Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction. Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems. The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption

Sustaining critical transport infrastructure space in megacities: multimodal assessment of railway and road systems in Kano & Lagos — Nigeria

Globalisation has the most tremendous negative effects on the changing landscapes of many cities because of the roles of cities as the de facto economy and haven of liveable socioeconomic advantages. As the urban population grows, particularly in developing countries' mega-cities where transport development faces the most complex challenges, a more sophisticated framework of assessment of critical transportation infrastructure and transportation planning is required. This research aims to investigate transport effects of the complex web of interactions of urban chain processes to bring about a more sustainable (and resilient) transport infrastructure development of mega-cities. The interdisciplinary research concepts which incorporate the development of scenario-based applications and prediction techniques involving qualitative and quantitative frameworks were applied to the two Nigerians most populous cities (Lagos and Kano). The framework includes the analysis of spatial-temporal relationship of transport space and urban land use change, congestion and accessibility, sustainability paradigm and themes and ordering of priorities of the intervention policies based on transportation demand management objectives. Data sources include Landsat images, traffic and demographic data, transportation infrastructure inventories, and collaborative engagement with stakeholders and policymakers via questionnaires, interviews, and checklists. First, spatial-temporal analysis was carried out using remote sensing GIS software for land use classification and CA-Markov model implemented in IDRISI SELVA for temporal prediction and its suitability quality. Next is the assessment of accessibility and congestion pattern of the two cities using a surrogate multi-layer feed-forward and back-propagation model involving input-output and curve fitting (NFTOOL) implemented in artificial neural network wizard of MATLAB. Also, the sustainable paradigm and themes were carried using questionnaire and interview instruments and analysed respectively using SPSS and NVivo softwares. Finally, the priorities of intervention policy decision and quality of infrastructure and services were analysed using hybrid SERVQUAL-AHP models. The spatial-temporal analysis of the two cities produced patterns of rising trends for transport and built-up areas while the other land use classes are receding. For example, Kano transport space had grown from 137km$^2$ in 1984 to 290km$^2$ in 2019 while that Lagos grew from 337km$^2$ to 535km$^2$ in the same period. The dynamics model predicts spatial land requirement of Kano city for transport to reach 410km$^2$ in 2050 while Lagos will be needing 692km$^2$ in the same period. Future prediction of the two cities will be highly unsustainable for transport infrastructure. The congestion profile results put the two cities within congestion indices ranging from 7.5 to 10 on a maximum scale of 10, indicating extreme traffic congestion regimes and inaccessibility in the two cities. The sustainability paradigm comprising literacy, sustainable choices and indicators of sustainable transport are below average exposing poor development in the area. Also, the thematic analysis revealed the preponderance of more negative sentiments from the interview over statements of optimism and progress and it corroborates the findings of sustainability paradigm. Finally, satisfaction quality assessment produced low quality scores of 48% and 49% for Kano and Lagos cities respectively. AHP equally allocated more weight to tangibility which defines infrastructure and service qualities. These values are suggestive of the necessity to infrastructure, public transit systems and management of transport demand in the decision policy making. To deal with rising urbanization trends in Nigerian cities and maintain liveable and accessible urban environments, aggressive push—and—pull policies that improve and increase transport infrastructure quality and drive sustainable transport, promote modal split, reduced motorization, and access control is recommended