Protected intersections are used to facilitate safe crossings for non-motorized users. As a relatively new treatment in North America, it is essential to understand how their design elements, such as bicycle intersection-crossing pavement markings and corner refuge island size, enhance bicyclist safety. A driving simulation experiment was developed to test the effectiveness of different design elements of protected intersections on driver speeds. Participants were exposed to different protected intersection designs that varied with respect to the corner refuge island width and bicycle intersection-crossing pavement marking levels. Their speed at two parts of the right turn, that is, approach and curve speed, was analyzed. A combination of design elements, participant demographics, or bicyclist presence at the intersection affects driver behavior at a protected intersection. The results indicate that the presence of a bicyclist crossing a protected intersection significantly reduces speeds for drivers performing a right turn. Corner refuge islands with larger width were found to reduce speed at the curve as they were accompanied by larger curb extensions which essentially reduce the space for the automobiles. Bicycle intersection-crossing pavement markings influenced only approach speeds prior to the actual turn since that is the location where they were the most visible. Age, gender, and bicycling frequency were observed to affect turning speeds, indicating that design elements alone cannot determine the safety effectiveness of a protected intersection. The findings of this study can guide the implementation of protected intersections.

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High quality data on road crashes, road design characteristics, and traffic are typically required to predict crash frequency. Surrogate Safety Measures (SSMs) are an alternative category of indicators that can be used in road safety analyses in order to quantify various unsafe traffic events. The objective of this research is to exploit road geometry data and SSMs toward various road crash investigations in motorway segments. To that end, for this analysis, a database containing data on injury and property-damage-only crashes, road design characteristics, and SSMs of 668 segments was compiled and utilized. The results of the developed negative binomial regression model revealed that crash frequency is positively correlated with the average annual daily traffic volume, the length of the segment, harsh accelerations, and harsh braking. Moreover, four distinct clusters representing crash risk levels of the examined segments emerged from the hierarchical clustering procedure, ranging from more risk-prone, potentially unsafe locations to more safe locations. These four clusters also formed the response variable classes of a random forest model. This classification model used various road geometry data and SSMs as predictors and achieved high classification performance for all classes, averaging more than 88% correct classification rates.

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The development of a large scale agent-based simulation model for the Greater Boston Area is presented, closing the gap between state-of-the art integrated demandsupply modeling techniques (SimMobility) with advanced energy estimation models (TripEnergy) and shedding light on its practical application to large urban areas. This paper describes the technical details of its three key components (activity-based demand, multi-modal dynamic supply, and trajectory-based energy models), the used data, the model estimation, integration and calibration processes. The proposed model can simulate any day with and without congestion in order to capture changes in energy use across all dimensions of a mobility system, namely temporal, spatial, modal or functional. For an average 24h in the Greater Boston Area the simulated travel of 4.5-million people resulted in 15-million trips and a total vehicle energy consumption of 548 thousand equivalent gallons of gasoline. Our proposed platform allows for the comprehensive and consistent assessment of energy related policies, technologies and services affecting traveler behavior, the transportation system's and vehicle energy performances.