MissII: Missing Information Imputation for Traffic Data

Cyber-Physical-Social Systems (CPSS) offer a new perspective for applying advanced information technology to improve urban transportation. However, real-world traffic datasets collected from sensing devices like loop sensors often contain corrupted or missing values. The incompleteness of traffic data poses great challenges to downstream data analysis tasks and applications. Most existing data-driven methods only impute missing values based on observed data or hypothetical models, thus ignoring the incorporation of social world information into traffic data imputation. The connection between real-world social activities and CPSS is crucial. In this paper, a novel theory-guided traffic data imputation framework, namely MissII, is proposed. In MissII, we first estimate the traffic flow between two PoIs (Points of Interest) according to spatial interaction theory by considering the physical environment information (e.g., population distributions) and human social interactions (e.g., destination choice game). Moreover, we further refine the estimated traffic flow by considering the effects of road interactions and PoIs. Then, the estimated traffic flow is input into the non-parametric GAN model as real samples to guide the training process. Extensive experiments are conducted on real-world traffic dataset to demonstrate the effectiveness of the proposed framework.

I. Introduction

In Recent years, there has been a growing interest in cyber-physical-social systems (CPSS), which are characterized by the interactions among the physical, cyber, and social worlds. Data serves as the foundation for connecting these three worlds, making it an indispensable component of CPSS. For example, traffic data generated by residents in the city reflects the spatial-temporal correlations among urban entities. Many downstream applications to intelligent transportation systems (ITS), such as traffic condition prediction, driving routes planning, and traffic flow prediction, are implemented with effective traffic data processing methods. Nevertheless, traffic data often suffers from corruption or missing values due to factors such as insufficient observations, power outages, and data transfer issues [1], which can hinder real-time monitoring of traffic conditions and restrict downstream applications.