The object of this research is to develop a travel demand model. The model predicts outdoor recreational travel of urban recreationists for camping. The development of this model is structured by a set of methodological criteria. These criteria relate to destination choice behavior in the context of recreation travel, and analytical structures of travel demand, in addition to the criterion of model performance. The thrust of this research is to define and evaluate a destination choice function with respect to recreational travel.

Developing real-time traffic diversion strategies is a major issue of Advanced Traffic Management Systems (ATMS), a component of Intelligent Vehicle Highway Systems (IVHS). Traffic diversion utilizes available capacity in the urban network during a congestion-causing event. If an alternative route selected for diversion is not congested at the current time, a certain part of the route may become congested by the time the diverted drivers reach that part of the network. Thus the ability to forecast future traffic variables on each link along various routes in a prompt and accurate fashion may be necessary to ensure the success of a diversion strategy. Forecasting future traffic variables would also be helpful for urban traffic control. In addition, the forecasting model may help assign the vehicles onto the alternate roads, if the information on driver destinations and the routes between a diversion point and the destinations are available. This dissertation is aimed at developing and evaluating two prediction models: link-based model and network-based model. The link-based prediction model has two components. One component is an Auto Regressive Integrated Moving Average (ARIMA) time series model based on the latest (current) traffic data. The other component is the smoothed historical traffic volume (historical average) for that period as obtained from previous days. These two components are combined to represent the dynamic fluctuations in the traffic flow behavior. The combined model is designed to produce the predicted traffic volumes for a look-ahead period of 30 minutes, divided into 6-minute time intervals. The results show that the combined model is promising for light to medium congested traffic conditions. The network-based prediction model combines current traffic, historical average, and upstream traffic. It is presumed that traffic volume on the upstream can be used to predict the downstream traffic in a specific time period. Three prediction models are developed for traffic prediction: a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models are evaluated through regression analysis. The third model is found to be the most applicable while the first model was the least. In order to consider current traffic conditions, a heuristic adaptive weighting system is devised based on the relationships between the origin of prediction and the previous periods. The developed models are applied to real freeway data in 15-minute time interval measured by regular induction loop detectors. The prediction models are shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30-minute or 45-minute. It is noted that the combined models usually produce more consistent forecasts than the historical average. / Ph. D.

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

This effort seeks to answer the question; can a transferable model be developed from easily obtainable, publicly available land-use, census, roadway, and network data for the use in safety performance functions? 474 stop-controlled minor legs were used as the training data set using ordinary least squares regression. A best-fit model of maximum independent variables, n=12 was chosen using an exhaustive approach using Mallow's Cp to select the model with least bias in the predictors. The results of the analysis revealed that the combination of variables from Washington, Ohio, and North Carolina did not have a strong relationship. The best-fit model incorporated functional class information of the major-leg, minor leg functional class information, longitudinal markings, access to a parking lot, and population density of census tract. Validation of the model demonstrated an average 59 percent error between the model estimated and actual AADT values for validation data set (n=54). Furthermore, separate models for each state revealed a lack of uniformity in the dependent variables, and more variance description of the state specific AADT.

[Truncated abstract] In an urban network, any change to the capacity of a road or an intersection will generally result in some traffic changing its route. In addition the presence of intersections creates the need for frequent stops. These stops increase the fuel consumption by anywhere between thirty to fifty percent as evidenced by published standardised vehicle fuel consumption figures for urban and for country driving. Other components of vehicle operating costs such as tyre and brake wear and time costs will also be increased by varying amounts. Yet almost all methods in use for economic evaluation of urban road projects use open road vehicle operating costs (sometimes factored to represent an average allowance for stopping at intersections) for one year or sometimes two years in the analysis period and then make assumptions about how the year by year road user benefits may change throughout the period in order to complete the analysis. This thesis will describe a system for estimating road user costs in an urban network that calculates intersection effects separately and then adds these effects to the travel costs of moving between intersections. Daily traffic estimates are used with a distribution of the flow rate throughout the twenty-four hours giving variable speed of travel according to the level of congestion at different times of the day. For each link, estimates of traffic flow at two points in time are used to estimate the year-by-year traffic flow throughout the analysis period by linear interpolation or extrapolation. The annual road user costs are then calculated from these estimates. Annual road user benefits are obtained by subtracting the annual road user costs for a modified network from the annual road user costs for an unmodified network. The change in the road network maintenance costs are estimated by applying an annual per lane maintenance cost to the change in lane-kilometres of road in the two networks. The Benefit Cost Ratio is calculated for three discount rates. An estimate of the likely range of error in the Benefit Cost Ratio is also calculated