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

Zhang，Zhiwen2; Wang，Hongjun1; Fan，Zipei1,2; Chen，Jiyuan1; Song，Xuan1; Shibasaki，Ryosuke2

10.1007/978-3-031-26422-1_30

2023

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

0302-9743

1611-3349

978-3-031-26421-4

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

13718 LNAI

489-504

SEP 19-23, 2022

null,Grenoble,FRANCE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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 (Time → Route), in turn, resulting in a more accurate time estimation (Route → Time). 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.

Graph convolutional network Internet of things Route recovery Travel time estimation Weakly supervised learning

通讯

英语

CPCI-S

National Key Research and Development Project[2021YFB1714400] ; Guangdong Provincial Key Laboratory[2020B121201001]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000999152800030

2-s2.0-85150991193

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.The University of Tokyo,Tokyo,Japan

Zhang，Zhiwen,Wang，Hongjun,Fan，Zipei,et al. Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2023:489-504.