1. Developing Predictive Border Crossing Delay Models Lei Lin, Ph.D. Qian Wang, Ph.D. Adel W. Sadek, Ph.D. First Annual Transportation Informatics Symposium University at Buffalo – SUNY Buffalo, NY August 14, 2015

2. Transportation Systems Engineering University at Buffalo The State University of New York Transportation Systems Engineering Motivation & Background Niagara Frontier International border crossing: one of North America’s busiest travel portals

3. Motivation & Background Economic vitality of the Golden Horseshoe Continued increase in travel demand + tighter inspection Current or instantaneous vs. Predicted or experienced

4. Methodology Two Steps: Border Crossing Traffic Volume Prediction Multi-server Queueing Model

5. Border Crossing Traffic Volume Prediction Data Processing & Analysis Individual Model Development SARIMA Model SVR Multi-model Combined Forecasting Fixed weight method Fuzzy Adaptive Variable Weight Method based on Fresh Degree Function The Spinning Network Method

6. Transportation Systems Engineering University at Buffalo The State University of New York Transportation Systems Engineering Border Crossing Traffic Volume Prediction Data Processing

7. Border Crossing Traffic Volume Prediction SARIMA Model Prediction Accuracy

8. Border Crossing Traffic Volume Prediction SVR Prediction Accuracy

9. Border Crossing Traffic Volume Prediction Multi-model Combined Forecasting SARIMA: Good for weekdays (9.84% vs. 10.37% for SVR) SVR: Good for game days (9.42% vs. 15.17% for SARIMA) Combining the forecasts from the two models: Fixed weight method Identifies the model that works best for a given hour Fuzzy Adaptive Variable Weight (Fresh Degree Function) weights assigned to each model based on how well each model performed on recent forecasts

10. Border Crossing Traffic Volume Prediction Models’ Performance Comparison

11. Spinning Network Forecasting Method A forecasting algorithm proposed by Huang & Sadek (2009). The method attempts to mimic some aspects of human memory and has the advantages of: Computational Efficiency Robustness

12. Spinning network (SPN)

13. Dynamic Time Warping (DTW)

14. Performance Evaluation

15. Method MAPE (%) Test Dataset (1,905 hours) MAPE (%) Hours showing Abrupt Change (36 hours) DTW-SPN10.6027.55 Euclidean-SPN16.4969.44 SARIMA16.3852.95 SVR14.5712.59

16. Methodology Two Steps: Border Crossing Traffic Volume Prediction Multi-server Queueing Model

17. Queueing Model Development Inter-arrival Time Distribution Service Time Distribution

18. Queueing Models

19. Queueing Model---VISSIM model

20. Comparison between Analytical Approach and Simulation Approach Traffic Volume (vph) No. of Service Stations Number of Vehicles in the Queue (Vehicles) Simulation in VISSIM Mean Standard Variance Mean Standard Variance Mean Standard Variance 400 340.735.1647.936.1541.97.24 4225.5822.936.2226.67.57 58.283.408.653.0915.45.29 65.61.495.261.779.93.72

21. A Smartphone app, the Toronto Buffalo Border Waiting (TBBW) app, designed to collect, share and predict waiting time at the three Niagara Frontier border crossings https://www.youtube.com/watch?v=t04n0bB73DM Putting it all together – TBBW App

22. Future Research Compare to blue-tooth delay measurement data Modeling station opening mechanism or rules Mechanism for on-line delay prediction adjustment A comprehensive border management framework for intelligent routing and traffic load balancing

23. THANK YOU ! QUESTIONS ! Adel W. Sadek, Ph.D. Professor University at Buffalo – SUNY Buffalo, NY 14260 Phone: (716) 645-4367 FAX: (716) 645-3733 E-mail: asadek@buffalo.edu