Forecast and Capacity Planning for Nogales’ Ports of Entry

1. Forecast and Capacity Planning for Nogales’ Ports of Entry Research Team: Dr. Rene Villalobos, Dr. Arnold Maltz, Liangjie Xue, Octavio Sanchez, & Laura Vasquez Arizona State University Sponsor:Arizona Department of Transportation

2. Objective & Activities Executive summary Literature Review Crossing Data Overview Chart Correlation Matrix Model Building Procedure Commercial Forecasts POV Forecasts Pedestrian Forecasts POV & Pedestrian Traffic Split Conclusions Agenda

3. Objective of the Study • The principal objective of this study is to forecast the number of border crossings by mode at the Nogales-Mariposa and DeConcini Ports of Entry • A secondary objective is the assessment of the interaction between the Mariposa and DeConcini Ports of Entry • A third objective is the assessment of future port of entry needs and opportunities

4. Executive Summary • We have developed models to predict border crossing for different modes of traffic • Different types of models are tested, including multivariate regression models and time series models. • We have prepared forecasts for five, ten and fifteen years into the future • The Mexican Peso to US Dollar exchange rate and the US Index of Industrial Production are good indices to build forecast models • Truck crossings may increase up to about 50% in 15 years compared to the crossings of 2008.

5. Executive Summary (cont.) • The POV traffic and Pedestrians are more sensitive to the changes of the economy climate and therefore their forecasts are less reliable than those obtained for commercial vehicles • The amount of bus passengers crossing the border is a small portion of the pedestrians and POV passengers. • The recent recession makes it hard for the model to forecast the near future trend

6. Literature Review: Summary • There are some studies conducted about the assessing and forecasting the volume of border crossing traffic, mainly about commercial traffic[1][2][3][4] • However, as far as we have seen, there are none for the ports at Nogales • The commonly used software package Border Wizard which is a capacity assessment software and not a forecast tool, it actually uses demand/volume as a input [5] • In order to come up with an accurate forecast we needed to determine which factors affect the number of border crossings [1] Thomas M. Fullerton, Jr. and Roberto Tinajero, “Cross Border Cargo Vehicle Flows,” International Journal of Transport Economics, vol. 29, 2002, pp. 201-213. [2] Thomas M. Fullerton Jr., “Currency movements and international border crossings,” International Journal of Public Administration, vol. 23, 2000, pp. 1113-1123. [3] M. Figliozzi, R. Harrison, and J. McCray, “Estimating Texas-Mexico North American Free Trade Agreement Truck Volumes,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1763, Feb. 2001, pp. 42-47. [4] IBI group, Canada-United States-Ontario-Michigan Border Transportation Partnership Planning/Need and Feasibility Study: Existing and Future Travel Demand, US, Canada: MDOT,URS Canada, . [5] Kimley-Horn and Associates, Inc. and BPLW Architects & Engineers, Inc. Planning Group, Feasibility Study Mariposa US Port of Entry, 2005.

7. Literature Review: Forecast Models • The Canadian-US border study • Explored regression model and time series model • Adopt regression model • Most of the models are no better than our preliminary assessment model in terms of the training R square • Fullerton’s study • El Paso Port of entry • Used time series model • Incorporated some external variables like the exchange rate, the index of industrial production of both nations, etc. • None of the existing studies address the periodical pattern on the data as we occurred in Nogales • No systematic methodology can be applied directly

8. Overview: Border Crossing Data 2008 * 9/11 * Testing Data

9. Correlation Matrix Note the difference before and after “9/11”

10. Commercial Traffic:

11. Commercial: Crossings Seasonality The relatively stable trend enables to build the model based on yearly data and split to monthly data This permits to use older data that is only available per year 14 years of data, 1995-2008 Truck Month

12. Model Building: Procedure • Define Models • Multivariate Regression Model • Time Series (ARIMA) Model • Variable Selection • Exhaustive method • LASSO method • Model testing & selection • Leave out the last three year data for testing purpose • Selection Criteria: • Theil’s U statistic (The smaller the better) • R-square value (The bigger the better) • VIF value of the variables (usually should be below 10) • Practical meaning of the model *LASSO: Least Absolute Shrinkage & Selection Operation

13. Commercial: Forecast Overview • External variables: • Mexican Peso to US Dollar Exchange Rate • US IIP • For each mode of traffic we will provide a five-year, ten-year and fifteen-year forecast • Use forecasts of the exchange rate and US IIP from forecasts.org, which is 36 months (3 years) forecast • Create different scenarios for the external variables beyond 36 month time frame

14. Comparison of Models Regression Model Coefficients: ARIMA parameters (p,d,q,P,D,Q, period)= (1,1,4,2,1,2,12)

15. Comparison of Models (cont.) • Time series model out performs the regression model in terms of both criteria • We will prefer to use time series model in this study * Validation data set

16. Commercial: 5-year Forecast Scenarios • Divide historical data into five year segments and each segment is fit separately (explained next) • Total of 9 scenarios for Exchange Rate and US IIP combinations

17. Commercial 5-year: Exchange rate and US IIP Levels

18. Commercial: 5-year Forecast US IIP - Growth Speed +

19. Commercial: 10-year Forecast Scenarios • Uses similar procedure as in the five-year forecast • Exchange-rate was never stable for a ten-year time span; therefore only two possible scenarios were considered for exchange rate • For the US IIP the same three scenarios were considered possible • Forecast is given in years instead of months due to uncertainty for longer time periods

20. Commercial: 10-year Forecast USIIP - Growth Speed +

21. Commercial: 15-year Forecast Scenarios • Historical data of exchange rate only available for 14 years • Rather than segmenting data used different methods of piecewise linear regression to create the forecast • We use a package called “segmented” in R to locate breakpoints

22. Commercial 15-year: X-Rate Levels

23. Commercial: 15-year Forecast USIIP - Growth Speed +

24. POV: Forecasts

25. POV: Forecast Overview • There is no external factor that is statistically significant to the POV crossings • ARIMA model is used to forecast the 5-year trend • A simple regression method is used to do the extended forecast

26. POV: 5-year Forecast

27. POV: 10-year & 15-year Forecast • We assume the crossing traffic will start to increase after the current recession is over (red dashed circle) • Circle drawn at 2014 only for a demonstration purposes however it will occur at an unknown time

28. Pedestrian: Forecasts

29. Pedestrian: Historical Data Review • The historical data can be divided into four different segments • Each segment has different increasing trend

30. Pedestrian: 5-year Forecast

31. Pedestrian: 10-year & 15-year Forecast

32. POV & Pedestrian: Traffic Split

33. POV: Traffic Split Before 2007, the portion is roughly 60:40 Since 2008, the portion is roughly 70:30 Both of the portions are quite stable

34. Pedestrians: Traffic Split The portion roughly maintain to 95:5

35. Conclusions • We have completed most of the activities • The short term prediction turns out to be very successful (see the example of 2009 data below) • One needs to be careful when using the long term prediction

36. Conclusion (cont.) • An example of the conclusions drawn for the industrial member: • The commercial traffic in 15 years may increase up to 50%. The POE should be prepared to handle the increase of the traffic with infrastructure and human resource. • Due to the lack of clearance, we did not get enough data for a new simulation model to assess the port capacity

37. Future Research Topics • Research why after at least a 6-year trend upwards, the POV traffic has kept shrinking since 9/11? • What is the economic impact of not having the proper infrastructure or procedures for the border crossing process that: • Prevents people from crossing the border? • Makes people cross walking rather than driving? • If the POV traffic doesn’t start to increase even when the current recession is over, at what level will it become stabilized (i.e. it can’t keep shrinking down to zero)?

38. Future Research Topics (cont.) Is there any relationship between the opposite trends (negative correlation) of POV and pedestrians crossings after 9/11? How is it that the traffic split between ports relates directly to the capacity at each one? How do people make the decision on what port to use that makes the overall system be “efficient”? Use of Delphi techniques for building plausible scenarios We need a professional, technical, independent “data clearing house” to serve as the repository of border data and research results

39. Questions?