Figure 1. Distribution of Trip Origins during the Morning Peak Hours.
This paper discusses the procedures of creating ArcInfo GIS databases of household and individual trip log data for the Treasure Coast Travel Characteristics Study recently conducted in south Florida. In this study, both household and individual trip log data were geocoded to the street-address level and point coverages of their locations were created. The derived ArcInfo coverages can show detailed geographic patterns of various travel characteristics using the ArcView 2 software. In addition, statistical analysis of these geographic variations can be performed on the data generated from GIS spatial analysis functions.
This paper discusses the procedures of using the ArcInfo GIS software to create GIS databases of the individual point locations of both household and trip log data collected in the Treasure Coast Travel Characteristics Study. The Treasure Coast Region consists of Indian River, St. Lucie, and Martin Counties located in south Florida. Unlike the continuous urban development pattern found in the Miami/Fort Lauderdale, and the West Palm Beach metropolitan areas, the Treasure Coast Region has five major urban centers (i.e., Sebastian, Vero Beach, Fort Pierce, Port St. Lucie, and Stuart) separated by less developed areas between them. Additionally, three major highways (US-1, I-95, and Florida's Turnpike) provide the key transportation linkages connecting these five urban centers which are linearly distributed within the Region as well as their connections to other major urban centers located north of and south of the Region.
The main objective of this study is to create GIS databases of the Treasure Coast Travel Characteristics Study such that the geographic variations of travel characteristics within the study area can be examined in detail. The next section discusses some important issues related to a travel characteristics survey design and its GIS database creation. It is followed by examples of analyzing the geographic variations of residential trip generation rates with respect to the proximity to urban centers and the proximity to the major highways within the Treasure Coast Region.
Another important consideration for trip log data is some trip ends may be located outside of the study area. If address coverages are unavailable for those areas, these trip ends will be rejected in the address-match process. One alternative to taking care of these rejected records is to group them by their city codes and then match them to the representative point location of each city. Although this alternative approach does not result in the exact locations of those rejected records, it gives a reasonable geographic distribution pattern of their approximate locations.
One major shortcoming of such "TRIP ENDS" databases is that only the trip origin locations are taken into account when the geographic pattern of a particular travel characteristics is displayed or analyzed. This could generate a misleading result for certain travel pattern analyses. It is possible to write an Arc Macro Language (AML) program to GENERATE a line coverage that shows the trip links. This can be accomplished by linking the trip end locations with reference to their "Household ID", "Individual ID", "Trip ID", and "Travel Date" items stored in the database. The derived "trip links" coverage gives a better representation of the geographic distribution of individual trips, but it could also result in a cluttered graphic display.
This limitation becomes even more obvious when we would like to analyze the trip-chaining behavior using the trip log data. A trip chain is defined as a connected sequence of trips that has the origin and the destination of the chain located at the same point in space (Meyer and Miller, 1984). One example of a multiple-stop trip chain could be a home-work-shop-home trip sequence. If the trip ends are geocoded by TAZ, trips made within the same zone are represented by the same centroid location. Therefore, it becomes difficult to analyze the trip-chaining behavior within a GIS environment. This defeats the purpose of using a GIS to create a trip log database for spatial analysis.
On the other hand, an ArcInfo point coverage created from the address-match function overcomes the above shortcomings. For example, simple queries based on the "trip start time" data item in the "TRIP ENDS" point coverage can easily show the trip origin locations for both the morning peak hours as well as the afternoon peak hours (see Figures 1 and 2, respectively). Additionally, GIS analysis functions, such as spatial search or map overlay, can be performed on this point coverage to identify individual trip locations for further geographic analysis.
Figure 1. Distribution of Trip Origins during the Morning Peak Hours.
Figure 2. Distribution of Trip Origins during the Afternoon Peak Hours.
The method of geocoding travel survey data down to the street address level also encounters a major challenge. Due to the requirement of recording the specific street address of each trip end, it is more likely to receive incomplete or incorrect locational data from the survey. Compounded with a less-than- perfect address coverage, the percentage of successful matches may be lower than the desired level from an automated address-match run. This address-matching rate can normally be improved if an interactive method of processing the rejected records is implemented. However, such an interactive processing method could mean a significant increase of database creation time.
Based on the above discussions, it is clear that the traditional method of geocoding travel characteristics data by traffic analysis zones is incompatible with the ArcInfo GIS database structure and cannot take full advantages of GIS spatial analysis capabilities. Although the creation of GIS databases for travel characteristics data using the address-match function also experiences some shortcomings, it shows significant improvements over the traditional method in data retrieval, data analysis, and data display within a GIS environment.
In addition to the "HOUSEHOLDS" and the "TRIP ENDS" point coverages created from the data collected in the Treasure Coast Travel Characteristics Study, two more ArcInfo coverages were created for analysis purposes. The first is a polygon coverage consisting of the city limits of the five major urban centers in the Treasure Coast region (Figure 3). The second is a line coverage including the three major highways (US-1, I-95, and Florida's Turnpike) located within the Region (Figure 4). In order to derive the trip generate rate of each household, the FREQUENCY command was used on the "Household ID" and the "Individual ID" in the "TRIP ENDS" coverage to derive the total number of trips made on a selected date by each individual. Since the "TRIP ENDS" coverage geocoded the origins of all trips plus the destination of the last trip made by each individual, the numbers derived from the FREQUENCY command represent one extra trip for each individual. Therefore, these numbers need to be adjusted by subtracting the total number of individuals in each household in order to derive the correct total number of trips made by all individuals in a household. Once the trip generate rates of each household are written into an Info table, this table is joined with (or related to) the PAT of the "HOUSEHOLDS" coverage using the "Household ID" as the relate item.
Figure 3. Distribution of the Major Urban Centers within the Study Area.
Figure 4. Distribution of Major Highways within the Study Area.
For a preliminary evaluation of the impact of geographic locations on the trip generation rates, one-mile interval was used to create buffer zones around the city limits and around the major highways. These buffer zones are then overlaid with the "HOUSEHOLDS" point coverage to assign a buffer zone ID to each household location. The data in the point attribute table of the "HOUSEHOLDS" coverage were then written out to an ASCII file for performing "difference-in-the-means" tests, using the SPSS statistical package, on the average trip generation rates between each pair of buffer zones. The test results provided some useful information. First of all, due to the geographic distribution of the sample households, the one-mile interval created some zones with zero or very few observations. Secondly, the one-mile interval appeared to be too small to reflect geographic variations between some zones. Lastly, the "t" statistic for different zonal pairs provided a good basis to estimate the best buffer intervals of dividing up the study area.
Based on the results derived from the preliminary tests, it was decided to divide the study area into two buffer zones for further evaluations of the impact of proximity to urban centers and the impact of proximity to major highways on the trip generation rates. For the "URBAN CENTERS" coverage, it was divided into a zone of within-four-miles from the city limits and another zone of beyond-four-miles from the city limits. With regard to the "MAJOR HIGHWAYS" coverage, the Florida's Turnpike was dropped from further analysis due to its proximity to limited number of household locations. In addition, the US-1 and the I-95 were combined, due to their close proximity to each other, to generate a less-than-or- equal-to 2 miles zone and a greater-than 2 miles zone. These new buffer zones were again overlaid with the "HOUSEHOLDS" point coverage and two separate ASCII files were created for statistical analyses (Figures 5 and 6).
Figure 5. Distribution of Households in the Buffer Zones around Major Urban Centers.
Figure 6. Distribution of Households in the Buffer Zones around US-1 and I-95.
The results of the "difference-in-the-means" tests for the two files are shown in Tables 1 and 2, respectively. Both cases indicate that the average trip generation rates of the households in the two geographic zones are statistically different at a 95% confidence level. In other words, geographic location appears to play a significant role in trip generation rates in terms of both their proximity to major urban centers and their proximity to the major highways.
Table 1. Results of the "Difference-in-the-Means" Test for
Buffer Zones around the Major Urban Centers.
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| Mean Trip Rate | t value | Sig. Level
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Within 4 miles | 8.244 | 2.80 | 0.007
Beyond 4 miles | 5.750 | |
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Table 2. Results of the "Difference-in-the-Means" Test for
Buffer Zones around the US-1 and I-95.
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| Mean Trip Rate | t value | Sig. Level
-------------------------------------------------------------
Within 2 miles | 8.417 | 2.00 | 0.048
Beyond 2 miles | 6.562 | |
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In order to further examine the relationships between the
geographic locations and the classification variables used in the
trip generation stage of the FSUTMS, same test procedures were
applied to the trip generation rates of households classified by
the three variables (dwelling types, household size, and auto
ownership) within each of the buffer zones created above. The
analysis results indicate that single-family households have
statistically different average trip generation rates between the
two zones around the major urban centers (Table 3) and around the
major highways (Table 4). Additionally, households with 2 or more
vehicles also show statistically different trip rates between the
two zones around the major highways (Table 5). Test results of all
other classes do not show statistically different trip rates
between the two geographic zones around either major urban centers
or the major highways.
Table 3. Results of the "Difference-in-the-Means" Test for
Single-Family Households in the Buffer Zones around
the Major Urban Centers.
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| Mean Trip Rate | t value | Sig. Level
-------------------------------------------------------------
Within 4 miles | 8.727 | 3.12 | 0.003
Beyond 4 miles | 5.815 | |
-------------------------------------------------------------
Table 4. Results of the "Difference-in-the-Means" Test for
Single-Family Households in the Buffer Zones around
the US-1 and I-95.
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| Mean Trip Rate | t value | Sig. Level
-------------------------------------------------------------
Within 2 miles | 8.868 | 2.94 | 0.004
Beyond 2 miles | 6.342 | |
-------------------------------------------------------------
Table 5. Results of the "Difference-in-the-Means" Test for
Households with 2 or more vehicles in the Buffer
Zones around the US-1 and I-95.
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| Mean Trip Rate | t value | Sig. Level
-------------------------------------------------------------
Within 2 miles | 9.333 | 2.50 | 0.014
Beyond 2 miles | 6.917 | |
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It is important to note that geographic information systems have presented a great challenge to the users community to re-think how we have been doing our works traditionally and what improvements could be achieved through the use of GIS. Hopefully, taking on this challenge will lead us to not only better ways to do our works but also further development of GIS capabilities.
Florida Department of Transportation (FDOT) District IV Office. 1995. Survey Design and methodology of Treasure Coast Travel Characteristics Study, prepared by Walter H. Keller, Inc., Coral Springs, FL.
Kanafani, A. 1983. Transportation Demand Analysis. New York, NY: McGraw-Hill Book Company.
Meyer, M.D. and Miller, E.J. 1984. Urban Transportation Planning: A Decision-Oriented Approach. New York, NY: McGraw-Hill Book Company.