MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation

TL;DR

MuST2-Learn is a novel multi-view learning framework that accurately predicts municipal service request times by modeling spatial, temporal, and type-specific factors, significantly improving prediction accuracy.

Contribution

The paper introduces MuST2-Learn, a multi-view learning framework that jointly models spatial, temporal, and service type information for municipal service time estimation, addressing complex real-world challenges.

Findings

Reduces mean absolute error by at least 32.5%

Outperforms existing state-of-the-art methods

Effectively captures heterogeneous request interactions

Abstract

Non-emergency municipal services such as city 311 systems have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfaction, and increase the number of follow-up inquiries. Predicting the service time for municipal service requests is challenging due to several complex factors: dynamic spatial-temporal correlations, underlying interactions among heterogeneous service request types, and high variation in service duration even within the same request category. In this work, we propose MuST2-Learn: a Multi-view Spatial-Temporal-Type Learning framework designed to address the aforementioned challenges by jointly modeling spatial, temporal, and service type dimensions. In detail, it incorporates an inter-type encoder to capture relationships among heterogeneous service request types and an intra-type variation encoder to model service time variation within homogeneous types. In addition, a spatiotemporal encoder is integrated to capture spatial and temporal correlations in each request type. The proposed framework is evaluated with extensive experiments using two real-world datasets. The results show that MuST2-Learn reduces mean absolute error by at least 32.5%, which outperforms state-of-the-art methods.