Paper Info
Title
Fine-grained travel time sensing in heterogeneous mobile networks: poster abstract
Abstract
Sensing and estimating real-time passenger travel time in urban mobile networks provides essential information for passengers to select different transportation modalities, improving their travel experience. Most existing work on travel time sensing has been focused on individual transportation modalities and its riding time based on the static time tables. However, passengers often consider different modes of transportation, e.g. taxis, subways, buses or personal vehicles, and a significant portion of the travel time is spent in the uncertain waiting or walking, which cannot be accurately obtained by static time tables. In this paper, we design a real-time data-driven framework FineTravel for fine-grained travel time sensing based on multi-source real-time data from multi-modal mobile networks including taxi, bus, subway, and private vehicle. The key challenge we address in FineTravel is to estimate implicit components (including walking, waiting and riding time) of the total travel time without direct measurement. In contrast to the existing work based on single-modal transportation networks mostly with offline data, the novelty of the FineTravel is based on its real-time multi-source data-driven (including both vehicle GPS and smartcard data) modeling for a comprehensive travel time sensing.
Year
DOI
Venue
2019
10.1145/3356250.3361955
Proceedings of the 17th Conference on Embedded Networked Sensor Systems
Keywords
Field
DocType
fine grained, heterogeneous mobile networks, travel time sensing
Computer science,Computer network,Travel time
Conference
ISBN
Citations 
PageRank 
978-1-4503-6950-3
2
0.36
References 
Authors
0
3
Name
Order
Citations
PageRank
Zhihan Fang1517.77
Fan Zhang282.55
Desheng Zhang335645.96