The wait time of bus patrons at bus stops is one of several measures for assessing reliability of transit services, especially in urban areas. The uncertainty associated with waiting affects bus patrons' perception of quality of the service provided. Studies in this subject area have therefore been of interest to transit service agencies and officials. This paper presents the findings of a study conducted to determine patrons' maximum acceptable wait times (beyond the scheduled arrival time) at bus stops in an urban area. In all, 3387 bus patrons at 71 selected bus stops were surveyed over a period of 9 months. The results of the survey showed that the least acceptable wait time beyond the scheduled arrival time was 1 minute, while the maximum acceptable wait time was reported to be 20 minutes. Also, only one-third (33%) of the total number of patrons surveyed were willing to wait up to 5 minutes beyond the scheduled arrival time of buses. In addition, patrons are willing to wait longer in warm weather. On average, white patrons were found to have the least maximum acceptable wait times, followed by Hispanics, Asians, and then Blacks.

The Washington, DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41 789 crashes at unsignalized intersections, which resulted in 14 168 injuries and 51 fatalities. The economic cost of these fatalities has been estimated to be in the millions of dollars. It is therefore necessary to investigate the predictability of the occurrence of theses crashes, based on pertinent factors, in order to provide mitigating measures. This research focused on the development of models to predict the injury severity of crashes using support vector machines (SVMs) and Gaussian naïve Bayes classifiers (GNBCs). The models were developed based on 3307 crashes that occurred from 2008 to 2015. Eight SVM models and a GNBC model were developed. The most accurate model was the SVM with a radial basis kernel function. This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%. The GNBC produced the worst-performing model with an accuracy of 48.5%. These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.

In 2015, about 20% of the 52,231 fatal crashes that occurred in the United States occurred at unsignalized intersections. The economic cost of these fatalities have been estimated to be in the millions of dollars. In order to mitigate the occurrence of theses crashes, it is necessary to investigate their predictability based on the pertinent factors and circumstances that might have contributed to their occurrence. This study focuses on the development of models to predict injury severity of angle crashes at unsignalized intersections using artificial neural networks (ANNs). The models were developed based on 3,307 crashes that occurred from 2008 to 2015. Twenty-five different ANN models were developed. The most accurate model predicted the severity of an injury sustained in a crash with an accuracy of 85.62%. This model has 3 hidden layers with 5, 10, and 5 neurons, respectively. The activation functions in the hidden and output layers are the rectilinear unit function and sigmoid function, respectively.

In this research, a strategy to improve mobility and reduce delay on road segments is explored via modeling and simulation. Thirty selected corridors with combination of signalized and unsignalized intersections were identified for this study. Each segment consists of at least one AWSC and two signalized intersections at which field data were obtained (lane configurations, signal timing, traffic volumes, etc.). The selected AWSC intersections on the segments were within 305 m (1000 feet) from the upstream or downstream signalized intersections. Synchro software program was utilized to model the existing condition of the segments based on which the strategy for mobility improvement was explored. The field data were used as input in Synchro software application to model two scenarios: existing or the "before" scenario, and the "after" scenario. The unsignalized intersections were signalized (and optimized) in the "after" scenario. The measures of effectiveness used to assess the efficiency of the strategy were average travel speed, control delay and 95 th percentile queue length. The analyses were conducted for both the morning (AM) and evening (PM) peak periods. The results of the analyses showed reductions in control delay and 95 th percentile queue lengths that were statistically significant, while the average travel speed of vehicles significantly increased at 5% level of significance. The evaluation determined that the signalization of some unsignalized intersections (which are 305 m or less from existing signalized intersections) may improve mobility despite the fact that these locations do not meet the MUTCD warrants for signalization. These findings would aid transportation engineers and planners to consider and evaluate this option when making decisions on signalization of intersections in urban areas.