Transportation network modeling
Transportation network models are central to operations and planning. It consists of network vehicle/passenger flow model and travel behavioral model.
Intelligent transportation system (ITS)
The explosive growth of mobile and sensor technologies is driving a sweeping wave of transformation towards improving the mobility and safety of today’s transportation. Enabling technologies, such GPS, mobile phone, and V2X, allow low-latency measurements of individual vehicle characteristics (such as second-by-second speeds, accelerations, directions, destinations, etc.) and vehicle control, which presents unprecedented opportunities for transportation agencies and location-based service organizations to learn travel behavior, and thus interact and engage with their customers.
photo credit: USDOT
Urban systems interdependency
Interdependencies across urban systems are increasingly acknowledged by city managers and planners. MAC focuses its research on the interdependency among three urban systems, transportation system, energy system and water/sewer systems.
The focus is on understanding travelers’ choices on routes, departure time, parking locations and traffic modes (such as solo-driving, carpool, transit, etc.). We use an economics instrument to incentivize travelers to change their choices towards the optimum of the entire transportation system. The incentives include various pricing mechanisms (tax, subsidy, toll, credit, lottery, time-of-day pricing, etc.), information provisions, and changes in travel time through cutting-edge communication technologies.
MAC uses state-of-the-art traffic engineering theories, models and algorithms to design transportation infrastructure and recommend operational schemes among the following perspectives:
• Traffic impact by road/bridge closure
• Transit scheduling, bunching mitigation
• Congestion pricing
• Managed lane
• Traffic control
• Parking management and pricing
• Traffic incident detection and management
Multi-modal transportation systems
Massive data, from various sources, provides unprecedented opportunity for the transportation industry to understand travel behavior and to propose efficient management strategies. However, those data sources are usually established by disparate public agencies and private sectors. They rarely communicate with each other and, as a result, data is only used and analyzed for a particular piece of a transportation system, such as an intersection, a stretch of freeway, or bus routes operated by the same agency. With disparate data sources, each part of the system is individually operated, making the entire transportation system far from socially optimal.
MAC has been collecting and integrating various multi-modal data sets around the Pittsburgh region, which include roadway traffic, probe vehicles, public transit, parking, incidents, bicycles, buildings, energy consumption, emissions, social media, etc. The data infrastructure is established on hierarchical systems, large-scale data archives and integration, statistical learning algorithms, and optimal decision making processes. Multi-modal transportation systems are analyzed integratively. The decision-making of one mode of transportation system must take into account its impact to other modes and vice versa. The data infrastructure is closely aligned to on-going research at Carnegie Mellon, including urban systems, air quality studies, climate change, connected/autonomous vehicles, energy policies, and infrastructure life cycle analysis. Interactions with those groups in other disciplines will have synergistic effects on the integration and optimization of sustainable urban systems.