Motorcycles are becoming increasingly popular, especially in developing countries. This increasing exposure, combined with the fact that they most likely result in injury crashes, necessitates new strategies to reduce the severity of crashes involving motorcycles. This study focused on analyzing the factors affecting the injury severity of crashes involving motorcycles in Dar es Salaam, Tanzania. A Bayesian Multinomial Logit Model with a Dirichlet random effect parameter was used to analyze four years (2013–2016) of crash data. The main benefit of this model is that it accounts for the groups of unobserved heterogeneity that exists in the data. The response variable is injury severity with three categories: fatal/severe injury, minor injury, and possible/no injury. The potential variables affecting motorcycle crashes were grouped into four categories: human, environment, roadway, and crash. Relative risk ratios and average pseudoelasticity were obtained to identify the factors influencing the severity of motorcycles crashes. The model results suggested that the following factors increase the probability of fatal/severe injury crashes: speeding, violations, head-on collisions, weekend, and off-peak hours. Several countermeasures were recommended based on the study findings. These countermeasures propose holistic safety improvement strategies encompassing the three E’s of highway safety, namely Engineering, Education, and Enforcement.

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.