This paper attempts to develop time-series models for forecasting traffic volume in urban arterials. The Box-Jenkins approach is used to estimate the time-series models. A 1-min data set representing traffic volume on five major urban arterials were available to estimate time-series models. The Box-Jenkins autoregressive integrated moving average (ARIMA) model of order (0, 1, 1) turned out to be the most adequate model in reproducing all original time series. The developed model is easy to understand and implement. Further, the model is computationally tractable, and only requires the storage of the last forecasted error and current traffic observation.

The objective of this study is to develop a methodology capable of modeling travelers' postwork activity patterns and thus tracing the movements of travelers through space and time. After reviewing the state of the art in activity participation and scheduling, a modeling system consisting of a set of discrete/continuous models and duration-based models is specified and estimated. The discrete/continuous models are used to predict the travelers activity-type preference (discrete), the travel time to the activity and back home (continuous), and activity duration (continuous). Duration models are used to model the travelers home-stay duration capturing the factors that motivate travelers to terminate their home-stay and undertake additional out-of-home activities.

This paper develops a system of disaggregate models that accounts for the effect of intersection, driver, and traffic characteristics on gap acceptance for left-turn maneuvers at urban T-intersections controlled by stop signs on the minor roads. The waiting time for each driver is first modeled using the hazard function. The binary probit model is then used to find the drivers' probabilities of accepting or rejecting a gap. These probabilities are used to calculate the critical gaps of individual drivers. The expected waiting time is included in the model as an explanatory variable. A multiple regression model is used for predicting the intersection mean critical gap. To estimate the parameters of the models, disaggregate data were collected by observing and interviewing drivers at 15 urban T-intersections in the Greater Amman area. The results show that the distribution of critical gaps is influenced by driver socioeconomic characteristics, expected waiting time, time of day, and trip purpose. The mean critical gap is influenced by total opposing traffic flow, number of major-approach lanes, presence of a median with a left-turn lane, maneuver type, speed of major road, and time of day. The proposed methodology can be used to calculate the critical gaps of individual drivers and, in turn, the mean critical gap at a specified intersection that is needed for delay and capacity analysis.