This note describes a simple procedure for assessing utility functions which avoids many difficulties of the standard techniques. The conventional methods suffer from at least three drawbacks; they (1) generate utility functions that depend on the probability levels used; (2) chain responses from one question to the next, so that any bias is propagated and even magnified; and (3) change ranges and reference points constantly, introducing range effects and other distortions. Noting the evidence linking the dependence of utility functions on the “certainty effect,” our method: (1) compares lotteries with other lotteries rather than certain amounts; (2) does not “chain” responses; and (3) consistently uses “elementary lotteries” which control for range and reference points. Experimental work supports the proposed procedure.

Roadway networks span large distances and can be difficult to monitor. Most efforts to collect roadway usage data either require a large fixed infrastructure or are labor intensive. Technological advances in electronics and communication have recently enabled an alternative, Unmanned Aerial Vehicles (UAVs). UAVs capable of carrying sensors and communications hardware to relay data to the ground are becoming available on the commercial market. UAVs can cover large areas and focus resources. They can travel at higher speeds than ground vehicles and are not restricted to traveling on the road network. In this paper we investigate the use of a UAV to monitor roadway traffic and develop and demonstrate several applications using data collected from a UAV flying in an urban environment. We describe our use of the data to determine level of service, average annual daily traffic, intersection operations, origin-destination flows on a small network, and parking lot utilization. Our ability to determine these measures illustrates the feasibility of extracting useful information from images sampled from a UAV for both off-line planning and real-time management applications, and our discussion of the methods indicates the challenges and opportunities images obtained from such a platform pose and entail.

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The advent of automatic passenger counter (APC) technologies is resulting in the collection of comprehensive boarding and alighting data on an ongoing basis across transit networks. The availability of APC data offers a new opportunity to determine origin–destination (O-D) flows on a frequent and comprehensive basis. In this paper, the performance of a simple procedure for route-level O-D flow determination requiring only boarding and alighting data is investigated. Specifically, the performance of the iterative proportional fitting (IPF) procedure used with a null base matrix is examined on the basis of a field experiment in which true O-D flows are observed. Because of the noninformative nature of the null matrix, using the IPF procedure with the null matrix as its input base may not be expected to produce good results. In a comparison of empirical results with those produced by other benchmark procedures, the IPF–null procedure is found to perform surprisingly well. The quality of the resulting matrices appears to be roughly similar to that of matrices derived from an onboard survey, the benchmark for what has been achieved in practice, but at much higher cost. The results indicate that much can be gained from using readily available APC data, even when the simple IPF–null procedure is applied. Moreover, using the better base obtained from an onboard survey with the IPF procedure improved performance, but less markedly compared with use of the null base; this difference indicates that combining onboard survey information with APC data provides a better O-D matrix than what can be derived from an onboard survey alone, even when the simple IPF procedure is used.

Vehicles can be identified in high-resolution satellite imagery that recently has become available to the civilian community. The vehicle information contained in this imagery, and in air-based imagery, could be used in annual average daily traffic (AADT) estimation, a task conducted by many transportation agencies around the world. However, because the imagery provides information equivalent to traffic counts of very short duration, it is possible that the information is too noisy to be of use. Empirical differences between AADT estimated from 14 satellite images and air photos of Interstate segments in Ohio and the corresponding AADT estimated from traditional, ground-based estimates are presented. The distribution in differences appears relatively unbiased, implying that averaging the estimates of several images of the same segment can decrease estimate errors. The empirical errors are small enough to indicate that AADT estimation errors and ground-based sampling efforts could both be reduced by combining satellite-based data with traditional ground-based data. The differences between the image-based and the ground-based estimates are smaller in the few cases in which ground-based estimates inspired greater confidence, implying that the image-based estimates may be better than what is indicated in the distribution of differences. Evidence also suggests that the differences tend to decrease when the image leads to longer equivalent traffic count duration, indicating the potential to condition the use of the image-based data on this readily available parameter.