|Planning||FHWA > HEP > Planning > Border > Resources > Studies|
|U.S./Mexico Joint Working Committee on Transportation Planning|
A Report to the Arizona Department of Transportation
Forecast and Capacity Planning for Nogales' Ports of Entry
Chapter 5 - Baseline Analysis of Current Conditions
A preliminary phase of this study was to assess the existing conditions of each of the ports of entry in the Nogales area. This baseline analysis consisted of several processes which are listed below:
The two international points of entry (POEs) connecting the cities of Nogales, Arizona with Nogales, Sonora in Mexico are vital for the economy of these two cities as well as the surrounding region. These two POEs, the Mariposa POE and the DeConcini POE, (see M and D in Figure 5-1) are also extremely important for trade between the United States and Mexico. For instance, one of the main economic drivers of Santa Cruz County and Nogales is the fresh produce industry which relies heavily on these POEs as they are the principal import points for winter fresh vegetables from Mexico to the United States.
Additionally, Nogales, Sonora is one of the Mexican border cities with a high level of industrial (maquiladoras) development. Consequently, the increased presence of American (and foreign in general) companies on the Mexican side of the border generates the need for daily transportation of materials across international boundaries. The shipping of goods proves to be a challenging task for the Logistics and Traffic departments of these businesses because the greater the congestion at the POEs in Nogales, the less competitive these companies become and alternatives such as moving to locations with more efficient POEs may then be considered.
In our analysis of current conditions, we assessed the existing conditions of the Commercial Traffic, POVs (Privately Owned Vehicles) and pedestrian traffic crossing the POEs in Nogales. First, we provide an overview of the historical data and then proceed to a more specific assessment of the commercial traffic which showed a cyclic pattern that, to the best of our knowledge, had not been addressed by any previous study. Next, we analyzed the traffic split between the Mariposa and DeConcini POEs. Last, we provided a brief description of the conclusions we drew from our research of relevant previous studies.
5.2 Historical Data
There are three principal modes of traffic which we explored: commercial traffic (trucks), POV, and pedestrians. In the original scope of the project, we had not planned on taking into account rail freight or bus traffic since they account for a very small percentage of traffic crossings. However it was later determined that they should also be considered. The data for these two modes of traffic is also presented in this section.
The historical monthly data (from January, 1994 - current) used was gathered from the Bureau of Transportation Statistics website (BTS). The daily truck crossing data of 2008 and the data regarding the traffic split between different POEs was obtained from US Customers and Border Protection Tucson Field Office (Donahue 2009).
The historical monthly crossing data for the commercial traffic, POVs and pedestrians are depicted in Figure 5-2. Note the vertical line marks the date of the event "9/11". We believe that the event "9/11" brought significant changes to border crossing traffic. Note that we plotted the number of Privately Owned Vehicles (POVs) crossing the border but not the number of persons crossing the border by POV because the POV crossings are processed vehicle by vehicle. However, the change in the number of POVs should be highly correlated with the number of persons crossing the border by POV.
As Figure 5-2 indicates, the truck data has very strong cyclic properties and subsequent statistical analysis quantified this behavior. As noted above, both POV and pedestrian traffic showed significant changes right after "9/11", while truck and bus crossings appear to be relatively unchanged. The correlation between these different modes of traffic is displayed in Table 5-1. Three approaches were used to calculate correlations: 1) using the entire range of data, 2) including only the data before "9/11" (until 2009/08); 3) including only the data after "9/11". From both the graph and our correlation data we can see that after "9/11" the changes in POV traffic and pedestrian traffic are negatively correlated with each other.
Table 5-1 Correlation between different traffic modes
From the first four rows of Table 5-1 we can also tell that out of any two modes of traffic the strongest correlation was between Pedestrian and Bus traffic followed by the correlation between POV and Pedestrian traffic. By separating the data into "before and after 9/11", we observed that the POV and pedestrian traffic had little correlation "before 9/11"; however, they showed a strong negative correlation "after 9/11". Also, we observed that the POV and bus traffic had strong positive correlation beforehand, but they showed a strong negative correlation after "9/11". The pedestrian and bus traffic are positively correlated. However, by separating the data, we can tell that this correlation mainly happened after "9/11". Thus, It appears that the preference for personal border crossing shifted from vehicle to foot and bus after 9/11.
Among the four modes of traffic, the pedestrian traffic contained the most variation, and the commercial vehicle traffic was the most stable. Note that for 2008, the pedestrian data exhibited a significant drop while the other two modes remained relatively stable. This could be interpreted as the pedestrian traffic being more sensitive to changes in the economic climate, considering the current recession.
Table 5-2 below lists the yearly number of crossings for each type of traffic. One interesting fact is that the number of POV crossings has been decreasing since 2001, and that 2007 and 2008 were both lower than POV crossings in 1995. In contrast, truck and pedestrian crossings have trended upward since 2001, with the exception of a decrease in pedestrian crossings in 2008.
Table 5-2 Yearly number of crossing of each mode
Figure 5-3 shows the historical data of the bus crossings and the number of passengers crossing by bus. The number of crossings by bus started to increase in the middle of 1997, and had a sharp jump in 1999. After that it was relatively stable with a slight decreasing trend until 2005. In 2005, there was another significant increase which lasted until 2007, when the number of bus crossings once again stabilized. Similarly, the number of passengers crossing by bus started to increase at the end of 1997 and stabilized during the year 2000. After that, the number of bus passengers remained relatively stable with a slight increasing trend. The only exception occurred during 2003, when an abnormally steep spike occurred. The bottom panel of Figure 5-3 is the average number of passengers per bus, which shows that the average number of passengers per bus started to decrease in 2005, although the downward trend is slight.
The number of bus passengers is much smaller than the number of passengers crossing by other modes. We found that although the number of bus passengers has increased very quickly during the last few years, it still only comprises a small fraction of the total number of passenger crossings. In 2008 for example, the average number of monthly pedestrian crossings was 547,351, the average monthly POV vehicles was 706,023, but the monthly bus passengers was 16,312, which was roughly 2.9% of the pedestrian crossings and 2.3% of the POV vehicles.
Figure 5-4 shows the number of trains crossing the border from January 1995 to December 2008. We did not have reported train crossings for February 1995 and April 1995. We used the average of the preceding and the following month to represent these missing values. Before 2000, the number of trains was relatively stable with a slight increasing trend. In the middle of 2000, there was a large spike and after this occurrence the number of trains followed a decreasing trend which continued until early 2005. 2005 saw another sudden increase, and since then train crossings have been relatively stable. Note that train crossings are partly dependent on the number of schedules Union Pacific chooses to run, and that the actual amount of freight crossing the border depends on the length and consists of the trains Union Pacific chooses to run.
5.3 Traffic split between the Nogales POEs
Commercial vehicles cross only at the Mariposa POV; therefore we did not have any data for the traffic split of the trucks. However, POV, pedestrians and bus crossings occurred at both of the POEs. We had a limited amount of data, starting from October 2004, for the traffic recorded by mode and by POE. Figure 5-5, Figure 5-6 and Figure 5-7 depict the split of the POV traffic, pedestrian traffic and bus traffic (number of buses) respectively. From Figure 5-5 we observe that the POV traffic has a ratio of roughly 60:40 (DeConcini: Mariposa) from 2004 to 2007, and then 70:30 (DeConcini: Mariposa) from late 2007 onward. Figure 5-6 shows that the majority of pedestrian traffic passes through the DeConcini POE, and this split has been relatively stable throughout the years. Figure 5-7 shows that the bus traffic has a ratio of roughly 25:75 (DEconcini: Mariposa) all over the years, except from April 2007 to September 2007. For the recent months (including the whole year of 2008), the ratio tended to be quite stable. We believe there are several causes for this stable trend in pedestrian traffic:
Figure 5-8 depicts the total number of persons crossing (POV passenger, bus passengers and pedestrian) crossing at both POEs in Nogales by month, where the red straight line is a fitted trend line The change in total number of persons crossings the border from 1995 to 2008 was relatively small however the fluctuations from month to month were at times very significant. The greatest change occurred between the months of July and September 2004 with a decrease of about 1.7 million crossings, which was preceded by a very large increase. We do not have any concrete explanation for these fluctuations however we hypothesize that it may have to do with changes in the measurement process.
5.4 Mariposa POE Site visitOur visit to the Mariposa POE was conducted on Tuesday May 25, 2009. The main purpose of this visit was to measure the time for a commercial vehicle (mainly referring to trucks) to cross the border. To gather our measurements we had 4 observation points, which were Weigh-in-Motion (WIM), SBs (Super Booths=primary inspection), ADOT inspection and the exit to the highway. The four observation points are marked in Figure 5-9.
We recorded the plate number of the vehicles passing every observation point and the time of passing. We also wrote a brief description of the vehicles in case we misread the plate or recorded different license plate numbers since it was very common for the border crossing vehicles to have multiple license plate numbers. We started our observations at 10:30 am and finished at 4:30 pm. These observations were only taken for commercial vehicles, since we were not granted access to observe other types of crossings. Also due to clearance issues, we were only able to gain a general idea of the amount of time spent at each location: WIM, SB, and ADOT (i.e. the time we recorded is a combination of the waiting time and processing time at these locations).
We observed approximately 600 trucks during the six hour time period. We summarize the time of passing of each observation point in Table 5-3. The histogram of number of trucks by hourly interval is provided in Figure 5-10. The bar marked as "14:17:27" is shorter, since the border was closed for half an hour during that time slot.
Table 5-2 Yearly number of crossing of each mode
After suitable processing we used this data to build a simulation model to assess the current capacity of the Mariposa POE.
5.5 Summary of Baseline Analysis
Our literature review also provided some useful insights for our model building. Economic indices like the Index of Industrial Production and the exchange rate were used in many previous studies. This motivated us to incorporate some of these indices in our model. Some preliminary analysis was also conducted to gain a more thorough understanding of the traffic characteristics. Through this process we identified the different modes of traffic to study and examined the historical data for each mode of traffic as well as the traffic split among the ports of entry. We noticed that the cyclic pattern shown in the truck crossing data was not addressed in other related work, although it has long been "taken for granted" in the Nogales import community. Valuable information was also obtained for our later capacity assessment simulation work through our visit to the Mariposa POE.