Methodology for Monitoring Border Crossing Delays with Connected Vehicle Data: United States and Mexico Land Crossings Case Study
1. Introduction
2. Literature Review
2.1. Land Border Crossing Performance Measurement Technology
2.2. Emerging Connected Vehicle Technology
3. Paper Objective
The objective of this paper is to address the need to provide the monitoring of land border crossing travel time delays. The research problem is then designed to use emerging CV data to formulate novel and easily scalable methodologies for this purpose that can be applied in a systematic manner across all land border crossings. This is important at the individual site level to manage staffing and at the regional and national level to monitor both short-term and long-term trends to most effectively allocate resources in a manner that holistically considers security and economic efficiency. The use of such methodologies is demonstrated using the land border crossings between the US and Mexico as a case study. The paper is organized as follows:
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Border crossings and select locations analyzed during this study (Section 4).
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Connected Vehicle data attributes available for analysis (Section 5).
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Scalable methodology for identifying trips and corresponding travel time (Section 6).
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Results from the analysis and graphics for agencies (Section 7).
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Summary of trips detected by location and direction (Section 7.1).
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Estimating travel time delay individually for identified trips (Section 7.2).
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Comparison of travel delays at border crossings (Section 7.3)
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Ranking the border crossings using four metrics (Section 7.4)
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Metric 3—Top Five border crossings with most trips with delays greater than 5 min (Section 7.4.3).
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Actionable insights from delay trends by Time of Day and Day of Week (Section 7.5).
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4. Study Locations
5. Data Description
Anonymized trajectory data were provided by a third party with information about each of the connected vehicles. A connected vehicle is defined as any vehicle that frequently transmits information to its original equipment manufacturer (OEM). A third party combines and anonymizes such data from multiple OEMs before distribution. Every connected vehicle provides a waypoint with the attribute information at intervals of 3 to 5 s. The attributes of each waypoint comprise an anonymous journey identifier, speed, location, heading and ignition status. The ignition status provides information about the vehicle after it has just been turned on or if the waypoint is mid-journey. A new anonymous journey identifier is generated every time the connected vehicle is turned on.
6. Methodology
where and are the number of origin and destination locations defined for the crossing. represents travel time from origin to destination for journey id j. represents the recorded times at origin for journey id j. represents the recorded times at destination for journey id j. represents the times at the intermediate location for journey id j.
7. Results
7.1. Summary of Trips Detected
7.2. Travel Time Delay Estimation
where is the travel time delay in minutes for trip j, is the detected travel time in minutes for trip j from origin (O) to destination (D) and is the distance in miles between origin (O) and destination (D). The travel time delay is 0 for a trip when the detected travel time is less than the expected travel time at 15 mph.
7.3. Delays at Border Crossings
Time lost waiting in queues constitutes a loss of productivity for millions of passengers. Delays at border crossings were estimated using Equation (2). Almost all trips to the US (97%) experienced delays, whereas only 76% of trips going to MX experienced delays. These delays were further analyzed by location, time of the day, and day of the week to identify potential areas for improvement.
7.4. Border Crossings Rankings
Although there are many ways to rank border crossings that will vary by use case, this paper describes four ranking techniques to illustrate how performance measures extracted from the CV data can be used. These include (1) trip counts, (2) median delay (minutes), (3) delayed trip counts and (4) cumulative delay (vehicle hours).
7.4.1. Trip Counts
7.4.2. Median Delay
7.4.3. Delayed Trip Counts
7.4.4. Total Cumulative Delay
The total delays of 23,738 and 1866 vehicle-hours were incurred by CV passenger cars going to the US and to MX, respectively. Assuming an average vehicle occupancy of two, the value of time lost estimates to more than USD 1 million per day at these border crossings. CA-SYS had the most vehicle-hours of delay for trips to MX and the second most for trips to the US. The top three border crossings with the highest number of total cumulative delays for trips to the US were all in California. For trips to MX, the top border crossing by cumulative delay was in California, and the next five border crossings were all in Texas. This measure provides the breadth of delays and potential possible benefits of improving travel times at a particular location.
7.5. Delay Trends by Time of Day and Day of Week
A systemwide analysis and comparison can help agencies narrow down focus areas to allocate limited resources to potentially maximize benefits and reduce delays. Once the border crossing location of interest is identified, further delay trends by day of the week or time of the day can be assessed for tactical initiatives. Border crossing locations with the highest number of total cumulative delays for both directions of travel are further analyzed.
Some improvement strategies might be to have more lanes open during high volume hours, adopt different shift plans on different days of the week to reduce delays, and improve overall system productivity with the same number of resources and implementing new technologies.
8. Conclusions
This paper focuses on techniques to collect travel time delays at border crossings to inform stakeholders and decision makers. Specifically, scalable methodologies for a systemwide assessment of travel time delays at land border crossings using passenger car CV data were presented in this study. Twenty-six border crossing locations between the US and Mexico were analyzed over a 25-day period in August 2020. Performance measures such as trip counts, median delay, delayed trips and total delay were compared across locations. The median and IQR delay for vehicles travelling from the US to Mexico was 1 and 2 min, respectively. In contrast, the median and IQR delay for vehicles travelling from Mexico to the US was 21 and 46 min. Hourly trends by day of the week revealed windows of opportunities for implementing improvement strategies and for reducing travel delays across borders. The graphics presented during this study can inform agencies of critical areas with high delays that could aid in focusing improvement efforts to yield maximum benefits. These can also provide regular feedback on delay metrics across all land borders for monitoring and/or before and after analysis. The study focuses on travel time delays for passenger cars at borders but can also be applied to trucks. Agencies and researchers worldwide can use granular trajectory-based data like CV data to monitor and analyze travel times and delays across borders or between any two locations of interest.
Some delay at border crossings is necessary to ensure appropriate checks and balances are provided to ensure mutual compliance with the agricultural, business, and government policies of both countries. This paper does not make any recommendations on what an appropriate delay is.
A limitation of this study is the varying penetration of CV data. With newer vehicles coming on roads, the penetration of CV data is expected to increase, providing a higher sample to be evaluated. It is important to note that the key on/off events might skew the sample size as they create new journey identifiers for the same vehicle. However, the authors believe that the occurrence of these events is sufficiently random to still be effective for characterizing delay.
Author Contributions
Conceptualization: D.M.B. and R.S.S.; methodology: R.S.S.; formal analysis: R.S.S.; data curation: R.S.S.; writing—original draft: R.S.S. and J.D.; writing—review and editing: D.M.B., E.D.S.-C., J.D. and R.S.S.; visualization: R.S.S.; supervision: D.M.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article.
Acknowledgments
The connected vehicle data in August 2020 used in this study were provided by Wejo Data Services, Inc. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein and do not necessarily reflect the official views or policies of the sponsoring organizations. These contents do not constitute a standard, specification, or regulation.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 3.
Trips detected at each of the land border crossings.
Figure 3.
Trips detected at each of the land border crossings.
Figure 4.
Travel time delay distribution across all crossings (Blue denotes trips from the US to MX and green denotes trips from MX to the US).
Figure 4.
Travel time delay distribution across all crossings (Blue denotes trips from the US to MX and green denotes trips from MX to the US).
Figure 5.
Summary for trips from MX to the US (MX → US).
Figure 5.
Summary for trips from MX to the US (MX → US).
Figure 6.
Summary for trips from the US to MX (US → MX).
Figure 6.
Summary for trips from the US to MX (US → MX).
Figure 7.
Ranked border crossings by total trip count.
Figure 7.
Ranked border crossings by total trip count.
Figure 8.
Ranked border crossings by average delay for delayed trips.
Figure 8.
Ranked border crossings by average delay for delayed trips.
Figure 9.
Ranked border crossings by total delay for delayed trips.
Figure 9.
Ranked border crossings by total delay for delayed trips.
Figure 10.
Trip counts and delays by hour and day of the week at Otay Mesa (CA-OTM), California, land border (MX → US).
Figure 10.
Trip counts and delays by hour and day of the week at Otay Mesa (CA-OTM), California, land border (MX → US).
Figure 11.
Trip counts and delays by hour and day of the week at San Ysidro (CA-SYS), California, land border (US → MX).
Figure 11.
Trip counts and delays by hour and day of the week at San Ysidro (CA-SYS), California, land border (US → MX).
Table 1.
Summary of land border crossings between US and Mexico analyzed during this study.
Table 1.
Summary of land border crossings between US and Mexico analyzed during this study.
US State | Mexico State | US Code | US Port of Entry | US Road/Highway | Mexico Port of Entry | Mexico Road/Highway | Opened in Year |
---|---|---|---|---|---|---|---|
California (CA) |
Baja California |
SYS | San Ysidro | I-5 | El Chaparral | Fed. 1 | 1906 |
OTM | Otay Mesa | SR 905 | Mesa de Otay | Boulevard Garita de Otay | 1983 | ||
TEC | Tecate | SR 188 | Tecate | Avenida Presidente Lázaro Cárdenas | 1919 | ||
CAL | Calexico West | Cesar Chavez Boulevard | Mexicali | Calzada de los Presidentes | 1902 | ||
IVP | Calexico East | SR 7 | Mexicali | Boulevard Abelardo L. Rodríguez | 1996 | ||
Texas (TX) |
Chihuahua | PDN | El Paso—PDN | El Paso Street | Vial Juan Gabriel | Vial Juan Gabriel | 1898 |
ELP | El Paso—Stanton | US 85 (Stanton St) | Lerdo | Calle Lerdo | 1898 | ||
BOA | El Paso—BOTA | I-110 | Cordova | Fed. 45 (Avd. Abraham Lincoln) | 1967 | ||
YSL | El Paso—Ysleta | Zaragoza Road | Zaragoza | Avenida Zaragoza | 1938 | ||
FAB | Tornillo | FM 1109 | Guadalupe | Fed. 2 | 2014 | ||
FHK | Fort Hancock | FM 1088 | El Porvenir | Praxedis Guerrero | 1936 | ||
PRE | Presidio | US 67 | Ojinaga | Fed. 16 | 1917 | ||
Coahuila | DLR | Del Rio | Loop 239 | Acuña | Francisco Javier Mina | 1919 | |
Nuevo León | LCB | Laredo–Colombia Solidarity | SH 255 | Colombia | Nuevo Leon State Highway Spur 1 | 1991 | |
Tamaulipas | LAR | Laredo Bridge 1 | Convent Ave | Nuevo Laredo | Avenida Guerrero | 1898 | |
LLB | Laredo Juarez/ Lincoln |
I-35 (San Dario Avenue) | Nuevo Laredo | Boulevard Leandro Valle | 1976 | ||
ROM | Roma | Estrella Street | Miguel Aleman | Avenida Venustiano Carranza | 1928 | ||
RIO | Rio Grande City | Pete Díaz Avenue | Camargo | Boulevard Ensenada | 1905 | ||
HID | Hidalgo | US 281 | Reynosa | Luis Echeverria Alvarez | 1905 | ||
PHR | Pharr | South Cage Boulevard | Reynosa | Fed. 40 | 1994 | ||
DNA | Donna | FM 493 | Río Bravo | Carretera Reynosa-Matamoros | 2010 | ||
PGR | Progreso | FM 1015 | Nuevo Progreso | Benito Juarez | 1952 | ||
IND | Los Indios | Cantu Road | Lucio Blanco | Carretera Reynosa-Matamoros | 1992 | ||
BBM | Brownsville—B&M | Mexico Street | Matamoros | Las Americas | 1909 | ||
GTW | Brownsville—Gateway | SH 4 (International Boulevard) | Matamoros | Alvaro Obregon | 1926 | ||
BRO | Brownsville—Veterans | I-69E/US 77/US 83 | Matamoros | Avenida 5 de Mayo | 1999 |
Table 2.
Sample data for OD pairs at CA-SYS crossing with travel time.
Table 2.
Sample data for OD pairs at CA-SYS crossing with travel time.
Date | Origin Time | OD Pair | Travel Time (min) |
---|---|---|---|
3 August 2020 | 19:31:42 | 56.5 | |
22 August 2020 | 14:18:39 | 30 | |
20 August 2020 | 08:45:34 | 14 | |
25 August 2020 | 06:18:12 | 8.95 | |
7 August 2020 | 17:32:16 | 5.35 | |
11 August 2020 | 13:16:51 | 4.1 | |
2 August 2020 | 11:53:18 | 2.27 | |
21 August 2020 | 17:31:12 | 8.87 |
Table 3.
Top five border crossings ranked by trips to the US experiencing travel time delays longer than 5 min.
Table 3.
Top five border crossings ranked by trips to the US experiencing travel time delays longer than 5 min.
Rank | Border Crossing | Trips (MX → US) |
Trips Delay < 2 min |
Trips Delay 2–5 min |
Trips Delay 5–10 min [a] |
Trips Delay 10–30 min [b] |
Trips Delay ≥ 30 min [c] |
Ranking by Trips Delay ≥ 5 min [a + b + c] |
---|---|---|---|---|---|---|---|---|
1 | CA-SYS | 4840 | 143 (3.0%) |
192 (4.0%) |
420 (8.7%) |
2077 (42.9%) |
2008 (41.5%) |
4505 (93.1%) |
2 | CA-OTM | 4978 | 616 (12.4%) |
336 (6.7%) |
488 (9.8%) |
755 (15.2%) |
2783 (55.9%) |
4026 (80.9%) |
3 | CA-CAL | 4289 | 774 (18.0%) |
423 (9.9%) |
410 (9.6%) |
678 (15.8%) |
2004 (46.7%) |
3092 (72.1%) |
4 | TX-BOA | 3093 | 204 (6.6%) |
121 (3.9%) |
212 (6.9%) |
854 (27.6%) |
1702 (55.0%) |
2768 (89.5%) |
5 | TX-HID | 2062 | 177 (8.6%) |
165 (8.0%) |
106 (5.1%) |
364 (17.7%) |
1250 (60.6%) |
1720 (83.4%) |
Table 4.
Top five border crossings ranked by trips to MX experiencing travel time delays longer than 5 min.
Table 4.
Top five border crossings ranked by trips to MX experiencing travel time delays longer than 5 min.
Rank | Border Crossing | Trips (US → MX) |
Trips Delay < 2 min |
Trips Delay 2–5 min |
Trips Delay 5–10 min [a] |
Trips Delay 10–30 min [b] |
Trips Delay ≥ 30 min [c] |
Ranking by Trips Delay ≥ 5 min [a + b + c] |
---|---|---|---|---|---|---|---|---|
1 | TX-LLB | 3583 | 1610 (44.9%) |
1207 (33.7%) |
548 (15.3%) |
200 (5.6%) |
18 (0.5%) |
766 (21.4%) |
2 | TX-BOA | 5510 | 3880 (70.4%) |
895 (16.2%) |
502 (9.1%) |
228 (4.1%) |
5 (0.1%) |
735 (13.3%) |
3 | CA-SYS | 12,773 | 10,712 (83.9%) |
1348 (10.6%) |
478 (3.7%) |
224 (1.8%) |
11 (0.1%) |
713 (5.6%) |
4 | TX-HID | 2603 | 1253 (48.1%) |
848 (32.6%) |
211 (8.1%) |
256 (9.8%) |
35 (1.3%) |
502 (19.3%) |
5 | TX-PRE | 1453 | 535 (36.8%) |
420 (28.9%) |
200 (13.8%) |
233 (16.0%) |
65 (4.5%) |
498 (34.3%) |
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