Infrastructure Engineering - Theses
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ItemToward Constant Service Quality Monitoring in Transport Nodes( 2022)Promoting public transport relies on developing effective tools for the proactive management of service quality in public transport nodes. However, the challenging environment of these nodes, characterised by crowding and congestion, limited physical space, and frequent changes, poses obstacles to existing monitoring approaches. This calls for novel data-driven methods to provide insight into the management of such a challenging environment. This study fills this gap by combining subjective and objective service quality measures, along with proposing state-of-the-art models, to enable constant assessment of service quality in the confined spaces of transport nodes. The resulting insights are expected to support prompt, context-aware, and flexible decision-making, benefiting local communities, transport authorities, and related industries. First, the feasibility of integrating unstructured data sources, such as social media content, for concurrent monitoring of service quality in transport nodes is investigated. To that end, tweets are mined to evaluate various service quality characteristics. To realize this, a novel framework based on a fine-tuned language model and sentiment analysis is proposed. This allows for the classification of tweets and the effective detection of unusual events impacting perceived service quality. Findings highlight the constraints of sparse yet valuable data, where extra knowledge from sentiment analysis improves the monitoring tool's sensitivity in a variety of settings. Next, a novel framework is designed to discover and predict events impacting service quality across a network of constrained public transport nodes. To that end, social media content offers a unique opportunity to effectively model such a complex phenomenon. Nonetheless, this is challenging. Apart from data sparsity, the asynchronous nature of the observations impedes pattern discovery and event prediction. To tackle this, Hawkes Point Process is used to model events without making them discrete. Moreover, sentiment analysis is used to strengthen the model with extra information. Results demonstrate the effectiveness of the approach in identifying the causal patterns and predicting events over the limited context. Then, Closed-Circuit Television (CCTV) footage as an authoritative dataset is used to monitor pedestrians' behaviour on platforms and objectively measure service quality in transport nodes. A pre-requisite is to accurately identify and localize pedestrians in the footage. However, this is challenging as pedestrians are often partially occluded in the crowded environment of transport nodes. To address this, a novel human pose-aware pedestrian localization framework is proposed to ensure a fast and accurate location of pedestrians. Results show the effectiveness of the approach in pedestrian localization. Finally, a novel framework for an effective combination of subjective and objective service quality measures in transport nodes is provided. To this end, multivariate Hidden Markov Models and vision-based Level of Service estimation are used to ensure robustness in the fusion of both datasets with significantly different characteristics. The model is strengthened by employing extracted sentiment information from social media feeds as additional knowledge about passengers' perceptions. Results show the efficacy of the framework compared to data-intensive state-of-the-art data fusion approaches. Overall, this study develops a range of new methods and brings new theories and technologies together, to enable concurrent monitoring of public transport infrastructure performance. The results of this study can be used as a contributing module in Cyber-Physical systems to constantly monitor passenger-infrastructure interactions. The findings would not only have a positive impact on people's quality of life but also inspire researchers from other disciplines, especially the public transport community, to employ data-driven solutions in a variety of interesting ways.