Integrating information and physical flow layers in transportation systems models

Many transportation systems are now integrated with information technologies and rely on these technologies for system operations and management. Effective system operation depends on the information flow as much as on the physical flow. Both information and physical flows need to be coordinated so that the overall system can operate properly. This thesis proposes a multi-layered modeling (MLM) paradigm that integrates information flow and physical flow layers in transportation systems models. This unified modeling paradigm, unlike traditional modeling practices, involves explicit representation of information flow dynamics (e.g., response time and information delay) and incorporates information flow related attributes into the models of transportation systems. Using network modeling as a framework, the thesis identifies and categorizes the important modeling issues that need to be considered in developing multi-layered models. It is shown that various dependencies that might exist in the information layer, which commonly arise due to resource sharing, can have significant implications in terms of understanding and modeling the system behavior.; To illustrate the application of the MLM, a simulation model for a hypothetical runway is built using colored Petri nets. The model consists of two interacting layers: a physical layer and an information layer. The physical layer models aircraft movements while the information layer deals with control of the air traffic. Aircraft communicate with the information layer to get clearance for takeoffs and landings. To investigate the impacts of delays in the information flow on system performance, a number of information delay scenarios are considered and modeled. The impacts of these information delays are measured in terms of airborne and takeoff delays.; In addition to the runway model, several queuing models are developed and numerical experiments are performed to demonstrate the significance of understanding various types of dependencies. Each model illustrates a different scenario in which the information system interacting with the physical system causes a behavior that cannot be captured unless the information flow is explicitly modeled. In general, the identified dependencies translate into correlated service times. When information flow is not modeled explicitly, service times are typically assumed to be independent. It is found that this assumption can lead to either overestimated or underestimated system capacity depending on the type of dependency. Furthermore, the magnitude of inaccuracy in the estimates increases with increasing level of congestion. Therefore, a MLM perspective plays a more critical role in accurately modeling and analyzing transportation systems that are operating at or near capacity. Accurately modeling system behavior is a key element in managing and operating transportation systems effectively.