The economic evaluation of a new airport investment requires the use of estimated future air passenger demand.Today it is well known that air passenger demand is basicly dependent on various socioeconomic factors of the country and the region where the planned airport would serve. This study is focused on estimating the future air passenger demand for planned airports in Turkey where the historical air passsenger data is not available.For these purposses, neural networks and multi-linear regression were used to develop forecasting models. As independent variables,twelve socioeconomic parameters are found to be significant and used in models. The available data for the selected indicators are statistically analysed and it is observed that most of the data is highly volatile, heteroscedastic and show no definite patterns. In order to develop more reliable models, various methods like data transformation, outlier elimination and categorization are applied to the data.Only seven of total twelve indicators are used as the most significant in the regression model whereas in neural network approach the best model is achieved when all the twelve indicators are included. Both models can be used to predict air passenger demand for any future year for Or-Gi and Zafer Airports and future air passenger demand for similar airports. Regression and neural models are tested by using various statistical test methods and it is found that neural network model is superior to regression model for the data used in this study.

Within the transit industry it is well known that transit buses have the potential to operate at weights that exceed vehicle weight limits. However, few attempts have been made to date to determine how often this occurs and to what degree. This research characterizes the current transit industry with respect to the regulatory environment, factors that have affected the weight of modern day transit buses, and methods for accommodating transit buses in pavement design. This research then develops and applies a methodology for calculating the in-service weights of standard 40-ft. transit buses using a combination of passenger characteristic data, transit bus curb weight data, and transit ridership data. The findings of this research suggest that the transit bus industry is in a state of competing interests. Weight estimates developed in this research identify that current transit bus models are unable to comply with vehicle weight limits in most jurisdictions even with no passengers on board. Further, these estimates indicate that transit buses have a significant impact on pavements – comparable to those of fully-loaded, five-axle semi-trucks on a per vehicle basis. To date this issue has been addressed in the Canadian Prairie Region by indefinitely granting transit buses overweight permits. However, based on the current state of the transit industry there is little incentive for transit agencies to operate lightweight transit buses and little incentive for transit bus manufacturers to produce lightweight transit buses in order to address pavement and regulatory concerns. Consequently, transit bus axle weight issues in the Canadian Prairie Region are expected to continue in the foreseeable future. / October 2015

The air transportation demand at large hubs in the U.S. is anticipated to double in the near future. Current runway construction plans at selected airports can relieve some capacity and delay problems, but many are doubtful that this solution is sufficient to accommodate the anticipated demand growth in the National Airspace System (NAS). With the worsening congestion problem, it is imperative to seek alternative solutions other than costly runway constructions. In this respect, many researchers and organizations have been building models and performing analyses of the NAS. However, the complexity and size of the problem results in an overwhelming task for transportation system modelers. This research seeks to compose an active design algorithm for an evolutionary airline network model so as to include network specific control properties. An airline network designer, referred to as a network architect, can use this tool to assess the possibilities of gaining more capacity by changing the network configuration. Since the Airline Deregulation Act of 1978, the airline service network has evolved from a point-to-point into a distinct hub-and-spoke network. Enplanement demand on the H&S network is the sum of Origin-Destination (O-D) demand and transfer demand. Even though the flight or enplanement demand is a function of O-D demand and passenger routings on the airline network, the distinction between enplanement and O-D demand is not often made. Instead, many demand forecast practices in current days are based on scale-ups from the enplanements, which include the demand to and from transferring network hubs. Based on this research, it was found that the current demand prediction practice can be improved by dissecting enplanements further into smaller pieces of information. As a result, enplanement demand is decomposed into intrinsic and variable parts. The proposed intrinsic demand model is based on the concept of 'true' origin-destination demand which includes the direction of each round trip travel. The result from using true O-D concept reveals the socioeconomic functional roles of airports on the network. Linear trends are observed for both the produced and attracted demand from the data. Therefore, this approach is expected to provide more accurate prediction capability. With the intrinsic demand model in place, the variable part of the demand is modeled on an air transportation network model, which is built with accelerated evolution scheme. The accelerated evolution scheme was introduced to view the air transportation network as an evolutionary one instead of a parametric one. The network model takes in intrinsic demand data before undergoing an evolution path to generate a target network. The results from the network model suggests that air transportation networks can be modeled using evolutionary structure and it was possible to generate the emulated NAS. A dehubbing scenario study of Lambert-St. Louis International Airport demonstrated the prediction capability of the proposed network model. The overall process from intrinsic demand modeling and evolutionary network modeling is a unique and it is highly beneficial for simulating active control of the transportation networks.

Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 142-144). Also available in electronic format on the Internet.

Advanced modeling tools and methods are essential components for the analyses of congested conditions and advanced Intelligent Transportation Systems (ITS) strategies such as Managed Lanes (ML). A number of tools with different analysis resolution levels have been used to assess these strategies. These tools can be classified as sketch planning, macroscopic simulation, mesoscopic simulation, microscopic simulation, static traffic assignment, and dynamic traffic assignment tools. Due to the complexity of the managed lane modeling process, this dissertation investigated a Multi-Resolution Modeling (MRM) approach that combines a number of these tools for more efficient and accurate assessment of ML deployments. This study clearly demonstrated the differences in the accuracy of the results produced by the traffic flow models incorporated into different tools when compared with real-world measurements. This difference in the accuracy highlighted the importance of the selection of the appropriate analysis levels and tools that can better estimate ML and General Purpose Lanes (GPL) performance. The results also showed the importance of calibrating traffic flow model parameters, demand matrices, and assignment parameters based on real-world measurements to ensure accurate forecasts of real-world traffic conditions. In addition, the results indicated that the real-world utilization of ML by travelers can be best predicated with the use of dynamic traffic assignment modeling that incorporates travel time, toll, and travel time reliability of alternative paths in the assignment objective function. The replication of the specific dynamic pricing algorithm used in the real-world in the modeling process was also found to provide the better forecast of ML utilization. With regards to Connected Vehicle (CV) operations on ML, this study demonstrated the benefits of using results from tools with different modeling resolution to support each other’s analyses. In general, the results showed that providing toll incentives for Cooperative Adaptive Cruise Control (CACC)-equipped vehicles to use ML is not beneficial at lower market penetrations of CACC due to the small increase in capacity with these market penetrations. However, such incentives were found to be beneficial at higher market penetrations, particularly with higher demand levels.

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared.

Transportation agencies face a challenging task to repair damaged roads in an aging transportation network with limited funding. In addition, the funding gap is forecasted to continue widening, which has direct impacts on the performance of surface transportation networks and the nation’s economy in the long run. Recently, transportation agencies were required by a newly enacted law to include national performance-based goals, such as environmental sustainability, in their programming and planning efforts for highway repair and rehabilitation. Therefore, the current practice in the area of highway rehabilitation planning is inadequate to handle this task and new practices are needed to improve the performance of transportation networks while maintain the national goal of maximizing environmental sustainability. Accordingly, this dissertation presents an innovative environmental-based decision-support model for planning highway construction programs. The model is developed in three main parts that are designed to: (1) model total vehicle fuel consumption and public benefits/costs of traveling on transportation networks; (2) evaluate the economic and environmental impacts of highway rehabilitation efforts; and (3) develop a multi-objective optimization model to identify and evaluate highway rehabilitation program(s) that are capable of simultaneously minimizing environmental impact and maximizing public benefits of rehabilitation decisions. First, mathematical models were developed to facilitate estimating the total vehicle fuel consumption and public benefits/cost for road users at the network-level. These models are deigned to estimate vehicle fuel consumption rate, tire depreciation cost, and vehicle repair and maintenance cost rate, in terms of major vehicle–road interaction factors, such as vehicle type, speed, and pavement conditions. The developed and statistically validated models are then used to estimate total vehicle fuel consumption and public benefits/costs at the network-level. Second, a new model was developed for evaluating the impact of decision making in highway rehabilitation efforts on greenhouse gas emissions and public travel costs. The model has the capabilities of: (1) identifying candidate rehabilitation treatment alternatives for damaged or aging pavement; (2) evaluating the impact of these treatments on pavement performance; (3) estimating network fuel consumption due to highway rehabilitation decisions; (4) estimating additional public costs as a result of travel-delay during road construction operations; and (5) evaluating the impact of rehabilitation efforts on public benefits expressed as expected savings in road user costs. Third, a multi-objective optimization model was developed to search for and identify highway rehabilitation programs that are capable of minimizing environmental impact in terms of CO2 emissions while maximizing public benefits under budget constraints. This newly developed model enables planners and decision makers to design and implement highway rehabilitation programs that are cost-effective and environmentally-conscious.

Autonomous Vehicles (AVs) are computer equipped vehicles that can operate without human driver’s active control using information provided by their sensors about the surrounding environment. Self-driving vehicles may have seemed to be a distant dream several years ago, but manufactures’ prototypes showed that AVs are becoming real now. Several car manufactures (i.e. Benz, Audi, etc.) and information technology firms (i.e. Google) have either showcased their fully AVs or announced their robot cars to be released in a few years. AVs hold the promise to transform the ways we live and travel. Although several studies have been conducted on the impacts of AVs, much remains to be explored regarding the various ways in which AVs could reshape our lifestyle. This dissertation addresses the knowledge gap in understanding the potential implications of AV technologies on travel behavior and system modeling. A comprehensive review of literature regarding AV adoption, potential impacts and system modeling was provided. Bass diffusion models were developed to investigate the market penetration process of AVs based on experience learned from past technologies. A stated preference survey was conducted to gather information from university population on the perceptions and attitudes toward AV technologies. The data collected from the Florida International University (FIU) was used to develop econometric models exploring the willingness to pay and relocation choices of travelers in light of the new technologies. In addition, the latest version of the Southeast Planning Regional Model (SERPM) 7.0, an Activity-Based Model (ABM), was employed to examine the potential impacts of AVs on the transportation network. Three scenarios were developed for short-term (2035), mid-term (2045) and long-term (2055) conditions. This dissertation provides a systematic approach to understand the potential implications of AV technologies on travel behavior and system modeling. The results of the survey data analysis and the scenario analysis also provide important inputs to guide planning and policy analysis on the impacts of AV technologies.

An examination of necessary infrastructure improvements in the United States.

During the 19th Century, the Mississippi River was the chief commercial highway in the United States. But for two impediments, the Upper and Lower (Des Moines) Rapids, its entire course of 2400 miles would have offered an untroubled thoroughfare to watercraft.The federal government, as well as private concerns, attempted throughout the better part of that century to alleviate the river of its barriers and to develop its rapids as a source of power. Those attempts were disappointingly unsuccessful, however, and not until the advent of the 20th Century, when the nation had matured both economically and technologically, was the Mississippi freed of its obstacles and developed on a large scale as a source of energy.