Journal of Transportation Engineering-asce 

Abstract: This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations.

Abstract: Several hybrid regression models that predict hot stabilized vehicle fuel consumption and emission rates for light-duty vehicles and light-duty trucks are presented in this paper. Key input variables to these models are instantaneous vehicle speed and acceleration measurements. The energy and emission models described in this paper utilize data collected at the Oak Ridge National Laboratory (ORNL) that included fuel consumption and emission rate measurements (CO, HC, and NOx) for five light-duty vehicles and three light-duty trucks as a function of the vehicle’s instantaneous speed and acceleration levels. The fuel consumption and emission models are found to be highly accurate as compared to the ORNL data, with coefficients of determination ranging from 0.92 to 0.99. Given that the models utilize the vehicle’s instantaneous speed and acceleration levels as independent variables, these models are capable of evaluating the environmental impacts of operational-level projects including intelligent transporta...

Abstract: Traffic accidents at urban intersections result in a huge cost to society in terms of death, injury, lost productivity, and property damage. Unfortunately, the elements that effect the frequency of intersection accidents are not well understood and, as a result, it is difficult to predict the effectiveness of specific intersection improvements that are aimed at reducing accident frequency. Using seven-yr accident histories from 63 intersections in Bellevue, Washington (all of which were targeted for operational improvements), this paper estimates a negative binomial regression of the frequency of accidents at intersection approaches. The estimation results uncover important interactions between geometric and traffic-related elements and accident frequencies. The findings of this paper provide exploratory methodological and empirical evidence that could lead to an approach to estimate the accident reduction benefits of various proposed improvements on operationally deficient intersections.

Abstract: The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will support proactive, dynamic traffic control. However, previous attempts to develop traffic volume forecasting models have met with limited success. This research effort focused on developing traffic volume forecasting models for two sites on Northern Virginia's Capital Beltway. Four models were developed and tested for the freeway traffic flow forecasting problem, which is defined as estimating traffic flow 15 minutes into the future. They were the historical average, time-series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the other models. A Wilcoxon signed-rank test revealed that the nonparametric regression model was easy to implement, and proved to be portable, performing well at two distinct sties. Based on its success, research is ongoing to refine the nonparametric regression model and to extend it to produce multiple interval forecasts.

Abstract: After reviewing the problem of short-term traffic forecasting, a nonparametric regression method, the \Ik\N-nearest neighbor (\Ik\N-NN) approach, is suggested as a candidate forecaster that might sidestep some of the problems inherent in parametric forecasting approaches. An empirical study using actual freeway data is devised to test the \Ik\N-NN approach, and compare it to simple univariate linear time-series forecasts. The \Ik\N-NN method performed comparably to, but not better than, the linear time-series approach. However, further research is needed to delineate those situations where the \Ik\N-NN approach may, or may not be, preferable. Particular attention should be focused on whether or not regression methods, which forecast mean values, are appropriate for forecasting the extreme values characteristic of transitions from the uncongested traffic regime to the congested regime. In addition, larger data bases may improve the accuracy of the \Ik\N-NN method.