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Ride Hailing service and Dynamic Shuttle are two key smart mobility practices, which provide on-demand door-to-door ride-sharing service to customers through smart phone apps. On the other hand, some big companies spend millions of dollars annually in third party vendors to offer shuttle services to pick up and drop off employees at fixed locations and provide them daily commutes for employees to and from work. Efficient fixed routing algorithms and analytics are the key ingredients for operating efficiency behind these services. They can significantly reduce operating costs by shortening bus routes and reducing bus numbers, while maintaining the same quality of service. This study developed an off-line optimization routing method for employee shuttle services including regular work shifts and demand based shifts (e.g. overtime shifts) in some regions.

It is estimated that up to 30% of traffic in cities is due to drivers searching for parking. Research suggests that drivers spend an average of 6-14 minutes looking for an available space in London. This increases individual stress levels as well as congestion and pollution. Parking Guidance Systems provide an effective way to reduce parking search time by presenting drivers with dynamic information on parking. An accurate prediction and recommendation analytics algorithm is the key part of the system combining real time cloud-based analytics and historical data trends that can be integrated into a smart parking user application. This paper develops a prediction algorithm based on transient queuing theory and Laplace transform to predict parking occupancy thus predicting open parking locations.

In recent years, electrification has emerged as an important means to reduce the carbon footprint of personal transportation. A key question for both policy makers and vehicle manufacturers is how quickly electric vehicles (EV) will be adopted by consumers. EV adoption will be impacted by external factors such as the price differential between gasoline and electricity, large incremental vehicle costs, and strong government policies that are far less significant for other advanced vehicle technologies such as hybrid electric vehicles (HEV) or the Ford Eco-Boost engine technology. The ability to reflect these additional externalities in adoption models will improve the reliability of EV market penetration forecasts and the quality of policy analysis. The Bass diffusion model is well established in studies of the adoption of new technologies, but it is not able to reflect those external factors related to EVs in its usual form.