Capturing the Spatiotemporal Evolution in Road Traffic Networks

Thumbnail Image
File version
Accepted Manuscript (AM)
Anwar, Tarique
Liu, Chengfei
Vu, Hai L
Islam, Md Saiful
Sellis, Timos
Griffith University Author(s)
Primary Supervisor
Other Supervisors
File type(s)

The urban road networks undergo frequent traffic congestions during the peak hours and around the city centre. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consume a minimum space with least redundancy at different time stamps.

Journal Title
IEEE Transactions on Knowledge and Data Engineering
Conference Title
Book Title
Thesis Type
Degree Program
Publisher link
Patent number
Grant identifier(s)
Rights Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Rights Statement
Item Access Status
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Access the data
Related item(s)
Pattern recognition
Data mining and knowledge discovery
Persistent link to this record