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NSF Annual report: 2007-2008
III-CXT: Spatio-temporal Graph Databases for Transportation Science
Project Participants
Senior personnel:
Name: Shekhar, Shashi
Worked for more than 160 hours: Yes
Contribution to Project:
Shashi Shekhar has been the PI on this project and also provided research guidance in
Spatio-temporal Graph Databases.
Name: Liu, Henry
Worked for more than 160 hours: Yes
Contribution to Project:
Henry Liu has been the Co-PI on this project and also provided research guidance in
Transportation Science, particularly related to the evacuation traffic flow modeling.
Graduate students:
Name: George, Betsy
Worked for more than 160 hours: Yes
Contribution to Project:
Research in modeling and developing algorithms in spatio-temporal networks.
Name: Celik, Mete
Worked for more than 160 hours: Yes
Contribution to Project:
Research in spatial and spatio-temporal data mining.
Name: Kang, James
Worked for more than 160 hours: Yes
Contribution to Project:
Research in spatio-temporal query processing
Name: Mohan, Pradeep
Worked for more than 160 hours: Yes
Contribution to Project:
Research in spatio-temporal pattern mining
Name: Xiaozheng, He
Worked for more than 160 hours: Yes
Contribution to Project:
Research in developing models and algorithms for solving dynamic system optimum
traffic assignment problems.
Name: Jabari, Saif
Worked for more than 160 hours: Yes
Contribution to Project:
Research in control strategies and resource allocation problems in transportation
networks under emergency.
Name:Shivarudrappa, Chetan
Worked for more than 160 hours: No
Contribution to Project:
Research in spatial analysis
Name:Subbarayalu, Preetha Lakshmi
Worked for more than 160 hours: No
Contribution to Project:
Research in spatial analysis
Name: Dietz, Mark
Worked for more than 160 hours: No
Contribution to Project:
Research in spatio-temporal data analysis.
Undergraduate students:
Technician, Programmer
Other participants:
Visiting professor:
Name: Jung, Sungwon
Worked for more than 160 hours: No
Contribution to Project:
Sunwong Jung has provided research guidance in streaming data processing.
Activities and findings
Research and Education Activities:
Our work is divided into three categories, details are given in below:
1. A Framework of Adaptive Control Strategies for Emergency Evacuation
2. A Solution Algorithm to Dynamic System Optimum Traffic Assignment
3. A Resource Allocation Strategy under Emergency
4. Modeling Spatio-temporal Graphs
5. Query Language for Spatio-temporal Graphs
6. Spatio-temporal Graph Query Processing Algorithms
Findings:
1. Model Reference Adaptive Control Framework for Real-Time Traffic
Management under Emergency Evacuation, H. Liu, J. Ban, W. Ma, P.
Mirchandani, ASCE Journal of Urban Planning and Development, 2007, Vol. 133(1),
43-50.
Recent natural or man-made disasters around the world have provided compelling
evidence that transportation system plays a crucial role in the emergency
evacuation and have stressed the need for effective evacuation traffic management
to maximize the utilization of the transportation system and to minimize fatalities
and losses. This paper presents a model reference adaptive control (MRAC)
framework for real-time traffic management under emergency evacuation.
Distinct from the well-studied evacuation planning, real-time traffic management
for evacuation aims to dynamically guide (control) traffic flow under evacuation
in such a way that certain system objective (e.g., minimization of fatalities or
property losses) could be achieved. The proposed framework is based on both
dynamic network modeling techniques and adaptive control theory, by
considering the traffic network under evacuation as a dynamic system. First, a
prescriptive dynamic traffic assignment model is applied to predict, in a shortterm and rolling-horizon manner, the desired traffic states based on certain system
optimal objectives. This model will serve as a reference point for the adaptive
control. Then, the adaptive control system integrates these desired states and the
current prevailing traffic conditions collected via the sensing system to produce
real-time traffic control schemes. Finally, these traffic control schemes are
implemented in the field to guide the real-world traffic flow to evolve towards the
desired states. Simulation studies provided in this paper show that the proposed
framework based on MRAC can significantly improve the performance of realtime evacuation traffic management.
2. Cell-Based Many-To-One Dynamic System Optimal Model and Its Heuristic
Solution Method for Emergency Evacuation, H. Liu, X. He, J. Ban, the 86th
Transportation Research Board Annual Meeting, Washington D.C., 2007.
An efficient prescriptive dynamic traffic assignment model is critical for the
effective traffic management under emergency evacuation. Although a number of
dynamic traffic assignment models have been proposed in the previous studies, it
is almost impossible to apply them for real-time emergency traffic management
due to the high computational cost. For the real-time emergency traffic
management, computational efficiency becomes essential, but reasonably detailed
representation of traffic flow dynamics has to be maintained. By assuming that all
evacuees have only one destination, the safe area, the prescriptive model is
established as a many-to-one dynamic system optimal problem and a linear
program is formulated based on the cellular representation of traffic network. To
achieve the required computational efficiency, instead of solving the linear
program directly, we develop a heuristic algorithm called HASTE and provide a
close approximation to the optimal solution. We applied the system optimal
model to a hypothetical emergency evacuation scenario in the downtown of
Minneapolis, Minnesota, and the solutions were generated both by a linear
program solver and by HASTE. The comparison between different solution
approaches indicates that the heuristic can provide close-to-optimal evacuation
strategies with a much higher computational efficiency, which is important to
real-time evacuation operations.
3. Find Critical Traffic Control Locations for Emergency Evacuation, R. He, J.
Ban, H. Liu, the 87th Transportation Research Board Annual Meeting,
Washington D.C., 2008.
Proper traffic control and guidance are imperative to reduce fatality and property
damages during emergency evacuation. Due to limited traffic control devices and
personnel under such unusual conditions, it is crucial to find the most critical
traffic control locations or intersections to deploy control devices or arrange
manual guidance for emergency evacuation so that the best system performance
can be achieved. This paper presents a mixed integer nonlinear programming
model (NLP) to identify the most crucial intersections and simultaneously the
optimal traffic control strategies at those locations to minimize the total system
costs for emergency evacuation. Numerical examples show that the model
performs reasonably well with valid results.
4. Time Aggregated Graphs for Modeling Spatio-temporal Network, B. George,
S. Shekhar, Journal on Semantics of Data (Editors: J.F. Roddick, S.
Spaccapietra), Vol XI, December, 2007.
Given applications such as location based services and the spatio-temporal queries
they may pose on a spatial network (e.g., road networks), the goal is to develop a
simple and expressive model that honors the time dependence of the road
network. The model must support the design of efficient algorithms for computing
the frequent queries on the network. This problem is challenging due to
potentially conflicting requirements of model simplicity and support for efficient
algorithms. Time expanded networks, which have been used to model dynamic
networks employ replication of the networks across time instants, resulting in
high storage overhead and algorithms that are computationally expensive. In
contrast, the proposed time-aggregated graphs do not replicate nodes and edges
across time; rather they allow the properties of edges and nodes to be modeled as
a time series. Since the model does not replicate the entire graph for every instant
of time, it uses less memory and the algorithms for common operations are
computationally more efficient than for time expanded networks. One important
query on spatio-temporal networks is the computation of shortest paths. Shortest
paths can be computed either for a given start time or to find the start time and the
path that lead to least travel time journeys (best start time journeys). Developing
efficient algorithms for computing shortest paths in a time variant spatial network
is challenging because these journeys do not always display optimal prefix
property, making techniques like dynamic programming inapplicable. In this
study, we propose algorithms for shortest path computation for a fixed start time.
We present the analytical cost model for the algorithm and compare with the
performance of existing algorithms.
5. Modeling Spatio-temporal Network Computations: A Summary of Results, B.
George, S. Shekhar, Proceedings of the Second International Conference on
GeoSpatial Semantics (GeoS 2007), Mexico City, Mexico, November, 2007.
Spatio-temporal network is defined by a set of nodes, and a set of edges, where
the properties of nodes and edges may vary over time. Such networks are
encountered in a variety of domains ranging from transportation science to sensor
data analysis. Given a spatio-temporal network, the aim is to develop a model that
is simple, expressive and storage efficient. The model must also provide support
for the design of algorithms to process frequent queries that need to be answered
in the application domains. This problem is challenging due to potentially
conflicting requirements of model simplicity and support for efficient algorithms.
Time expanded networks which have been used to model dynamic networks
employ replication of the network across time instants, resulting in high storage
overhead and algorithms that are computationally expensive. This model is
generally used to represent time-dependent flow networks and tends to be
application-specific in nature. In contrast, the proposed time-aggregated graphs do
not replicate nodes and edges across time; rather they allow the properties of
edges and nodes to be modeled as a time series. Our approach achieves physical
data independence and also addresses the issue of modeling spatio-temporal
networks that do not involve flow parameters. In this paper, we describe the
model at the conceptual, logical and physical levels. We also present case studies
from various application domains.
6. STSG: A Data Model for Representation and Knowledge Discovery in Sensor
Data, B. George, J. Kang, S. Shekhar, Proceedings of Workshop on Knowledge
from Sensor Data (SensorKDD `07) at ACM-SIGKDD, San Jose, CA, August,
2007.
Developing a model that facilitates the representation and knowledge discovery
on sensor data presents many challenges. With sensors reporting data at a very
high frequency, resulting in large volumes of data, there is a need for a model that
is memory efficient. Since sensor data is spatio-temporal in nature, the model
must also support the time dependence of the data. Balancing the conflicting
requirements of simplicity, expressiveness and storage efficiency is challenging.
The model should also provide adequate support for the formulation of efficient
algorithms for knowledge discovery. Though spatio-temporal data can be
modeled using time expanded graphs, this model replicates the entire graph across
time instants, resulting in high storage overhead and computationally expensive
algorithms. In this paper, we propose Spatio-Temporal Sensor Graphs (STSG) to
model sensor data, which allow the properties of edges and nodes to be modeled
as a time series of measurement data. Data at each instant would consist of the
measured value and the expected error. Also, we present several case studies
illustrating how the proposed STSG model facilitates methods to find interesting
patterns (e.g., growing hotspots) in sensor data.
7. Discovering and Quantifying Mean Streets: A Summary of Results, M. Celik,
S. Shekhar, B. George, J. P. Rogers, J. A. Shine, University of Minnesota,
Department of Computer Science, Technical Report, no. 07-025, 2007.
Mean streets represent those connected subsets of a spatial network whose
attribute values are significantly higher than expected. Discovering and
quantifying mean streets is an important problem with many applications such as
detecting high-crime-density streets and high crash roads (or areas) for public
safety, detecting urban cancer disease clusters for public health, detecting human
activity patterns in asymmetric warfare scenarios, and detecting urban activity
centers for consumer applications. However, discovering and quantifying mean
streets in large spatial networks is computationally very expensive due to the
difficulty of characterizing and enumerating the population of streets to define a
norm or expected activity level. Previous work either focuses on statistical rigor at
the cost of computational exorbitance, or concentrates on computational
efficiency without addressing any statistical interpretation of algorithms. In
contrast, this study explores computationally efficient algorithms for use on
statistically interpretable results. We describe alternative ways of defining and
efficiently enumerating instances of subgraph families such as paths. We also use
statistical models such as the Poisson distribution and the sum of independent
Poisson distributions to provide interpretations for results. We define the problem
of discovering and quantifying mean streets and propose a novel mean streets
mining algorithm. Experimental evaluations using synthetic and real-world
datasets show that the proposed method is computationally more efficient than
naive alternatives.
Training and development:
The graduate students for Computer Science learned existing techniques used in their
research.
Outreach activities:
Journal Publications
B. George, J. M. Kang, S. Shekhar Spatio-Temporal Sensor Graphs (STSG): A Data
Model for the Discovery of Spatio-Temporal Patterns, International Journal of Intelligent
Data Analysis (JIDA). (Submitted)
H. Liu, X. He, S. Jabari. "A Heuristic Method for Dynamic System Optimal Routing
Under Emergency Evacuation". Submitted to the IEEE ITS Transaction. 2008.
H. Liu, J. Ban, W. Ma, P. Mirchandani. Model Reference Adaptive Control Framework
for Real-Time Traffic Management under Emergency Evacuation, ASCE Journal of
Urban Planning and Development, Vol. 133(1), 43-50. 2007.
Y.-C. Chiu, and H. Liu, Dynamic Transportation Planning and Operations Models and
GIS for Emergency Mass Evacuation, Encyclopedia of Geographical Information
Science, Springer Publishers, 2008 (ISBN: 978-0-387-30858-6). 2007.
S. Shekhar, J. Kang, V. Gandhi Spatial Databases, Encyclopedia of Computer Science
and Engineering, Wiley, Cassie Craig (Eds.). (Accepted)
S. Shekhar, J. Kang, V. Gandhi Spatial Data Mining, Encyclopedia of Database Systems,
Springer Publishers, Jennifer Carlson (Eds.). (Accepted)
S. Shekhar, V. Gandhi, J. Kang, Spatial Data Mining, Encyclopedia of Geographical
Information Science, Sage Publications, Karen Kemp (Eds.). (Accepted)
J. M. Kang, Voronoi Diagrams, Encyclopedia of Geographical Information Science,
Springer Publishers, 2008 (ISBN: 978-0-387-30858-6).
B. George, S. Shekhar, Time Aggregated Graphs for Modeling Spatio-temporal
Networks, Journal on Semantics of Data (Editors: J.F. Roddick, S. Spaccapietra),
(Special Issue of selected papers from ER 2006), Volume XI, December, 2007.
B. George, S. Shekhar, Modeling Spatio-temporal Network Computtions: A Summary of
Results, Proceedings of the Second International Conference on GeoSpatial Semantics
(GeoS 2007), Mexico City, Mexico, November, 2007.
B. George, J. M. Kang, S. Shekhar, Spatio-Temporal Sensor Graphs (STSG): A Sensor
Model for the Discovery of Spatio-Temporal Patterns, In the first SIG-KDD International
Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD '07), San Jose CA,
August 12, 2007 (Best Paper Award).
V. Gandhi, J. M. Kang, S. Shekhar, Spatial Databases, Technical Report TR07-020,
Department of Computer Science, University of Minnesota, Minneapolis, USA, 2007.
M. Celik, S. Shekhar, B. George, J. P. Rogers, J. A. Shine, Discovering and Quantifying
Mean Streets: A Summary of Results, University of Minnesota, Department of Computer
Science, Technical Report, no. 07-025, 2007.
Talks
S. Shekhar, Invited Presentation, Panel on Cyberinfrastructure and Geographic
Information Sciences at the “Annual Meeting of the American Association of
Geographers”, Boston, MA, April 2008.
H. Liu, X. He, S. Jabari, (). "A Heuristic Method for Dynamic System Optimal Routing
Under Emergency Evacuation”, Presented at the 2008 Annual Meeting of Transportation
Research Board, Washington DC. 2008.
S. Shekhar, Invited Presentation, Minnesota Population Center, University of Minnesota,
MN, February, 2007.
S. Shekhar, Invited Presentation, Electrical Engineering and Computer Science
Department, University of Tennessee, Knoxville, TN, December, 2007.
S. Shekhar, Invited Presentation, National Geo-spatial Intelligence Agency, Workshop on
Complicated Features, Airlie Conference Center, Virginia, Novemeber, 2007.
S. Shekhar, Invited Presentation, NSF Workshop on Next Generation Data Mining
(NGDM), Baltimore, October, 2007.
S. Shekhar, Invited Presentation, National Geo-spatial Intelligence Agency PIWorkshop,
National Academies,Washington D.C., September, 2007.
S. Shekhar, Invited Presentation, Transportation Research Board / Federal Highway
Authority Workshop on Advanced Research in Geospatial Information Technologies for
Transportation, National Academy of Science, Washington DC, September, 2007.
H. Liu, J. Ban, W. Ma, P. Mirchandani, " Model Reference Adaptive Control Framework
for Real-Time Traffic Management Under Emergency Evacuation". Presented at the 2007
Annual Meeting of Transportation Research Board, Washington DC. 2007.
H. Liu, X. He, J. Ban, "A cell-based dynamic system optimal model and its heuristic
solution method for emergency evacuation". Presented at the 2006 INFORMS Annual
Meeting, Pittsburgh, and the 2007 Annual Meeting of TRB, Washington DC. 2006/2007.
X. He, H. Liu. "A dynamic data-driven traffic operations model for effective evacuation".
Presented at the 2007 Annual Meeting of CTS Transportation Research Conference, Saint
Paul. 2007.
S. Shekhar, Invited Presentation, China National Laboratory on Machine Perception,
Center for Information Sciences, Peking/Beijing University, Beijing, China, August,
2007.
S. Shekhar, Invited Presentation, China State Key Laboratory of Software Engineering,
Wuhan University, Wuhan, China, August, 2007.
S. Shekhar, Invited Presentation, China State Key Lab of Information Engineering in
Surveying Mapping and Remote Sensing , (LIEMARS), Wuhan University, Wuhan,
China, August, 2007.
J.M. Kang, Continuous Evaluation of Monochromatic and Bichromatic Reverse Nearest
Neighbors, Presented in an NSF Integrative Graduate Education and Research
Traineeship (IGERT) Seminar, University of Minnesota, October 8, 2007.
J.M. Kang, Computational and Data Analysis Techniques of Global Change, Presented in
an NSF Integrative Graduate Education and Research Traineeship (IGERT) Seminar,
University of Minnesota, December 4, 2007.
M. Celik, Master of GIS, Spatial Databases and Spatial Data Mining , Guest lecturer,
October 2007.
B. George, S. Shekhar, Modeling Spatio-temporal Network Computtions: A Summary of
Results, Proceedings of the Second International Conference on GeoSpatial Semantics
(GeoS 2007), Mexico City, Mexico, November, 2007.
B. George, J. Kang, S. Shekhar, STSG: A Data Model for Representation and Knowledge
Discovery in Sensor Data, Proceedings of Workshop on Knowledge from Sensor Data
(SensorKDD `07) at ACM-SIGKDD, San Jose, CA, August, 2007.
Books or other one-time publications
S. Shekhar and J. M. Kang, Spatial Databases, Book Chapter in Data Engineering,
Springer Publishers. (Accepted)
S. Shekhar, V. Gandhi, J. M. Kang, and M. Mokbel, Spatial Databases, Handbook of
Database Technologies (Ed. Markus Schneider and Joachim Hammer), CRC Press, 2007.
(Accepted)
Shashi Shekhar, Ranga Raju Vatsavai, and Mete Celik. "Spatial and Spatiotemporal Data
Mining: Recent Advances" in Next Generation of Data Mining by Hillol Kargupta et. al
(Eds). CRC press. 2008 (submitted).
Web/Internet site
Other specific products
Contributions
Contributions within discipline:
1. Modeling Adaptive Control Framework for Evacuation:
This study helps in developing a sophisticated system dealing with adaptive
controls responding to real-time traffic dynamics. The proposed adaptive control
framework differs from conventional planning models; rather it focuses more on
controlling traffic according to its real-time dynamics which may not be
enumerated in planning models. And thus, the proposed framework introduces
real-time traffic pattern as an input, and produces optimal controls by an
embedded adaptive control model, referring to a system-optimal based
prescriptive model. The proposed framework serves as guidance for developing
effective control systems for traffic management, especially under unpredictable
incidences.
2. Solution Algorithm for Prescriptive System Optimum Traffic Pattern:
This study helps in developing effective prescriptive module in the adaptive
control system. Since the high computational efficiency is of essence to the realtime controls, an efficient solution algorithm has been developed to provide in
time reference results for the adaptive control system. This algorithm
approximates system-optimum traffic pattern which takes traffic queue dynamics
into consideration.
3. Modeling Critical Control Location under Emergency:
This study helps in developing control module in the proposed adaptive control
framework. Critical control locations are important in the adaptive control system,
due to the limited control facilities in reality. When incidents happen, this model
will provide the locations and control strategies for deploying limited control
facilities, to achieve maximal benefits.
4. Modeling spatio-temporal networks:
This study help in developing a time aggregated graph approach for dynamic
networks. The proposed time-aggregated graphs do not replicate nodes and edges
across time; rather they allow the properties of edges and nodes to be modeled as
a time series. The proposed approach provides an efficient and flexible model to
support queries on dynamic networks. Further computational and analytical
analysis aspects as well as novelty of the proposed approach were studied.
5. Modeling Spatio-temporal Network Computations:
This study help in describing the time aggregated graph model at the conceptual,
logical and physical levels and present case studies from various application
domains. The model provides support for the design of algorithms to process
frequent queries that need to be answered in the application domains.
6. A Data Model for Representation and Knowledge Discovery in Sensor Data:
This study help in developing Spatio-Temporal Sensor Graphs (STSG) to model
sensor data, which allow the properties of edges and nodes to be modeled as a
time series of measurement data. Data at each instant would consist of the
measured value and the expected error. Also, several case studies presented
illustrating how the proposed STSG model facilitates methods to find interesting
patterns (e.g., growing hotspots) in sensor data.
7. Discovering and Quantifying Mean Streets:
This study help in developing a graph-based hotspot, e.g. mean street, discovery
method The proposed mean street discovery algorithm takes into account the
graph structure of the dataset. This study uses statistical models such as the
Poisson distribution and the sum of independent Poisson distributions to provide
interpretations for results. It also proposes computationally efficient algorithms
for the discovery of mean streets. Further computational and analytical analysis
aspects as well as novelty of the proposed approach were studied.
Contributions to other disciplines:
Contributions to human resource development:
1. Involvement of many graduate/undergraduate students in this project.
2. Ph.D. dissertation/Master’s project
(i) Betsy George
Ph.D. in Computer Science (To be defended Summer 2008)
Thesis Title: Modeling and Algorithms for Spatio-temporal Networks
(ii) Mete Celik
Ph.D. in Computer Science (To be defended Summer 2008)
Thesis Title: Modeling Spatial and Spatio-temporal Co-occurrence Patterns
(iii) Betsy George
M.S. Thesis, Computer Science (Plan C, 2008)
Title: “Map Entry Program for Chimpanzee Locational Data”
(iv) Xiaozheng He
M.S. Thesis, Civil Engineering (Plan A Summer 2007)
Thesis Title: A Time-Constrained Heuristic Solution Algorithm for Dynamic
System Optimal Routing under Emergency Evacuation
(v) Saif Jabari
M.S. Thesis, Civil Engineering (To be defended Fall 2008)
Thesis Title: Real Time Intersection Traffic Control under Emergency Evacuation
3. Undergraduate Honors project.
Contributions to resources for research and education:
Contributions to beyond science and engineering:
Special requirements
Special reporting requirements: None
Changes in objectives or scope: None
Unobligated funds:
Animal, human subjects and biohazards: None.
Categories for which nothing is reported:
Any book
Any web/internet site
Any product
Contributions: To any resources for research and education
Contributions: To any beyond science and engineering