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a b s t r a c tWaiting time in transit travel is often perceived negatively and high-amenity stops and stations are becoming increasingly popular as strategies for mitigating transit riders' aversion to waiting. However, beyond recent evidence that realtime transit arrival information reduces perceived waiting time, there is limited empirical evidence as to which other specific station and stop amenities can effectively influence user perceptions of waiting time. To address this knowledge gap, the authors conducted a passenger survey and video-recorded waiting passengers at different types of transit stops and stations to investigate differences between survey-reported waiting time and video-recorded actual waiting time. Results from the survey and video observations show that the reported wait time on average is about 1.21 times longer than the observed wait time. Regression analysis was employed to explain the variation in riders' reported waiting time as a function of their objectively observed waiting time, as well as station and stop amenities, weather, time of the day, personal demographics, and trip characteristics. Based on the regression results, most waits at stops with no amenities are perceived at least 1.3 times as long as they actually are. Basic amenities including benches and shelters significantly reduce perceived waiting times. Women waiting for more than 10 min in perceived insecure surroundings report waits as dramatically longer than they really are, and longer than do men in the same situation. The authors recommend a focus on providing basic amenities at stations and stops as broadly as possible in transit systems, and a particular focus on stops on low-frequency routes and in less safe areas for security measures.

This study seeks to examine transit's role in promoting social equity by assessing the before-after impacts of recent transit changes in the Twin Cities, including the opening of the Hiawatha light-rail line, on job accessibility among workers of different wage categories. Geospatial, descriptive, and regression analyses find that proximity to light-rail stations and bus stops offering direct rail connections are associated with large, statistically significant gains in accessibility to low-wage jobs. These gains stand out from changes in accessibility for the transit system as a whole. Implications of the study results for informing more equitable transit polices are discussed.

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Physical planning can benefit from deeper insight into the space-use options that individuals have. This paper examines how individuals’ uses of space are related to urban form factors at their residences, after controlling for traffic congestion, weather, and individual or household characteristics. The behavioral data analyzed came from the 2006 Greater Triangle Region Travel Study in North Carolina. Individuals’ uses of space were measured by daily activity space–the minimum convex polygon that contains all the daily activity locations–and daily travel distance, and were estimated by the use of spatial regression models. The results showed that the residents of densely developed neighborhoods with more retail stores and better-connected streets generally have a smaller area of daily activity space and a shorter daily travel distance. In addition, urban form factors were compared in terms of their importance in explaining individuals’ space-use behavior. It was found that retail mix and street connectivity are key factors relating to individuals’ uses of space, whereas building density was less important. The findings shed light on possible land use solutions toward the better coordination of services in space.

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We propose and compare combinations of several methods for classifying transportation activity data from smartphone GPS and accelerometer sensors. We have two main objectives. First, we aim to classify our data as accurately as possible. Second, we aim to reduce the dimensionality of the data as much as possible in order to reduce the computational burden of the classification. We combine dimension reduction and classification algorithms and compare them with a metric that balances accuracy and dimensionality. In doing so, we develop a classification algorithm that accurately classifies five different modes of transportation (i.e., walking, biking, car, bus and rail) while being computationally simple enough to run on a typical smartphone. Further, we use data that required no behavioral changes from the smartphone users to collect. Our best classification model uses the random forest algorithm to achieve 96.8% accuracy.