Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework

Developing a Whole-Life Value Model for the Irish National Road Network

Planning of road maintenance helps to spend available budgets efficiently and aims to keep the network in a safe and useable condition for road users. Road pavement maintenance models have traditionally excluded externalities as part of quantitative assessments of maintenance options. However, road maintenance affects wider society and therefore any maintenance decisions should integrate externalities into the decisions and tools that are used to generate maintenance programmes. This thesis investigates how externalities of carbon and noise emissions from maintenance can be included in a pavement maintenance model and the associated impacts on developing a maintenance programme. Pavement maintenance models were studied and it showed that there is a general omission of externalities within the core of the models. A review of externalities (with an emphasis on environmental externalities) demonstrated that road authorities do have policies to take account of externalities but often in a qualitative assessment and often only at a project level, not at a strategic level. This research developed a whole-life cost model into which novel methodologies for modelling carbon and noise were included, with the methodologies developed so that they can be used in other pavement management systems. The result was a model that took account of a wider range of value parameters as part of the economic analysis. Two in-depth case studies were completed to investigate the impact that the methodologies had on a road network. Using current government prices for carbon and noise, noise had a significantly greater impact on the resulting maintenance programme. Sensitivity analysis showed that the resulting maintenance programmes were a lot less sensitive to changes in the price of carbon, although both parameters did lead to changes in the resulting maintenance programme, especially when specific environmentally focused maintenance options were included as treatments