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An effective real-time estimation of the travel time for vehicles, using AVL (Automatic Vehicle Locators) has added a new dimension to the smart city planning. In this paper, the authors used data collected over several months from a transit agency and show how this data can be potentially used to learn patterns of travel time during specially planned events like NFL (National Football League) games and music award ceremonies. The impact of NFL games along with consideration of other factors like weather, traffic condition, distance is discussed with their relative importance to the prediction of travel time. Statistical learning models are used to predict travel time and subsequently assess the cascading effects of delay. The model performance is determined based on its predictive accuracy according to the out-of-sample error. In addition, the models help identify the most significant variables that influence the delay in the transit system. In order to compare the actual and predicted travel time for days having special events, heat maps are generated showing the delay impacts in different time windows between two timepoint-segments in comparison to a non-game day. This work focuses on the prediction and visualization of the delay in the public transit system and the analysis of its cascading effects on the entire transportation network. According to the study results, the authors are able to explain more than 80% of the variance in the bus travel time at each segment and can make future travel predictions during planned events with an out-of-sample error of 2.0 minutes using information on the bus schedule, traffic, weather, and scheduled events. According to the variable importance analysis, traffic information is most significant in predicting the delay in the transit system. 

Effective public transit operations are one of the fundamental requirements for a modern community. Recently, a number of transit agencies have started integrating automated vehicle locators in their fleet, which provides a real-time estimate of the time of arrival. In this paper, we use the data collected over several months from one such transit system and show how this data can be potentially used to learn long term patterns of travel time. More specifically, we study the effect of weather and other factors such as traffic on the transit system delay. These models can later be used to understand the seasonal variations and to design adaptive and transient transit schedules. Towards this goal, we also propose an online architecture called DelayRadar. The novelty of DelayRadar lies in three aspects: (1) a data store that collects and integrates real-time and static data from multiple data sources, (2) a predictive statistical model that analyzes the data to make predictions on transit travel time, and (3) a decision making framework to develop an optimal transit schedule based on variable forecasts related to traffic, weather, and other impactful factors. This paper focuses on identifying the model with the best predictive accuracy to be used in DelayRadar. According to the preliminary study results, we are able to explain more than 70% of the variance in the bus travel time and we can make future travel predictions with an out-of-sample error of 4.8 minutes with information on the bus schedule, traffic, and weather.