This paper evaluates different factors and parameters contributing to likelihood of bicycle crash injury severity levels. Multinomial Logit (MNL) model was used to analyze impact of different roadway features, traffic characteristics and environmental conditions associated with bicycle crash injury severities. The multinomial model was used due to its flexibility in quantifying the effect of the independent variables for each injury severity categories. Model results showed that, severity of bicycle crashes increases with increase in vehicles per lane, number of lanes, bicyclist alcohol or drug use, routes with 35 -45 mph posted speed limits, riding along curved or sloped road sections, when bicyclists approach or cross a signalized intersection, and at driveways. In addition, routes with a high percentage of trucks, roadway sections with curb and gutter, cloudy or foggy weather and obstructed vision were found to have high probability of severe injury. Segments with wider lanes, wide median and wide shoulders were found to have low likelihood of severe bicycle injury severities. Limited lighting locations was found to be associated with incapacitating injury and fatal crashes, indicating that insufficient visibility can potentially lead to severe crashes. Other findings are also presented in the paper.

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Estimation of flexible-statistical models of travel demand involves tuning varying parameters, hyperparameters, manually and iteratively. Proper tuning of hyperparameters results in superior models. However, considerable expertise, including technical knowledge of statistics, data mining or machine learning, and experience are required to tune hyperparameters and consequently generate appropriate models. Moreover, tuning hyperparameters is prone to subjective error and consequently produces travel demand models that are difficult to reproduce and extend, and makes the development more an art than a science. There is a need for methods to reduce or eliminate subjectivity during the tuning process. This study proposed a framework to reduce subjectivity during the tuning of hyperparameters required for the estimation of nonparametric models of activity-duration. That is, a flexible-statistical framework, which leverages state-of-the-art innovations in Bayesian optimization (BO), was proposed to estimate Gaussian process models of activity duration and associated hyperparameters. The framework was applied to estimate duration models for five types of out-of-home non-mandatory activity episodes for household individuals in the greater Los Angeles area. Experiments demonstrate that the accuracy of results from the proposed framework are superior to those from the current tuning process, and are obtained in a fraction of the time. The proposed framework could potentially increase the productivity of modelers by reducing time required to tune hyperparameters.

The paper analyses integrating origin-destination (O-D) survey results with stochastic user equilibrium (SUE) in traffic assignment. The two methods are widely used in transportation planning but their applications have not yet fully integrated. While O-D gives a generalized trip patterns, purpose and characteristics, SUE provides optimal trip distributions using the characteristics found in O-D survey. The paper utilized O-D and SUE in route relocation study for the town of Coamo in Puerto Rico. The O-D survey was used initially in studying possible trip distribution and assignment for the new route. Initial distribution and assignment of traffic to the existing roadway networks and the proposed route were allocated utilizing the O-D survey findings. The SUE was then used to optimize the assignments considering roadway characteristics such as number of lanes, capacity limits, free flow speed, signal spacing density, travel time and gasoline cost. The travel time was optimized through the Bureau of Public Roads (BPR) equation found in 2000 HCM. The optimal trips found from the SUE were then used to propose the final alignment of the new route. Traffic assignment from the SUE was slightly different from those initially assigned using O-D, indicating there was optimization. The assignment on new route was increased by 13.8% from the one assigned using O-D while assignment on the existing link was reduced by 22%

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