DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks

TL;DR

DiSGMM is a novel method that accurately completes time-varying microscopic road network weights by modeling complex distributions with Gaussian mixtures, addressing data sparsity and variability.

Contribution

The paper introduces DiSGMM, combining sparsity-aware embeddings with spatiotemporal modeling to improve microscopic weight completion on road networks.

Findings

DiSGMM outperforms existing methods on real-world datasets.

It effectively captures complex weight distributions, including heavy tails and multiple clusters.

The approach handles dual-layer sparsity in road network data.

Abstract

Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees. We study the problem of time-varying microscopic weight completion. During a time slot, the available weights typically cover only some road segments. Weight completion recovers distributions for the weights of every road segment at the current time slot. This problem involves two challenges: (i) contending with two layers of sparsity, where weights are missing at both the network layer (many road segments lack weights) and the segment layer (a segment may have insufficient weights to enable accurate distribution estimation); and (ii) achieving a weight distribution representation that is closed-form and can capture complex conditions flexibly, including heavy tails and multiple clusters. To address these challenges, we propose DiSGMM that combines sparsity-aware embeddings with spatiotemporal modeling to leverage sparse known weights alongside learned segment properties and long-range correlations for distribution estimation. DiSGMM represents distributions of microscopic weights as learnable Gaussian mixture models, providing closed-form distributions capable of capturing complex conditions flexibly. Experiments on two real-world datasets show that DiSGMM can outperform state-of-the-art methods.