Traffic congestion is a serious problem in the United States and is likely to get worse. A number of strategies encompassing increasing supply and managing demand have been suggested to mitigate the problem of traffic congestion. These strategies seek to reduce travel time and/or make travel time more reliable. The use of managed lanes is one such strategy. Faced with successful implementation of a managed lane strategy, it is important to understand potential public perception of the managed lane as well as estimate the number of travelers willing to use managed lanes. Such an estimate would help estimate the toll rates for optimal usage of managed lanes by carpoolers and toll paying travelers. An online survey augmented by paper and laptop survey was conducted in Houston and Dallas to collect information about travelers’ travel behavior, socio-economic characteristics, managed lane perception, and potential use of managed lanes. A comparison of interest in using managed lanes revealed that in majority of cases there was no difference in interest in using managed lanes across user groups. Travel time reliability and ability to travel faster were indicated as top two reasons for interest in managed lanes. This was true for all travelers regardless of mode. Mode choice model using multinomial logit modeling were estimated for Houston and Dallas. Simulation studies were conducted using these mode choice models to estimate the percentage of Single Occupant Vehicle (SOV) travelers on managed lane (ML), High Occupancy Vehicle with two travelers (HOV2) on ML, High Occupancy Vehicle with three or more travelers (HOV3+) on ML, SOV travelers on general purpose lane (GPL), HOV2 travelers on GPL, and HOV3+ travelers on GPL. These scenarios compared the managed lane usage for different speeds on GPL (25 miles per hour, 30 miles per hour, and 35 miles per hour). For the case when general purpose lane speed is 25 miles per hour, an increase of $11.75 in SOV tolls ($18 from $6.25) decreases the modal share of SOV travelers on Houston ML from 23.3 percent to 16.9 percent. A similar increase in Dallas tolls decreases the modal share of SOV ML travelers from 22.0 percent to 16.3 percent.

Traffic congestion is a serious problem in the United States and is likely to get worse. A number of strategies encompassing increasing supply and managing demand have been suggested to mitigate the problem of traffic congestion. These strategies seek to reduce travel time and/or make travel time more reliable. The use of managed lanes is one such strategy. Faced with successful implementation of a managed lane strategy, it is important to understand potential public perception of the managed lane as well as estimate the number of travelers willing to use managed lanes. Such an estimate would help estimate the toll rates for optimal usage of managed lanes by carpoolers and toll paying travelers. An online survey augmented by paper and laptop survey was conducted in Houston and Dallas to collect information about travelers’ travel behavior, socio-economic characteristics, managed lane perception, and potential use of managed lanes. A comparison of interest in using managed lanes revealed that in majority of cases there was no difference in interest in using managed lanes across user groups. Travel time reliability and ability to travel faster were indicated as top two reasons for interest in managed lanes. This was true for all travelers regardless of mode. Mode choice model using multinomial logit modeling were estimated for Houston and Dallas. Simulation studies were conducted using these mode choice models to estimate the percentage of Single Occupant Vehicle (SOV) travelers on managed lane (ML), High Occupancy Vehicle with two travelers (HOV2) on ML, High Occupancy Vehicle with three or more travelers (HOV3+) on ML, SOV travelers on general purpose lane (GPL), HOV2 travelers on GPL, and HOV3+ travelers on GPL. These scenarios compared the managed lane usage for different speeds on GPL (25 miles per hour, 30 miles per hour, and 35 miles per hour). For the case when general purpose lane speed is 25 miles per hour, an increase of $11.75 in SOV tolls ($18 from $6.25) decreases the modal share of SOV travelers on Houston ML from 23.3 percent to 16.9 percent. A similar increase in Dallas tolls decreases the modal share of SOV ML travelers from 22.0 percent to 16.3 percent.

One of the primary reasons of the escalating rates of injuries and fatalities in the construction industry is the ever so complex, dynamic and continually changing nature of construction work. Use of cranes has become imperative to overcome technical challenges, which has lead to escalation of danger on a construction site. Data from OSHA show that crane accidents have increased rapidly from 2000 to 2004. By analyzing the characteristics of all the crane accident inspections, we can better understand the significance of the many variables involved in a crane accident. For this research, data were collected from the U.S. Department of Labor website via the OSHA database. The data encompass crane accident inspections for all the states. The data were divided into categories with respect to accident types, construction operations, degree of accident, fault, contributing factors, crane types, victim’s occupation, organs affected and load. Descriptive analysis was performed to compliment the previous studies, the only difference being that both fatal and non-fatal accidents have been considered. Multinomial regression has been applied to derive probability models and correlation between different accident types and the factors involved for each crane accident type. A log likelihood test as well as chi-square test was performed to validate the models. The results show that electrocution, crane tip over and crushed during assembly/disassembly have more probability of occurrence than other accident types. Load is not a significant factor for the crane accidents, and manual fault is more probable a cause for crane accident than is technical fault. Construction operations identified in the research were found to be significant for all the crane accident types. Mobile crawler crane, mobile truck crane and tower crane were found to be more susceptible. These probability models are limited as far as the inculcation of unforeseen variables in construction accidents are concerned. In fact, these models utilize the past to portray the future, and therefore significant change in the variables involved is required to be added to attain correct and expedient results.