggest that simple models with few independent variables perform as well as more complicated and costly models, and that external factors have a pronounced effect on air-travel demand.