Socially Responsible Road Charging for Online Retailers to Support Disadvantaged Urban Communities

Project Abstract

This research project will investigate a socially responsible model for road charging for Online Merchandise Delivery Vehicles (OMDVs). This concept is planned to be achieved through a ‘cost and reward’ traffic control priority system. The methodology will first develop the ‘right formula’ to charge extra fees for OMDVs, which will be implemented as a traffic control priority at signalized intersections and other relevant parts of the urban intersection. Then, such policy-based priority decision support system will be tested under several policy and traffic demand and multimodal use scenarios. It is expected that the results will be able to show a right trade off between priority given to OMDVs and the extracted fees which can be utilized to improve road infrastructure. The results of this research are expected to be disseminated at various professional and scientific events both nationally and internationally. Also, the technology transfer will be sought with the most relevant industry partners while educational benefits will be achieved through training of PhD scholars.

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Universities Involved

University of Pittsburg

Principle Investigators

Dr. Lev Khazanovich

Dr. Aleksandar Stevanovic

Funding Sources and Amounts

USDOT: $100,000

Start Date

September 1, 2023

Completion Date

September 1, 2024

Expected Research Outcomes & Impacts

The existing research methods do not connect traffic control priority (usually implemented at signalized intersection) with the concept of road-pricing, especially when such pricing is applied to specific road users which utilize our infrastructure for a profit. Also, strategies and methods where such priorities are combined with ‘traditional’ priorities for multimodal users (e.g., bus rides and pedestrians) are not well covered in the existing state of the art knowledge. This research will apply various soft-computing methods and heuristic techniques, possibly including Machine Learning (ML) methods, to find out optimal policies which perform best in such a multidimensional operational environment. The research is expected to bring original contribution to the existing body of knowledge, which will result in new knowledge that will be applicable for practical traffic operations.

Subject Areas

Electric Vehicles, Urban Transit, E-commerce