Search

The development of science and technology brings novel directions, data sources and methods to deal with transport issues. In the past few decades, especially, a large amount of researches in the transport field have been undertaken based on applying emerging data sources or methods. In light of their findings, emerging data sources are considered to have relatively easier accessibility, larger user coverage and richer information. Meanwhile, novel modelling methodologies are better at mining the latent relationship between the various aspects of the data. Therefore, combining emerging data sources and novel large-scale data analysis methodologies is considered to be valuable for improving existing models and solving emerging problems in transport research. This thesis will mainly concentrate on three related problems. The first is to apply these models to a traditional problem – travel demand estimation. This study demonstrates that social media is an appropriate data source for improving the travel demand estimation model. It also presents the factors which may affect the usage of social media data in the existing models. The second problem addressed is an emerging problem – carsharing system design and demand forecasting. The aim is to estimate the latent demand by spatio-temporal autocorrelation model using historical big data. The last problem is potentially a future problem called a ‘Network Inferring Problem’ which is helpful in sketch network designing. This problem is defined as finding the number of links which can feasibly carry all the point-to-point demand. Meanwhile the travel time on those links is closest to what is actually observed. Following the ‘storyline’ of ‘traditional-emerging-future’, we aim to demonstrate that novel data sources and the analysis methods to enable them are appropriate for the different requirements of various transport research problems. Even though gaps could still be found in both quantity and quality between emerging data

This study aims to provide a decision support framework for designing a sustainable Integrated Solid Waste Management (ISWM) system. Sustainability of the ISWM system is addressed by incorporating economic, environmental and social objectives. The proposed study focuses on the issues of location selection, routing and waste flow to optimise the system. To address the location selection problem, Multi-Criteria Decision Making (MCDM) methods are utilised in novel frameworks under the Geographic Information System (GIS) environment. The methods of Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Decision Making Trial and Evaluation Laboratory (DEMATEL) are employed to propose two hybrid methods in adaptive frameworks: AHP-TOPSIS and AHP-DEMATEL. To verify the effectiveness of the proposed MCDM methods, these methods are tested with a case of New South Wales (NSW), Australia. The locations found by the MCDM methods can serve as an input (i.e. alternative locations for siting the system’s components) for the holistic optimisation models in the frame of Location-Routing Problem (LRP). To concurrently optimise location selection and waste allocation in the system, an Operation Research (OR) technique, specifically Mixed-Integer Linear Programming (MILP) is utilised to develop novel mathematical models in the frame of LRP. To address the NP-hardness (non-deterministic polynomial-time hard) of the problem, heuristic methods are developed in novel frameworks. The developed models are successfully applied to real-life cases in NSW and Tehran, Iran to provide a consolidated plan for supporting MSW management systems. The results obtained by the proposed heuristic methods are also validated by comparing them with the exact results obtained by the MILP model. Further, this research has developed an integrated method (i.e. integration of MCDM and MILP models) by proposing a novel multi-dimensional modelling ap