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Traffic delay caused by incidents is closely related to three variables: incident frequency, incident duration, and the number of lanes blocked by an incident that is directly related to the bottleneck capacity. Relatively, incident duration has been more extensively studied than incident frequency and the number of lanes blocked in an incident. In this study, we provide an investigation of the influencing factors for all of these three variables based on an incident data set that was collected in New York City (NYC). The information about the incidents derived from the identification can be used by incident management agencies in NYC for strategic policy decision making and daily incident management and traffic operation. In identifying the influencing factors for incident frequency, a set of models, including Poisson and Negative Binomial regression models and their zero-inflated models, were considered. An appropriate model was determined based on a model decision-making tree. The influencing factors for incident duration were identified based on hazard-based models where Exponential, Weibull, Log-logistic, and Lognormal distributions were considered for incident duration. For the number of lanes blocked in an incident, the identification of the influencing factors was based on an Ordered Probit model which can better capture the order inherent in the number of lanes blocked in an incident. As identified in this study, rain is the only factor that significantly influenced incident frequency. For incident duration and the number of lanes blocked in an incident, various factors had significant impact. As concluded in this study, there is a strong need to identify the influencing factors in terms of different types of incidents and the roadways where the incidents occured.

Even though the MOBILE5 model has been widely used in air-quality analysis, it cannot be used in the evaluation of transportation improvements that result in a reduction of vehicle acceleration or deceleration. In this study, least-square regression models were developed to take into account the effect of acceleration or deceleration. To fully capture the dynamics of acceleration or deceleration, the value of acceleration or deceleration in the current period and those in previous periods were included. In addition, the durations of acceleration or deceleration were also considered. Grade was incorporated into the models by using it to adjust the values of acceleration or deceleration. In addition to these independent variables, variables that represent tractive power were also introduced into the models. The results from the calibration of the models indicated that most of the coefficients can be reasonably explained. The emissions model developed in this study was evaluated from microscopic and macroscopic perspectives. The results show that the emissions model developed in this study performs better than two other existing emissions models. DOI: 10.1061/(ASCE)0733-947X(2004)130:3(348) CE Database subject headings: Air quality; Emissions; Models; Air pollution.

The CUSUM (cumulative sum of log-likelihood ratio) algorithm is a detection algorithm that shows potential for the improvement of incident detection algorithms because it is designed to minimize the mean detect delay for a given false alarm constraint and it can also detect changes with different patterns. In this study, the CUSUM algorithm was applied to freeway incident detection by integrating traffic measurements from two contiguous loop detectors and the non-stationarity of traffic flows. The developed algorithm was tested based on incident data from the PATH program, with consideration given to the impact of different geometric conditions on algorithm performance. It was also compared with two existing algorithms, in order to address the influence of traffic patterns. The evaluation results show that the CUSUM incident detection algorithm can perform equally well in comparison with the selected algorithms. [ABSTRACT FROM AUTHOR]