Robust Fairness in Spatial-Temporal Resource Allocation
Creators: name: Kang, Yufan
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 460308
subject: Pattern recognition

description: Smart cities aim to optimise city operations, enhance the quality of life, and minimise ecological footprints by integrating computer science, information, and communication technologies. The motivations behind developing smart cities include improved efficiency in public services to reduce costs and enhance service delivery. In this thesis, titled 'Robust Fairness in Spatial-Temporal Resource Allocation', we address the challenges in spatial-temporal resource allocation for various urban mobility and public transportation tasks.

Addressing robust fairness while optimising utility is vital in spatial-temporal resource allocation as unfairness potentially lead to negative economic (e.g., service deliverers with lower wages abandoning their jobs, leading to a shortage) and societal (e.g., unethical issues) consequences. Platforms must balance optimising utility with ensuring fairness, as focusing solely on fairness may inadvertently create a scenario where no one benefits, paradoxically resulting in a form of extreme fairness. The main challenges in strike a balance between robust fairness and utility in real-time optimisation platforms are i) the dynamic changing correlation between the two objectives, ii) the difficulties of achieving fairness increase along with the number of agents in the system, iii) the instability of the fairness achieved. Regarding these challenges we made several contributions in this thesis toward constructing a robust and multi-party fair spatial-temporal optimisation platform which will be essential towards constructing a smart city.

First, we introduced Multi-Level Fair Genetic Algorithm (MLFA) to tackle the challenge of increment complexity of promoting fairness among customers in spatial-temporal optimisation platforms (e.g., customers in service delivery systems, disaster locations in disaster management) along with the increment of the number of customers. The proposed approach incorporates a multi-level fairness scheme, with three distinctive characteristics: i) targeting fairness with a hierarchical structure to reduce the complexity of the problem, ii) incorporating the proposed multi-level fairness scheme into a Genetic Algorithm-based matching algorithm to allow the algorithm to consider fairness and utility at the same time, and iii) allowing the algorithm to adjust the weight of fairness by adjusting the number of fair iterations run in the algorithm. 


Second, we take a further step toward constructing a robust and multi-party fair spatial-temporal optimisation platform. We introduced Two-sided Fairness-aware Genetic Algorithm (2FairGA), which expands the genetic algorithm from the original objective solely focusing on utility to multiple objectives that incorporate two-sided fairness. Most existing studies focusing on promoting fairness in a spatial-temporal optimisation system focus on improving fairness from a single side. There has been limited research addressing two-sided fairness that simultaneously considers the needs of both parties in an optimisation system—service deliverers and customers—along with utility optimisation. Subsequently, the impact of injecting two fairness definitions into the utility-focused model and the correlation between any pair of the three objectives are explored. 

Third, we put the focus onto improving robustness in a fair spatial-temporal optimisation platform. We introduced a dynamic Markov Decision Process (MDP)-based model to address long-term fairness among agents. Existing approaches to promote fairness in spatial-temporal optimisation fall short in maintaining fairness in a long time horizon. The challenges in maintaining long-term fairness are due to i) short sight, ii) concept drift, and iii) disparity between utility and fairness increase across time. To address these challenges, we propose a dynamic Markov Decision Process (MDP)-based model that incorporates a prediction module to foresee the future requests (e.g., future demands change in a delivery service platforms or frequency of disasters at areas) and a scalarisation function to dynamically balance between efficiency and fairness. 

Fourth, recognising that event prediction plays a crucial role in our third proposed approach, we introduced STEMO - a multi-objective Reinforcement Learning-based approach towards building an adaptive early spatial-temporal predictor. Early prediction allows for more proactive decisions in resource allocation. By anticipating events or demands before they occur, a system can prepare and respond more effectively (e.g., allow the MDP-based scheduling platform change decision making pattern early by modifying the transition probabilities). This capability not only forecasts demands but also identifies shifts in patterns that could impact fairness, allowing for timely and equitable adjustments. With the conflict between timeliness and accuracy in mind, the adaptive characteristic in STEMO guarantees accurate predictions for different events due to their spatial-temporal characteristics. We proposed the method to address the three primary challenges in adaptive early forecasting: i) balancing robustness and accuracy due to their often negative correlation, ii) varying negative correlations based on spatial-temporal characteristics of events, and iii) spatial-temporal data alignment.

Putting all the steps together, we eventually contributed towards building future smart cities in terms of urban mobility and public transportation. To evaluate the applicability of the proposed methods, we adapted the models into scenarios such as ridesharing, officer patrolling problems, and dynamic arc routing problems.