In the early phases of planning an airport, the evaluation of the number of aircraft gates required is one of the first steps to be taken. This paper presents an analytical methodology to evaluate the gate position requirement for a terminal accommodating a mix of aircraft that includes conventional jets, wide-bodied aircraft, and the new large aircraft. The methodology takes into consideration the shared use of gates by the wide range of aircraft sizes to improve the efficiency of terminal operations. It also allows the terminal to be built and expanded in stages so as to minimize a cost function. Significant savings are shown to be achievable with the use of space sharing and stage construction.

This paper presents a need-based methodology to prioritize and select highway projects for improvement. This approach is based on developing a multiattribute need function that quantifies relative concerns of a highway agency and the traveling public about various physical and operational deficiencies on different highway segments. It is an effective alternative to the traditional cost-benefit analysis. While the cost-benefit analysis may be useful in evaluating a small number of project alternatives at a fixed location (e.g., alternative alignments for a single corridor), it has major limitations when applied to a large-scale (e.g., statewide) highway construction program. This paper discusses some of the philosophical and practical limitations of the cost-benefit analysis and how the need-based approach overcomes these limitations. A successful implementation of the need-based approach to Kansas’s statewide highway improvement program is also described.

This paper provides a detailed overview on the state of practice in the area of data collection, modeling, calibration, and validation of route choice models. This paper presents a new approach for modeling route choice behavior using stated preference data coupled with rating (scoring) technique. The data collection was carried out over three stages; travelers’ scores of route attributes, attributes’ levels identification, and experimental templates for validation. A factorial experiment-design model was developed and validated through a two-stage procedure: the validation of the scoring technique, and the validation of the developed factorial experiment design model. Results indicated the dominant influence of travel time, and queuing time information on the route choice decisions. Other factors such as the speed, highway class, familiarity, and highway/pavement conditions were also found significantly important. The results include the percentages of the factors’ contributions to the route utility, and the relationships derived to illustrate the effect of the factors’ interactions on the travelers’ route utility.

The neuro-fuzzy refers to the recent technology that couples the traditional fuzzy logic developments with neural nets training capabilities to compose the fuzzy logic’s knowledge base and fuzzy sets’ parameters optimally. This paper discusses the calibration methodology of a neuro-fuzzy logic for modeling the route choice behavior. The logic accounts for the various factors of potential effect on the route choice utility perceived by the traveler. The structure of the fuzzy control stages, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model. The results of the fuzzy logic model are utilized for in-depth analyses of the travelers’ perceptions of the route utility in response to the various traffic states.

Transportation network improvements are commonly evaluated by estimating average speeds from a transportation/traffic model and converting them into emission estimates using an environmental model such as MOBILE or EMFAC. Unfortunately, recent research has demonstrated that average speed, and perhaps even simple estimates of the amount of delay and the number of vehicle stops on a roadway, is insufficient to fully capture the environmental impacts of Intelligent Transportation System (ITS) strategies such as adaptive traffic signal control. Specifically, for the same average speed, one can observe widely different instantaneous speed and acceleration profiles, each resulting in very different fuel consumption and emission levels. In an attempt to address this limitation, the paper presents the INTEGRATION model framework for quantifying the environmental impacts of ITS alternatives. The model combines car-following, vehicle dynamics, lane changing, energy, and emission models to estimate mobile source emissions directly from instantaneous speed and acceleration levels. The validity of the model is demonstrated using sample test scenarios that include traveling at a constant speed, traveling at variable speeds, stopping at a stop sign, and traveling along a signalized arterial. The study also demonstrates that an adjustment in driver aggressiveness can provide environmental benefits that are equivalent to the benefits of adaptive traffic signal control.

A method for precisely formulating intersection construction costs including earthwork costs, right-of-way costs, and pavement costs is introduced in this paper. Intersection design characteristics are reviewed and intersection cost components are formulated. A highway alignment is described by parametric representation in space, and vector manipulation is used to find the coordinates of intersections and other points of interest. A simplified typical fill section is studied to obtain actual ground and road elevation coordinates that are crucial for setting the boundaries and computing the costs. Because estimating right-of-way costs requires identifying affected properties and structures in the vicinity of an intersection, two examples based on geographic information system (GIS) databases from Maryland and an artificial study area are presented to show how the developed cost functions can be used in real applications. The developed intersection construction cost functions can be incorporated in previously developed highway alignment optimization models that neglected the characteristics and costs of intersections.

This paper investigates the use of two well-known artificial neural network (ANN) paradigms: the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks in analyzing driver injury severity. The objective of this study is to investigate the viability and potential benefits of using the ANN in predicting driver injury severity conditioned on the premise that a crash has occurred. The performance of the ANN was compared to a calibrated ordered probit model. Modeling results showed that the testing classification accuracy was 73.5% for the MLP, 70.6% for the fuzzy ARTMAP, and 61.7% for the ordered probit model. This result indicates a more accurate prediction capability of injury severity for ANN (particularly the MLP) over other traditional methods. The results of the models showed that gender, vehicle speed, seat belt use, type of vehicle, point of impact, and area type (rural versus urban) affect the likelihood of injury severity levels.

This paper presents the development of a dynamic user equilibrium (DUE) traffic-assignment model for the congested urban road network with signalized intersections. A simulation-based approach is employed for the case of multiple-origin multiple-destination traffic flows. The modified method of successive averages (MSA) is developed to arrive at the user equilibrium condition. The proposed method is applied to an example network, and its results are compared with the results obtained by using the artificial intelligence technique of genetic algorithms (GAs). It is concluded that the formulated DUE model avoids the all-or-nothing type of assignment and gives results closer to the equilibrium condition, and the modified MSA gives better results with lesser computation time as compared to GAs.

This research involves the development of a tool that can be used to assess the changes in traffic safety tendencies that result from changes in traffic flow. The tool uses data from single inductive loop detectors, converting 30-second observations of volume and occupancy for multiple freeway lanes into traffic flow regimes. Each regime has a specific pattern of crash types, which were determined through nonlinear multivariate analyses of over 1,000 crashes on freeways in Southern California. These analyses revealed ways in which differences in variances in speeds and volumes across lanes, as well as central tendencies of speeds and volumes, combine in complex ways to explain crash taxonomy. This research may provide the foundation to forecast the crash rates, in terms of vehicle miles of travel, for vehicles that are exposed to different traffic flow conditions.

Most transport models for short-term strategic planning are required to be calibrated with current planning parameters and updated traffic data. There are basically two steps involved in transport model calibration, namely, trip matrix updating and network calibration. These two steps are carried out iteratively in the conventional approach and may not achieve a stable solution. This paper proposes a bilevel programming model that integrates the two steps of trip matrix updating and network calibration into one stage. A solution algorithm is developed on the basis of genetic algorithms. Numerical examples are presented to illustrate the performance of the proposed model and solution algorithm under various conditions where different source data are available.

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.

State highway agencies strongly desire to develop and use performance-related specifications (PRS) for highway construction. A PRS for hot mix asphalt (HMA) construction, titled HMA Spec, was recently developed as part of the WesTrack project. The beta version of HMA Spec was applied to compare pay factors and actual rutting performance to predicted performance for four Nevada Department of Transportation (NDOT) test sections on Interstate 80 (I-80) east of Reno, Nevada. Some of these sections were similar in terms of materials and construction techniques to coarse-graded sections constructed at WesTrack, so a comparison to performance at WesTrack was also made. NDOT placed the test sections on I-80 in an effort to assess the applicability of the Superpave mix design process and HMA Spec for Nevada’s conditions. Sections were designed using both Hveem and Superpave mix design methods. Several interesting observations were made while evaluating the HMA Spec output. These observations and field performance data are described in this paper.

Intersection sight distance (ISD) is the sight distance to be provided at intersections between a minor road and a major road. Current AASHTO policy provides an equation and charts for the required at-grade ISD so that a driver on the minor road can depart (crossing, turning left, or turning right) safely, even though an approaching vehicle on the major road comes into view as the stopped vehicle begins to depart. The AASHTO model is based on two assumptions: (1) both minor and major roads are assumed to be straight without any vertical or horizontal curvature; and (2) the intersection angle is assumed to be 90°. In many practical situations, however, sight distance is required to be checked for an existing or proposed 3D intersection alignment where vertical curves (crest or sag) and horizontal curves overlap. This paper presents a new mathematical model for the analysis of stop-controlled ISD on 3D highway alignments that allows the major road to have vertical and horizontal curves with skewed angle, and the minor road to have a longitudinal grade. Design aids are developed to determine the available ISD for different geometric alignment variables (e.g., radius of horizontal curve, lane width, number of lanes, and vertical curve parameters). Application of the methodology is illustrated using numerical examples.