Abstract: A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Abstract: A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Abstract: Embodiments include fare evasion inference using personal mobility data in a public transportation system. Aspects include receiving personal mobility data and constructing a plurality of personal trajectories from the personal mobility data. Aspects also include mapping each of the plurality of personal trajectories to a route and time of the public transportation system and calculating an estimated occupancy for each route and time in the public transportation system based on a number of personal trajectories mapped to each route and time. Aspects further include comparing the estimated occupancy with a paying passenger data received from the public transportation system and assigning a score to each route and time in the public transportation system based on the comparison of the estimated occupancies and the paying passenger data, wherein the score is indicative of a probability of a fare evasion occurring.

Abstract: Embodiments of the disclosure include a method for providing a consolidated estimated time of arrival in a scheduled transportation system. The method includes receiving at least one of a scheduled time of arrival, a real-time estimated time of arrival and a predictive estimated time of arrival and assigning a weight to each of the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival. The method also includes calculating a confidence level and the consolidated estimated time of arrival based on the weights and the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival.

Abstract: Embodiments of the disclosure include a method for providing a consolidated estimated time of arrival in a scheduled transportation system. The method includes receiving at least one of a scheduled time of arrival, a real-time estimated time of arrival and a predictive estimated time of arrival and assigning a weight to each of the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival. The method also includes calculating a confidence level and the consolidated estimated time of arrival based on the weights and the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival.

Abstract: A method for inferring inconvenient travel experiences may include analyzing, with a processing device, a record of a displacement of a traveler, detecting a traveler displacement pattern indicative of a potential inconvenience, verifying a transport network status corresponding to the potential inconvenience, and validating an inferred inconvenience based on the potential inconvenience and the transport network status.

Abstract: A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Abstract: A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Abstract: Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

Abstract: Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

Abstract: Embodiments of the disclosure include a method for providing a consolidated estimated time of arrival in a scheduled transportation system. The method includes receiving at least one of a scheduled time of arrival, a real-time estimated time of arrival and a predictive estimated time of arrival and assigning a weight to each of the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival. The method also includes calculating a confidence level and the consolidated estimated time of arrival based on the weights and the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival.

Abstract: Embodiments of the disclosure include a method for providing a consolidated estimated time of arrival in a scheduled transportation system. The method includes receiving at least one of a scheduled time of arrival, a real-time estimated time of arrival and a predictive estimated time of arrival and assigning a weight to each of the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival. The method also includes calculating a confidence level and the consolidated estimated time of arrival based on the weights and the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival.

Abstract: Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.