Assessment of Commuting Crossing Cost at El Paso – Ciudad Juárez International Bridge of the Americas by

Luis D. Galicia, Gabriel A. Valdez, David Salgado, Iraki Ibarra, and Edwin Varela

Project performed by Center for International Intelligent Transportation Research

Report No. CIITR Project: Assessment of Commuting Crossing Cost and Mobility Index at El Paso – Ciudad Juárez International Bridge of the Americas Project No. 186052-00006

April 2014

Report prepared by Center for International Intelligent Transportation Research Texas Transportation Institute 4050 Rio Bravo, Suite 151 El Paso, Texas 79902

TEXAS TRANSPORTATION INSTITUTE The Texas A&M University System College Station, Texas 77843-3135

TABLE OF CONTENTS

Page

List of Figures ...... iii

List of Tables ...... iv

Disclaimer and Acknowledgments ...... vi

Executive Summary ...... 1

Chapter 1: Introduction ...... 2 1.1 Area of Study ...... 2 1.2 Literature Review...... 3 1.2.1 The Value of Travel Time Overview ...... 3 1.2.2 Studies in the United States ...... 4 1.2.3 International Studies ...... 11 1.3 Chapter Summary ...... 12

Chapter 2: Stated Preference and Origin-Destination Survey ...... 15 2.1 Survey Design ...... 15 2.2 Survey Execution ...... 15 2.2.1 Placement and Schedule ...... 15 2.2.2 Protocol ...... 15 2.3 Survey Findings ...... 16 2.3.1 Background Questions ...... 16 2.3.2 Stated Preference Questions ...... 21 2.3.3 Vehicle Information Questions ...... 23 2.4 Chapter Summary ...... 26

Chapter 3: Identification of Commuters’ Origins and Destinations...... 27 3.1 Overview ...... 27 3.2 TAZ and AGEB in the El Paso Juárez Region ...... 27 3.3 OD Matrix TransCAD ...... 28 3.4 Summary ...... 30

Chapter 4: Microsimulation Model ...... 31 4.1 Model Overview ...... 31 4.1.1 BOTA Booth Inspection Time ...... 31 4.1.2 BOTA Border Wait Times ...... 31 4.1.3 BOTA Operating Booths ...... 33 4.2 Network Construction ...... 34 4.3 Operational Modeling ...... 35 4.3.1 Simulated Travel Time and General Cost ...... 36 4.4 Model Calibration and Validation ...... 38 4.5 Analysis of Results ...... 39

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Chapter 5: Estimation of Value of Waiting Time and Communing Costs ...... 41 5.1 Estimation of Personal Costs ...... 41 5.1.1 Work/Work Related ...... 43 5.1.2 School or College ...... 43 5.1.3 Shopping Related ...... 44 5.1.4 Other Purposes Related ...... 45 5.1.5 Calculation of VoTT ...... 45 5.2 Estimation of Commuting Costs ...... 46 5.2.1 Vehicle Costs ...... 46 5.2.2 Environmental Costs ...... 53 5.3 Results ...... 56 5.3.1 Total Vehicle Costs ...... 56 5.3.2 Total Environmental Costs ...... 56 5.3.3 Commuting Costs...... 57 5.4 Calculation of VoWT ...... 58 5.4.1 Formulation ...... 58 5.4.2 VoWT Calculation ...... 59 5.5 Conclusions and Final Remarks...... 61

References ...... 62

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LIST OF FIGURES

Page Figure 1. Area of Study (El Paso, Ciudad Juárez)...... 3 Figure 2. Commuters’ Place of Residency...... 16 Figure 3. Commuters’ Place of Residency per Period of Time Surveyed...... 16 Figure 4. Monthly Income...... 17 Figure 5. Drivers’ Main Trip Purpose...... 18 Figure 6. Drivers’ Main Trip Purpose according to Place of Residency...... 18 Figure 7. Weekly Trips’ Frequency...... 19 Figure 8. Monthly Trips’ Frequency...... 19 Figure 9. Yearly Trips’ Frequency...... 19 Figure 10. Status of Employment...... 20 Figure 11. Age Distribution among Interviewees...... 20 Figure 12. Crossing Times Obtained...... 21 Figure 13. Money Spent with a 25 Percent Crossing Time Reduction...... 22 Figure 14. Money Spent with a 50 Percent Crossing Time Reduction...... 22 Figure 15. Money Spent with a 75 Percent Crossing Time Reduction...... 23 Figure 16. Makes Obtained from the Survey...... 24 Figure 17. Vehicle Classification...... 24 Figure 18. Vehicle Year...... 25 Figure 19. Number of Passengers per Vehicle...... 25 Figure 20. License Plates Obtained from the Survey...... 26 Figure 21. Desire Lines between El Paso and Ciudad Juárez...... 28 Figure 22. Frequent OD Pairs Using BOTA...... 29 Figure 23. Booth Inspection Time – Probability Distribution Function...... 32 Figure 24. Average Waiting Time at BOTA...... 33 Figure 25. BOTA VISSIM Network...... 34 Figure 26. VISSIM Dwell Time Distribution for Inspection Booths...... 35 Figure 27. BOTA VISSIM Base Model with 9 Lanes Open and 11 Booths Operating...... 36 Figure 28. VISSIM Edge between Node 2 and 5...... 37 Figure 29. BOTA Microscopic Model Calibration Results...... 38 Figure 30. Fuel Consumption at BOTA...... 40 Figure 31. Total Delay at BOTA...... 40 Figure 32. Hierarchical Structure of VoWT...... 41 Figure 33. VoTT Calculation Process...... 42 Figure 34. Hierarchical Structure of Commuting Costs...... 46 Figure 35. Trend Curve of Gasoline Price...... 51 Figure 36. Gas Consumed and CO2 Emitted for Each Scenario...... 55 Figure 37. Commuting Cost for Each Scenario...... 57 Figure 38. VoWT for Each Scenario...... 61

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LIST OF TABLES

Page Table 1. Commuter VoTT in European Countries...... 5 Table 2. VoTT Recommended by the USDOT (Expressed in Percent of the Hourly Wage)...... 6 Table 3. VoTT Recommended by the USDOT (Expressed in US$/hr)...... 6 Table 4. VoTT Recommended by AASHTO...... 7 Table 5. VoTT Recommended by AASHTO for the Industry...... 7 Table 6. VoTT Estimated by Tuzel (12)...... 8 Table 7. VoTT Estimated by Tilahun and Levinson for Non-Subscribers ($US/hr)...... 8 Table 8. VoTT Estimated by Tilahun and Levinson for Subscribers ($US/hr)...... 9 Table 9. VoTT Proposed by FDOT...... 9 Table 10. VoTT Estimated by Kang and Stockton...... 9 Table 11. VoTT Estimated by for Motorists (19)...... 10 Table 12. VoTT Estimated by Brownstone and Small (20)...... 10 Table 13. VoTT Proposed by ODOT...... 11 Table 14. VoTT Estimated by Tseng and Verhoef (4)...... 11 Table 15. VoTT Estimated by Antoniou et al. (6)...... 12 Table 16. Summary of VoTT Studies Reviewed...... 13 Table 17. Example of Demand Table for VISSIMs DTA...... 31 Table 18. Booth Inspection Time – Descriptive Statistics...... 32 Table 19. Descriptive Statistics for BOTA Operating Booths – 10:00 a.m. to 11:00 a.m. Period...... 33 Table 20. VISSIM Model Calibration – Percent Error...... 39 Table 21. BOTA Results Based on No. of Booths Operating...... 39 Table 22. Income Data for Work/Work Related Trips...... 43 Table 23. Income Data for School or College Related Trips...... 44 Table 24. Income Data for Shopping Related Trips...... 44 Table 25. Income Data for Other Purposes Trips...... 45 Table 26. Average Hourly Wage and VoTT...... 45 Table 27. Number of Vehicles and Gas Consumption and VMT per Day at BOTA...... 47 Table 28. Percentage of Vehicles per Category...... 47 Table 29. Number of Vehicles per Category and per Day at BOTA...... 47 Table 30. Insurance Costs per Category...... 48 Table 31. Cost of the Full Coverage Insurance per Scenario per Year...... 49 Table 32. Routine Maintenance, Tires, Repair, and Depreciation Costs per Year...... 51 Table 33. Total Gas Costs...... 52 Table 34. Annual Texas Vehicle Inspection and Engomado Ecologico Costs...... 53 Table 35. Metric Tons of CO2 Emitted per Day at BOTA...... 54 Table 36. Price of CO2 Emitted (€/Metric Ton)...... 55 Table 37. Price of CO2 Emitted (US$/ Metric Ton)...... 56 Table 38. Daily CO2 Emission Costs at BOTA...... 56 Table 39. Vehicle Costs Summary...... 56 Table 40. Environmental Costs Summary...... 57 Table 41. Total Annual Cost Summary...... 57

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Table 42. Number of Commuters per Year at BOTA...... 58 Table 43. Delay at BOTA...... 59 Table 44. Percentage of Commuters Included in Each Trip Purpose Group...... 59

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DISCLAIMER AND ACKNOWLEDGMENTS

The authors wish to acknowledge the support of the Instituto Municipal Investigación y Planeación staff in Ciudad Juárez, Chihuahua, for the successful implementation of surveys. Additionally, authors would like to acknowledge Texas A&M Transportation Institute staff, especially Iraki Ibarra and Edwin Varela, for their support during the surveys data processing. The contents and views expressed in this paper is the sole responsibility of the authors.

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EXECUTIVE SUMMARY

Passenger vehicles that commute on a daily basis through the El Paso-Ciudad Juárez international ports-of-entry (POEs) have always experienced long queues and prolonged crossing times. During peak periods, vehicle delay at a POE can reach between 2 to 3 hours, depending on the amount of lanes opened and the average inspection time. Currently, among the POEs available, the Bridge of the Americas (BOTA) charges no fee to passenger vehicles. As a consequence, BOTA shows the highest crossing delay time. In many ways, this delay time has a real economic cost. For example, businesses in El Paso lose revenue when potential shoppers from Ciudad Juárez or other Mexican cities and communities decide not to cross the border because of the excessive wait. Also, there is a significant impact on commuters that cross on a regular basis, and this impact can be calculated in monetary terms depending on their trip purpose, vehicle operating costs, and value of waiting time. There was currently no measured relationship between the number of inspection lanes made available and the reality of how they affect the total commuting costs.

This study quantifies the monetary impact upon the northbound traffic on BOTA based on the number of inspection lanes open. The sum of the Value of Waiting Time, vehicle operating costs, and environmental costs (CO2 emissions) provides a clearer and broader look at the increasing cross border waiting time problem at BOTA. Results of the 1,504 socioeconomic/origin-destination person survey showed that the majority has a monthly income between 0 and 10,000 pesos. The main purpose of their trips in descending order was other, shopping, work, and school. Approximately 75 percent of the people surveyed cross to El Paso at least once per week. At least half of them work full time and have an age between 25 and 54 years. Commuters are willing to spend money ($1 to $2 USD toll payment) if they could reduce their crossing time by 50 percent, and $3 up to $5 if they could reduce their crossing time by 75 percent. The average crossing time of commuters was 65 minutes. The total estimated Value of Travel Time per hour per commuter resulted in $1.44 (2012 US$). After applying the suggested method to estimate the total Commuting Cost and Value of Waiting Time, researchers found that these two variables were dependent of the total number of booths operating at the POE. After running four scenarios (14, 13, 12, and 11 booths available for inspection), the results showed a substantial (exponential) decrease in the VoWT and the commuting cost when more booths are operating at the BOTA POE.

Key Words: Value of Time, Value of Waiting Time, Border Commuting, Travel Cost, Border Mobility, State Preference Survey, Origin Destination, Passenger Car Value of Time

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CHAPTER 1: INTRODUCTION

Cross border commuting in the El Paso/Ciudad Juárez region is defined as the practice where people cross any of the four international bridges in the region in their regular journey between the traveler’s home and their place of work, study, or other purposes. The El Paso/Ciudad Juárez international border crossing region contributes billions of dollars in trade every year, providing the regional population with access to schools and businesses, and sharing regional culture and lifestyle. In recent years, this international border has experienced an increase in motorized travel time and waiting time at check points, producing travel time delays. This research quantifies annual commuting costs at Bridge of the Americas (BOTA) international bridge by estimating and adding the value of waiting time, vehicle operating costs, and environmental costs. To achieve this objective, the research team quantified the average annual monetary impact of northbound traffic on BOTA depending upon the number of inspection lanes available for crossing.

According to a recent study developed by Cambridge Systematics, current traffic volumes along the region’s six international crossings are beginning to impact wait times at the border, some of which are already significantly high (1). The study placed special emphasis on the typical peak period wait times at BOTA, where they already exceed two hours for passenger traffic during average days, and where on busy days, wait times can be even longer. These findings are indications of a deficiency in operational capacity, identifying the point at which the crossing can no longer efficiently handle travel demand.

1.1 AREA OF STUDY

The El Paso/Ciudad Juárez region is a binational metropolitan area on the border between Mexico and the United States. Both cities are linked by four major ports-of-entry (POE) shown in Figure 1. This region contributes largely to the total amount of traffic across the U.S.–Mexico border. A significant amount of this traffic is local and generates large amounts of cross-border trips with origins and destinations (OD) on both sides of the border. These cross-border interactions play a crucial role in the economy and daily activities of the region.

According to Cambridge Systematics, the El Paso and Ciudad Juárez economies are tightly linked, and long and unpredictable border waiting times will adversely impact the overall economic health of the region (1). Additionally, they predict that if the problem remains unchecked through 2035, forecasted congestion and waiting times in the El Paso/Ciudad Juárez region will diminish the economy by $54 billion (21.8 percent), and cause a net job loss of about 850,000 (17.4 percent). Although the Cambridge Systematics analysis was made using a Regional Economic Model Inc. technique, there are no estimations on the individual characteristics of commuters, their travel time behaviors, value of travel time (VoTT), the value of waiting time (VoWT), and most importantly, the operational behavior at the POEs (and recommended actions to improve mobility and avoid traffic congestion at the international bridges). Therefore, this study also addresses the hypothetical operation of booths at the BOTA POE.

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Figure 1. Area of Study (El Paso, Ciudad Juárez).

1.2 LITERATURE REVIEW

This section presents a review of existing literature related to the value of travel time, focusing on the definition of this concept and the existing studies in the U.S. and worldwide. Additionally, this section explores potential influences upon the different methods for estimating the VoTT and the proper criteria to be calculated.

1.2.1 The Value of Travel Time Overview

The VoTT is one of the largest costs of transportation. VoTT commonly accounts for costs of consumers of personal time spent on travel and the costs to business of paid employee time spent in travel and is expressed in monetary units per unit of time (e.g., US$/hr). Different studies reviewed tend to use the term Value of Travel Time Savings (VTTS) as a synonym of VoTT since VTTS represent VoTT saved due to an improvement on the infrastructure. Hence, VTTS is theoretically equal to VoTT. For example, if the VoTT of a commuter is 23.05 US$/hr, the VTTS per hour of that commuter would be 23.05 US$/hr. In this report, the term VoTT will be used through the text and will be expressed in US$ unless indicated otherwise.

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After a comprehensive literature review, a unique definition of VoTT was not found. However, the following definitions could closely describe the meaning of the term VoTT. • VoTT is the amount of money that a commuter would be willing to pay in order to save time, or the amount that a commuter would accept as a compensation for lost time (2). • “The Value of Travel Time (VoTT) refers to the cost of time spent on transport, including waiting as well as actual travel. It includes costs to consumers of personal (unpaid) time spent on travel, and costs to businesses of paid employee time spent in travel” (3).

Tseng and Verhoef assume that “the VoTT is theoretically derived from a budget constrained allocation problem of time across various activities, such as work, leisure, and the time required consuming goods and services” (4). In the U.S. the studies reviewed express VoTT in US$/hr.

The VoTT is usually used to assess how feasible it is to consider the construction of new infrastructures. By simulating the before and after the construction scenarios of new infrastructure, the estimated VoTT will reveal the best scenario in monetary terms. For example, to assess the feasibility of constructing a new toll road, economists and engineers calculate the VoTT of the commuters. This VoTT shows the amount of money that commuters would pay for saving time. Indirectly, this VoTT will show the toll price that commuters would be willing to pay. This information is important in order to estimate the revenue of the toll roads.

The major objective of transportation investments is to reduce delay in passenger or freight transportation without compromising safety. VoTT is an important element in evaluating the benefits of investments in infrastructure (5).

Normally, transportation infrastructure projects can be justified by quantifying the benefits to society. By calculating the VoTT of the commuters and the time savings after implementing the respective infrastructure, the stakeholders may analyze the social benefits of the infrastructure in economic terms (6). For example, Lehtonen and Kulmala used VoTT figures to estimate the travel time savings due to signal prioritization and real-time passenger information enhancements along two transit lines in the city of Helsinki, Finland (7).

In order to calculate VoTT, it is necessary to build a model that takes account of mode choice, route choice, and socioeconomic status of the commuters. These data are commonly collected through Stated Preference (SP) surveys and Revealed Preference (RP) surveys. RP surveys collect data related to actual behavior of commuters, and SP surveys collect data related to commuter’s hypothetical situations (6, 8). Due to the traffic characteristics and traffic conditions at the BOTA POE, the research team selected the SP survey as the most suitable method for collecting information.

1.2.2 Studies in the United States

This subsection presents some of the VoTT studies published in the U.S. Since the United States Department of Transportation (USDOT) and the American Association of State Highway and Transportation Officials (AASHTO) are two of the most relevant transportation authorities in the U.S., the research team made a deeper analysis of their publications. This subsection provides a complete description of some of the most relevant VoTT studies documented (data, methodology, and results).

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1.2.2.1 U.S. Department of Transportation (The Value of Travel Time)

The USDOT is a federal cabinet department of the United States government in charge of transportation matters nationwide (5). The USDOT states that VoTT estimation depends on trip purpose, personal characteristics, hourly income, mode and distance, and comfort. The factors are explained in detail below: 1. Trip purpose. The different trip purposes considered are business travel time, for which a market wage is paid, and personal or leisure travel time. Sometimes, commuting is treated as a separate category, intermediate between personal and business, but more frequently it is included in personal travel. In some cases, VoTT might be negative if the individual is willing to pay to spend more time traveling (e.g., driving a sport car, cruise ship, or steam railroad). 2. Personal characteristics. Demographic variables such as age, education, sex, employment status or position, and activity during the trip (e.g., driver, commuter reading e-mails) influence VoTT. 3. Hourly income. This study calculated VoTT based on percentages of the hourly wage. This percentage is lower while the person is in a paid business travel than while he is commuting to the office. 4. Mode. Mode choice is based on different factors such as location, distance, and quality of transit in the area, etc. Regarding the different modes that operate within a city (metro, bus, BRT, LRT, vehicle, etc.), they should be distributed with identical probabilities. However, for a long trip the probabilities of choosing high speed train or airplane increases probabilities due to the limited amount of time available for taking a long trip. 5. Comfort. Travelers will vary widely in willingness to pay to shorten the time during which they are subject to uncomfortable conditions such as walking, bicycling, and standing on platforms or in vehicles.

The USDOT reviewed different national and international sources. Some of these sources were based on stated preference surveys and discrete choices techniques such as logit model analysis or meta-analysis. Additionally, USDOT explored studies resulted from efforts of other nations to standardize the practice of valuing travel time for public investment evaluation. Based on this literature, USDOT developed the proposed VoTT.

USDOT documented the calculated VoTT for different European countries. Table 1 illustrates the VoTT for some European countries expressed in 2012 US$/hr. In the USDOT report, VoTT was expressed in 2008 USD. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 1. Commuter VoTT in European Countries.

Country VoTT 2008$/hr VoTT 2012$/hr Denmark $12.46 $13.30 France $13.27 $14.16 Norway $9.33 $9.96 Spain $18.52 $19.77 Sweden $6.77 $7.23 Switzerland $18.41 $19.65 UK $9.15 $9.77

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Table 2 shows the values and ranges recommended by USDOT expressed as a percentage of the total earning per hour. This table is categorized in local and intercity travels, and inside these categories the subcategories are personal or business travel. Additionally, there is an extra category in the table that represents the vehicle operators. The reader must note that high speed rail was not included as a surface mode.

Table 2. VoTT Recommended by the USDOT (Expressed in Percent of the Hourly Wage).

Category % of the Average Confidence Interval Wage (% of the Average Wage) Local Travel Personal 50 35–60 Business 100 80–120 Intercity Travel Personal 70 60–90 Business 100 80–120 Vehicle Operators 100 80–120

USDOT recommends using the U.S. Census Bureau as the only one source of hourly income for calculating VoTT. Based on this recommendation and the information presented in Table 2, these quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9). The following table contains that information. Again, reader must note that high speed rail was not included as a surface mode.

Table 3. VoTT Recommended by the USDOT (Expressed in US$/hr).

Category VoTT Estimated VoTT Estimated (2011 US$/hr) (2012 US$/hr) Local Travel Personal 23.90 24.42 Business 22.90 23.40 Intercity Travel Personal 23.90 24.42 Business 22.90 22.90

1.2.2.2. AASHTO Study Regarding Users and Non-Users Benefit Analysis for Highways

AASHTO is a nonprofit, nonpartisan association representing highway and transportation departments in the 50 states, the District of Columbia, and Puerto Rico. It represents all five transportation modes: air, highways, public transportation, rail, and water. Its primary goal is to foster the development, operation, and maintenance of an integrated national transportation system (10). AASHTO’s study “User and Non-User Benefit Analysis for Highways” considers that the cost of travel includes not only expenses such as gasoline, insurance, tires, etc., but also the time spent traveling (11). The VoTT is directly related with the after-tax wage of the traveler, because working is an alternative in which that travel time may be spent. The value that users assign to their travel time will depend upon the opportunity cost of that time, and the consumption opportunities that the user associates with traveling on highways.

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Table 4 shows the VoTT provided by AASHTO. These values are based in the values calculated by USDOT. However AASHTO classifies the travelers based on their transportation mode and trip purposes. The table shows the VoTT expressed as a percentage of the wage rate.

Table 4. VoTT Recommended by AASHTO.

Transportation Mode and Trip Purpose % of the Average Wage AUTO Drive Alone Commute 50 Carpool Driver Commute 60 Carpool Passenger Commute 40 Personal (Local) 50 Personal (Intercity) 70 Business 100 TRANSIT BUS In-Vehicle Commute 50 In-Vehicle Personal 50 Excess (Waiting, Walking, or Transfer Time) Non-business 100 Business (All Time) 100

According to AASHTO, the VoTT of a person who is in business while traveling should be higher than a 100 percent of his/her salary. This compensation should be paid due to the opportunity cost lost by the person. Table 5 provides the VoTT recommended by AASHTO after taking into account the compensation for the lost opportunity cost. It is proposed on average an increase of 18 percent over the wage. Table 5 shows the VoTT expressed in percentage of the wage for different types of industries.

Table 5. VoTT Recommended by AASHTO for the Industry.

Industry Type Total Compensation as a Percent of the Average Wage All employees 118 Private industry 116 Transportation & public utilities 119 Trucking and warehousing 120 Finance, real estate, insurance 115 Services 114 Private household services 103 Government 131 Federal non-military 148 State and local 125

AASHTO recommends obtaining the data for calculating VoTT from the National Income and Product Accounts of the United States.

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1.2.2.3 Value of Travel Time Estimation Using Hierarchical Bayesian Mixed Logit Approach

This study collected data from commuters traveling on toll facilities operated by the Port Authority of New York and New Jersey and the New Jersey Turnpike (12). The surveys collected SP, trip, and socioeconomic information of the commuter. The methodology proposed by this study was a Hierarchical Bayesian Mixed logit model. Table 6 shows the confidence interval of VoTT for peak and non-peak hours. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 6. VoTT Estimated by Tuzel.

Time Confidence Interval Confidence Interval (2005 US$/hr) (2012 US$/hr) Peak Hours (15.15–16.5) (15.59–17.18) Non-Peak Hours (5.72–6.45) (6.02–6.79)

1.2.2.4 The Value of Travel Time according to Tilahun and Levinson

The data used in this study were collected on the I-394 MnPASS (Minnesota Department of Transportation's electronic toll collection system) High Occupancy/Toll (HOT) lane project recently implemented in the Minneapolis/St. Paul region. In order to estimate VoTT of those groups of commuters, the researchers performed a SP survey (13). The survey asked commuters if they took the HOT lane or not to arrive to their destinations. The analysis grouped the commuters into subscribers and non-subscribers of the MnPASS (electronic toll collection transponder) and if they experienced a delay or not. Trip times were divided into morning peak, afternoon peak, and off peak. Trip experience was divided into delayed and not delayed. A total of six groups were created. Finally, these data were analyzed by using a random parameter logit model to obtain the VoTT. Table 7 shows the estimated VoTT for non-subscribed commuters for each of the six groups expressed in 2009 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 7. VoTT Estimated by Tilahun and Levinson for Non-Subscribers ($US/hr).

Groups VoTT Confidence VoTT Confidence Estimated Interval (2009 Estimated Interval (2009 US$/hr) (2012 US$/hr) (2012 US$/hr) US$/hr) Off peak-early/on time (VOTa) 11.94 (10.03, 13.84) 12.79 (10.74–14.83) Off peak-late (VOTb) 14.82 (10.4, 19.23) 15.88 (11.14–20.60) Morning-early/on time (VOTc) 13.36 (11.06, 15.65) 14.31 (11.85–16.76) Morning-late (VOTd) 10.10 (7.49, 12.72) 10.82 (8.02–13.63) Afternoon-early/on time (VOTe) 13.96 (8.62, 19.29) 14.95 (9.23–20.66) Afternoon-late (VOTf) 18.95 (12.95, 24.94) 20.30 (13.87–26.72)

Table 8 shows the estimated VoTT for subscribed commuters for each of the six groups expressed in 2009 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

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Table 8. VoTT Estimated by Tilahun and Levinson for Subscribers ($US/hr).

Groups VoTT Confidence VoTT Confidence Estimated Interval (2009 Estimated Interval (2012 (2009 US$/hr) (2012 US$/hr) US$/hr) US$/hr) Off peak-early/on time (VOTa) 10.47 (8.03, 12.92) 11.22 (8.60–13.84) Off peak-late (VOTb) 15.11 (7.51, 22.71) 16.19 (8.04–24.33) Morning-early/on time (VOTc) 12.73 (10.84, 14.61) 13.64 (11.61–15.65) Morning-late (VOTd) 9.54 (5.97, 13.11) 10.22 (6.40–14.04) Afternoon-early/on time (VOTe) 10.62 (4.7, 16.55) 11.38 (5.03–17.73) Afternoon-late (VOTf) 25.43 (18.75, 32.1) 27.24 (20.09–34.09)

1.2.2.5 The Value of Travel Time according to the Florida Department of Transportation

This study compiled and synthesized the work done on VoTT in the last 40 years. From this extensive literature review, the Florida Department of Transportation (FDOT) provided a set of recommended values for calculating VoTT (14). Table 9 shows the VoTT recommended by FDOT expressed in percentage of the hourly wage. The VoTTs recommended are categorized by trip purpose.

Table 9. VoTT Proposed by FDOT.

Purpose % of the Average Wage Personal 50 Commercial (on-the-clock) 100 + Benefits

1.2.2.6 The Value of Travel Time according to Kang and Stockton

The study site was a segment of the President George Bush Turnpike toll road in northwest Dallas, Texas, known as the “SuperConnector,” and the preexisting non-toll road route on I-35/I-635 (15). The research team collected travel times for both routes. Additionally, the North Texas Tollway Authority provided the home location of the toll road users. This home location was used to estimate income based on the data from the U.S. Census Bureau. After obtaining the income data, this study based the VoTT calculation on previous studies. Table 10 expresses VoTT values estimated by this study. VoTT values are expressed in 2008 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 10. VoTT Estimated by Kang and Stockton.

Time VoTT Estimated VoTT Estimated (2008 US$/hr) (2012 US$/hr) Peak Hours 14.81 15.81 Non-Peak hours 5.44 5.81

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1.2.2.7 The Value of Travel Time according to Small et al.

The data for this study were collected during a 10 month period in 1999–2000 (16). The location was the California State Route 91 (SR91) in the Los Angeles area. The estimation of VoTT was based in revealed and stated preference data from motorists. Motorists were able to decide whether to pay a toll for congestion-free express travel or not. The methodology used in this study was a mixed logit model. Table 11 illustrates the motorists’ VoTT expressed in 2005 US$ and in percentage of the average wage rate. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 11. VoTT Estimated by for Motorists.

VoTT Estimated (2005 VoTT Estimated (2012 % of the Average US$/hr) US$/hr) Wage 21.46 25.25 93

1.2.2.8 The Value of Travel Time according to Brownstone and Small

The data deployed in this study were collected in toll facilities of two major roads of California. The first facility was located in State Route 91 (SR91) in Orange County (CA). The second facility was located in a segment of I-15, which links San Diego employment centers with inland northern suburbs (17). The data collected were divided into RP and SP. This study estimated VoTT by developing two binary logit models, one of them with random parameters, and the other one without random parameters. Table 12 shows the confidence interval of VoTT on morning commute expressed in 2005 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 12. VoTT Estimated by Brownstone and Small.

Confidence Interval Confidence Interval (2005 US$/hr) (2012 US$/hr) (20–40) (23.53–47.07)

1.2.2.9 Oregon Department of Transportation

The present study is a review of the report published by Oregon Department of Transportation (ODOT) entitled The Value of Travel Time: Estimates of the Hourly Value of Time for Vehicles in Oregon 2003. ODOT used national and state wage data (Oregon) to calculate VoTT (18). The methodology used was based on the method developed by the Federal Highway Administration (FHWA) in the Highway Economic Requirements System (HERS) (19). Table 13 expresses VoTT values proposed by this study. VoTT values are expressed in 2003 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

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Table 13. VoTT Proposed by ODOT.

Vehicle Class VoTT Estimated VoTT Estimated (2003 US$/hr) (2012 US$/hr) Automobiles 15.31 19.1 Light Trucks 19.53 24.4

1.2.3 International Studies

This subsection presents a brief description of some of the most relevant international VoTT studies documented.

1.2.3.1 The Value of Travel Time according to Tseng and Verhoef

This study used SP data for representing departure time choices during the morning commute. The data were obtained from an internet survey among Dutch commuters (4). These commuters were selected on the criterion of experiencing at least 10 minutes of congestion, at least 3 days per week. The authors presented two statistical models for estimating VoTT. The first one was a conventional multinomial logit model (MNL) and the second one a mixed logit model (ML). The models developed in this study relate real and expected arrival time with different VoTT. Table 14 presents VoTT estimated for different commute situations. These values are expressed in 2007 euros per hour. The 2007 euros were converted into 2007 US$ by using the 2007 average exchange rate 1.37 US$ per euro (20). After the exchange, the 2007 US$ were inflated to 2012 US$ by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 14. VoTT Estimated by Tseng and Verhoef.

Time Interval VoTT Estimated VoTT Estimated (2007 US$/hr) (2012 US$/hr) MNL ML MNL ML Between 30–60 minutes before ideal arrival time 8.9 5.28 9.8 8 Between 60–90 minutes before ideal arrival time 17.9 12.75 19.8 19.4 Between 90–120 minutes before ideal arrival time 22.4 14.44 24.8 21.9 Between 120–150 minutes before ideal arrival time 25.9 16.69 28.7 25.3 Between 150–180 minutes before ideal arrival time 34.5 18.16 38.2 27.6 Between 0–15 minutes after ideal arrival time 14.3 6.75 15.8 10.2 Between 15–30 minutes after ideal arrival time 30.8 13.97 34.1 21.1 Between 30–60 minutes after ideal arrival time 24.8 17.67 27.4 26.8

1.2.3.2 The Value of Travel Time according to Antoniou et al.

The data were collected from a random sample of 289 people. Researchers designed a SP survey to collect trip-based and socioeconomic characteristics. Based on the SP data collected, it was developed three logit models: ordered logit model, generalized linear mixed logit, and binary logit model (6). They concluded that the overall performance of the ordered logit and the generalized linear mixed model has been found to be superior to the binary logit model. Table 15 shows the VoTT calculated by each of the three models for two sample groups: all commuters

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and young commuters. VoTT was expressed in 2007 US$/hr. These quantities were inflated to the year 2012 by using the Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9).

Table 15. VoTT Estimated by Antoniou et al.

Generalized Linear Mixed Ordered logit Model Binary logit Model Model VoTT VoTT VoTT VoTT VoTT VoTT Estimated Estimated Estimated Estimated Estimated Estimated (2007 US$/hr) (2012 US$/hr) (2007 US$/hr) (2012 US$/hr) (2007 US$/hr) (2012 US$/hr) 7.2 8.0 6.9 7.6 8.1 9.0

1.3 CHAPTER SUMMARY

This chapter provided a literature review of several studies that define VoTT. The research team summarized the findings and relevant information of each study in Table 16.

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Table 16. Summary of VoTT Studies Reviewed.

Reference Data Methodology VoTT Estimated USDOT (5) U.S. Census Bureau for Review of national and Category Recommended % Plausible ranges % for Surface Modes for Surface Modes income data international studies Local Travel Personal 50% 35%-60% Business 100% 80%-120% Intercity Travel Personal 70% 60%-90% Business 100% 80%-120% Vehicle Operators 100% 80%-120% National Income and Product USDOT (5) Transportation Mode and Trip Purpose % of the Average Wage AASHTO (11) AUTO Drive Alone Commute 50% Accounts of the United States Carpool Driver Commute 60% Carpool Passenger Commute 40% for income data Personal (local) 50% Personal (intercity) 70% Business 100% TRANSIT BUS In-Vehicle Commute 50% In-Vehicle Personal 50% Excess (waiting, walking, or transfer time) Non-business 100% Business (all time) 100% Groups VoTT Estimated VoTT Estimated Tilahun and Levinson (13) Stated preference survey in a Random parameter logit model for Subscribers for Non- (2012 US$/hr.) Subscribers (2012 US$/hr.) toll road facility Off peak-early/on 11.22 12.79 time (VOTa) Off peak- late 16.19 15.88 (VOTb) Morning-early/on 13.64 14.31 time (VOTc) Morning-late 10.22 10.82 (VOTd) Afternoon- 11.38 14.95 early/on time (VOTe) Afternoon-late 27.24 20.30 (VOTf)

Small et al. (16) Revealed and stated Mixed logit model VoTT Estimated (2005 VoTT Estimated (2012 % of the Average preference data from US$/hr.) US$/hr.) Wage 21.46 25.25 93% motorists in a toll facility

Brownstone and Small (17) Revealed and stated preference Binary logit models with random data in a toll facility parameters and Confidence Interval (2012 US$/hr.) binary logit models without random (23.53-47.07) parameters

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Reference Data Methodology VoTT Estimated Yanmaz-Tuzel et al. (12) Stated preference, trip and Hierarchical Bayesian mixed logit Time Confidence Interval (2012 US$/hr.) socioeconomic information in a model Peak Hours (15.59-17.18) toll facility Non-Peak Hours (6.02-6.79)

Concas and Kolpakov (14) NA Literature reviewed Purpose % of the Average Wage Personal 50% Commercial (on-the-clock) 100% + Benefits Stockton and Kang (15) Stated preference data collected in Literature reviewed Time VoTT Estimated VoTT Estimated a toll facility and income data (2008 US$/hr.) (2012 US$/hr.) Peak Hours 14.81 15.81 collected from the U.S. Census Non-Peak hours 5.44 5.81 Bureau

Oregon Department of National and State of Oregon wage Method developed by the Federal Vehicle Class VoTT Estimated VoTT Estimated Transportation (18) data Highway Administration (FHWA) (2003 US$/hr.) (2012 US$/hr.) in the Highway Economic Automobiles 15.31 19.1 Requirements System (HERS) Light Trucks 19.53 24.4

Time Interval VoTT Estimated (2007 VoTT Estimated (2012 Tseng and Verhoef (4) Stated preference data from an on- Multinomial logit model (MNL) US$/hr.) US$/hr.) MNL ML MNL ML Between 30-60 minutes before ideal 8.9 5.28 9.8 8 arrival time line survey and mixed logit model (ML) Between 60-90 minutes before ideal 17.9 12.75 19.8 19.4 arrival time Between 90-120 minutes before ideal 22.4 14.44 24.8 21.9 arrival time Between 120-150 minutes before ideal 25.9 16.69 28.7 25.3 arrival time Between 150-180 minutes before ideal 34.5 18.16 38.2 27.6 arrival time Between 0-15 minutes after ideal arrival 14.3 6.75 15.8 10.2 time Between 15-30 minutes after ideal arrival 30.8 13.97 34.1 21.1 time Between 30-60 minutes after ideal arrival 24.8 17.67 27.4 26.8 time

Antoniou et al. (6) Stated preference data Ordered logit model, generalized Generalized Linear Mixed Ordered logit Model Binary logit Model Model linear mixed logit and binary logit VoTT VoTT VoTT VoTT VoTT VoTT Estimated Estimated Estimated Estimated Estimated Estimated model (2007 US$/hr.) (2012 US$/hr.) (2007 US$/hr.) (2012 US$/hr.) (2007 US$/hr.) (2012 US$/hr.) 7.2 8.0 6.9 7.6 8.1 9.0

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CHAPTER 2: STATED PREFERENCE AND ORIGIN-DESTINATION SURVEY

From this literature review, the TTI researchers anticipated the use of OD and SP surveys as a main source of information to estimate the VoTT in the BOTA international POE. The following sections of this chapter describe the survey design, implementation, execution, and findings.

2.1 SURVEY DESIGN

Stated preference methods have become the most popular method to estimate values of time in recent years (21). This section describes the stated preference survey design and implementation in the BOTA POE. The final version of this survey combines the stated preference technique with an OD questionnaire that allowed the research team to save time and effort. The OD questionnaire was used to double-check the average income the respondents answered during the interview and to understand the commuters’ travel behavior and match the OD pairs with their stated trip purposes.

2.2 SURVEY EXECUTION

2.2.1 Placement and Schedule

The scope of this survey was to determine the value of time of the people who cross BOTA in their vehicles. The survey was conducted for five working days in three different time frames. Two of them were in the peak hours (7:00 a.m.–9:00 a.m., 4:00 p.m.–6:00 p.m.) and the third one was from 10:00 a.m. to 12:00 p.m.

2.2.2 Protocol

A minimum of 1500 surveys were solicited to the surveyors for this study. Participants were selected only among northbound travelers due to the scope of this study. This is also due to the northbound border crossing waiting times, because northbound commuters have to stay in the queue for several minutes, facilitating participation.

The survey team interacted directly with the people who were interviewed. Before starting the questionnaire, surveyors had to identify themselves and explain the purpose of the survey. Although the right to interrupt the interview was clearly granted to participants, the vast majority concluded the questionnaire. It is important to keep in mind that due to violence and insecurity that is affecting Ciudad Juárez, the responses regarding the monthly income may be tampered due to the participants’ reluctance.

Appendix A includes the survey questionnaires with all the questions in both languages (English and Spanish). The following sections describe the survey findings and a detailed analysis of each specific question.

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2.3 SURVEY FINDINGS

2.3.1 Background Questions

2.3.1.1 Question 1

The first question was related to the place of residency of the people who answered the survey. Figure 2 shows that 73 percent live in Ciudad Juárez and 27 percent in El Paso. Figure 3 shows that the amount of people from Ciudad Juárez that cross in each time frame is more consistent. Regarding the people from El Paso that cross, it can be seen that at least half of them do it between 4:00 p.m. and 6:00 p.m.

Figure 2. Commuters’ Place of Residency.

Figure 3. Commuters’ Place of Residency per Period of Time Surveyed.

2.3.1.2 Question 2 and 3

Questions 2 and 3 determined each commuter’s origin and destination. Researchers used the information gathered in these two questions as an input to perform an OD pair analysis. The results of this analysis are covered more in detail in Chapter 3.

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2.3.1.3 Question 4

Question 4 dealt with the average monthly income of the interviewees. Figure 4 shows that approximately 32 percent answered that their monthly income is between 0 and 5,000 pesos ($0 to $385 USD). Having half of the respondents with a monthly income lower than 10,000 pesos ($770 USD) might be an indication of insecurity among the residents of the two cities, since many of the commuters refused to give an honest answer. This type of behavior was anticipated; consequently, the answers where double checked indirectly using the OD and trip purpose information as well as the minimum income reported by the Instituto Municipal de Investigación y Planeación in the corresponding Transportation Analysis Zones (TAZs) data.

Figure 4. Monthly Income.

2.3.1.4 Question 5

In Question 5, people were asked about what was the main purpose of their trip. Figure 5 shows that 24 percent cross to go to work, 11 percent go to school, 30 percent go shopping, and 35 percent answered did not specify their purpose of crossing to El Paso. Again, it should be taken into consideration that due to the violence, people might have decided not to specify the purpose of their trip. Figure 6 shows the same data as Figure 5 but according to their place of residency. As expected, the majority of the people from Ciudad Juárez cross to work or go shopping.

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Figure 5. Drivers’ Main Trip Purpose.

Figure 6. Drivers’ Main Trip Purpose according to Place of Residency.

2.3.1.5 Question 6

Question 6 determines the frequency the trip is made by each person interviewed. The responses were given in a weekly, monthly, and yearly basis as shown in Figure 7, Figure 8, and Figure 9. The graphs revealed that 75 percent of the people interviewed cross every week to El Paso.

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Figure 7. Weekly Trips’ Frequency.

Figure 8. Monthly Trips’ Frequency.

Figure 9. Yearly Trips’ Frequency.

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2.3.1.6 Question 7

When answering about their employment status in Question 7, 58 percent said they work full time while 12 percent work part time, 8 percent were students, 7 percent were students and had a job, and finally 15 percent selected other as their status (Figure 10).

Figure 10. Status of Employment.

2.3.1.7 Question 8

Figure 11 shows the age distribution of surveyed people as asked in Question 8. There is a gradual increase between the first three age categories (18–24, 25–34, and 35–44) and from there it starts to decrease for the rest of the categories.

Figure 11. Age Distribution among Interviewees.

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2.3.1.8 Question 9

Observing the cross waiting times, the majority of the respondents indicated they experience waiting times between one and two hours. From the total people surveyed, 27 percent said they have been waiting between 45 and 60 minutes, and 42 percent have been waiting between 76 and 90 minutes. Figure 12 illustrates all the crossing times obtained within a time frame of 180 minutes.

Figure 12. Crossing Times Obtained.

2.3.2 Stated Preference Questions

2.3.2.1 Question 10

This question asked about how much the interviewees were willing to pay if this could save them 25 percent, 50 percent, and 75 percent of their crossing time. Figure 13 shows that almost half of them would not pay anything even if they could decrease their crossing time by 25 percent. In Figure 14, it can be observed that half of the people are willing to pay between $1–2 in order to reduce their crossing time by 50 percent. Finally Figure 15 shows that as their crossing time can be reduced by 75 percent, people are willing to pay on average one more dollar and includes even up to $5.

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Figure 13. Money Spent with a 25 Percent Crossing Time Reduction.

Figure 14. Money Spent with a 50 Percent Crossing Time Reduction.

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Figure 15. Money Spent with a 75 Percent Crossing Time Reduction.

2.3.3 Vehicle Information Questions

2.3.3.1 Question 11

The last part of the survey gathered data about the vehicle specifications (make, model, and year), vehicle occupancy, and its license plate (U.S. or Mexican). Figure 16 shows all the makes obtained from the people being surveyed. It can be seen that the main companies are Ford, Chevrolet, and Nissan. Figure 17 shows the four vehicle types in which the vehicles were classified (car, SUV, truck, and van/minivan). Half of the vehicles surveyed fall under the car category. The year of the vehicles was also obtained and the year range went from 1968 to 2012. Figure 18 shows that almost 90 percent of the total vehicles surveyed were at least five years old. This is very important because as the vehicle gets old and it is not properly maintained, the probability of it polluting the air increases.

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Figure 16. Makes Obtained from the Survey.

Figure 17. Vehicle Classification.

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Figure 18. Vehicle Year.

2.3.3.2 Question 12

Figure 19 illustrates that the majority of the people that cross, do it just by themselves or at most cross with one other person. Figure 20 shows that also the majority of the people that cross have vehicles with Mexican license plates.

Figure 19. Number of Passengers per Vehicle.

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Figure 20. License Plates Obtained from the Survey.

2.4 CHAPTER SUMMARY

The majority of the people surveyed have their residency in Ciudad Juárez and have a monthly income between 0 and 10,000 pesos. The main purpose of their trip in descending order was other, shopping, work, and school. Approximately 75 percent of the people surveyed cross to El Paso at least once per week. At least half of them work full time and have an age between 25 and 54 years. Almost 27 percent answered that their average crossing time was between 46 and 60 minutes while 42 percent answered it took them between 76 and 90 minutes. People were not willing to spend any money when they were told they could reduce their crossing time by 25 percent. When questioned if they could reduce their crossing time by 50 percent, they were willing to pay between $1–2 to do so, and this amount doubled when asked if their crossing time could be reduced by 75 percent. From the survey sample obtained, the number of vehicle makes was 38. The three main companies were Ford, Chevrolet, and Nissan, and half of the vehicles were cars while the rest were SUVs, pickups, and vans/minivans. Almost 70 percent of the vehicles fall within the year range of 1998 and 2007. From the total people surveyed, 62 percent drove alone and 28 percent drove with one more person. Finally, from the 1502 vehicles, 62 percent had Mexican license plates while the rest had U.S. license plates.

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CHAPTER 3: IDENTIFICATION OF COMMUTERS’ ORIGINS AND DESTINATIONS

3.1 OVERVIEW

Once the stated preference survey was implemented, the major origin-destination pairs were explored by using a tool in TransCAD software called “desire lines.” This tool helps illustrate the flow of transportation demand on a map based on the commuters’ OD answers. This OD information was processed and mapped to obtain the correspondent TAZ in the El Paso region and the Basic Geographic and Statistical Area (Área Geográfica Estadística Básica [AGEB]), which is its counterpart in Ciudad Juárez region. The process determined the major OD pairs between Ciudad Juárez and El Paso (northbound only). At the end of this process researchers analyzed the resulting desire line maps and studied the characteristics of commuters crossing through the BOTA POE and what could cause this travel behavior.

3.2 TAZ AND AGEB IN THE EL PASO JUÁREZ REGION

Planning agencies/organizations use Transportation Analysis Zones to obtain household travel characteristics to create a database of information regarding the number of trips, trip length, and trip purpose by mode and time-of-day. TAZs are not covered by any official entity in Mexico; instead, data by supplement comparable zones called AGEBs are being used. The total number of TAZs in El Paso is 770, and their equivalent AGEBs numbered 555 in Ciudad Juárez. To make it easier to locate and identify certain zones, the combined number of TAZs and AGEBs had to be reduced. The new zones were created paying careful attention to the land uses of each individual TAZ or AGEB when merging them; merging was performed among zones of their own class only (i.e., a TAZ was never merged with an AGEB). As a result the pre-existing 770 TAZs in El Paso were merged to create 148 Super TAZs. Similarly, the predefined 555 AGEBs were merged to create 116 Super AGEBs. The resulting groups were then exported into a new GIS layer. A GIS point layer was subsequently created, placing a point in the general center of each Super-TAZ area. Based on the results from the database of 1500 surveys conducted at BOTA, the research team identified the origins and destinations based on the geographic locations using first ArcGIS software and then TransCAD software. Each point was located at the center of each Super TAZ that fell into that zone being a neighborhood, street, or intersection. Some complications arose within the process due to the poor description of the origin and destination during the surveys (frequently origins in Ciudad Juárez). The survey needed more accurate OD labels example: intersecting street names, zip code, mile markers. Another issue the researchers faced was the fact that some of the AGEBs were too large and led to a misinterpretation of the data specially when given a neighborhood or single street name that fell in two or more zones (it was difficult to analyze without a more accurate location, an option of having intersecting streets on the survey would be very beneficial). All these problems were satisfactory solved to finally obtain an OD matrix of the BOTA commuters.

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3.3 OD MATRIX TRANSCAD

After locating the origins and destinations and inputting the data into spreadsheet the research team created an OD matrix using the pivot table option in excel; this step helped create the data file that would be imported into TransCAD. Once the OD matrix was complete, the TAZ and AGEB shape files were opened in TransCAD. The next step was to run the desire line command. By applying filters to the desire lines researchers saw a better view of the commuter trips from the origins to the destinations. Four filters were made: show all trips per zone, show greater than 1 trip per zone, show greater than 2 trips per zone, and show greater than 3 trips per zone. From this researchers evaluated the most visited places in El Paso (destination) from Ciudad Juárez (origin).

Figure 21. Desire Lines between El Paso and Ciudad Juárez.

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Figure 22. Frequent OD Pairs Using BOTA.

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3.4 SUMMARY This chapter presented a list of the data collected for Juárez and El Paso and its organization for its use in the TRANUS (transportation software) binational model including socioeconomic and demographic data. Three economic sectors were defined to aggregate available employment data in TRANUS. Similarly, six income brackets were defined to aggregate population per socioeconomic strata: three for Ciudad Juárez, and three for El Paso, given the intrinsic differences in socioeconomic strata between the two cities. The methodology applied to bring outdated variables to the base year (2009) was presented. Finally, this section described the process used to create analysis zones compatible with TRANUS capabilities by reducing the granularity of the data-aggregation by creating bigger TAZs and bigger AGEBs (called Super TAZs and Super AGEBs). The next chapter explains how TRANUS uses these data and the structure of the binational model.

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CHAPTER 4: MICROSIMULATION MODEL

4.1 MODEL OVERVIEW The first step to develop the simulation based model was to determine the key parameters needed to represent current conditions at BOTA. Speed limits, number of lanes, and geometry of the BOTA were gathered through maps and field inspections. Next, the vehicle demand matrices were created to cover a total simulation period of 13 hours. One text file was needed for each hour of demand. The number of trips coded into the text files was estimated based on previous traffic count efforts done by TTI at BOTA.

Table 17 shows an example of a demand table from 7:00 a.m. to 8:00 a.m. The research team then proceeded to collect data for the booth inspection time, border wait times, and the number of lanes open throughout the day at BOTA to help code and calibrate the simulation model.

Table 17. Example of Demand Table for VISSIMs Dynamic Traffic Assignment.

* time interval [hh.mm] 7.0 8.0 * scaling factor 1 * number of zones 3 * zones 1 2 3 * number of trips between zones 0 0 120 0 0 295 0 0 0

4.1.1 BOTA Booth Inspection Time

Data were gathered on-site for the booth inspection time at BOTA. A total of 220 observations were tabulated in order to develop a probability distribution function (PDF). The descriptive statistics analysis showed that the inspection time ranged between 17 seconds up to 417 seconds with an average of 76 seconds. All of the descriptive statistics are shown in Table 18. Furthermore, a PDF was created and is depicted in Figure 23. As expected, the PDF is skewed to the right due to the occasional high inspection time readings encountered throughout the data collection process. However, most of the readings were still below the 3 minute mark with the majority of the readings being between 45 to 60 seconds.

4.1.2 BOTA Border Wait Times

The border wait times for the BOTA POE were obtained from the U.S. Customs and Border Protection (CBP) website. The website provides an estimated wait time (updated hourly) for

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reaching the primary inspection booth as well as the amount of booths operating. However, in order to get a significant sample for each hour of interest the data for the whole year of 2011 was extracted into a database. Based on a 780 sample size for each hour, the border wait time’s descriptive statistics were calculated. Table 18 shows the average waiting time at BOTA throughout the period of interest. The results showed that on average there was a one hour delay at the BOTA POE.

Table 18. Booth Inspection Time – Descriptive Statistics.

Booth Inspection Time (sec) Mean 76.81 Standard Error 3.91 Median 58.1 Mode 44 Standard Deviation 58.02 Sample Variance 3367.25 Kurtosis 10.14 Skewness 2.76 Range 400.3 Minimum 17 Maximum 417.3 Sum 16898.3 Count 220 Confidence Interval (95.0%) 7.71

60 110.00%

100.00%

50 90.00%

80.00% 40 70.00%

60.00% 30 50.00% Frequency 40.00% 20 30.00%

10 20.00% 10.00%

0 0.00%

Inspection Time (sec)

Frequency Cumulative %

Figure 23. Booth Inspection Time – Probability Distribution Function.

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70.00 65.17 65.26 65.44 65.31 63.48 64.13 60.68 60.18 60.00 56.55 55.33 50.28 50.00 43.23 39.67 40.00

30.00

20.00

10.00 Average Waiting Time at BOTA (min)

0.00 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM

Figure 24. Average Waiting Time at BOTA.

4.1.3 BOTA Operating Booths

In the same fashion as the border wait times, the descriptive statistics were obtained for the amount of booths operating at BOTA. The sample size for each hour varied between 700 and 850 depending on the data available for the year 2011. The data analysis showed that the POE usually had 11 lanes open for passenger car inspections. Table 19 shows an example of the resulting statistics for the time period between 10:00 a.m. to 11:00 a.m. The same analysis was done for the remaining hours considered for this study.

Table 19. Descriptive Statistics for BOTA Operating Booths – 10:00 a.m. to 11:00 a.m. Period.

BOTA Operating Booths Mean 10.54 Standard Error 0.04 Median 11 Mode 11 Standard Deviation 1.23 Sample Variance 1.50 Kurtosis 3.39 Skewness −1.02 Range 11 Minimum 2 Maximum 13 Sum 8556 Count 812 Confidence Interval (95.0%) 0.08

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4.2 NETWORK CONSTRUCTION

The BOTA POE model was developed using the microscopic software platform VISSIM. The international bridge network was coded with two main entry points as seen in Figure 23. The east and south inbound links were included given that the queue usually goes past the junction point (i.e., zone 5 in the network) during peak hours. The network included two origin nodes and one destination node past the primary inspection booths. Speed limits, reduction speed areas, and vehicle inputs were all coded as needed based on the data collection efforts done by the research team.

Destination Node

BOTA Entry Points

Origin Node

Figure 25. BOTA VISSIM Network.

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Stop signs were coded into the network to replicate the primary inspection booths. Based on the PDF obtained from the inspection time statistics, a dwell time cumulative distribution was assigned to each stop sign as seen in Figure 24. However, at BOTA there are two lanes that have two inspection booths, a total of 14 (Figure 27). In other words, vehicles queued in such lanes can advance to the inspection booth as one of the two becomes available. A similar dwell time distribution was coded for these two lanes but with half of the average time to account for the double inspection booth. Finally, all of the measures of effectiveness parameters such as travel time, fuel consumption, and vehicle delay were specified in the model for further analysis.

Figure 26. VISSIM Dwell Time Distribution for Inspection Booths.

4.3 OPERATIONAL MODELING

Typically, VISSIM assigns simulated vehicles through routes in the network that are manually defined by the user. The so called static assignment assumes that there will be no changes in regard to the travel demand or in the road network itself. However, a POE such as BOTA can have changes in capacity because of the number of inspection booths operating throughout the day (e.g., 11 booths open during morning peak hours and 8 during the mid-day period). As a result, the research team decided to assign the vehicles in VISSIM via dynamic traffic assignment. The four scenarios modeled allowed the research team to quantify the various benefits of having a different amount of lanes available for the BOTA traffic (Figure 27). The scenarios simulated for this study were as follows: • BOTA with 9 lanes open (base case scenario with 11 booths operating).

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• BOTA with 10 lanes open (12 booths). • BOTA with 11 lanes open (13 booths). • BOTA with 12 lanes open (all inspection booths operating).

BOTA Lanes with Two Inspection Booths

BOTA has 14 Inspection Booths and 12 Lanes

Figure 27. BOTA VISSIM Base Model with 9 Lanes Open and 11 Booths Operating.

4.3.1 Simulated Travel Time and General Cost

In VISSIM, the simulated travel time when using DTA is measured per edge and per evaluation interval (Figure 28). Every vehicle that leaves a specific edge reports its travel time back to the system. Then, all travel times during one evaluation interval are averaged and make the resulting travel time for that edge. For this study, an evaluation interval of 1800 seconds (30 minutes) was specified given that is recommended to have at least double temporal resolution of the demand changes (i.e., 1 hr for this model). However, vehicles at BOTA might experience longer wait times than 30 minutes, spending more than one evaluation period on an edge. In this case, vehicles report their dwell time back to the system to get the necessary information about congested links.

Aside from travel time, route choice in VISSIM is also influenced by travel distance and financial costs. To account for this, a general cost equation is used to influence route choice for each of the edges in the network. The weighted sum equation is as follows:

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= + + (1)

All퐺푒푛푒푟푎 of the푙 퐶표푠푡 coefficients훼 ∙ 푇푟푎푣푒푙 α, β, and푇푖푚푒 γ are user훽 ∙ 푇푟푎푣푒푙specified.퐷푖푠푡푎푛푐푒 However, for훾 ∙ this퐹푖푛푎푛푐푖푎푙 particular퐶표푠푡 model only travel time was considered (i.e., α = 1, β= 0, γ=0). The reason behind this is that the distance is not a factor when deciding which sides of the bridge vehicles are headed to. In addition, there are no link costs associated in the model (e.g., tolls).

Figure 28. VISSIM Edge between Node 2 and 5.

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4.4 MODEL CALIBRATION AND VALIDATION

The microscopic simulation model was calibrated based on the average wait times experienced at BOTA (see section 3.1.2). The base model was simulated until the user specified convergence criteria was met (i.e., difference of travel time on paths between iterations less than 15 percent). Once convergence was achieved, the average travel time for each hour was calculated from the model measures of effectiveness (MOEs). The model results were then compared to the observed travel time at BOTA to identify the time periods where less/more demand was needed. Then, adjustments to the demand tables were done by ±10 percent to better represent current conditions at the POE. No more adjustments were performed to the demand matrices once all of the simulated wait times were within the ±10 percent absolute error range. Figure 29 shows the plot of observed wait time vs. the simulated wait time after the calibration process. In addition, the percent error was calculated for each hour of interest as seen in Table 20.

80

70

60

50

40

30

Waiting Time (min) 20

10

0

Observed Average Wait Time (min) Simulated Average Wait Time (min)

Figure 29. BOTA Microscopic Model Calibration Results.

Throughout the calibration process the research team observed the queuing behavior in the model to ensure no errors or discrepancies were present (e.g., stuck vehicles). Once the base model was fully calibrated and validated, three more scenarios were developed with a different amount of lanes open (i.e., 10, 11, and 12 lanes). The additional scenarios were simulated until the convergence criterion was met. The MOEs for each scenario were then tabulated for further analysis.

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Table 20. VISSIM Model Calibration – Percent Error.

Observed Simulated Hour of Day Average Wait Time Average Wait Time Error (hh:min) (hh:min) 7:00 a.m. 8:00 a.m. 0:43 0:40 −5.21% 8:00 a.m. 9:00 a.m. 0:50 0:54 8.56% 9:00 a.m. 10:00 a.m. 0:56 0:55 −1.62% 10:00 a.m. 11:00 a.m. 1:00 0:59 −1.30% 11:00 a.m. 12:00 p.m. 1:03 1:03 0.67% 12:00 p.m. 1:00 p.m. 1:05 1:09 6.65% 1:00 p.m. 2:00 p.m. 1:05 1:09 5.91% 2:00 p.m. 3:00 p.m. 1:05 1:04 −1.09% 3:00 p.m. 4:00 p.m. 1:05 1:02 −3.63% 4:00 p.m. 5:00 p.m. 1:04 1:03 −1.42% 5:00 p.m. 6:00 p.m. 1:00 1:06 9.83% 6:00 p.m. 7:00 p.m. 0:55 0:54 −1.14%

4.5 ANALYSIS OF RESULTS

The BOTA simulation model results showed that having additional inspection booths made a significant impact on the amount of fuel consumed and queue length. In comparison to the base case scenario (i.e., 11 booths open), the other three scenarios showed improvement as seen in Figure 30. For example the addition of one more booth (i.e., 12) showed an improvement of 60 percent when compared to the base model. Table 21 shows the results obtained from the Vissim model for each scenario per day.

Table 21. BOTA Results Based on No. of Booths Operating.

Scenario Number of Fuel Consumed VMT per Day Total Delay per Vehicles per per Day (Gallons) (miles) Day Day (hr) 11 Booths Operating 5904 11363 6553 5629 12 Booths Operating 6276 4509 6966 2490 13 Booths Operating 6277 1944 6967 1095 14 Booths Operating 6279 1465 6970 820

Fuel consumption is highly influenced by the number of inspection booths operating. Figure 30 establishes a fuel consumption comparison between the scenarios considered. The amount of fuel saved can reach 10,000 gallons per day. In the same fashion, the total delay is significantly improved when all 14 inspection booths are operating (Figure 31).

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12000

10000

8000

6000 Fuel Consumed 4000

Gallons of Fuel Fuel Saved

2000

0 11 12 13 14 No. of Booths Operating at BOTA

Figure 30. Fuel Consumption at BOTA.

6000

5000

4000

3000

2000

1000 Total Delay (hours)Day per

0 11 12 13 14 No. of Booths Operating at BOTA

Figure 31. Total Delay at BOTA.

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CHAPTER 5: ESTIMATION OF VALUE OF WAITING TIME AND COMMUNING COSTS

This chapter aims to determine the cost components related to border crossing by passenger car through BOTA. VoWT is the summation of commuting costs and personal costs. On one hand, commuting costs consist of vehicle costs and environmental costs. On the other hand, personal costs consist of VoTT (Figure 32). The following two sections explain the procedure followed by the research team to calculate commuting and personal costs at BOTA. The third section provides the VoWT calculated.

Figure 32. Hierarchical Structure of VoWT.

5.1 ESTIMATION OF PERSONAL COSTS

The present section illustrates the procedure followed by the research team to calculate the VoTT of commuters at BOTA. VoTT is calculated by using the income data extracted from the survey. The survey asked interviewees for their location within the seven income groups considered. Additionally, the survey collected trip purpose data. By combining income and trip purpose data, the research team calculated VoTT per trip purpose. The four purposes considered in the survey were: work/work related, school or college, shopping, and other purposes.

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The research team grouped commuters based on their trip purpose (four groups). Within each group, commuters were classified according to their monthly income into seven income groups. The income groups consisted of seven different income ranges. Therefore, the research team calculated the average monthly income of each income group to use it in the weighted equation. Then, it was identified the percentage of commuters included in each income group. After that, the research calculated the weighted monthly income based on a weighted equation using the percentage of commuters and the average monthly income of each income group. Finally, the weighted monthly wage was divided into the average of labor hours per month (174 hours/month) resulting the weighted hourly wage (Figure 33). Finally, the research team calculated VoTT for each trip purpose by using the weighted hourly wages previously calculated. The VoTT for each trip purpose group is expressed in US$/hr.

Trip Purpose and Create Income Monthly Income Create Trip Purpose Groups within Trip Data from the Groups Purpose Groups Survey

Calculate the Apply Weighted Calculate the Percentage of Equation to Average Monthly Commuters Included Calculate Weighted Income for Each in Each Income Monthly Income Income Group Group

Calculate Weighted Daily Wage for Each Calculate VoTT Trip Purpose Group

Figure 33. VoTT Calculation Process.

The weighted equation applied to each trip purpose group is:

% = 7 × 100 표푓 퐶표푚푚푢푡푒푟푠푖 푊푒푖푔ℎ푡푒푑 푀표푛푡ℎ푙푦 퐼푛푐표푚푒 � 퐴푣푒푟푎푔푒 푀표푛푡ℎ푙푦 퐼푛푐표푚푒푖 푖=1=

푖 퐼푛푐표푚푒 퐺푟표푢푝

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According to the Secretary of Finance and Public Credit of Mexico (SHCP for its acronym in English), the minimum daily wage in 2012 for the State of Chihuahua is 62.33 Mexican Pesos (22). This amount converted into 2012 US$ is equal to $4.74 (around $0.60 per hour) (23). It is important to account for this fact to interpret correctly the VoTT results at BOTA. The following subsections present the VoTT results for each trip purpose.

5.1.1 Work/Work Related

Researchers identified 359 out of 1502 commuters whose trip purpose was work or work related. This is 24 percent of the sample. Table 22 shows the necessary data to calculate the weighted monthly income for work or work related trips.

Table 22. Income Data for Work/Work Related Trips.

Monthly Income (US$) Average Percentage calculated based on Monthly the records founded (359) Income (US$) 0–392 $ 196 26% 393–784 $ 392 26% 785–1176 $ 588 14% 1177–1568 $ 784 14% 1569–1960 $ 980 7% 1961–2352 $ 1176 5% >2352 > $ 1176 8%

= (196 × 0.26) + (392 × 0.26) + (588 × 0.14) + (784 × 0.14) 푊푒푖푔ℎ푡푒푑 푀표푛푡+ (ℎ980푙푦 퐼푛푐표푚푒× 0.07) + (1176 × 0.05) + (1177 × 0.08) = $ 567

The research team assumed that the number of hours worked in a month is in average푝푒푟 푚표푛푡174. ℎ Thus, in an average month the wage ratio is $567/174 hr, which is equal to $3.26/hr.

5.1.2 School or College

Researchers identified 168 out of 1502 commuters whose trip purpose was school or college. This is 11 percent of the sample. Table 23 shows the necessary data to calculate the weighted monthly income for school or college related trips.

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Table 23. Income Data for School or College Related Trips.

Monthly Income (US$) Average Percentage calculated based on Monthly the records founded (168) Income (US$) 0–392 $ 196 58% 393–784 $ 392 20% 785–1176 $ 588 11% 1177–1568 $ 784 6% 1569–1960 $ 980 2% 1961–2352 $ 1176 2% >2352 > $ 1176 1%

= (196 × 0.58) + (392 × 0.20) + (588 × 0.11) + (784 × 0.06) 푊푒푖푔ℎ푡푒푑 푀표푛푡+ (ℎ980푙푦 퐼푛푐표푚푒× 0.02) + (1176 × 0.02) + (1177 × 0.01) = $ 359

푝푒푟 푚표푛푡ℎ The research team assumed that the number of hours worked in a month is in average 174. Thus, in an average month the wage ratio is $359/174 hr that is equal to $ 2 per hour.

5.1.3 Shopping Related

Researchers identified 443 out of 1502 commuters whose trip purpose was shopping related. This is 29 percent of the sample. Table 24 shows the necessary data to calculate the weighted monthly income for shopping related trips.

Table 24. Income Data for Shopping Related Trips.

Monthly Income (US$) Average Percentage calculated based on Monthly the records founded (443) Income (US$) 0–392 $ 196 33% 393–784 $ 392 26% 785–1176 $ 588 19% 1177–1568 $ 784 9% 1569–1960 $ 980 5% 1961–2352 $ 1176 3% >2352 > $ 1176 5%

= (196 × 0.33) + (392 × 0.26) + (588 × 0.19) + (784 × 0.09) 푊푒푖푔ℎ푡푒푑 푀표푛푡+ (ℎ980푙푦 퐼푛푐표푚푒× 0.05) + (1176 × 0.03) + (1177 × 0.05) = $ 492

The research team assumed that the number of hours worked in a month is in average푝푒푟 푚표푛푡174. ℎ Thus, in an average month the wage ratio is $492/174 hr that is equal to $2.83/hr.

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5.1.4 Other Purposes Related

Researchers identified 532 out of 1502 commuters whose trip purpose was different than the ones considered in the survey. This is 36 percent of the sample. Table 25 shows the necessary data to calculate the weighted monthly income for these trips.

Table 25. Income Data for Other Purposes Trips.

Monthly Income (US$) Average Percentage calculated based on Monthly the records founded (532) Income (US$) 0–392 $ 196 26% 393–784 $ 392 22% 785–1176 $ 588 24% 1177–1568 $ 784 13% 1569–1960 $ 980 7% 1961–2352 $ 1176 4% >2352 > $ 1176 4%

= (196 × 0.26) + (392 × 0.22) + (588 × 0.24) + (784 × 0.13) 푊푒푖푔ℎ푡푒푑 퐸푞푢푎푡푖표푛+ (980 × 0.07) + (1176 × 0.04) + (1177 × 0.04) = $ 543

푝푒푟 푚표푛푡ℎ The research team assumed that the number of hours worked in a month is in average 174. Thus, in an average month the wage ratio is $543/174 hr that is equal to $3.12/hr.

5.1.5 Calculation of VoTT

After extensively reviewing the VoTT literature, the research team decided to calculate the VoTT at BOTA by using the AASHTO method (11). According to this reference, VoTT for drive alone commute is 50 percent of the average hourly wage. Table 26 shows the average hourly wage for each trip purpose group and the VoTT calculated.

Table 26. Average Hourly Wage and VoTT.

Trip Purpose Average Hourly VoTT ($/hr) Wage ($/hr) Work/Work Related $ 3.2 $ 1.6 School or College $ 2 $ 1 Shopping Related $ 2.8 $ 1.4 Other Purposes Related $ 3.1 $ 1.5

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5.2 ESTIMATION OF COMMUTING COSTS

The research team calculated commuting costs by adding vehicle and environmental costs. On one hand, vehicle costs consist on insurance, fuel, Texas Vehicle Inspection or Engomado Ecológico (Mexican counterpart), and routine maintenance, tires, repair, and depreciation costs. On the other hand, environmental costs consist on the costs of emitting CO2 (Figure 34).

Commuting Costs

Environmental Vehicle Costs Costs

Routine Texas Vehicle maintenance, Inspection or Insurance tires, repair Fuel CO Emissions Engomado 2 and Ecologico depreciation

Figure 34. Hierarchical Structure of Commuting Costs.

The present section explains the procedure followed for estimating vehicle and environmental costs associated to the border crossing through BOTA. The section consists in two subsections: vehicle costs and environmental costs.

5.2.1 Vehicle Costs

In order to simplify the vehicle cost calculation, the research team decided to split the passenger vehicles into two different categories: automobile (all size of sedan cars) and pickup/SUV/van (all pickups, SUVs, and vans). Moreover, the research team identified four vehicle cost components: • Insurance costs. • Routine maintenance, tires, repair, and depreciation costs. • Fuel costs. • Texas Vehicle Inspection or Engomado Ecológico costs.

Table 27 shows the results obtained from VISSIM model at BOTA. The international bridge was modeled from 6:00 a.m. to 7:00 p.m. (13 hours). The research team assumed that the 13 hours modeled is a good representation of an entire day (24-hour) model for VoTT estimation

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purposes. Table 27 shows number of vehicles, gallons of fuel consumed, and total vehicle miles traveled (VMT) per day (from 6:00 a.m. to 7:00 p.m.) at BOTA. Table 27 is referenced several times along this section.

Table 27. Number of Vehicles and Gas Consumption and VMT per Day at BOTA.

Scenario Number of Vehicles Fuel Consumed per VMT per Day (Miles) per Day Day (Gallons) 11 Lanes Opened 5,904 11,363 6,553 12 Lanes Opened 6,276 4,509 6,966 13 Lanes Opened 6,277 1,944 6,967 14 Lanes Opened 6,279 1,465 6,970

The survey reveals that 53 percent of vehicles at BOTA may be included in the automobile category, and 47 percent of the vehicles may be included in the pickup/SUV/van category (Table 28).

Table 28. Percentage of Vehicles per Category.

Vehicle Category Percentage Automobile 53 % Pickup/SUV/Van 47 %

The number of vehicles per day and per category was calculated by applying these percentages to number of vehicles per day at BOTA extracted from Table 27. Table 29 expresses the number of automobiles and pickups/SUVs/vans at BOTA in one day for each scenario.

Table 29. Number of Vehicles per Category and per Day at BOTA.

Scenario Number of Vehicles Number of Number of per Day Automobiles Pickup/SUV/Van 11 Lanes Opened 5904 3129 2775 12 Lanes Opened 6276 3326 2950 13 Lanes Opened 6277 3327 2950 14 Lanes Opened 6279 3328 2951

5.2.1.1 Insurance Costs

The American Automobile Association (AAA), in Your Driving Costs 2011 Edition, establishes full covered insurance costs in the U.S. (24). It provides insurance costs for six different vehicle categories. Some examples of car models included in each category can be founded below: • Small sedan: Chevrolet Cobalt, Ford Focus, Honda Civic, Nissan Sentra, and Toyota Corolla. • Medium sedan: Chevrolet Impala, Ford Fusion, Honda Accord, Nissan Altima, and Toyota Camry. • Large sedan: Buick Lucerne, Chrysler 300, Ford Taurus, Nissan Maxima, and Toyota Avalon.

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• SUV: Chevrolet Traverse, Ford Explorer, Jeep Grand Cherokee, Nissan Pathfinder, and Toyota 4Runner. • Minivan: Dodge Grand Caravan, Kia Sedona, Honda Odyssey, and Toyota Sienna.

The research team decided to group these five categories created by the AAA within the two categories used in the study. Therefore, sedans were included in the automobile category, and the SUV and Minivans were included in the pickup/SUV/van category. The AAA report did not include the full insurance price for pickups. However, the research team assumed that the pickup’s full insurance price is equal to the SUV’s and van’s insurance. Although most of the cars that cross the border by using BOTA have Mexican plates, CBP requires American car insurance. Because of this, insurance costs were based on American insurance prices rather than Mexican insurance prices.

Table 30 shows the costs documented and the costs assumed by the research team. The insurance prices used for this study were calculated by averaging the categories made by AAA, which in turn were included in the categories created by the research team.

Table 30. Insurance Costs per Category.

AAA Category Full Coverage Categories Used in Prices Used in the Insurance Price the Report Study (2011 US$/Year) (US$/Year) Small Sedan $ 951.00 Medium Sedan $ 948.00 Automobile $ 968.00 Large Sedan $ 1,006.00 SUV $ 912.00 Pickup/SUV/Van $ 883.00 Minivan $ 853.00

Table 30 illustrates in its last column the insurance costs used by the research team for estimating vehicle costs. Insurance costs are $968.00 per year for the automobile category and $883.00 per year for the pickup/SUV/van category. In order to calculate this cost for this case study, the research team used the percentage of automobiles and pickups/SUVs/vans at BOTA from Table 28. By using the data presented in Table 28, the research team developed a weighted equation to accurately estimate the insurance cost per year at the BOTA POE.

$

퐼푛푠푢푟푎푛푐푒 푊푒푖푔ℎ푡푒푑 퐸푞푢푎푡푖표푛 � � $ = 푌푒푎푟 ×

�퐴푢푡표푚표푏푖푙푒 퐼푛푠푢푟푎푛푐푒 퐶표푠푡 � � 푃푒푟푐푒푛푡푎푔푒$ 표푓 퐴푢푡표푚표푏푖푙푒푠� + / / 푌푒푎푟 ×

� 푃푖푐푘 − 푈푝 푆푈푉 푉푎푛 퐼푛푠푢푟푎푛푐푒 퐶표푠푡 � � 푃푒푟푐푒푛푡푎푔푒 표푓 푃푖푐푘 / / 푌푒푎푟

− 푈푝 푆푈푉 푉푎푛�

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$ = (968 × 0.53) + (883 × 0.47) = 928

퐼푛푠푢푟푎푛푐푒 푊푒푖푔ℎ푡푒푑 퐸푞푢푎푡푖표푛 � � $ 1 $ 푦푒푎푟 = 928 × = 2.5 365 푦푒푎푟 퐷푎푖푙푦 퐶표푠푡 � � � � � � The daily cost estimated was inflated to푦푒푎푟 the year 2012푑푎푦 by using the 푑푎푦Consumer Price Index Inflation Calculator from the Bureau of Labor Statistics (9). As a result, the research team assumed $2.60 as the daily insurance average cost. Then, it was assumed that each vehicle crosses once per day. It can be considered that the cost per day is equal to the cost per vehicle. Therefore, the research team could assume that the insurance cost is equal to $2.60 per vehicle.

The research team evaluated four scenarios (11, 12, 13, and 14 inspection lanes opened) that were modeled by using VISSIM software. Table 31 presents the results of the insurance costs per year for each scenario modeled. Total vehicles per year were calculated by using the number of vehicles per day extracted from Table 27 and multiplying them by 365 days. Total annual insurance cost was calculated multiplying average insurance cost per vehicle ($2.60 per vehicle) by the number of vehicles per year.

Table 31. Cost of the Full Coverage Insurance per Scenario per Year.

Scenario Total Vehicles per Daily Insurance Average Total Annual Insurance Year Cost (2012 US$/vehicle) Cost (2012 US$/Year) 11 Lanes Opened 2,154,960 2.6 $ 5,602,896 12 Lanes Opened 2,290,740 2.6 $ 5,955,924 13 Lanes Opened 2,291,105 2.6 $ 5,956,873 14 Lanes Opened 2,291,835 2.6 $ 5,958,771

5.2.1.2 Routine Maintenance, Tires, Repair, and Depreciation Costs

Routine maintenance, tires, repair, and depreciation costs were calculated in U.S. dollars per mile based on a study developed by the University of Minnesota entitled The Per-Mile Costs of Operating Automobiles and Trucks (25). This study is focused on how the costs increase when the number of miles covered by the vehicle also increases. In this study insurance and finance costs were ignored.

The University of Minnesota’s study developed a cost model based on operating costs primarily from consumer guides. The vehicle categories considered were autos and pickups/SUVs/vans. By using this model, the research team introduced the following data from BOTA to create a new model:

• Operating characteristics. Users at BOTA experience severe congestion during all periods of time that were modeled. The University of Minnesota’s cost model assumed for severe congestion an average speed of 10 mph. At BOTA the congestion is even more severe. However, there was no option for considering this congestion in the cost model. After analyzing the cost model, the research team founded that the degree of congestion

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severity at BOTA would not significantly affect the total costs. The research team assumed an average speed of 10 mph at BOTA waiting lines. • Pavement roughness expressed in the Present Serviceability Index (PSI) units. After visiting the bridge, the research team observed that the pavement was in good condition. In this study it was assumed that the PSI is equal to 3.0. • Maintenance and repair cost adjustment. This factor addresses the inflation of the maintenance and repair prices from the base year (2003). The inflation was calculated based on the Consumer Price Index Inflation Calculator (26). The inflation obtained was a 23 percent from 2003 to 2012. • Tire cost adjustment. The research team assumed that the price of tires increased by 58 percent since 2003. This information was obtained from the Bureau of Labor Statistics (27). • Depreciation cost adjustment. The research team assumed that there were almost no changes in the price of depreciation of cars since 2003, due to the lack of specific information available. • Fraction of fleet cars: According to the data collected from the surveys performed, the research team decided not to consider fleet cars in the operating costs model. • Fraction of the cars 5 years old and less: Based on data obtained from the survey conducted, 230 cars out of 1502 were 5 years old or less (15.3 percent). • Fraction of total repair costs in year 5: The research team assumed that annual repair costs from the fifth until the end of the service life of the car are 50 percent of the total repair costs during the first 4 years of the car.

Once the routine maintenance, tires, repair, and depreciation cost model were estimated, the total costs for automobile and pickup/SUV/van vehicle types were 18.83 and 20.83 cents per mile (2012 US$), respectively. Table 32 shows routine maintenance, tires, repair, and depreciation costs per day expressed in 2012 USD. The information provided by each column of Table 32 is: 1. VMT Column. VMT per year extracted from Table 27. 2. VMT per category Colum. This column was calculated by multiplying daily VMT by the percentage of each vehicle category from Table 28. 3. Daily cost per category column. This column expresses the routine maintenance, tires, repair, and depreciation costs per vehicle category. This result was obtained by multiplying the VMT of each vehicle category by the unitary cost calculated for each vehicle category (18.83 and 20.83 cents per mile). 4. Total cost column. This column shows the total routine maintenance, tires, repair, and depreciation costs per day for each scenario. This column results from the sum of the costs expressed in the previous column (daily cost per category).

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Table 32. Routine Maintenance, Tires, Repair, and Depreciation Costs per Year.

Scenario VMT VMT per Category (per day) Daily Cost Per Category Total (per Automobiles Pickup/SUV/Van Automobiles Pickup/SUV/V Cost day) (53%) (47%) (2012 US$) an per Day (2012 US$) 2012 US$ 11 Lanes 6,553 3,473 3,080 654 642 $ 1,296 Opened 12 Lanes 6,966 3,692 3,274 695 682 $ 1,377 Opened 13 Lanes 6,967 3,693 3,274 695 682 $ 1,377 Opened 14 Lanes 6,970 3,694 3,276 696 682 $ 1,378 Opened

5.2.1.3 Fuel Costs

Since trucks were not considered in this study, the research team assumed that all vehicles at BOTA use unleaded regular gasoline. The unleaded regular gasoline price per gallon was calculated by using the data published by the Bureau of Labor Statistics (28). Figure 35 shows the pattern followed by unleaded regular gasoline prices during last year.

$4.00 3.93 3.87 3.79 $3.80 3.70 3.65 3.63 3.61 3.57 $3.60 Mean = 3.61 $/Gal 3.47 3.42 3.40 $3.40 3.28 $3.20 Price of Unleaded Regular Gasoline $3.00

$2.80

Figure 35. Trend Curve of Gasoline Price.

The price of gasoline assumed in this study was $3.61/gal (i.e., the average of gas prices in the last 12 months). Fuel costs were calculated by multiplying gallons of fuel consumed on each scenario (from Table 27) by the unitary cost of unleaded regular gasoline documented ($3.61/gal). Table 33 presents the total gas costs for each scenario considered.

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Table 33. Total Gas Costs.

Scenario Gas Consumption per Unitary Gas Price Total Gas Costs per Day (Gallons) (2012 US$/Gal) Day (2012 US$) 11 Lanes Opened 11,363 3.61 $ 41,020 12 Lanes Opened 4,509 3.61 $ 16,277 13 Lanes Opened 1,944 3.61 $ 7,018 14 Lanes Opened 1,465 3.61 $ 5,289

5.2.1.4 Texas Vehicle Inspection and Engomado Ecológico Costs

U.S. CBP requires up-to-date Texas Vehicle Inspection (for Texan vehicles) and Engomado Ecologico (for Chihuahuan Vehicles). The Texas Vehicle Inspection cost is $14.25 for the emission test plus $14.50 for the one-year safety inspection. Therefore, the total cost of the Texas Vehicle Inspection is $28.75 per year (i.e., $0.079 per day). Additionally, the cost of the Engomado Ecologico 2012 is approximately $8 per year (i.e., $0.022 per day). The research team assumed that each vehicle crosses once per day. It can be supposed that the cost per day is the cost per vehicle. In conclusion, the Texas Vehicle Inspection and Engomado Ecologico costs are $0.079 and $0.022 per vehicle, respectively.

After performing observations at BOTA, the research team realized that the number of vehicles without plates from Chihuahua or Texas was not significant. Then, it was applied Texas Vehicle Inspection costs to all American vehicles and Engomado Ecologico costs to all Mexican vehicles.

According to the survey conducted, the percentage of U.S. and Mexican vehicles that cross the border on daily basis is 38 percent and 62 percent, respectively. These percentages were applied to the number of vehicles per day extracted from Table 27 to calculate U.S. and Mexican vehicles per day. Total Texas Vehicle Inspection and Engomado Ecológico costs per day were calculated by means of the following formulation:

$

푇표푡푎푙 퐶표푠푡 � � $ 퐷푎푦= (# . × 푉푒ℎ푖푐푙푒 표푓 푈 푆 퐷푎푖푙푦 푉푒ℎ푖푐푙푒푠 � � 푇푒푥푎푠 퐼푛푠푝푒푐푡푖표푛 퐶표푠푡푠 � � + (# 퐷푎푦 × 푉푒ℎ푖푐푙푒 푉푒ℎ푖푐푙푒 표푓 푀푒푥푖푐푎푛 퐷푎푖푙푦 푉푒ℎ푖푐푙푒푠 � $ � × 퐷푎푦 퐸푛푔표푚푎푑표 퐸푐표푙표푔푖푐표 퐶표푠푡푠 � � 푉푒ℎ푖푐푙푒

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# . 푉푒ℎ푖푐푙푒 표푓 푈 푆 퐷푎푖푙푦=푉푒ℎ푖푐푙푒푠 � � × ( . . ) 퐷푎푦 100 푉푒ℎ푖푐푙푒푠 푇표푡푎푙 표푓 푉푒ℎ푖푐푙푒푠 푝푒푟 퐷푎푦 푈 푆 푃푒푟푐푒푛푡푎푔푒 표푓

# 푉푒ℎ푖푐푙푒 표푓 푀푒푥푖푐푎푛=퐷푎푖푙푦 푉푒 ℎ푖푐푙푒푠 � � × ( ) 퐷푎푦 100 푉푒ℎ푖푐푙푒푠 푇표푡푎푙 표푓 푉푒ℎ푖푐푙푒푠 푝푒푟 퐷푎푦 푀푒푥푖푐푎푛 푃푒푟푐푒푛푡푎푔푒 표푓 The total daily cost was multiplied by 365 days to covert it in total costs per year. Table 34 shows the total Texas Vehicle Inspection and Engomado Ecológico costs per year.

Table 34. Annual Texas Vehicle Inspection and Engomado Ecológico Costs.

# of Vehicles per Inspection Nationality Price Total of Percentages Total Cost Scenario Vehicles per (2012 (2012 US$) Year US$/vehicle) per Year U.S. MX U.S. MX U.S. MX 11 Lanes 2,154,960 38 62 818,885 1,336,075 0.079 0.022 $94,086 Opened 12 Lanes 2,290,740 38 62 870,481 1,420,259 0.079 0.022 $100,014 Opened 13 Lanes 2,291,105 38 62 870,620 1,420,485 0.079 0.022 $100,030 Opened 14 Lanes 2,291,835 38 62 870,897 1,420,938 0.079 0.022 $100,062 Opened

5.2.2 Environmental Costs

The research team monetized the environmental costs of CO2 emissions. Since CO2 is present in the stock market, the price of the metric ton of CO2 emitted is known. However, other compounds such as carbon monoxide (CO) or mono-nitrogen oxides (NOx) are not present in the stock market. Hence, the research team did not consider other compounds apart from CO2.

5.2.2.1 CO2 Emission Costs

CO2 emissions were calculated from the VISSIM model for each scenario. Table 27 facilitates the amount of gallons consumed in the model per day. The number of gallons obtained -3 was multiplied by 8.92 × 10 metric tons CO2/gallon of gasoline to get the metric tons of CO2 emitted. The average number of CO2 metric tons emitted by a car engine per each gallon -3 consumed is 8.92 × 10 (29). Table 35 shows the amount of CO2 emitted by the vehicles at BOTA in metric tons per day.

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Table 35. Metric Tons of CO2 Emitted per Day at BOTA.

Scenario Gas Consumption per Metric Tons per Gallon Metric Tons of CO2 Day (Gallons) of Gas Consumed Emitted per Day 11 Lanes Opened 11,363 8.92 × 10-3 101 12 Lanes Opened 4509 8.92 × 10-3 40 13 Lanes Opened 1944 8.92 × 10-3 17 14 Lanes Opened 1465 8.92 × 10-3 13

12000 100

90 10000 80 CO2 (Metric Tons) Saved

12 Hr Period 70 - 8000 60

6000 50

40 4000 30

20 2000 Total Fuel Consumed (gal) Consumed Fuel Total 10

0 0 11 12 13 14 No. of Booths Operating at BOTA

Figure 36 establishes a comparison between fuel consumption and CO2 emission in the four scenarios modeled. It can be easily observed a marked decrease in gas consumption and CO2 emissions produced by an increase in the number of inspection points at BOTA.

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12000 100

90 10000 80 CO2 (Metric Tons) Saved

12 Hr Period 70 - 8000 60

6000 50

40 4000 30

20 2000 Total Fuel Consumed (gal) Consumed Fuel Total 10

0 0 11 12 13 14 No. of Booths Operating at BOTA

Figure 36. Gas Consumed and CO2 Emitted for Each Scenario.

The price of the CO2 emitted was calculated based on the data collected from the price of the Carbon Credits in the stock market. These trading data were obtained from the European Energy Exchange AG website. Currently, the U.S. has not ratified the Kyoto Protocol, which means that the U.S. does not participate in CO2 exchange market. However, Mexico ratified this protocol. This fact justifies the use of carbon credits in this project due to its binational nature. Table 36 expresses the price in euros of emitting a ton of CO2. The information summarized in the table was extracted on May 14, 2012. The Settlement Price establishes the average price for this specific day. Best Bid represents the highest price offered per ton of CO2, and the Best Ask

represents the lowest price asked per ton of CO2 (30).

Table 36. Price of CO2 Emitted (€/Metric Ton).

Year Best Bid (€/ton Best Ask (€/ton Settlement Price CO2) CO2) (€/ton CO2) 2012 3.60 3.66 3.63

Table 37 illustrates the conversion of the price of carbon credits from €/ton CO2 to US$/ton CO2 based on the average exchange rate in the last year. According to OANDA Corporation, from May 14, 2011, to May 14, 2012, the average exchange rate was 1.3625 US$ per 1 euro (23).

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Table 37. Price of CO2 Emitted (US$/ Metric Ton).

Year Best Bid (2012 Best Ask (2012 Settlement Price US$/ton CO2) US$/ton CO2) (2012 US$/ton CO2) 2012 4.90 4.99 4.95

The research team assumed the settlement price as the price of emitting a metric ton of CO2 (Table 37). Table 38 shows the metric tons generated per year and the environmental cost expressed in 2012 US$ per year.

Table 38. Daily CO2 Emission Costs at BOTA.

Scenario Metric Tons of CO2 Emitted Unitary Cost Total CO2 Emission per Day (2012 US$/Metric Ton) Cost per Day (2012 US$) 11 Lanes Opened 101 4.95 500 12 Lanes Opened 40 4.95 198 13 Lanes Opened 17 4.95 84 14 Lanes Opened 13 4.95 64

5.3 RESULTS

This section summarizes all commuting costs calculated by the research team for this project. These costs are expressed in 2012 US$ per year.

5.3.1 Total Vehicle Costs

Table 39 summarizes all vehicle costs estimated in the present study.

Table 39. Vehicle Costs Summary.

Scenario Total Annual Routine Fuel Costs Texas Vehicle Insurance Maintenance, Inspection or Cost (2012 Tires, Repair, Engomado US$/Year) and Ecológico Costs Depreciation Costs 11 Lanes Opened $5,602,896 $472,868 $14,972,300 $94,086 12 Lanes Opened $5,955,924 $502,670 $5,941,105 $100,014 13 Lanes Opened $5,956,873 $502,742 $2,561,570 $100,030 14 Lanes Opened $5,958,771 $502,959 $1,930,485 $100,062

5.3.2 Total Environmental Costs

Table 40 summarizes the environmental costs estimated in the present study.

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Table 40. Environmental Costs Summary.

Scenario CO2 Emission Costs (US$/day) 11 Lanes Opened $ 182,482 12 Lanes Opened $ 72,270 13 Lanes Opened $ 30,715 14 Lanes Opened $ 23,488

5.3.3 Commuting Costs

Table 41 summarizes the commuting costs estimated in the present study.

Table 41. Total Annual Cost Summary.

Scenario Commuting Costs 11 Lanes Opened $21,324,788 12 Lanes Opened $12,572,162 13 Lanes Opened $9,151,871 14 Lanes Opened $8,515,636

The data showed in Table 41 are represented in Figure 37. This figure aims to establish a comparison between commuting costs generated for each scenario considered. It can be easily observed a marked decrease in commuting costs when more booths are opened. In fact, by opening 14 booths instead of 11, almost $13 million per year can be saved in commuting costs.

Commuting Costs $25,000,000 $21,324,788 $20,000,000

$15,000,000 $12,572,162 $10,000,000 $8,515,636 $9,151,871 $5,000,000 Commuting Costs $-

Figure 37. Commuting Cost for Each Scenario.

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5.4 CALCULATION OF VOWT

As mentioned at the beginning of this chapter, the VoWT is a result of adding commuting costs and personal costs (Figure 32). This section explains in detail the process researchers followed to calculate VoWT. At the end of this section, the VoWT results are compared for each scenario that was considered in the modeling chapter.

5.4.1 Formulation

The research team developed the following methodology to calculate the VoWT at BOTA for each scenario.

5.4.1.1 Occupancy Rate

The occupancy rate is expressed in passengers per vehicle. The survey performed at BOTA reveals that the occupancy rate is 1.72 passengers per vehicle.

= 1.72 푃푎푠푠푒푛푔푒푟푠 푂푐푐푢푝푎푛푐푦 푅푎푡푒 � � 5.4.1.2 Number of Commuters 푉푒ℎ푖푐푙푒 The number of commuters per year was calculated by multiplying the number of vehicles per day (Table 27) by the occupancy rate and by the number of days in a year (i.e., 365 days). The number of commuters will vary for each scenario considered. The research team applied the equation below to calculate the number of commuters per year for each scenario at BOTA (Table 42).

푁푢푚푏푒푟 표푓 퐶표푚푚푢푡푒푟푠= 1.72 × 퐶표푚푚푢푡푒푟푠 푉푒ℎ푖푐푙푒 � � 푁푢푚푏푒푟 표푓 푉푒ℎ푖푐푙푒푠 푓표푟 푒푎푐ℎ 푆푐푒푛푎푟푖표 � � × 365 푉푒ℎ=푖푐푙푒 퐷푎푦 퐷푎푦 퐶표푚푚푢푡푒푟푠 � � 푁푢푚푏푒푟 표푓 퐶표푚푚푢푡푒푟푠 � � 푌푒푎푟 푌푒푎푟

Table 42. Number of Commuters per Year at BOTA.

Scenario Number of Commuters per Year 11 Lanes Opened 3,706,531 12 Lanes Opened 3,940,073 13 Lanes Opened 3,940,701 14 Lanes Opened 3,941,956

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5.4.1.3 Delay

The delay (expressed in hours) was derived from the VISSIM model. The following table (Table 44) shows the delay at BOTA for each scenario.

Table 43. Delay at BOTA.

Scenario Delay (Hr) 11 Lanes Opened 5,629 12 Lanes Opened 2,490 13 Lanes Opened 1,095 14 Lanes Opened 820

5.4.1.4 VoTT

The research team calculated the VoTT for each trip purpose at BOTA (Table 26). The VoTT used to calculate the VoWT results from applying a weighted equation to the trip purpose VoTTs calculated (Table 26). The variable used to weight the VoTT was the percentage of commuters included in each of the trip purpose groups (Table 44).

Table 44. Percentage of Commuters Included in Each Trip Purpose Group.

Trip Purpose Percentage of Commuters Work/Work Related 24% School or College 11% Shopping Related 29% Other Purposes Related 36%

The research team applied the following equation to calculate the weighted VoTT:

= (0.24 × $1.6) + (0.11 × $1) + (0.29 × $1.4) + (0.36 × $1.5) $ = 1.44 푊푒푖푔ℎ푡푒푑 푉표푇푇 .× � � 퐻푟 퐶표푚푚푢푡푒푟 5.4.2 VoWT Calculation

After calculating all the variables involved in VoWT calculation, the research team applied the following equation:

$ $ = × ( ) × # × 퐶표푚푚푢푡푒푟 푉표푊푇Then,�푌푒푎푟 research� 푉표푇푇ers calculated�퐻표푢푟 퐶표푚푚푢푡푒푟 the VoWT� for퐷푒푙푎푦 each of퐻표푢푟 the four scena표푓 퐶표푚푚푢푡푒푟푠rios considered� in푌푒푎푟 the � modeling:

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• Scenario 1: 11 Booths Operating

$ = 1.44 × 5,629( ) × 3,706,531 × 퐶표푚푚푢푡푒푟 푉표푊푇 � � 퐻표푢푟 � � =퐻표푢푟30 퐶표푚푚푢푡푒푟 푌푒푎푟 퐵푖푙푙푖표푛 퐷표푙푙푎푟 � � • Scenario 2: 12 Booths Operating푌푒푎푟

$ = 1.44 × 2,490 ( ) × 3,940,073 × 퐶표푚푚푢푡푒푟 푉표푊푇 � � 퐻표푢푟 � � =퐻표푢푟14 퐶표푚푚푢푡푒푟 푌푒푎푟 퐵푖푙푙푖표푛 퐷표푙푙푎푟 � � • Scenario 3: 13 Booths Operating푌푒푎푟

$ = 1.44 × 1,095 ( ) × 3,940,701 × 퐶표푚푚푢푡푒푟 푉표푊푇 � � 퐻표푢푟 � � =퐻표푢푟6 퐶표푚푚푢푡푒푟 푌푒푎푟 퐵푖푙푙푖표푛 퐷표푙푙푎푟 � � • Scenario 4: 14 Booths Operating푌푒푎푟

$ = 1.44 × 820 ( ) × 3,941,956 × 퐶표푚푚푢푡푒푟 푉표푊푇 � � 퐻표푢푟 � � =퐻표푢푟5 퐶표푚푚푢푡푒푟 푌푒푎푟 퐵푖푙푙푖표푛 퐷표푙푙푎푟 � � Figure 38 illustrates the difference푌푒푎푟 in VoWT among the four scenarios involved. A marked decrease in VoWT is evident when more inspection booths are operating. For instance, if 14 booths are opened instead of 11 booths, the total saving in monetary terms are about $25 billion per year.

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$35

$30

$25

$20

$15 Billions

$10

$5

$0 11 Booths 12 Booths 13 Booths 14 Booths operating operating operating operating

Figure 38. VoWT for each Scenario.

5.5 CONCLUSIONS AND FINAL REMARKS Although there is no standard definition for the Value of Travel Time and the Value of Waiting Time, the research team adapted a definition from the literature reviewed to calculate both values at BOTA. After running the four scenarios (14, 13, 12, and 11 booths available for inspection), the results showed a substantial decrease in the VoWT and the commuting cost when more booths are available at the BOTA POE. This phenomenon suggested that the more lanes opened at rush hours, the lesser the waiting delay and faster the boarding crossing are. For instance, a marked decrease was observed in commuting costs when more booths are opened. In fact, by opening 14 booths instead of 11, the savings in commuting cost reached almost $13 million per year.

Environmentally speaking, delay at the POE is equivalent to emissions generation. The research team estimations regarding the environmental cost show that the savings in emissions are exponential when comparing the number of lanes opened.

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APPENDIX A

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