Infrastructure project delays and cost overrun are caused by ineffective use of organizational skills, processes, and resources by project managers in the construction industry. Cost overrun and schedule delay in Qatari infrastructure projects have had damaging effects on the national economy by way of claims and litigation, contractual disputes, delays in dependent projects, and project abandonment. The purpose of this qualitative case study was to explore the perceptions of project managers regarding how they utilize capacity-planning practices to mitigate project schedule delay and cost overrun in government-funded infrastructure projects in Qatar. This study was framed by three conceptual models developed by Gill to outline the capacity management needs within a construction company: (a) the time horizon model, (b) the individual-organization-industry levels model, and (c) the capacity development across components model. Date were collected from semistructured interviews with 8 participants, observational field notes, and archival data regarding Qatari infrastructure project managers' experiences in capacity-planning practices. Thematic analysis of textual data and cross-case synthesis analysis yielded 5 conceptual categories that encompassed 15 themes. The conceptual categories were (a) resources to meet performance capacity, (b) knowledgeable and skillful staff, (c) short- and long-term planning strategy, (d) cost overrun issue, and (e) time management. Findings may be used to promote timely completion of infrastructure projects, which may benefit citizens, construction companies, and the economy of Qatar.

The aim of this thesis is to propose a decision support tool for contractors before the bidding stage to quantify the probability of schedule delay in construction projects by using fuzzy logic incorporated with relative importance index (RII) method. Eighty three (83) different schedule delay factors were identified through detailed literature review and interview with experts from a leading Turkish construction company, then categorized into nine (9) groups and visualized by utilizing Ishikawa (Fish Bone) Diagrams. The relative importances of schedule delay factors were quantified by relative importance index (RII) method and the ranking of the factors and groups were demonstrated according to their importance level on schedule delay. A schedule delay assessment model was proposed by using Fuzzy Theory in order to determine a realistic time contingency by taking into account of delay factors characterized in construction projects. The assessment model was developed by using Fuzzy Logic Toolbox of the MATLAB Program Software. Proposed methodology was tested in a real case study and probability of schedule delay was evaluated by the assessment model after the required inputs were inserted to software. According to the case study results, the most contributing factors and groups (that need attention) to the probability of schedule delays were discussed. The assessment model results were found to be conceivably acceptable and adequate for the purpose of this thesis.

A Decision Support model for planning Intercity Railways is presented in this research. The main aim of the model is to generate inputs for the logit model existing in the Virginia Tech Transportation Systems Analysis Model (TSAM). The inputs required by the TSAM logit model are travel time, travel cost and schedule delay. Travel times and travel costs for different rail technologies are calculated using a rail network and actual or proposed rail schedules. The concept of relational databases is used in the development of the network topology. Further, an event graph approach is used for analysis of the generated network. Shortest travel times and their corresponding travel costs between origin-destination pairs are found using Floyd's algorithm. Complete itineraries including transfers (if involved) are intrinsically held in the precedence matrix generated after running the algorithm. A standard mapping technique is used to obtain the actual routes. The algorithms developed, have been implemented in MATLAB. Schedules from the North American Passenger rail system AMTRAK are used to generate the sample network for this study. The model developed allows the user to evaluate what-if scenarios for various route frequencies and rail technologies such as Accelerail, High Speed Rail and Maglev. The user also has the option of modifying route information. Comparison of travel time values for the mentioned technology types in different corridors revealed that frequency of service has a greater impact on the total travel time in shorter distance corridors, whereas technology/line-haul speed has a greater influence on the total travel time in the longer distance corridors. This tool could be useful to make preliminary assessments of future rail systems. The network topology generated by the algorithm can further be used for network flow assignment, especially time-dependent assignment if used with dynamic graph algorithms. / Master of Science

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825

Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data. / QC 20100825