Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

Metadata-based Needs Assessment for Emergency Transportation Operations with a Focus on an Aging Population: A Case Study in Florida

In the aftermath of disasters, evacuating aging victims and maintaining an optimal flow of critical resources in order to serve their needs becomes problematic, especially for Gulf Coast states in the USA such as Florida, where more than 6.9 million (36.9%) of the population are over age 50. Scanning the literature, there is no substantial prior work that has synthesized the requirements for a multi-modal emergency needs assessment that could facilitate the safe and accessible evacuation of aging people, and optimize the flow of resources into the affected region to satisfy the needs of those who remain. This paper presents a review of the aging population-focused emergency literature utilizing a knowledge base development methodology supported with a geographic information system-based case study application set in Florida. Importance is given to both ensuring the resiliency of the transportation infrastructure and meeting the needs of aging populations. As a result of this metadata-based analysis, critical research needs and challenges are presented with planning recommendations and future research directions. Results clearly indicate that transportation agencies should focus on clear and fast dissemination of disaster-related information to the aging populations. The use of paratransit services for evacuating aging people, especially those living independently and/or in rural areas, is also found to be of paramount importance.     

Finite element analysis of collapse of front side rails with new types of crush initiators

Today’s vehicles are designed with lighter weight to increase performance and to lower fuel consumption, while at the same time meeting the demands of safety requirements. Reducing the cross-section of structural elements to achieve weight reduction can lead to adverse effects on passive safety of the vehicle. In such cases, necessary design modifications must be created to overcome the adverse effects. For this purpose, front rail columns with crush initiators are used in the front zone of cars. These shock-absorbing elements act as energy consuming devices that convert impact energy (kinetic energy) into plastic deformation energy. Simulation of this energy conversion phenomenon is the subject of this paper. The primary objective of this study is to computationally determine how various crush initiators can reduce the maximum crushing force and how different types of structural modifications affect the observed folding form. The ribs near the crash area are placed in two rows and four different configurations on all facing sides of the column in order to decrease reaction forces and absorb more kinetic energy. These structures are analyzed under axially loaded crushing forces using the explicit nonlinear finite element analysis solver ANSYS/LS-DYNA.