Influence-Aware Predictive Density Queries under Road-Network Constraints

Lasanthi Heendaliya; Michael Wisely; Dan Lin; Sahra Sedigh; A. R. Hurson

doi:10.1007/978-3-319-22363-6_5

Influence-Aware Predictive Density Queries under Road-Network Constraints

Lasanthi Heendaliya
, Michael Wisely
, Dan Lin
, Sahra Sedigh
, A. R. Hurson

Electrical and Computer Engineering

Missouri University of Science and Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp t ₁ , the prediction of the condition on other roads after t ₁ should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.

Original language	American English
Article number	A5
Pages (from-to)	80-97
Number of pages	18
Journal	Lecture Notes in Computer Science: Advances in Spatial and Temporal Databases
Volume	9239
DOIs	https://doi.org/10.1007/978-3-319-22363-6_5
State	Published - Aug 1 2015
Event	14th International on Symposium on Spatial and Temporal Databases, SSTD 2015 - Hong Kong, China Duration: Aug 26 2015 → Aug 28 2015

Keywords

Roads And Streets
Street Traffic Control
Traffic Congestion
Transportation
Dense Region
Effectiveness And Efficiencies
Predictive Density
Query Algorithms
Road Network
Traffic Flow
Traffic Jams
Traffic Prediction
Traffic Control

Disciplines

Computer Sciences
Electrical and Computer Engineering

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https://doi.org/10.1007/978-3-319-22363-6_5

Cite this

@article{299765e5c914461e99814c05a3f3fd5f,

title = "Influence-Aware Predictive Density Queries under Road-Network Constraints",

abstract = " Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp t 1 , the prediction of the condition on other roads after t 1 should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.",

keywords = "Roads And Streets, Street Traffic Control, Traffic Congestion, Transportation, Dense Region, Effectiveness And Efficiencies, Predictive Density, Query Algorithms, Road Network, Traffic Flow, Traffic Jams, Traffic Prediction, Traffic Control",

author = "Lasanthi Heendaliya and Michael Wisely and Dan Lin and Sahra Sedigh and Hurson, \{A. R.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 14th International on Symposium on Spatial and Temporal Databases, SSTD 2015 ; Conference date: 26-08-2015 Through 28-08-2015",

year = "2015",

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doi = "10.1007/978-3-319-22363-6\_5",

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T1 - Influence-Aware Predictive Density Queries under Road-Network Constraints

AU - Heendaliya, Lasanthi

AU - Wisely, Michael

AU - Lin, Dan

AU - Sedigh, Sahra

AU - Hurson, A. R.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp t 1 , the prediction of the condition on other roads after t 1 should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.

AB - Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp t 1 , the prediction of the condition on other roads after t 1 should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.

KW - Roads And Streets

KW - Street Traffic Control

KW - Traffic Congestion

KW - Transportation

KW - Dense Region

KW - Effectiveness And Efficiencies

KW - Predictive Density

KW - Query Algorithms

KW - Road Network

KW - Traffic Flow

KW - Traffic Jams

KW - Traffic Prediction

KW - Traffic Control

UR - https://scholarsmine.mst.edu/comsci_facwork/499

UR - https://doi.org/10.1007/978-3-319-22363-6_5

U2 - 10.1007/978-3-319-22363-6_5

DO - 10.1007/978-3-319-22363-6_5

M3 - Article

VL - 9239

SP - 80

EP - 97

JO - Lecture Notes in Computer Science: Advances in Spatial and Temporal Databases

JF - Lecture Notes in Computer Science: Advances in Spatial and Temporal Databases

M1 - A5

T2 - 14th International on Symposium on Spatial and Temporal Databases, SSTD 2015

Y2 - 26 August 2015 through 28 August 2015

ER -

Influence-Aware Predictive Density Queries under Road-Network Constraints

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