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Estimation of short-term bus travel time is an essential component of effective intelligent transportation systems (ITS), including traveler information systems and transit signal priority (TSP) strategies. Several technologies, such as automated vehicle location (AVL) systems, can provide real-time information for estimation of bus travel time. However, low resolution of data from such technologies presents a challenge to accurate estimation of travel time. Several models for estimation of bus travel time at signalized urban arterials were developed and tested. These models used low-frequency AVL data and required only knowledge of network specifications such as locations of bus stops and intersections. First, a linear regression model was developed; it decomposed total travel time into its components, including running travel time, dwell time at bus stops, and delay at signalized intersections. Second, various machine learning models, including support vector regression (SVR) with nonlinear kernel, ridge, Lasso, decision tree, and Bayesian ridge were trained by using Python libraries such as scikit-learn and evaluated. A segment of Washington Street in Boston, Massachusetts, was selected as the study site. The results indicate that the SVR model outperformed other regression models in generalized error measures, in particular those of mean absolute error and root mean square error. The findings of this study can lead to improved traveler information systems and more-efficient TSP strategies and, overall, can contribute to better transit quality of service.

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Comfort is an important aspect of the transit passenger experience. Crowding can significantly decrease passenger comfort and disrupt service delivery, causing passenger travel times to increase and even resulting in passengers being unable to board an arriving vehicle. This research explores the use of automatically collected vehicle location data, fare transaction data, and passenger origin-destination inference to measure crowding on buses. Three model components are involved: scaling vehicle trip-level origin-destination transfer data, measuring crowding as perceived by passengers through performance measures defined for this purpose, and determining the sources of crowding. The latter is important to identify the most effective means of addressing crowding in each case. The models are tested on data from the Massachusetts Bay Transportation Authority, and examples of graphical applications already being used by planners are presented.

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