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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., Introduction Traffic congestion can be caused by recurrent events such as the cyclical AM or PM peaks of traffic demand on any given day and by non-recurrent events such as [...]

Before the implementation of a parking information system, it is necessary to evaluate the parking difficulty, technology choice, and system costs. In this study, the parking problem was quantified by [...]

This paper presents preliminary results of the development of a virtual flight data recorder (VFDR) for commercial airliners. Federal Aviation Administration (FAA) regulations, currently being revised, mandate the recording of 11 dynamic parameters, not including the control surface deflections. The absence of these data can be critical for crash investigation purposes. This paper proposed the introduction of a VFDR based on a neural network simulator (NNS) and a neural network reconstructor (NNR). The NNS is trained, using flight data for the particular aircraft, to simulate any desired control surface deflections (or any other parameter of interest not recorded by the FDR), minimizing a cost function based on the differences between the available data from the FDR and the output from the NNS. The VFDR scheme has been introduced, tested, and validated with flight data from a Boeing 737-300 with an FDR with extended recording capabilities showing accurate reconstruction of the control surface deflections' time histories. The VFDR can be considered a tool for crash investigations where control surface failures are believed to be a factor.

Busways are controlled-access facilities dedicated for bus service separated from general traffic. The concept of busways was first given serious consideration in the 1960s; however, only a few of them have been constructed in North America. This paper examines the potential of busway transit in providing urban environments with cost-effective mobility. The review makes the case that there are some misconceptions concerning the cost and level-of-service characteristics of busways. In the final section, a comparison is made between busways and their most prominent competitor, light rail. The comparison is done in the framework of the four most cited advantages of light rail, and concludes that busways, in most cases, are likely to be a superior mode of transit to light rail.

Vehicle delay is one of the most serious impacts of highway workzones on existing traffic operations. The present study provides a comprehensive and detailed estimation of traffic delay due to a freeway workzone where the roadway in one direction is closed and the traffic usually on the closed roadway is diverted to share the roadway in the opposite direction. The total delay was decomposed into speed reduction delay and congestion delay. Different procedures were developed to estimate speed reduction delay for several roadway conditions. A mathematical model was developed to find the length of the queue upstream of a workzone and a procedure was generated to estimate daily congestion delay under any given condition. Alternative roadway closure patterns along the length of a given project were evaluated in terms of traffic control cost and additional road user costs. The optimal workzone length for a given project was studied. Procedures were developed to determine the optimal workzone length.

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]