Table of Links

Abstract and 1 Introduction.

Methodology

Results

Concluding comments, Acknowledgement and References

This paper illustrates the estimation of the nationwide ZIP-to-ZIP drive time matrix in the U.S. The drive times derived by the Google Maps API on randomly-sampled OD pairs serve two purposes: facilitating empirical models to further improve the preliminary estimates based on road networks or simply geodesic distances, and validating our design of the methods of varying computational complexity and differential sampling intensity. As trip lengths increase, the approach requires less data preparation and uses less computational power without much compromising the quality of results.

Our own motivation for undertaking this endeavor is to facilitate a study that examines a national health care market structure. We hope that the derived matrix becomes an important resource for researchers who may need it in spatial analysis of a national scope or a large region. For instance, a recent study on measuring and improving accessibility to public libraries in the U.S. (Donnelly, 2015) could benefit from a more accurate measure of drive time from us. In addition, the estimated coefficients and other parameters from the regression models in both Algorithm 2 and Algorithm 3 can be used as a reference in other studies when such information at the national scale is not available. For studies being performed in other geographic scales, such as census tract, or other geographic areas, the derived parameters can be also referenced as a baseline. The proposed research method (or framework) is also useful for one to imitate in a different country (region) of a similar scale. The method has been wrapped into a convenient ArcGIS tool with a user interface, where researchers can easily select input data and make changes to key parameters, such as the constant travel speed, the predefined three hierarchical levels, and the number of requests sent to Google Maps API, to make the tool work for their own data. We will provide both the tool and the matrix for free download.

Several limitations of this study merit discussion. First, this research considers driving as the only transportation mode. The omission of other modes could be problematic especially for studies focusing on other trip purposes or in other areas where public transit service coverage is high. For example, the General Transit Feed Specification (GTFS) data can be integrated into the road network for calculating drive time by transit. In addition, potential users of the derived matrices are suggested to proceed with caution when using travel times of some medium- or long-range trips, such as from Alaska to the contiguous U.S., if they favor more accurate estimates down to minutes. Some of these trips are likely to be made by other modes such as air or train, which are not accounted for by the proposed approach. Another related issue is that the use of ferry is permitted in Algorithm 2 by default, which yields much shorter travel between areas separated by water, e.g., between Michigan and Wisconsin, or with island barriers in coastal areas, than otherwise. If it is desirable to avoid the use of ferry, one can simply specify one parameter (avoid=“ferries”) in Algorithm 2, according to the Google Maps API. In any case, the estimated drive times are a good proxy for travel impedance.

Secondly, more work is needed to improve the baseline estimation on the current division of three hierarchical levels in Algorithm 1. Instead of using people’s perceptions, one may design a simulation procedure that examines the national road network and identifies at what distances it would be most appropriate to simplify the road network. Other types of times warrant consideration for more reasonable estimates, especially for long-range trips in Level 3, such as stopping time for bathroom breaks, gas refill, or sleep. Another issue may arise from the current selection of travel speeds, such as 50 mph in Algorithm 1 and 25 mph in Algorithm 3, in estimating drive times. These values may overestimate drive times of distant zip code pairs in Algorithm 1 or large zip code zones in Algorithm 3 in which case highways and interstates with higher speed limits are more likely to be utilized. Similarly, the selection of an appropriate distance threshold to snap locations onto a road network may depend heavily on the geography being studied. More experiments are needed to determine the most appropriate values. In addition, as discussed in Shi (2007), the Monte Carlo randomization in Algorithm 3 would benefit from a process that considers population distribution, such as the block-level population data, rather than the zip code zone itself. Such a finer geographic resolution would demand additional computation, however. Another potential improvement to Algorithm 3 could be the consideration of the number of road segments or the total length of road segments within ZIP code areas besides perimeter and area.

hree sources of uncertainty are relevant in this study: (1) the three defined hierarchical levels, (2) the centroid-based representation of a zip code zone, and (3) the random sampling of zip code pairs in Algorithm 2. For example, a possible solution to address the random sampling issue might be to consider different geographies and population sizes (Delmelle et al., 2019). Furthermore, other road network data sources such as the OpenStreetMap (OSM) could be employed, especially for regions or countries that do not have access to high-quality road network data. Finally, it is worthwhile to make the proposed method available in a non-ArcGIS environment since ArcGIS is not free to the public, especially for researchers in other counties.

Acknowledgement

Financial support from the National Cancer Institute (NCI), National Institutes of Health, under Grant R21CA212687, is gratefully acknowledged. Points of view or opinions in this article are those of the authors, and do not necessarily represent the official position or policies of NCI. Hu also would like to acknowledge the support by the Ralph E. Powe Junior Faculty Enhancement Awards from the ORAU (Oak Ridge Associated Universities). Comments from 3 anonymous reviewers helped us prepare a much improved final version of the paper

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