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Research in the choice behavior of air travelers has evolved to include an analytical focus on variation in the sensitivities of travelers to factors influencing itinerary choice. Some choice studies have moved beyond a focus on assumed representative, mean-level sensitivities toward a goal of representing the distribution of preferences across a sample. The mixed multinomial logit model has served as a valuable means of estimating such distributions of air travel preferences, including studies of business travelers, impacts of airport level-of-service attributes, distributions of willingness-to-pay (WTP), and information-processing strategies. Does the insight gained in previous studies, focusing on preferences in mature markets with relatively high per capita rates of air travel activity, apply to markets with low frequencies of airline patronage? This study centers on a survey of travelers in Tehran, Iran, a low-frequency air travel market. The analysis incorporates tests of a full range of distributions of random parameters to determine whether the impacts of restricting distributions allow only normality and confirms the potential to improve model fit with alternative distributions. The estimated distributions of WTP measures confirm the value of accounting for preference heterogeneity in the analysis of choice of air itinerary behavior in a low-frequency market and yield lower mean WTP values relative to analysis that omits the effects of preference heterogeneity.

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Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.

Abstract. Contraflow or lane reversal is an efficient way for increasing the outbound capacity of a network by reversing the direction of in-bound roads during evacuations. Hence, it can be considered as a potential remedy for solving congestion problems during evacuation in the context of homeland security, natural disasters and urban evacuations, especially in response to an expected disaster. Most of the contraflow studies are performed offline, thus strategies are generated beforehand for future implementation. Online contraflow models, however, would be often computationally demanding and time-consuming. This study contributes to the state of the art of contraflow modelling in two regards. First, it focuses on the calibration of a Logit choice model which predicts the online contraflow directions of strategic lanes based on the set of directions obtained from offline scenarios. This is the first effort to adjust offline results to be applied for an online case. The second contribution of this paper is the generation of calibration data set from a novel approach through simulation. The calibrated Logit model is then tested for the network of the City of Fort Worth, Texas. The results show a high performance of this approach to generating beneficial strategies, including an increase in up to 16% in throughput compared to no contraflow case.