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Pu W.,Metropolitan Washington Council of Governments
Transportation Research Record | Year: 2011

Travel time reliability is measured in various ways. Measures used in the transportation engineering field include the 90th or 95th percentile travel time, standard deviation, coefficient of variation, percent of variation, buffer index, planning time index, travel time index, skew statistic, misery index, frequency of congestion, and on-time arrival. Correlations and inconsistencies between these measures were observed on a case-by-case basis in past studies, without a full explanation or examination of the fundamental causes of such differing relationships. This paper analytically examines a number of reliability measures and explores their mathematical relationships and interdependencies. With the assumption of lognormal distributed travel times and the use of percent point function, a subset of reliability measures is expressed in relation to the shape parameter or the scale parameter of the lognormal distribution or to both. This process enables a clear understanding of the quantitative relationships and variation tendencies of different measures. Contrary to some previous studies and recommendations, this paper finds that the coefficient of variation, instead of the standard deviation, is a good proxy for several other reliability measures. The use of the average-based buffer index or average-based failure rate is not always appropriate, especially when travel time distributions are heavily skewed, in which case the median-based buffer index or failure rate is recommended. Source


Son H.T.,Metropolitan Washington Council of Governments | Kweon Y.-J.,Virginia Center for Transportation Innovation and Research | Park B.T.,University of Virginia
Transportation Research Part C: Emerging Technologies | Year: 2011

Typical engineering research on traffic safety focuses on identifying either dangerous locations or contributing factors through a post-crash analysis using aggregated traffic flow data and crash records. A recent development of transportation engineering technologies provides ample opportunities to enhance freeway traffic safety using individual vehicular information. However, little research has been conducted regarding methodologies to utilize and link such technologies to traffic safety analysis. Moreover, traffic safety research has not benefited from the use of hurdle-type models that might treat excessive zeros more properly than zero-inflated models.This study developed a new surrogate measure, unsafe following condition (UFC), to estimate traffic crash likelihood by using individual vehicular information and applied it to basic sections of interstate highways in Virginia. Individual vehicular data and crash data were used in the development of statistical crash prediction models including hurdle models. The results showed that an aggregated UFC measure was effective in predicting traffic crash occurrence, and the hurdle Poisson model outperformed other count data models in a certain case. © 2011 Elsevier Ltd. Source


Martchouk M.,Metropolitan Washington Council of Governments | Mannering F.,Purdue University | Bullock D.,Purdue University
Journal of Transportation Engineering | Year: 2011

Travel time has long been an important performance measure for assessing traffic conditions and the extent of highway congestion. However, recently, more and more attention has been given to understanding the uncertainty regarding the variability in travel time from hour to hour and day to day-variability that is known to be a source of great frustration among road users. In this paper, travel-time variability is studied by collecting travel-time data using probe data on freeway segments in Indianapolis obtained using anonymous Bluetooth sampling techniques. The data show considerable travel-time variability is induced by adverse weather, but also show that variability results from unexpected changes in traffic flow rates and driver behavior. Various statistical models are estimated to understand the effect that traffic-related variables have on variability in individual vehicle travel times as well as average travel times. For individual vehicle travel times, a model is estimated to study how the probability of a vehicle's duration of time spent on freeway segments changes over time. Interestingly, this model shows that the point where the conditional probability of travel times becoming longer occurs roughly at the onset of level-of-service F conditions-an important finding that supports the traffic-density definitions of level of service F used in the highway capacity manual. Another model, estimated using seemingly unrelated regression estimation, studies 15-min interval average vehicle travel times and the standard deviation of these travel times on the basis of speed and volume (available from remote traffic microwave sensors) and time of day indicators. This model provides interesting insights into the traffic parameters that affect average travel time and its variability. © 2011 American Society of Civil Engineers. Source


Duduta N.,World Resources Institute | Zhang Q.,Massachusetts Institute of Technology | Kroneberger M.,Metropolitan Washington Council of Governments
Transportation Research Record | Year: 2014

The existing research and modeling applications for predicting pedestrians' decision to cross on red at signalized intersections have most commonly modeled this decision as a function of pedestrian characteristics (such as age and molality restrictions), traffic conditions, and signal delay. For this study, field observations suggested that the physical configuration of the crosswalk should also he an important predictor of the decision to cross on red, because pedestrians should be less likely to cross on red at major intersections with longer crosswalks. In addition, the overall configuration and complexity of the signal also have an impact on signal compliance. The study tested these hypotheses by collecting video data of pedestrian behavior at signalized intersections in Washington, D.C., and by building a binary logit model to predict the probability of crossing on red. The study found that certain signal phases, such as protected left turns, were strongly correlated with a higher probability of crossing on red. The study also found that the length of crosswalks was inversely correlated with the probability of crossing on red. Through tests of the model's predictive accuracy under various specifications, the design variables (e.g., geometry and types of signal phases) were found to he key in improving the model's predictive ability. The findings offer insights for the design of signalized intersections and shed light on the complexity of the relationship between crossing on red and pedestrian safety, because some characteristics that are associated with a lower incidence of pedestrian crashes are also associated with more crossing on red. Source


Pu W.,Metropolitan Washington Council of Governments
Transportation Research Record | Year: 2013

The newly enacted U.S. transportation act Moving Ahead for Progress in the 21st Century requires the reporting of highway performance in terms of congestion and reliability. With unprecedented coverage and detail, private-sector probe-based traffic data are one of the most promising sources for the establishment of a highway performance monitoring system that can track congestion and reliability on a national scale. But the data alone are not enough; many variants in the data and the data-processing procedures can cause significantly different results even from the same set of data. As demonstrated in this paper, the space mean speed feature of the probe data, the referencing of the segment locations, the frequency of data archiving, the calculation procedures, and the difference between experienced travel time and instantaneous travel time could play a role in determining the values of certain performance measures. Standardized data elements and data processing procedures should be established in the effort to use proprietary probe data to measure highway performance. Source

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