A description is given of a methodology for estimating transit walk accessibility at the home end of transit trips and for forecasting transit walk accessibility at the home end for a future year, given forecast population and employment data, transit route information, and type of street configuration. The methodology for estimating transit walk accessibility overcomes the problems associated with natural and man-made barriers such as water bodies and community walls and the problem of uneven distribution of population. A comparison of the results with those from the traditional buffer method, as well as with network ratio methods that consider actual walk distance along streets, showed that both the buffer method and network ratio methods tended to overestimate transit walk accessibility. Regression analysis also showed that the new transit walk accessibility measure was a stronger predictor of transit use than that produced using the buffer method. The methodologies may be applied to transit planning, urban design for sustainable development, and long-range transit demand modeling.

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Secondary crashes are generally understood as crashes that occur as a result of primary incidents. Research is needed to determine the nature and cause of these crashes. However, to date, this research has been limited for two reasons. First, there is no uniform definition of secondary crashes. Second, the usually poor quality of incident data and the unavailability of related traffic data make any analysis of secondary crashes difficult. This paper describes a study that uses a comprehensive incident database from District 4 of the Florida Department of Transportation to identify freeway secondary crashes and their contributing factors. A method based on a cumulative arrival and departure traffic delay model was developed to estimate the maximum queue length and the associated queue recovery time for incidents with lane blockages. Descriptive statistics and logistic regression analysis were applied to understand the factors contributing to secondary crashes. The logistic regression analysis indicates that the following four factors have significant effects on the likelihood of secondary crashes: primary incident type, primary incident lane-blockage duration, time of day, and whether the incident occurred on northbound I-95.

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