This project is intended to assess the observed impacts of the delivery restrictions that have been enacted and proposed in large cities such as New York (USA), Mumbai, Delhi, and Chennai (India), Bogotá and Barranquilla (Colombia), and Sao Paulo (Brazil). This comparative study is important because it would provide a real life view on this complex subject, and would confirm or reject the research conducted that suggests the possibility of negative unintended effects on traffic congestion and pollution.
The group also conducts cutting-edge research on intelligent transportation systems (ITS). The current focus is on developing transportation big data analytics to apply emerging technologies such as mobile sensing and connected vehicles to evaluate transportation system performances and to develop novel system management strategies. The group also works on understanding whether/how such technologies could transform the ways transportation system components behave and interact with each other.
This project focuses on freight systems as crucial economic and quality of life contributor, and a major source of environmental pollution, unwanted noise and safety hazards. The primary goal of this project is to improve the overall performance of the urban freight industry.
The project will define a pragmatic and conceptually well-grounded planning guide that includes both supply and demand strategies (including hybrids), that is supported by solid guidelines to establish effective and proactive stakeholder engagement processes and software tools to estimate freight trip generation in urban areas. The project will provide practitioners with comprehensive, pragmatic, and actionable guidelines on how to plan, design, and implement both supply and demand strategies.
The project’s main goal is to develop a state-of-the-art Decision Support System (DSS) that, using network condition and disaster impact assessments provided by Commercial Remote Sensing (CRS), will compute optimal Access Restoration Plans (ARP). This will help responders optimally use their scarce resources to orchestrate the road openings, road repairs, and other similar actions; and subdivide the work so that multiple groups could seamlessly cooperate to reach maximum effectiveness. The DSS will process multi-modal temporal data feeds (GIS, social media feeds, etc.) to update estimates of disaster impacts, and will use modern optimization procedures to update the ARP as new data become available. To achieve the overall goal, the team will collaborate to:
- Develop CRS techniques that produce rapid assessments of network conditions and disaster impacts integrating multi-modal / multi temporal data
- Develop mathematical models to produce ARP using appropriate priority metrics
- Create a DSS that is smoothly transitioned to practice, fully validated, and useful to responders.
- Develop appropriate procedures for integration of outside assistance.