The data assimilation algorithm developed during the MW-R project implements the Kalman Filter model in a novel way as far as the transportation engineering community is concerned due to the implementation of the control term. Furthermore, the model has potential for rapid implementation thanks to employment of a standard model for which software libraries are readily available.
The impact of the project is related first with the operators. The project aims to provide a significant increase in the power of the data that the operators use for performing planning activities thus increasing the efficiency of the planning activities and, consequently, increasing the profit of operations. By performing better planning operations, MW-R contributes in creating a better network and service for the passenger all the while including crowd sourced data. Thus, MW-R is an enriching platform that improves the power and usage of existing datasets, by performing fusion/assimilation and filtering. However, MW-R is not only targeted to operators. It is also targeted to the passenger that is on the road. Specifically, MW-R aims to use the short-term space model that is used for data assimilation, in order to provide improved recommendations to passengers. In simple terms, the feedback is inserted in the simulation engine (state space model) and achieves improved estimations in the short term (15 min interval). This estimation is aimed to be transmitted to passengers in order for them to plan their trip better. Imagine that MW-R can take the form that “rain-alarm” take for weather prediction apps. If a problem is detected in the short-term, MW-R generates a feedback request, creates a new prediction and disseminates it to passengers.
MW-R achieved some indirect impacts as a project which relate with the hire of an experienced Innovation Associate that excelled in lifting the idea off and in providing useful information to the company by sharing knowledge retrieved in different working environments and in different fields (academia and industry). This knowledge and in combination with the INNOSUP work plan assisted the company in creating a new education scheme for the employees where each employee can take 4 days per 6 months to participate in a course or education seminar for which the company provides vouchers.
Finally, the company and the innovation associate have created plans for the future of MW-R. The created model/algo can be implemented through different business models, either as a standalone application or as an API connected with existing application, thus, working as an ad/subscription-based app or as a pay-per-call API app. Both models will be examined during the next research cycle which will be coupled by business analytics.
