Abstract:

High population density urban areas have increasingly been the targets of terrorism in recent years due to the possibility of inflicting a large number of casualties in a crowded environment and causing high impact disruption especially in the outdoor environment. The project develops new approaches to complement and validate results produced by existing approaches based on video surveillance and human population density estimation via cellphone usage to monitor anomalies and threat level in urban area. It will benefit agencies and local governments that require the planning and allocation of resources to secure locations with higher levels of threat in a timely manner. 

The goals of the three-year project are to (i) develop a new data-driven hybrid differential equations (DE) modeling approach for object (e.g., human, bike, etc.) density and flow estimation to model human dynamics, and (ii) develop a real-time anomaly detection algorithm utilizing the DE model and real-time observed data to identify anomalous crowd density and traffic in an urban environment.  Due to the interdependency of locations when modeling human dynamics, spatiotemporal data are transformed into time-evolving graphs as the data representation.

Duration: August 15, 2018 - January 31, 2022

PI: Dr. Shen-Shyang Ho (Rowan University)

co-PI: Dr. Min Wang (Kennesaw State University)

Participating Undergraduate/Graduate Students:

Former:

1. Kaitlyn Myers (Graduated in Spring 2019)

2. Wen Fei Cao (Graduated in Spring 2019)

3. Alex Lam (BSc. CS + MSc. DA, graduated in Summer 2021)

4. Matthew Schofield (BSc. CS/Accelerated MSc. - Thesis, graduated in Fall 2021)

5. Kiefer Montenero (BSc. CS)

6. Autumn Chadwick (MSc. CS - Thesis, graduated in Spring 2022)

7. Owen Anderson (BSMS)

8. Nirav Patel (BSc. CS)

Publications:

1. John R. Graef, Shen-Shyang Ho, Lingju Kong, Min Wang, A fractional differential equation model for bike share systems, Journal of Nonlinear Functional Analysis, Vol. 2019 (2019), Article ID 23, pp. 1-14

2. Alex Lam, Matthew Schofield, and Shen-Shyang Ho. 2019. Detecting (Unusual), Events in Urban Areas using Bike-Sharing Data. In 3rd ACM SIGSPATIAL InternationalWorkshop on Analytics for Local Events and News (LENS’19), November 5, 2019, Chicago, IL, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3356473.3365190

3. Wang, Min. "On the resilience of a fractional compartment model." Applicable Analysis (2022): 3161 – 3173 

4. J. R. Graef, L. Kong, A. Ledoan, and M. Wang, Stability analysis of a fractional online social network model, Math. Comput. Simulat. 178 (2020), 625 – 645.

5. Autumn Chadwick, Shen-Shyang Ho, Yinan Li, and Min Wang , A Discrete Model for Bike Sharing Inventory, International Journal of Differential Equations, Vol. 15, Issue 2, 2020.

6. L Kong and M Wang, Optimal control for an ordinary differential equation online social network model, Differential Equations & Applications 14 (2022), 205 – 214.

7. L Kong and M Wang, Existence of positive solutions for a fractional compartment system, Electron. J. Qual. Theory Differ. Equ. 2021 No. 60, 1 – 9.

Other Related Publications (Not funded by this project):

1. Shen-Shyang Ho, Matthew Schofield, Bo Sun, Jason Snouffer, Jean Kirschner, A Martingale-based Approach for Flight Behavior Anomaly Detection, 20th IEEE International Conference on Mobile Data Management (MDM 2019), Hong Kong, China, June 10-13, 2019. (Sponsored by ASRC Federal Mission Solutions). 

2. Schofield, M., Ho, S.S. and Wang, N., 2021, September. Handling Rebalancing Problem in Shared Mobility Services via Reinforcement Learning-based Incentive Mechanism. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 3381-3386). IEEE.

3. Schofield, M., 2021. Rebalancing Shared Mobility Systems by User Incentive Scheme via Reinforcement Learning. Rowan University (Thesis).