Border Crossing Delay Cost Analysis: Integration of Various Data Sources in the Direct Cost Estimation Tool

Abstract

While previous studies have estimated border delay costs at specific locations or for limited vehicle types, there has been a lack of comprehensive, U.S.-wide tools that integrate multiple data sources to quantify direct economic impacts for both commercial and passenger vehicles. This study presents the findings of the Direct Cost Estimation Tool (DCET), a comprehensive framework for quantifying the direct economic impact of delays at U.S. land ports of entry. The research integrates multiple data sources to calculate delay costs for both commercial and passenger vehicles at 49 major border crossings. The methodology employs an approach that considers commodity-specific costs for commercial vehicles and value of time calculations for passenger drivers and passengers. Using 2024 data, the analysis reveals that, for U.S.-bound traffic at the selected crossings, border delays cost more than $1.5 billion annually, with $337 million attributed to commercial vehicles and $1.25 billion to privately owned vehicles. California experienced the highest passenger delay costs (58% of the national total), while Texas accounted for the largest share of commercial vehicle delay costs (61%). DCET serves as a valuable decision support tool for transportation planners, policymakers, carriers, and shippers to evaluate infrastructure investments and operational improvements at international border crossings. By quantifying these costs, stakeholders can better understand the economic implications of border inefficiencies and make data-driven decisions to enhance cross-border transportation.

Keywords

delay costs cross-border transportation economics direct cost estimation tool

Introduction

Since the introduction of the North American Free Trade Agreement (NAFTA), Mexico and Canada have remained some of the biggest trade partners with the U.S. Over the last decade, U.S. trade with Mexico and Canada has continued to grow despite a brief downturn during the COVID-19 pandemic. This increase in cross-border trade, coupled with mandatory inspections at the U.S. border, has created substantial delays that affect manufacturers, shippers, carriers, and, ultimately, consumers. Meanwhile, the volume of passenger vehicle crossings has nearly returned to pre-pandemic levels, resulting in significant delays for cross-border passenger vehicles and travelers as well. Excessive delays at the border directly translate into economic costs both for drivers and the broader regional economy.

Reducing the wait time without compromising safety at the border remains a complex challenge. However, quantifying the economic impacts of delays is crucial in understanding their effects on trade, regional economic activity, and tourism across North America. Translating excessive delays into measurable financial losses can provide transportation planners and policymakers with the necessary information to better allocate resources, prioritize infrastructure investments, and make better-informed decisions to improve border efficiency.

To quantify the economic impact of border crossing delays at land ports of entry, the authors developed the Direct Cost Estimation Tool (DCET). Initially implemented as a limited spreadsheet in 2009, DCET utilized key variables including traffic volume, wait times, commodity distribution, cargo value, and associated costs to estimate direct delay costs for U.S.-bound commercial traffic. Since its inception, the tool has undergone significant enhancements and now features expanded capabilities to calculate delay costs for both commercial motor vehicles (CMVs) and privately owned vehicles (POVs) in both northbound and southbound directions across the U.S., although this study focuses on traffic entering the U.S.

This paper presents a comprehensive case study employing DCET to quantify the direct economic impacts of delays at 49 border crossings along the U.S.-Mexico and the U.S.-Canada borders. The study systematically documents the DCET development process, details the robust methodology for calculating CMV and POV delay costs, and analyzes case study findings to demonstrate the tool’s efficacy as a decision support mechanism for stakeholders to understand border economics.

Study in Context

This section establishes the contextual framework for the study by examining existing research on the economic implications of delays at international crossings, specifically focusing on cost of delay and value of time (VoT) metrics for both commercial and passenger vehicles. Through systematic review of literature spanning over 2 decades, this section presents a critical analysis of key studies that have shaped the understanding of cross-border transportation economics.

Cost of Delay Studies

Ojah et al. conducted one of the initial studies aimed at raising awareness about coordination problems in commercial border-crossing processes between Mexico and the U.S. ( 1 ). Their research estimated that delays at the southern border cost approximately $60 million annually. Building on this work, Taylor et al. compiled a comprehensive collection of studies examining the multifaceted impacts and costs of transportation and logistics at the U.S.-Canada border ( 2 ). Their analysis explored causes of delays and potential solutions, covering topics from customs inspection impacts to infrastructure conditions and relevant trade policies.

Goodchild et al. investigated crossing time variability at the Blaine, Washington, border crossing and its effects on regional supply chains ( 3 ). Using descriptive statistics, qualitative carrier interviews, and an examination of mitigation strategies, they found that, while typical variability in crossing times was not a significant concern for carriers, extreme delays could substantially disrupt operations and result in increased costs for customers. The researchers defined “crossing time” as the total time spent waiting and being processed at the border.

Haralambides and Londoño-Kent provided a detailed analysis of inefficiencies in U.S.-Mexico trade routes, particularly at the Laredo crossings ( 4 ). Their study examined the border crossing system’s complexities, analyzing time and cost factors at each process step. The research identified institutional constraints contributing to delays and congestion, including non-standardized procedures and limited implementation of information and communication technologies. The authors specifically identified delays as the most significant problem at the crossing within Laredo port of entry.

Rajbhandari et al. subsequently detailed a dashboard tool designed to communicate delays and economic costs using the original DCET ( 5 ). This dashboard provided stakeholders with key performance indicators related to delays and costs to support informed planning and policy decisions. The authors examined various aspects of the dashboard’s design and implementation, including the guiding principles that ensured its effectiveness.

The Joint Interim Committee on Border Wait Times Interim Report analyzed challenges faced by ports of entry along the Texas-Mexico border and recommended efficiency improvements ( 6 ). The report covered various topics, including border crossing processes and a comprehensive literature review of recent studies on the economic impact of border delays, referencing the research by Rajbhandari et al. ( 5 ).

Aldrete et al. presented the updated DCET version, designed to provide real-time estimates of commercial vehicle border delay economic impacts ( 7 ). This enhanced version introduced several improvements, including separate crossing times for expedited lanes, updated commodity distribution estimates, and more accurate average cargo value estimates. While the original version used average crossing times, the updated tool employed the 10^th percentile crossing time as the free-flow threshold. Despite these enhancements, the new DCET maintained the same cost calculation methodology as the original version to ensure consistency. The cost of delay estimations for Bridge of the Americas and Ysleta Bridge crossings in El Paso, Texas, demonstrated the substantial economic impact of border delays.

While these studies provide valuable insights into delay costs at specific crossings or regions, they often lack a standardized methodology for nationwide application and do not integrate multiple data sources for comprehensive cost estimations. Additionally, prior tools have focused primarily on commercial vehicles, leaving a gap in understanding the economic impact of delays on passenger vehicles. Recent studies have explored innovative approaches for estimating border crossing times using emerging data sources and advanced analytics. For example, connected vehicle and crowdsourced data have been leveraged to monitor crossing times with high correlation to ground truth observations, achieving promising accuracy for real-time applications ( 8 ). Machine learning models, such as predictive analytics, have also been applied to forecast border crossing times. These methods demonstrate the potential of big data and AI-driven solutions to complement traditional approaches and improve reliability in delay estimation. While the current study focuses on enhancing DCET using established data sources, future research can evaluate the integration of real-time crowdsourced data and predictive modeling techniques to further improve the tool’s capabilities. Future research could compare DCET-based delay cost estimates with localized estimates reported in state border master plans, such as the Texas-Mexico Border Transportation Master Plan, to assess consistency and identify regional differences in cost estimation approaches.

Value of Time (VoT) Studies

VoT in transportation refers to the monetary value individuals place on time spent traveling. This concept is fundamental for transportation planners and policymakers when making informed decisions about infrastructure investments and prioritizing transportation projects. VoT varies, based on several factors including traveler’s income, employment status, and trip purpose. Understanding these variables is crucial when calculating VoT for specific traveler groups, particularly in cross-border contexts.

Because of space constraints for TRB paper submissions, this literature review focuses on the most relevant VoT studies for border crossing analysis, though numerous other valuable studies exist in the broader transportation literature. The U.S. Department of Transportation revised its valuation of travel time in economic analysis in 2016 ( 9 ). This guidance updates VoT savings calculations using median household income data from the Census Bureau and salary information from the Bureau of Labor Statistics. To establish comparable values, annual household income is converted to an hourly rate by dividing by 2,080 hours per year. According to these guidelines, business travel VoT should equal the gross hourly employment cost, including payroll taxes and fringe benefits, while local personal travel VoT is estimated at 50% of hourly median household income. Similarly, the Texas Department of Transportation established adjusted VoT values for the state, declaring delay time values at $37.20 per passenger vehicle hour and $52.75 per commercial truck hour, based on the previous year’s Consumer Price Index ( 10 ).

The research by Burris et al. examines VoT and its critical role in transportation planning and infrastructure investment, defining it as the monetary value travelers are willing to pay to reduce trip duration ( 11 ). This study estimated VoT through travel choices on a Texas freeway. The results were lower than expected and what was currently used in practice. Litman investigated how new technologies affect travel time valuation while exploring equity and efficiency considerations ( 12 ). The research emphasized that understanding tradeoffs and limitations across transportation modes is essential for effective resource allocation. Similar to Burris et al.’s findings, Litman noted that transportation agencies typically value personal travel time at 35%–60% of average wages ( 11 , 12 ). However, when tested with optional congestion-avoiding tools, the average willingness to pay was generally much lower, suggesting travelers often prioritize cost savings over time savings.

Shelton and Martin conducted a comprehensive study on how VoT affects road user costs during work zone closures ( 13 ). Their research employed various VoT calculations based on origin zone, destination zone, and trip purpose. To better understand these different values’ impact, the study analyzed resulting traffic congestion and total delay using simulation-based dynamic traffic assignment models. The findings demonstrated that assigning VoT based on trip purpose, and using these differentiated values to represent road user costs in the simulation-based dynamic traffic assignment, provided the greatest improvement in overall traffic congestion and yielded the lowest total delay. This insight is particularly relevant for cross-border travelers, who encounter unique challenges affecting their time valuation because of potentially lengthy border crossing times. In a related study, Shelton and Martin explored optimal toll rates for border regions using a dynamic congestion-responsive approach to accurately reflect ideal tolling rates ( 14 ). Their comprehensive comparative analysis of the El Paso region revealed that VoT values ranged between $12.88 and $22.09 per hour (2019 USD), with trip purpose significantly influencing individual time valuation (see Figure 1).

Figure 1.

Value of time comparison for El Paso.

Galicia et al. focused on cross-border travelers’ commuting costs at an international border crossing. After an extensive literature review on VoT, the researchers adopted 50% of the average hourly wage as their baseline ( 15 ). Moreover, the study involved detailed calculations of vehicle costs, environmental impacts, and commuting expenses across scenarios with varying numbers of inspection booths. Their cost model for routine maintenance, tires, repairs, and depreciation estimated costs for automobiles and pickups/sport utility vehicle/vans at 18.83 and 20.83 cents per mile, respectively, using 2012 values.

Existing VoT research has largely concentrated on general travel or work zone contexts, with limited attention to cross-border travel where trip purposes and income levels vary significantly between countries. This gap underscores the need for a framework that incorporates binational demographic and economic factors to accurately estimate delay costs for both drivers and passengers.

Framework Development

This section outlines the framework development for the current DCET tool. Key improvements in this version include: 1) expanded coverage to analyze both commercial and passenger vehicles, 2) ability to generate monthly direct cost of delay estimates for all crossings nationwide, and 3) consistent delay measurement methodology using the 10^th percentile crossing time as the baseline. The following sections provide detailed explanations of the direct cost calculations for both vehicle categories.

Commercial Motor Vehicle (CMV) Cost of Delay Calculations

This section presents the comprehensive CMV cost of delay calculation framework, as illustrated in Figure 2. The flowchart provides a visual representation of the systematic approach used to quantify economic impacts of border delays for commercial vehicles. The analysis details key inputs required, calculation processes applied to these inputs, and the resulting outputs that enable stakeholders to assess financial implications. The framework is organized into three interconnected input components—cost factors (shown in blue), vehicle characteristics (shown in orange), and time measurements (shown in green)—each represented by a distinct color-coding scheme to enhance clarity and facilitate interpretation. Purple rectangles (numbered 1–6) represent interim calculations for specific commodity categories: 1) just-in-time commodities, 2) non-just-in-time commodities, 3) perishables, 4) non-perishable agricultural goods, 5) other commodity groups, and 6) empty trucks. The final outputs appear in red rectangles on the right-hand side, labeled 2a to 2f, corresponding same commodity categories (2a for just-in-time, 2b for perishables, 2c for non-just-in-time, 2d for non-perishables, 2e for other commodities, and 2f for empty trucks).

Figure 2.

Commercial motor vehicle cost of delay calculation flowchart.

Blue Components (Cost Factors)

Idling fuel consumption rate: The amount of fuel consumed by a stationary vehicle with its engine running, measured in gallons per hour. Because of the stop-and-go nature of border crossings, this study estimates the average CMV’s idling fuel consumption rate at 4 gal/h, consistent with the value used in previous versions of the tool. The idling fuel consumption rate of 4 gal/h was adopted based on prior versions of the DCET tool. While typical long-haul truck idling consumes around 0.8–1.3 gal/h under standard rest conditions, a study by the Argonne National Laboratory indicates that fuel use can increase significantly when auxiliary systems such as refrigeration units, air conditioning, and heating are engaged during idle periods ( 16 , 17 ). These conditions are common for trucks carrying goods at border crossings, where engines run at elevated RPMs to maintain cargo integrity and cabin climate. Additionally, there is inherent uncertainty because of the diversity of vehicle fleets entering from Mexico and Canada, which may include older engines with lower fuel efficiency. Therefore, the 4 gal/h assumption reflects a conservative estimate for border-crossing operational scenarios, rather than average long-haul idling rates.

Truck fuel price: Monthly diesel price values obtained from the U.S. Energy Information Administration ( 18 ).

Driver’s wage: The mean wage for heavy and tractor-trailer truck drivers in the U.S., as reported by the U.S. Bureau of Labor Statistics ( 19 ).

Other non-fuel operating costs: Additional operating costs for carriers estimated at $5.71 per hour, consistent across all time periods and crossing locations. This value was calculated using Consumer Price Index inflation rates to update the previous tool’s value.

Average cargo values: Calculated using monthly cargo values and volumes from Bureau of Transportation Statistics (BTS) data for selected crossings ( 20 ). These values vary between months, crossings, and commodity groups.

Annual inventory carrying cost: Based on Council of Supply Chain Management guidelines, the inventory capital cost is set at 3.25% of the average cargo value per annum ( 21 ). The hourly value is derived by converting from yearly rates using average cargo values for each commodity group.

Annual inventory carrying cost for time-sensitive cargo: Inventory risk costs are calculated at 18% of average cargo value for just-in-time commodities and 9% for the perishables ( 21 ). These percentages, drawn from supply chain management practice to reflect higher risk and obsolescence costs for just-in-time commodities and perishable goods compared with others. Hourly values are derived from average cargo values for each commodity group using yearly-to-hourly conversion.

Additional logistic cost from buffer time: “Buffer time” refers to the extra time built in to schedules to account for potential delays or uncertainties in transportation and delivery. It is intended to improve reliability and reduce the risk of missed deadlines, but it also incurs additional costs when resources remain idle during this period. Reliability costs for buffer time are established at $198.7 per hour for both just-in-time and perishable commodity groups. This value was calculated using Consumer Price Index inflation rates to update the previous tool’s value.

Additional logistic cost from scheduled delay: Reliability costs for schedule delay amount to $440.2 per hour for just-in-time commodities. This factor is only applied when using 95^th percentile crossing times as an additional cost component to cover the lengthy delays. This value was calculated using Consumer Price Index inflation rates to update the previous tool’s value.

Direct hourly cost: Delay costs linked to carriers calculated for each hour includes truck operating and non-operating costs, plus driver wages. These hourly costs apply universally across all commodity types.

Orange Components (Vehicle Characteristics)

Truck crossings: Monthly truck volumes are obtained from the U.S. BTS for both loaded and empty trucks. These monthly figures are converted to average daily values to facilitate analysis.

Operational days: The analysis accounts for the number of days each crossing operates per month, excluding closures. Many crossings are closed on Sundays, while Saturdays typically experienced reduced operations with limited hours, resulting in lower traffic volumes than weekdays.

Loaded truck volume: The number of loaded trucks crossings is obtained from the BTS database. Monthly totals are converted to average daily figures by dividing the number of operational days at each crossing.

Commodity distribution: BTS provides monthly data on the weight and value of inbound trade commodities, categorized by the first two digits of their harmonized system (HS) codes ( 22 ). These HS codes are organized into three main categories (i.e., manufacturing, agricultural, and other) with five subgroups (i.e., just-in-time, non-just-in-time, perishables, nonperishables, and other). The commodity distribution percentages vary by month and crossing, and are multiplied by the average daily loaded truck volumes to estimate the number of trucks carrying each commodity type daily.

Green Components (Time Measurements)

Crossing times: The tool obtains crossing times from U.S. Customs and Border Protection (CBP) border wait times ( 23 ). To capture more border crossings nationwide and have a fair comparison, this study only looked for general lane border wait times; no expedited lane crossing times were used for the cost of delay calculations. Monthly border wait time distributions are analyzed to establish baseline (free flow) times and calculate average delays.

10th percentile crossing time: This metric represents the crossing time at the 10^th percentile of the dataset and serves as the baseline for measuring delay. Any crossing time exceeding this threshold is considered delay. The 10th percentile crossing time was selected as the free-flow baseline because it represents a realistic lower bound of observed crossing times under normal operating conditions, excluding outliers that may occur during very low traffic periods (e.g., overnight hours). Using the absolute minimum crossing time could underestimate typical processing requirements, while 10th percentile provides a statistically robust threshold that reflects efficient operations without congestion. It is important to note that the choice of the 10th percentile is a significant assumption that directly influences the magnitude of the estimated delay costs. This threshold was selected for consistency with prior research and to maintain comparability across crossings and with previous studies.

90th percentile crossing time: This metric identifies the crossing time at the 90th percentile of the dataset. It represents the threshold for implementing additional logistic costs from schedule delays in the analysis framework.

95th percentile crossing time: The crossing time at the 95th percentile of the dataset serves as the threshold for applying additional shipper logistic costs associated with exceeding buffer time allowances.

Percentage of trucks experiencing delay: This measure is calculated by counting trucks with crossing times exceeding the 10th percentile threshold, then dividing by the total number of truck crossing during the month. The result quantifies the proportion of vehicles affected by delay.

Average delay time above 10th percentile: Consistent with the previous tool’s methodology, any crossing time exceeding the 10th percentile is classified as delay. All crossing times meeting this criterion are averaged to determine the mean delay time for CMVs during the month.

Average delay time above 90th percentile: For time-sensitive commodities, additional logistic costs from schedule delays are applied to vehicles with crossing times exceeding the 90th percentile. This metric captures the average magnitude of these extended delays.

Average delay time above 95th percentile: Crossing times exceeding the 95th percentile are classified as unscheduled delays for special commodities. The average of these extended crossing times quantifies the typical magnitude of severe delays. These lengthy delays incur significantly higher costs because of the addition of logistic and buffer costs, particularly for just-in-time and perishable commodities.

Process and Outputs

Number of Trucks Experiencing Delay: This metric represents trucks with crossing times exceeding the 10th percentile threshold. In the tool, this value is converted to a daily estimate for use in the final cost of delay calculations.

Commodity-specific delay costs (2a–2f): Delay costs for each commodity category are calculated by multiplying the hourly delay cost by the average delay duration (in hours) and the daily number of trucks experiencing delays. Because of their time sensitive nature, just-in-time and perishable commodities incur additional costs beyond the standard delay calculations.

Shipper direct cost of delay: The tool maintains separate calculations for shipper and carrier costs. Shipper hourly delay costs incorporate inventory risk and capital costs, as well as logistic and schedule delay expenses. The final shipper cost is calculated by multiplying the hourly delay cost by the average delay duration and the number of trucks experiencing delays.

Carrier direct cost of delay: Carrier hourly delay costs comprise truck operating and non-operating costs, plus driver wages. The final carrier cost is calculated using the same methodology as shipper costs: the product of hourly delay costs, average delay duration, and the number of affected trucks. Although these carrier direct costs are calculated separately in DCET, they may ultimately be passed on to shippers or receivers through freight rates and contractual agreements.

Total monthly direct cost of delay: This comprehensive metric is derived by summing the direct delay costs for both shippers and carriers across all commodity groups, including empty trucks. The resulting daily cost figure is then extrapolated to estimate monthly costs and calculate the average cost per truck.

Figure 3 presents the user interface of DCET for commercial vehicles, using the World Trade Bridge in Laredo, Texas, as an example. The interface is divided into two main sections: the top portion contains input fields where users enter required parameters, while the lower section shows the calculated results in USD values—presented separately for carriers and shippers, with daily and monthly totals, as well as cost per truck.

Figure 3.

User interface for commercial vehicles.

Privately Owned Vehicle (POV) Cost of Delay Calculations

As described in the previous section, DCET used the original tool as a foundation for improving direct cost of delay estimations for CMV crossings. However, POV crossings were never incorporated into the original DCET framework. Although the direct cost of delay for a single POV is significantly lower than that of a CMV experiencing the same border delay, the substantial volume of POV crossings necessitates their inclusion in comprehensive impact analyses. To address this gap, this study developed a parallel methodology for POV crossings that complements the CMV direct cost of delay calculations (see Figure 4).

Figure 4.

Privately owned vehicle cost of delay calculation flowchart.

Blue Components (Cost Factors)

Idling fuel consumption rate: The amount of fuel consumed by a stationary vehicle with its engine running, measured in gallons per hour. Considering the stop-and-go nature of border crossings, this study estimates the average POV’s idling fuel consumption rate at 1.5 gal/h.

Vehicle fuel price: Monthly gasoline prices obtained from the U.S. Energy Information Administration ( 18 ).

Rate of incoming country residents: The proportion of incoming country residents among total cross-border travelers. This rate can be determined through local cross-border surveys by dividing the number of incoming country resident travelers by the total number of cross-border travelers. When specific data is unavailable, border crossing times are used to estimate the rate. The morning peak crossing times and afternoon peak crossing times were compared, and their proportion were used to estimate the rate of residents from the incoming country. This approach relies on the assumption that differences between morning and afternoon peak crossing times reflect the proportion of incoming country residents. While this proxy is practical in the absence of survey data, it introduces uncertainty because peak patterns may also be influenced by other factors such as trip purpose, seasonal variations, or local events.

Median household income: Data on median household income from the incoming region, incoming country, outgoing region, and outgoing country is necessary to estimate the VoT for cross-border travelers. This information is sourced from the U.S. Census Bureau and Statistics Canada for the northern border, and the U.S. Census Bureau and the Instituto Nacional de Estadística y Geografía (INEGI) for the southern border.

Rate of local traffic: The proportion of cross-border travelers who remain within the selected regions on both sides of the border. This information, combined with regional and national median household income data, enables calculation of a weighted average median household income and, subsequently, the VoT for an average cross-border traveler. This study used monthly National Household Travel Survey results to estimate the local traffic for the border regions for the U.S. and estimated a similar pattern is valid for the other side of the border. Assuming the local patterns derived from U.S. side are representative of similar patterns on the other side of the border is a significant assumption that may not fully capture differences in travel behavior. Future research is recommended to validate this assumption using binational survey data or region-specific studies.

Other non-fuel operating costs: Additional vehicle operating costs estimated at 4 cents per hour, applied consistently across all time periods and crossing locations.

VoT: This study employs standard VoT calculations from the literature, primarily using employment rate and median household income as key parameters. The innovative aspect of this approach is the integration of input parameters from different countries to determine the VoT for an average cross-border traveler. The model calculates separate values for drivers and passengers in POVs, with distinct values for weekdays and weekends.

Direct cost of delay for drivers and passengers: Because of the different input variables affecting the VoT for drivers versus passengers, the tool provides separate hourly direct cost of delay calculations for each category:

(a) Driver cost: Reflects the economic impact of delays on vehicle operators.

(b) Passenger cost: Captures the economic impact of delays on vehicle occupants.

Direct cost of delay for vehicles: These delay costs encompass hourly fuel consumption costs and other non-fuel operating costs resulting from the stop-and-go traffic pattern typical at border crossings.

Orange Components (Vehicle Characteristics)

Vehicle crossings: Monthly vehicle volumes are obtained from BTS and converted to average daily crossings, with separate calculations for weekdays and weekends to account for traffic pattern variations.

Passenger crossings: BTS data on monthly passenger crossings are similarly converted to average daily figures, differentiated between weekday and weekend traffic to ensure accurate representation of border crossing patterns.

Operational calendar: The number of days each crossing operates is documented to facilitate accurate conversion from monthly to daily crossing volumes. Weekends are weighed differently, mainly because the trip purposes are different at the weekends. While weekday crossings are primarily job-related, weekend crossings tend to be for leisure, shopping, or family visits.

Demographic data: Population and employment statistics for both the border region and national level are incorporated to estimate the workforce crossing the border. This information informs the VoT calculations for drivers and passengers during both weekdays and weekends. Following U.S. Department of Transportation methodology, values range from 35% to 100% of median household income, depending on whether trips are for work or leisure purposes.

Employment rate: Calculated as the ratio of the total number of employed persons to the driving-age population.

Green Components (Time Measurements)

Crossing times: The tool retrieves crossing time data from CBP border wait times, specifically focusing on POV crossings. Monthly crossing time data is analyzed to calculate both the free flow time and average delay time, accounting for baseline conditions.

10th percentile crossing time: This metric represents the crossing time at the 10^th percentile of the dataset and serves as the baseline threshold for measuring delay.

Average delay time above 10th percentile: Consistent with the original tool, any crossing time exceeding the 10th percentile is classified as delay. All crossing times meeting this criterion are averaged to determine the mean delay time for POVs during the month.

Percentage of vehicles experiencing delay: This measure is calculated by counting vehicles with crossing times exceeding the 10th percentile threshold, then dividing by the total number of vehicle crossings during that month. The result quantifies the proportion of POVs affected by delay.

Process and Outputs

Number of Vehicles Experiencing Delay: This metric represents vehicles with crossing times exceeding the 10th percentile threshold. In the tool, this value is converted to a daily estimate for calculating the final cost of delay.

Direct cost of delay for drivers and passengers: The tool calculates separate delay costs for drivers and passengers based on their distinct VoT variables. These costs can be reported as either total values or on a per-person basis (2a: driver cost, 2b: passenger cost).

Direct cost of delay for vehicles: These costs encompass hourly fuel consumption and other non-fuel operating expenses resulting from the stop-and-go traffic patterns at border crossings. Results can be presented as either total values or on a per-vehicle basis.

Total monthly direct cost of delay: This comprehensive metric is calculated by summing the direct delay costs for drivers, passengers, and vehicles. The result represents the monthly economic impact and can also be expressed as a daily average.

Figure 5 presents the user interface of the DCET for POVs, using a border crossing in Detroit, Michigan, as an example. The interface is divided into two main sections: the top portion contains input fields where users enter required parameters, while the lower section shows the calculated results in USD values—presented separately for drivers, passengers, and vehicles, with daily and monthly totals, as well as cost per vehicle.

Figure 5.

User interface for passenger vehicles.

To complement the narrative description of the framework, the following section presents the core calculation logic in a concise mathematical form. These Equations 1 and 2 summarize how DCET computes delay costs for CMVs and POVs using the key input parameters such as delay duration, number of affected vehicles, and cost factors.

\begin{matrix} Total CMV Delay Cost = \\ \sum_{c \in Commodity Groups} [N_{c} \times D_{c} \times (C_{carrier, c} + C_{shipper, c})] \end{matrix}

(1)

where

$N_{c}$ = number of trucks experiencing delay for commodity group $c$ ,

$D_{c}$ = average delay duration (hours),

$C_{carrier, c}$ = carrier hourly cost (fuel + driver wages + operating costs), and

$C_{carrier, c}$ = shipper hourly cost (inventory + buffer + schedule delay costs).

\begin{matrix} Total POV Delay Cost = \\ \sum_{v \in Vehicles} [N_{v} \times D_{v} \times (C_{driver} + C_{passenger} + C_{vehicle})] \end{matrix}

(2)

where

$N_{v}$ = number of POVs experiencing delay,

$D_{v}$ = average delay duration (hours),

$C_{driver}$ = VoT for drivers,

$C_{passenger}$ = VoT for passengers, and

$C_{vehicle}$ = fuel + operating costs.

Limitations and Findings

This case study demonstrates DCET’s application in calculating direct delay costs at 49 land border crossings between the U.S., Mexico, and Canada. The analysis estimated costs for both CMVs and POVs entering the U.S. in 2024.

A significant challenge faced during the study was the limited availability and consistency of border crossing data. While BTS provides data on vehicles entering the U.S., there is no comparable information for outbound crossings. More critically, U.S. CBP only provides border wait time measurements, which represent just a portion of total border crossing experience. These measurements fail to capture secondary inspections, processing time, and queues on the opposite side of the border, resulting in underestimated total crossing times. Additionally, CBP data is limited to daily averages for each month, potentially missing peak delay periods and extreme wait time events that significantly affect economic costs. While CBP remains the only source providing consistent wait time measurements across all land crossings, the data collection methodologies vary between locations—some relying on manual observations while others use automated systems. Despite these limitations, this study utilized the best available data, acknowledging that the resulting cost estimates are conservative since they capture only a subset of actual delays experienced at border crossings. Consequently, this study focused exclusively on traffic entering the U.S. for this analysis. This analysis does not account for exchange-rate fluctuations between the U.S. dollar and currencies of Mexico or Canada, which could influence cost valuations for cross-border trade and travel. Incorporating currency variability in future studies would improve the robustness of economic impact studies.

After reviewing the available data, a set of criteria was used to ensure the study captured costs for border crossings that experience a large volume of vehicular traffic. To that end, any border crossings that recorded an average of more than 5,000 CMV crossings or 50,000 POV crossings per month during the year 2024 were selected. As a result, over a third of border crossings in the U.S. were captured in this study. More importantly, the locations that were selected represent 90% of total POV crossings and 94% of total CMV crossings in the U.S., as shown in Table 1. Future research could extend the DCET application to include at least the highest-volume crossing in currently unrepresented border states, to assess whether their delay cost patterns are consistent with those observed states included in this study.

Table 1.

Selected versus Total Border Crossings and 2024 Traffic Volumes by State

State	Number of crossings		Privately owned vehicle crossings		Passenger crossings		Loaded truck crossings
State	Total	Captured	Total	Captured	Total	Captured	Total	Captured
Alaska	3	0	114,449	0	216,845	0	11,778	0
Arizona	6	4	9,484,088	8,732,320	17,552,835	15,965,245	481,331	433,228
California	6	5	31,303,572	30,693,820	52,194,062	51,057,537	1,581,974	1,581,974
Idaho	2	0	175,995	0	353,870	0	53,893	0
Maine	16	2	1,681,198	343,371	2,846,996	686,474	259,924	154,286
Michigan	4	3	5,300,811	4,727,500	9,858,440	8,760,501	2,346,843	2,314,096
Minnesota	7	0	696,793	0	1,358,006	0	48,914	0
Montana	12	1	471,518	165,681	1,020,517	356,910	188,796	158,337
New Mexico	3	1	1,200,288	780,912	2,478,249	1,618,532	187,671	169,241
New York	16	5	7,178,638	5,737,326	15,552,217	13,383,011	1,434,856	1,363,416
North Dakota	18	1	484,969	239,674	1,073,740	578,858	402,037	247,746
Texas	28	21	34,896,618	32,516,751	66,145,500	61,597,648	5,366,231	5,325,248
Vermont	14	2	1,079,132	920,216	2,324,337	2,017,842	196,020	179,340
Washington	13	4	5,379,980	4,573,030	10,157,397	8,528,451	607,399	452,195
Total	148	49	99,448,049	89,430,601	183,133,011	164,551,009	13,167,667	12,379,107

Out of the 49 crossings selected for this study, 35 accommodate commercial vehicle traffic. In addition to the number of commercial vehicles that crossed the border and their crossing time measurements, other cost factors and vehicle characteristics, such as fuel price, average cargo value, cargo distribution, and hourly driver expenses, were input into the tool to estimate the cost of CMV delays in 2024.

Delays along 35 border crossings for CMVs resulted in an estimated total cost of $336,741,408. Table 2 breaks down the total cost by state, along with the average delay and the cost per truck for carriers and shippers. Texas, which accounted for 45% of total CMV crossings, experienced the highest total cost of delays, just over 60% of the national total, followed by California and Michigan with 15% and 13%, respectively. In contrast, states such as Vermont, Maine, and Montana were found to have the shortest delays and, ultimately, lower costs—accounting for less than 1% of the national total combined. The map in Figure 6 shows the magnitude of the total CMV costs for each crossing along the northern and southern borders.

Table 2.

Direct Cost Estimation Tool Output for Commercial Motor Vehicles by State

State	Loaded trucks	Empty trucks	Carrier cost per truck	Shipper cost per truck	Average delay (min)	Total cost
Arizona	433,228	120,370	$7.27	$20.39	9	$18,478,356
California	1,581,974	548,498	$10.75	$17.76	12	$50,408,065
Maine	154,286	60,132	$1.70	$ 0.91	2	$138,410
Michigan	2,314,096	445,672	$8.34	$10.23	10	$42,570,002
Montana	158,337	31,224	$4.37	$7.30	5	$1,202,549
New Mexico	169,241	66,701	$6.44	$14.66	8	$3,725,957
New York	1,363,416	256,067	$3.57	$2.80	4	$7,921,706
North Dakota	247,746	41,407	$6.54	$11.83	8	$3,266,776
Texas	5,325,248	1,786,652	$7.68	$18.66	10	$206,053,006
Vermont	179,340	29,533	$1.59	$1.07	2	$8,330
Washington	452,195	154,590	$3.10	$1.95	4	$2,968,251

Figure 6.

Cost of commercial motor vehicle delays by magnitude.

Since the World Trade Bridge is the only selected crossing that does not facilitate POVs, the study was able to estimate costs related to POV delays at 48 border crossing locations. As opposed to CMVs, other cost components, such as the rate of incoming residents, median household income, rate of local traffic, population of driving age, total employment, and other related costs, were entered into DCET to estimate costs for POVs in 2024.

Delays along 48 border crossings for POVs resulted in an estimated total cost of $1,254,477,466. Along with the total cost in each state, Table 3 presents the average cost per vehicle, the average cost per person in the vehicle, and average delay. Although California only accounted for 34% of total POV crossings in the country, the costs of delays in the state were estimated to be nearly 58% of the national total. This is more than their counterparts in Texas, which saw the largest number of POV crossings but only attributed 25% of the national total. In addition to the highest total cost, California was also estimated to have the highest cost per vehicle and cost per person in the vehicle, followed by Arizona and Texas with $2.65 and $1.52, respectively. The map in Figure 7 shows the magnitude of the total POV costs for each crossing along the northern and southern borders.

Table 3.

Direct Cost Estimation Tool Output for Privately Owned Vehicles (POVs) by State

State	POVs	Passengers	Total cost	Cost per vehicle	Cost per person in POV	Average delay (min)
Arizona	8,732,320	15,965,245	$111,688,224	$2.65	$5.25	31
California	30,693,820	51,057,537	$731,770,873	$3.84	$8.67	38
Maine	343,371	686,474	$616,469	$ 0.35	$ 0.52	5
Michigan	4,727,500	8,760,501	$16,807,179	$ 0.57	$1.64	8
Montana	165,681	356,910	$728,995	$ 0.67	$1.65	9
New Mexico	780,912	1,618,532	$5,961,404	$1.21	$3.10	18
New York	5,737,326	13,383,011	$29,897,589	$ 0.60	$1.75	8
North Dakota	239,674	578,858	$781,617	$ 0.50	$ 0.96	7
Texas	32,516,751	61,597,648	$318,348,611	$1.52	$3.70	23
Vermont	920,216	2,017,842	$5,242,311	$ 0.86	$1.77	12
Washington	4,573,030	8,528,451	$29,089,571	$ 0.75	$2.47	9

Figure 7.

Cost of privately owned vehicle delays by magnitude.

Figure 8 shows the magnitude of the combined costs incurred by POV and CMV delays along each border crossing in this study. The two border crossings with the highest combined costs were San Ysidro and Otay Mesa in California. These two crossings are the busiest in POVs, servicing over 21 million vehicles in 2024. The total combined costs for these two crossings were estimated to be over $645 million—nearly 40% of the national total. Altogether, the combined cost of POVs and CMVs among all 49 border crossings using the DCET was estimated to be more than $1.5 billion.

Figure 8.

Combined costs of privately owned vehicle and commercial motor vehicle delays by magnitude.

Conclusion

As cross-border trade and travel between the U.S., Mexico, and Canada continues to expand, understanding the economic implications of border crossing delays becomes increasingly critical. This study introduces DCET to quantify these impacts through a comprehensive analysis of delay costs. A thorough review of existing literature on international crossing delays informed the methodological framework, particularly focusing on delay cost metrics and VoT calculations for both POVs and CMVs.

The study implemented DCET through a detailed case study of 49 U.S. land border crossings using 2024 data. The analysis incorporated multiple variables, including vehicle characteristics, crossing times, and region-specific cost factors, to estimate both aggregate and per-vehicle delay costs. Results revealed that California experienced the highest total and POV-related costs, while Texas led in CMV delay costs. California also recorded the highest delay cost per commercial vehicle among the analyzed crossings, indicating that both passenger and freight movements in the state experience relatively high per-vehicle delay burdens. The total national cost of delays reached over $1.5 billion, with POV delays accounting for a significantly larger portion despite lower per-vehicle values. This disparity is primarily attributed to the substantially higher volume of POV crossings and longer average wait times for POVs, particularly at high-volume crossings.

A key limitation of this analysis is its focus solely on traffic entering the U.S. Future research opportunities exist to expand the analysis to bidirectional flows, which would provide a more complete understanding of cross-border economic impacts.

This study provides a structured approach to estimating the financial impact of current delays at the borders using DCET. While the methodology integrates multiple data sources and established cost factors, the estimates should be interpreted as indicative rather than definitive, because of data limitations and lack of formal validation against observed economic outcomes. The results of this study are significant for transportation planners and policymakers by providing a comprehensive estimation of the financial impact that border delays pose to drivers, passengers, carriers, and shippers.

With this information, better decisions about border improvements and infrastructure investments can be made. Similarly, if shippers and carriers understand the financial impact of excessive delays, they can be better equipped to make decisions about which ports of entry to use and they can prepare for the financial implications of anticipated delays. Overall, using DCET can provide valuable insight to stakeholders, policymakers, and cross-border travelers that allows them to make better decisions about travel, transportation planning, and policies that can improve the overall efficiency of international border crossings.

Future research directions include focus on validating DCET through empirical comparisons with observed impacts, and sensitivity analyses. Expanding the tool to incorporate bidirectional traffic flows, seasonal variations, and exchange-rate effects would improve accuracy and applicability. Additionally, developing a user-friendly interface or dashboard for transportation planners and policymakers can be a critical next step to facilitate decision-making. Integrating predictive analytics and scenario modeling could further enhance the tool’s capability to support infrastructure investment decisions and operational strategies at border crossings.

Footnotes

Acknowledgements

The authors thank the project team and funding partners for their support.

Authors’ Note

The authors used generative AI tools (Notion AI and Copilot) only to assist with improving the readability of the language in this paper. All content was reviewed and edited by the authors to ensure technical accuracy and originality.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: R. Aldrete, O. Gurbuz, D. Madrid; data collection: D. Madrid, O. Gurbuz; analysis and interpretation of results: D. Madrid, O. Gurbuz, R. Aldrete; draft manuscript preparation: D. Madrid, E. Vargas, O. Gurbuz. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Center for International Intelligent Transportation Research, a part of the Texas A&M Transportation Institute with a grant number of 185925-00009.

Data Accessibility Statement

Some or all data or models that support the findings of this study are available from the corresponding author on reasonable request.

ORCID iDs

Danielle Madrid

Okan Gurbuz

Erik Vargas

Rafael M. Aldrete

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