Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories

TL;DR

This paper introduces an EM-based method for joint travel time estimation and route recovery from sparse GPS trajectories, addressing challenges posed by low sampling rates and uncertain labels in IoT-enabled travel data.

Contribution

It formulates travel time estimation and route recovery as an inexact supervision problem and proposes a novel EM algorithm to improve both tasks simultaneously.

Findings

Effective on three real-world datasets

Improves accuracy of travel time estimation

Enhances route recovery from sparse data

Abstract

Due to the rapid development of Internet of Things (IoT) technologies, many online web apps (e.g., Google Map and Uber) estimate the travel time of trajectory data collected by mobile devices. However, in reality, complex factors, such as network communication and energy constraints, make multiple trajectories collected at a low sampling rate. In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points. We formulate this problem as an inexact supervision problem in which the training data has coarsely grained labels and jointly solve the tasks of TTE and route recovery. And we argue that both two tasks are complementary to each other in the model-learning procedure and hold such a relation: more precise travel time can lead to better inference for routes, in turn, resulting in a more accurate time estimation). Based on this assumption, we propose an EM algorithm to alternatively estimate the travel time of inferred route through weak supervision in E step and retrieve the route based on estimated travel time in M step for sparse trajectories. We conducted experiments on three real-world trajectory datasets and demonstrated the effectiveness of the proposed method.