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A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Conclusion, Acknowledgement & References

Authors:
(1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611;
(2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803;
(3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005;
(4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Methodology Results Concluding comments, Acknowledgement and References Results Based on the 2010 block population data, population weighted centroids for the 32,840 ZIP codes in the U.S are created. Together with the two network datasets of different road levels, they are fed into Algorithm 1. This process results in a massive OD cost matrix of 1,078,432,760 records, each of which consists of estimation of drive time and distance for a ZIP code pair. It takes about 76 hours for Algorithm 1 to compute and export the national ZIP-to-ZIP time matrix, and the breakdowns are: Level 1 consumes 6 hours, Level 2 60 hours, and Level 3 10 hours. Algorithm 2 is implemented on a randomly-sampled subset of OD pairs derived from Algorithm 1. Given the free usage constraints discussed previously, Algorithm 2 is run to collect Google drive times over a period of four months. It finally gathers 124,350 (nsample) valid ZIP-to-ZIP trips, which are associated with 32,478 unique ZIP codes (about 99% of the national 32,840 ZIP codes). Each trip includes Google’s estimates of drive time and distance. Table 3 lists the frequency distribution of OD trips derived from Algorithms 1 and 2. The sampling intensity for the short-range trips (Level 1) is about triple those medium-range trips (Level 2) and quadruple those long-range trips (Level 3). Oversampling shorter trips is to enhance their representation and ensure the quality of subsequent interpolation for greater interests in acquiring shorter-range travel times. Figure 3 shows sampled ZIP code pairs of Level 1. Figures 4A, 4C, and 4E (and 4B, 4D, 4F) plot drive times (distances) estimated by Algorithm 1 against times (distances) by Algorithm 2 at Level 1, 2 and 3, respectively. Only the data points within 24 hours (1,200 miles, or 1,931 km) for Level 3 are shown. Clearly, the pattern is largely consistent between the two algorithms for ZIP code pairs across the three levels. A few observations merit discussion. It is obvious in Figures 4A and 4C that Algorithm 1 tends to underestimate travel times. One likely cause for this trend is the omission of congestion in Algorithm 1. Interestingly, such a trend of downward estimates is also observed in distance measurements in Figures 4B and 4F. This is because the distances returned by Algorithm 2 are simply the lengths in mileage of those quickest routes in terms of drive times, which are commonly through highways. Therefore, distances measured by Algorithm 1—the “shortest” routes in terms of mileage—are consistently lower than the values estimated by Algorithm 2. The exact fitting power of each needs to be examined by regression. Corresponding to each of the three levels in Algorithm 1, a regression is run to infer the relationship between Google drive times by Algorithm 2 (dependent variable) and Algorithm 1 drive times (independent variable). Results are summarized in Table 4. It shows that Algorithm 1 times explain the variation in the matched Google times by 91 percent at Level 1, 93 percent at Level 2, and 96 percent at Level 3. Table 4 also reports the results for regression models on distance measures from the two algorithms, with the R2 = 0.95, 0.93, and 0.99 for Levels 1, 2 and 3, respectively. The higher fitting powers by the travel distance models than the drive time ones are due to the uncertainty in traffic congestion effect on drive time measurement. As travel distances are not used as often as drive times in measuring spatial impedance, our discussion focused on drive time Note: Ta1 represents Algorithm 1 derived drive times and distances, and N denotes the number of observations. ***Significant at the 0.001 level. area Originally designed by the United States Postal Service (USPS), ZIP code is a fivedigit number corresponding to address points in the U.S., and such data have never been released by the USPS for public access. It is, therefore, not an areal unit where a ZIP code has a specified physical boundary. For the sake of spatial analysis, many entities have attempted to delineate ZIP code areas, such as the ZCTAs by the U.S. Census (Khan et al., 2019). Our recent experience in using this drive time matrix indicates that some USPS ZIP codes are not included in the ZCTAs data. Most of those missing ZIP codes have no associated area such as post office box ZIP codes and single site ZIP codes (government, building, or large volume customer). One may use the following algorithm to interpolate the drive times on corresponding missing OD pairs: For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. The above proposition assumes that the location of a missing ZIP code can be approximated by the average of its three nearest ZIP codes. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Authors: Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Abstract and 1 Introduction. Methodology Methodology Results Results Concluding comments, Acknowledgement and References Concluding comments, Acknowledgement and References Results Based on the 2010 block population data, population weighted centroids for the 32,840 ZIP codes in the U.S are created. Together with the two network datasets of different road levels, they are fed into Algorithm 1. This process results in a massive OD cost matrix of 1,078,432,760 records, each of which consists of estimation of drive time and distance for a ZIP code pair. It takes about 76 hours for Algorithm 1 to compute and export the national ZIP-to-ZIP time matrix, and the breakdowns are: Level 1 consumes 6 hours, Level 2 60 hours, and Level 3 10 hours. Algorithm 2 is implemented on a randomly-sampled subset of OD pairs derived from Algorithm 1. Given the free usage constraints discussed previously, Algorithm 2 is run to collect Google drive times over a period of four months. It finally gathers 124,350 (nsample) valid ZIP-to-ZIP trips, which are associated with 32,478 unique ZIP codes (about 99% of the national 32,840 ZIP codes). Each trip includes Google’s estimates of drive time and distance. Table 3 lists the frequency distribution of OD trips derived from Algorithms 1 and 2. The sampling intensity for the short-range trips (Level 1) is about triple those medium-range trips (Level 2) and quadruple those long-range trips (Level 3). Oversampling shorter trips is to enhance their representation and ensure the quality of subsequent interpolation for greater interests in acquiring shorter-range travel times. Figure 3 shows sampled ZIP code pairs of Level 1. Figures 4A, 4C, and 4E (and 4B, 4D, 4F) plot drive times (distances) estimated by Algorithm 1 against times (distances) by Algorithm 2 at Level 1, 2 and 3, respectively. Only the data points within 24 hours (1,200 miles, or 1,931 km) for Level 3 are shown. Clearly, the pattern is largely consistent between the two algorithms for ZIP code pairs across the three levels. A few observations merit discussion. It is obvious in Figures 4A and 4C that Algorithm 1 tends to underestimate travel times. One likely cause for this trend is the omission of congestion in Algorithm 1. Interestingly, such a trend of downward estimates is also observed in distance measurements in Figures 4B and 4F. This is because the distances returned by Algorithm 2 are simply the lengths in mileage of those quickest routes in terms of drive times, which are commonly through highways. Therefore, distances measured by Algorithm 1—the “shortest” routes in terms of mileage—are consistently lower than the values estimated by Algorithm 2. The exact fitting power of each needs to be examined by regression. Corresponding to each of the three levels in Algorithm 1, a regression is run to infer the relationship between Google drive times by Algorithm 2 (dependent variable) and Algorithm 1 drive times (independent variable). Results are summarized in Table 4. It shows that Algorithm 1 times explain the variation in the matched Google times by 91 percent at Level 1, 93 percent at Level 2, and 96 percent at Level 3. Table 4 also reports the results for regression models on distance measures from the two algorithms, with the R2 = 0.95, 0.93, and 0.99 for Levels 1, 2 and 3, respectively. The higher fitting powers by the travel distance models than the drive time ones are due to the uncertainty in traffic congestion effect on drive time measurement. As travel distances are not used as often as drive times in measuring spatial impedance, our discussion focused on drive time Note: Ta1 represents Algorithm 1 derived drive times and distances, and N denotes the number of observations. ***Significant at the 0.001 level. area Originally designed by the United States Postal Service (USPS), ZIP code is a fivedigit number corresponding to address points in the U.S., and such data have never been released by the USPS for public access. It is, therefore, not an areal unit where a ZIP code has a specified physical boundary. For the sake of spatial analysis, many entities have attempted to delineate ZIP code areas, such as the ZCTAs by the U.S. Census (Khan et al., 2019). Our recent experience in using this drive time matrix indicates that some USPS ZIP codes are not included in the ZCTAs data. Most of those missing ZIP codes have no associated area such as post office box ZIP codes and single site ZIP codes (government, building, or large volume customer). One may use the following algorithm to interpolate the drive times on corresponding missing OD pairs: For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between a missing ZIP code (say, origin) and a known ZIP code (say, destination): identify the 3 nearest known ZIP codes from the missing ZIP code, locate the 3 drive times between each of the 3 nearby known ZIP codes (origin) and the known ZIP (destination) from the provided matrix, and use their average drive time as the one between the missing ZIP code and the known ZIP code. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. For drive time between two missing ZIP codes: identify the 3 nearest known ZIP codes from the origin ZIP code and also the 3 nearest known ZIP codes from the destination ZIP code, locate the corresponding 9 drive times between each of the 3 nearby origins and each of the 3 nearby destinations from the provided OD cost matrix, and use their average drive time as the one between the two missing ZIP codes. The above proposition assumes that the location of a missing ZIP code can be approximated by the average of its three nearest ZIP codes. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Results

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Methodology

Authors:
(1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611;
(2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803;
(3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005;
(4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Methodology Results Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770 Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Authors: Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Abstract and 1 Introduction. Methodology Methodology Results Results Concluding comments, Acknowledgement and References Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Abstract and Introduction

The direction of federal vapor product regulation in the United States hangs in the balance following leadership changes within the U.S. Department of Health and Human Services. While many vapers and industry advocates welcome the departure of certain vocal anti-vaping officials, a sense of uncertainty prevails. Meaningful policy reform is still needed to translate President Trump’s renewed commitment to "save flavored vaping" into tangible reality for millions of American adults who rely on these products. The current regulatory framework, critics argue, remains deeply flawed, hindering access to potentially life-saving alternatives to combustible cigarettes. leadership changes save flavored vaping Vaping's Emergence Coincides with Record Low Smoking Rates Despite being commercially available for nearly two decades, electronic cigarettes and nicotine vapor products face ongoing condemnation from influential public health groups, numerous policymakers, and a significant portion of the public. This persistent opposition exists even as their market presence has directly paralleled a dramatic, historic reduction in cigarette smoking among American adults. This shift marks a stark departure from the mid-century "Mad Men" era where tobacco advertising reigned supreme. Underscoring this trend, the U.S. Centers for Disease Control and Prevention (CDC) highlighted in a March 2025 Morbidity and Mortality Weekly Report that “adult cigarette smoking prevalence remains at its lowest level in 60 years.” While some conventional tobacco control measures, like targeted taxes or public use restrictions, likely contributed, proponents of harm reduction argue that the introduction and adoption of innovative vaping technology correlates with the most significant declines in deadly combustible cigarette use. They posit that offering smokers less harmful ways to consume nicotine is a powerful public health tool. a March 2025 Morbidity and Mortality Weekly Report A History Rooted in Harm Reduction, Not Big Tobacco Challenging popular anti-vaping rhetoric, the modern e-cigarette was not a creation of Big Tobacco aimed at addicting a new generation. Its origins trace back to Hon Lik, a Chinese pharmacist seeking a less harmful alternative to smoking, effectively inventing it as a smoking cessation device. Since entering the U.S. market around 2007, e-cigarettes have undergone significant evolution driven largely by independent companies responding to consumer needs. Initial "cigalikes" mimicking traditional cigarettes gave way to more powerful, customizable "open system" devices, followed by convenient "pod mods," and most recently, the ubiquitous disposable vapes. It wasn't until 2012, a full five years after vaping's US debut, that a major tobacco company made its first significant move by acquiring the blu brand. The landscape truly shifted when the FDA asserted regulatory authority over e-cigarettes as tobacco products, a move upheld by courts, leading to the 2016 "deeming rule." Only then did Big Tobacco begin introducing its own vaping products, navigating a complex and often ambiguous regulatory pathway. Hon Lik acquiring the blu brand deeming rule The PMTA Gauntlet: Stifling Innovation, Favoring Giants Before this federal oversight intensified, American vapor manufacturers operated with agility, quickly adapting products based on user feedback and technological advancements. The 2016 deeming rule, however, implemented the burdensome Premarket Tobacco Product Application (PMTA) process. This regulation mandated that all vaping products introduced after February 15, 2007, undergo rigorous, expensive FDA review and receive explicit marketing authorization before being legally sold. Critics describe the PMTA process as a nearly insurmountable barrier for the small and medium-sized independent businesses that pioneered the industry. The extensive data requirements and high costs associated with submitting a PMTA created a regulatory bottleneck that effectively stifled innovation and favored companies with deep pockets – namely, Big Tobacco. PMTA all before The results are stark: As of April 1, 2025, the FDA had authorized only 34 vapor products for sale nationwide. Tellingly, all these authorized products are owned by just three major tobacco corporations, entities that continue to derive substantial profits from selling the very combustible cigarettes vaping aims to replace. Furthermore, the agency continues to authorize far more new combustible cigarette products than innovative, potentially harm-reducing vapor alternatives. FDA had authorized only 34 vapor products The Tobacco Control Act: The Underlying Hurdle? While changes in FDA leadership or enforcement priorities might offer temporary shifts, many argue the fundamental problem lies within the 2009 Family Smoking Prevention and Tobacco Control Act (TCA). This legislation, championed by Senator Dick Durbin (D-Ill.)—a long-standing opponent of both traditional tobacco and vaping products—imposes severe limitations on adult access to reduced-risk nicotine alternatives. Any product launched after the 2007 predicate date faces the daunting PMTA pathway. In the decade-plus since its implementation, a mere 76 novel tobacco/nicotine products (including those 34 vape products) have successfully navigated this process, compared to the thousands of combustible cigarette products grandfathered in or approved through less stringent pathways. 2009 Family Smoking Prevention and Tobacco Control Act TCA Global Harm Reduction Acceptance vs. US Hesitancy During President Trump’s first term, FDA leadership expressed some recognition of tobacco harm reduction's potential. However, under the subsequent Biden administration, this forward momentum appeared to stall, replaced by a more prohibitive stance. This contrasts sharply with public health approaches in other developed nations. Health bodies in Canada, the United Kingdom, and New Zealand, among others, publicly acknowledge that e-cigarettes are significantly less harmful than combustible cigarettes. Some, like Public Health England (now the Office for Health Improvement and Disparities), have actively encouraged adult smokers unable to quit nicotine entirely to switch to vaping. Yet, the US regulatory environment largely remains entrenched in what critics call a regressive, fear-based mindset that downplays or ignores the potential benefits for adult smokers. appeared to stall Canada Canada United Kingdom United Kingdom New Zealand New Zealand Allegations of Disinformation and Market Capture This policy stagnation, some contend, may also reflect the increasing influence of Big Tobacco companies within the regulatory sphere. Critics allege these companies, having gained the President's ear, have engaged in spreading misinformation – ranging from amplifying fears about youth vaping (often used to justify flavor bans that disproportionately affect adult smokers) to promoting sensationalist claims about illicit fentanyl-laced vapes from China. Concurrently, state legislatures nationwide are advancing "vapor product registry" bills. While often framed as state-level enforcement tools, these bills frequently restrict sales to only those products that have received federal PMTA authorization – effectively locking out competitors and handing the market to the few Big Tobacco companies whose products dominate the short list of FDA-approved vapes, all enforced at taxpayer expense. vapor product registry The Overlooked Adult Vaper and the Call for Reform Big Tobacco, though late to innovate in the vapor space, shouldn't necessarily be barred from offering potentially safer nicotine alternatives. However, critics argue vehemently against allowing them to monopolize a grassroots harm reduction movement initiated by former smokers and independent entrepreneurs. The demographic reality is that among adults aged 25 and older who vape, the vast majority are either current or, more often, former smokers specifically seeking a less dangerous way to consume nicotine. Yet, hampered by outdated legislation like the TCA and a federal regulatory framework seemingly detached from real-world science and consumer behavior, these adults face growing uncertainty and shrinking access to the products, particularly the flavors, that helped them quit smoking. President Trump made a clear promise to protect flavored vaping options. With over 20 million adult vapers in America – many of whom are former smokers potentially saved from returning to deadly cigarettes – the expectation for decisive action and meaningful regulatory reform is immense. They await policies that acknowledge harm reduction, foster responsible innovation, and prioritize the needs of adults seeking safer alternatives. 20 million adult vapers in America

So Are Vapes Making a Comeback Under the Trump Administration?

Over the past 6 months, we benchmarked multiple tracking technologies for mobile, unpowered assets: construction gear, medical rentals, tools, shipping containers, and more. The goal: build a system that reliably tells you: Where is this item now?
When was it last seen?
What’s the cost per asset to do this at scale? Where is this item now? Where is this item now? When was it last seen? When was it last seen? What’s the cost per asset to do this at scale? What’s the cost per asset to do this at scale? We tested: Cellular GPS (LandAirSea, Samsara)
Passive/Active RFID (Zebra, Alien)
BLE Trackers (Tile, One-Key, etc.)
Apple AirTags + programmatic layer Cellular GPS (LandAirSea, Samsara) Passive/Active RFID (Zebra, Alien) BLE Trackers (Tile, One-Key, etc.) Apple AirTags + programmatic layer Only one approach gave us: High-fidelity passive tracking
Minimal labor
Predictable location updates in the wild
Sub-$4/month total cost High-fidelity passive tracking Minimal labor Predictable location updates in the wild Sub-$4/month total cost 🔴 GPS Trackers — Power-Hungry & Operationally Fragile lessCopyEdit[ Asset ]
   |
[ GPS Module ]
   |
[ LTE Modem ]---[ Carrier Network ]---[ Cloud Platform ]
   |
[ Battery ] lessCopyEdit[ Asset ]
   |
[ GPS Module ]
   |
[ LTE Modem ]---[ Carrier Network ]---[ Cloud Platform ]
   |
[ Battery ] What it’s good at: What it’s good at: Real-time tracking when you have cell signal
Fine-grained updates (every 10s–60s) Real-time tracking when you have cell signal Fine-grained updates (every 10s–60s) Failure modes in the field: Failure modes in the field: Battery depletion → dead device in 7–30 days
Indoor/urban canyons → GPS lock fails
SIM provisioning → ~3% failure rate during setup
Techs forget to charge or miswire installs Battery depletion → dead device in 7–30 days Indoor/urban canyons → GPS lock fails SIM provisioning → ~3% failure rate during setup Techs forget to charge or miswire installs Measured in field: Measured in field: Avg install time (non-hardwired): 18 min
Avg labor revisit per year: 5.7x
Location latency indoors: >1hr or failed ping Avg install time (non-hardwired): 18 min 18 min Avg labor revisit per year: 5.7x 5.7x Location latency indoors: >1hr or failed ping >1hr or failed ping GPS works for powered fleets. It breaks down hard for passive assets. GPS works for powered fleets. It breaks down hard for passive assets. Annual TCO per device: ~$400 \  ($150 hardware + $240 data + $50 labor) 🟠 RFID — Great for Warehouses, Not the Field lessCopyEdit[ RFID Tag ]---(Passive)---[ RFID Reader ]
                             |
                        [ Backend DB ] lessCopyEdit[ RFID Tag ]---(Passive)---[ RFID Reader ]
                             |
                        [ Backend DB ] Useful for: Useful for: Inventory control at fixed chokepoints
Bulk scanning in highly structured environments Inventory control at fixed chokepoints Bulk scanning in highly structured environments Limitations: Limitations: No real tracking — just “seen at gate X”
Requires infrastructure at every decision point
0 coverage outdoors, in transit, on job sites
Readers are expensive and fragile (EM interference prone) No real tracking — just “seen at gate X” Requires infrastructure at every decision point 0 coverage outdoors, in transit, on job sites Readers are expensive and fragile (EM interference prone) Cost Breakdown: Cost Breakdown: Passive tags: $0.12–$0.80


Reader infrastructure/site: $2,500–$10,000


Integration overhead: high Passive tags: $0.12–$0.80 Passive tags: $0.12–$0.80 Reader infrastructure/site: $2,500–$10,000 Reader infrastructure/site: $2,500–$10,000 Integration overhead: high Integration overhead: high Unless you control the entire environment, RFID creates visibility gaps you can't close. Unless you control the entire environment, RFID creates visibility gaps you can't close. Annual TCO (real deployments): $8k–$40k/site Annual TCO (real deployments): $8k–$40k/site 🟡 BLE Trackers — “Tracking” That Usually Isn’t cssCopyEdit[ BLE Beacon ] --x--> [ Needs Someone Running App ] cssCopyEdit[ BLE Beacon ] --x--> [ Needs Someone Running App ] Examples tested: Examples tested: Tile Pro
Milwaukee One-Key
Chipolo Business Beta Tile Pro Milwaukee One-Key Chipolo Business Beta Core issue: They rely on proprietary apps running in the background — no real mesh, just marketing. Core issue: no real mesh Mesh Density (urban tests): Mesh Density (urban tests): Tile: 2 updates/week/asset in SF
One-Key: 0 updates outside Milwaukee job sites
Battery life: ~12 months (replaceable CR2032) Tile: 2 updates/week/asset in SF One-Key: 0 updates outside Milwaukee job sites Battery life: ~12 months (replaceable CR2032) Key limitation: Key limitation: If no one’s running the app, the device is invisible. In practice, that means ~99%+ dead zones. If no one’s running the app, the device is invisible. In practice, that means ~99%+ dead zones. No way to manage at scale. No geofences. No asset dashboard. Just a few stale pings, if you're lucky. TCO: ~ $30–$60/year  (still too high for something that barely works) TCO: ~ $30–$60/year  (still too high for something that barely works) 🟢 AirTags — When Paired with the Right Stack, They Win lessCopyEdit[ AirTag ]---(BLE)---[ Any iPhone Nearby ]
                         |
              [ Apple Find My Mesh ]
                         |
                 [ Programmatic Layer ]
                         |
                   [ AirPinpoint Dashboard ] lessCopyEdit[ AirTag ]---(BLE)---[ Any iPhone Nearby ]
                         |
              [ Apple Find My Mesh ]
                         |
                 [ Programmatic Layer ]
                         |
                   [ AirPinpoint Dashboard ] Key technical advantages: Key technical advantages: BLE beaconing interval: ~800ms (adaptive)
U1 chip + BLE triangulation improves spatial accuracy
Global passive mesh: 2.2B+ iOS devices relay encrypted updates
Battery: 2–4 years on CR2032
Range: We observed ~0.2–2.3 miles real-world handoff distance in cities BLE beaconing interval: ~800ms (adaptive) U1 chip + BLE triangulation improves spatial accuracy Global passive mesh: 2.2B+ iOS devices relay encrypted updates 2.2B+ iOS devices Battery: 2–4 years on CR2032 Range: We observed ~0.2–2.3 miles real-world handoff distance in cities Test results: Test results: Urban ping latency: <8 minutes
Suburban latency: ~12–45 minutes
Dense indoor location accuracy: ~10–15m radius
Deployment time: <60 seconds per asset
No SIM, no charge cycles, no field config Urban ping latency: <8 minutes <8 minutes Suburban latency: ~12–45 minutes ~12–45 minutes Dense indoor location accuracy: ~10–15m radius Deployment time: <60 seconds per asset <60 seconds No SIM, no charge cycles, no field config This is the only BLE-based system with actual nationwide mesh reliability. This is the only BLE-based system with actual nationwide mesh reliability. AirTag Comprehensive CR2032 Battery Brand Comparison Based on extensive empirical testing of CR2032 batteries under conditions similar to AirTag operation, here's how different brands perform: Brand

Runtime (Hours)

Runtime (Approx. Months)*

Cost per Cell

Value Rating (Hours/$)



Panasonic

1500

12.5

$1.04

1442



Voniko

1448

12.1

$0.83

1745



Maxell

1444

12.0

$0.58

2490



Toshiba

1325

11.0

$0.64

2070



Harbor Freight

1307

10.9

$0.75

1743



Energizer

1307

10.9

$1.28

1021



Duracell

1298

10.8

$1.81

717



AC Delco

1223

10.2

$0.79

1548



Nightkonic

1186

9.9

$0.24

4942



Murata

1175

9.8

$0.45

2611



Hua Dao

642

5.4

$0.14

4586 Brand

Runtime (Hours)

Runtime (Approx. Months)*

Cost per Cell

Value Rating (Hours/$)



Panasonic

1500

12.5

$1.04

1442



Voniko

1448

12.1

$0.83

1745



Maxell

1444

12.0

$0.58

2490



Toshiba

1325

11.0

$0.64

2070



Harbor Freight

1307

10.9

$0.75

1743



Energizer

1307

10.9

$1.28

1021



Duracell

1298

10.8

$1.81

717



AC Delco

1223

10.2

$0.79

1548



Nightkonic

1186

9.9

$0.24

4942



Murata

1175

9.8

$0.45

2611



Hua Dao

642

5.4

$0.14

4586 Brand

Runtime (Hours)

Runtime (Approx. Months)*

Cost per Cell

Value Rating (Hours/$) Brand Brand Runtime (Hours) Runtime (Hours) Runtime (Approx. Months)* Runtime (Approx. Months)* Cost per Cell Cost per Cell Value Rating (Hours/$) Value Rating (Hours/$) Panasonic

1500

12.5

$1.04

1442 Panasonic Panasonic 1500 1500 12.5 12.5 $1.04 $1.04 1442 1442 Voniko

1448

12.1

$0.83

1745 Voniko Voniko 1448 1448 12.1 12.1 $0.83 $0.83 1745 1745 Maxell

1444

12.0

$0.58

2490 Maxell Maxell 1444 1444 12.0 12.0 $0.58 $0.58 2490 2490 Toshiba

1325

11.0

$0.64

2070 Toshiba Toshiba 1325 1325 11.0 11.0 $0.64 $0.64 2070 2070 Harbor Freight

1307

10.9

$0.75

1743 Harbor Freight Harbor Freight 1307 1307 10.9 10.9 $0.75 $0.75 1743 1743 Energizer

1307

10.9

$1.28

1021 Energizer Energizer 1307 1307 10.9 10.9 $1.28 $1.28 1021 1021 Duracell

1298

10.8

$1.81

717 Duracell Duracell 1298 1298 10.8 10.8 $1.81 $1.81 717 717 AC Delco

1223

10.2

$0.79

1548 AC Delco AC Delco 1223 1223 10.2 10.2 $0.79 $0.79 1548 1548 Nightkonic

1186

9.9

$0.24

4942 Nightkonic Nightkonic 1186 1186 9.9 9.9 $0.24 $0.24 4942 4942 Murata

1175

9.8

$0.45

2611 Murata Murata 1175 1175 9.8 9.8 $0.45 $0.45 2611 2611 Hua Dao

642

5.4

$0.14

4586 Hua Dao Hua Dao 642 642 5.4 5.4 $0.14 $0.14 4586 4586 *Converted from accelerated test conditions to approximate real-world months based on typical AirTag usage patterns Converted from accelerated test conditions to approximate real-world months based on typical AirTag usage patterns Test Methodology: All batteries were tested with a consistent 80μA base load (approximately 16x the typical AirTag sleep current) with periodic higher current pulse loads similar to Bluetooth transmissions. Tests were normalized to account for minor variations in test fixtures. Test Methodology 🧮 Real Cost Comparison (Per 100 Assets, Per Year) Tech

Hardware

Labor

Subscriptions

TCO (Year 1)



GPS

$15,000

$5,000

$36,000

$56,000



RFID

$2,000

$4,000

$0

$6,000+



BLE

$3,000

$3,000

$0

$6,000



AirTag + Platform

$2,000

$500

$47.88 (flat)

~$2,548 Tech

Hardware

Labor

Subscriptions

TCO (Year 1)



GPS

$15,000

$5,000

$36,000

$56,000



RFID

$2,000

$4,000

$0

$6,000+



BLE

$3,000

$3,000

$0

$6,000



AirTag + Platform

$2,000

$500

$47.88 (flat)

~$2,548 Tech

Hardware

Labor

Subscriptions

TCO (Year 1) Tech Tech Hardware Hardware Labor Labor Subscriptions Subscriptions TCO (Year 1) TCO (Year 1) GPS

$15,000

$5,000

$36,000

$56,000 GPS GPS $15,000 $15,000 $5,000 $5,000 $36,000 $36,000 $56,000 $56,000 $56,000 RFID

$2,000

$4,000

$0

$6,000+ RFID RFID $2,000 $2,000 $4,000 $4,000 $0 $0 $6,000+ $6,000+ $6,000+ BLE

$3,000

$3,000

$0

$6,000 BLE BLE $3,000 $3,000 $3,000 $3,000 $0 $0 $6,000 $6,000 $6,000 AirTag + Platform

$2,000

$500

$47.88 (flat)

~$2,548 AirTag + Platform AirTag + Platform AirTag + Platform $2,000 $2,000 $2,000 $500 $500 $500 $47.88 (flat) $47.88 (flat) $47.88 (flat) ~$2,548 ~$2,548 ~$2,548 ✅ Final Notes AirTags on an ios device alone are not sufficient for business use. They lack: Asset naming / grouping


Geofencing


Bulk management


Alerting / integrations


Silent mode (you don’t want customers getting “you’re being tracked” alerts) Asset naming / grouping Asset naming / grouping Geofencing Geofencing Bulk management Bulk management Alerting / integrations Alerting / integrations Silent mode (you don’t want customers getting “you’re being tracked” alerts) Silent mode (you don’t want customers getting “you’re being tracked” alerts) But when paired with a 3rd party platform that solves those (like AirPinpoint) they become arguably the highest-leverage, lowest-friction tracking layer available for unpowered asset fleets. highest-leverage, lowest-friction tracking layer available If you’re trying to track equipment, rentals, forklifts, pumps, dollies, carts, containers, or anything that moves but doesn’t have power — this is the stack to beat. this is the stack to beat

We Tested GPS, RFID, BLE, and AirTags for Asset Tracking — Only One Actually Worked

Humans have always had the desire to be free and control their fate. This drive for autonomy— to be seen, heard, and included in decision-making—has fueled democracy, taking it from a mere idea in ancient Greece into the most practiced form of government today. And when the internet was invented, everyone thought it would be a tool for freedom and connection. Instead, it's been hijacked. Today, only a measly 17% of Internet users worldwide enjoy total freedom (Statista, 2024). Tech giants silence voices they don’t like. Governments cut off access when it suits them. This is nothing like the freedom the internet was supposed to give. Statista, But a shift is coming. The Decentralized Internet is about to break that monopoly, handing power back to users. This article looks at how a decentralized internet will level the playing field and what its future holds. What Is Decentralized Internet? The decentralized internet is an internet infrastructure that operates on peer-to-peer (P2P) networks rather than centralized servers. peer-to-peer (P2P) networks Let’s break things down. For instance, under the traditional structure, internet service providers like AT&T and Comcast allow you to access the internet. Companies like Google and Amazon operate data centers that store and process your data. And of course, government agencies and social media giants enforce regulations and control content algorithms. In contrast, with a decentralized internet, it's not just companies providing the services—everyone is involved in the process. For example, instead of depending on centralized providers like Amazon Web Services (AWS) or Google Cloud, individual users or small groups could run independent data centers or participate in a distributed network. Simon St. Laurent (Content Manager at LinkedIn Learning) puts it this way: Simon St. Laurent A Decentralized Web is a network of resources in which no one player can control the conversation or spin it to [his or her] exclusive advantage. A Decentralized Web is a network of resources in which no one player can control the conversation or spin it to [his or her] exclusive advantage. A Decentralized Web is a network of resources in which no one player can control the conversation or spin it to [his or her] exclusive advantage. In a decentralized internet, independent computers and privately owned networks work together to provide internet services, without relying on corporate-controlled infrastructure. However, beyond this shift from corporate-controlled networks to a decentralized model, the big question is: Does it really matter much if the Internet is centralized or decentralized? Are there any real-world benefits for the average Joey if the internet is decentralized? Let’s break down the key advantages of a decentralized Internet. really Beyond Big Tech: 5 Real Benefits of a Decentralized Internet Beyond Big Tech: 5 Real Benefits of a Decentralized Internet ‘ 1. Data Privacy 1. Data Privacy If there is one thing that has been misused so much under a centralized internet system, it is users' data. For instance, Google’s location tracking scandal revealed that Google still tracks users' location even when they turn off their "location tracking feature" on their mobile devices. Imagine someone spying on you everywhere you go, every minute. Google’s location tracking scandal With a decentralized internet, this is less likely to happen because users are in complete control of their data. The rules and permissions are handled via transparent smart contracts on the Blockchain. This means no one can redefine privacy settings behind the scenes. smart contracts on the Blockchain 2. Resistance to Censorship In 1996, the Chinese government created the Great Firewall, an internet censorship system that allows citizens to access only a heavily filtered version of the internet. This means that people in China cannot access global websites like Google, YouTube, Facebook, Twitter, and even certain news outlets like BBC and The New York Times. Great Firewall And in 2025, that initiative is still in place. That’s nearly three decades of being shut off from the rest of the world. However, the Chinese government is not the only one that tampers with its citizens’ digital freedom. There have also been several countries that shut down the internet completely on several occasions. countries that shut down the internet With a decentralized internet, it becomes hard for censorship to happen since the infrastructure is now owned and controlled by different people in different places. For instance, one of the methods the Chinese government uses for the firewall program is controlling Internet service providers. And that was easy to do because only a few corporations were involved. If it were under a decentralized structure, getting everyone involved to agree would be difficult. 3. No Single Point of Failure Under a centralized internet, if you have a situation where there is a failure of a single component it can cause the entire system to stop working. A good example of this is the Rogers Telecom Outage. Rogers Telecom Outage. On July 8, 2022, Rogers Communications, one of Canada's largest telecom providers, suffered a massive nationwide outage that left 12 million Canadians stranded. People couldn't call 911, pay for goods, or access online banking. And what was the cause? Well, Rogers runs a centralized infrastructure and was trying to do some upgrades. That was it. Just one error leading to total blackout. That’s one of the many dangers of centralization. In a decentralized network, data is routed through many independent nodes, not one giant backbone. So, if one provider or server fails, others take over automatically. 4. More Efficient Content Delivery In a centralized internet, most websites and services rely on central servers or cloud data centers to store and deliver content. This means all users must connect to the same few locations to access a site or stream media. But what happens when those servers are overloaded or far from the user? The users will experience slow loading times, and sometimes it leads to a complete outage. loading times In a decentralized internet, the content is stored across many distributed nodes and delivered from the closest or fastest available source, rather than a single fixed server. This means the user will enjoy a great experience even when there is high traffic. 5. Open and Transparent Governance Presently, the average user has no say in what happens on the internet. Tech giants just decide at will to make changes without even considering the impact it may have on users. For example, Facebook’s algorithm changes have destroyed entire businesses overnight, with no warning and no recourse. In contrast, Decentralized networks are often governed through community-driven protocols and on-chain governance, where users vote on key changes. Decisions are made by stakeholders and not just CEOs. And this is not just some big idea. Projects like Ethereum, ENS (Ethereum Name Service), and Mastodon are already showing that decentralized communities can run complex systems fairly and openly. What Does the Future Hold? What Does the Future Hold? While the decentralized internet is still in the developmental stage, one thing is clear: it is a shift that will forever change our lives. Oke Umurhohwo (Head of Global Marketing, Zap Africa) says: Oke Umurhohwo The decentralized internet represents a seismic shift in the way we think about online interactions, one that prioritizes community, security, and transparency over centralized control. The decentralized internet represents a seismic shift in the way we think about online interactions, one that prioritizes community, security, and transparency over centralized control. Already, we have started seeing promising implementations of the decentralized internet. Projects like Filecoin are creating decentralized storage networks. Brave Browser is redefining how we interact with content online. And initiatives like Spacecoin are taking decentralization beyond Earth's surface with satellite-based internet nodes. With the Spacecoin project, internet accessibility will greatly improve even in remote areas where traditional internet infrastructure is lacking. However, despite this progress, there are still some challenges plaguing the decentralized internet, with user adoption being one of the major problems. To many people, the idea of a decentralized internet still sounds abstract. But it is only a matter of time before the gospel of a decentralized internet becomes mainstream. The benefits are just too much to be ignored. Decentralization is our only chance of restoring the internet to what it should be: a tool of freedom. And we all should start embracing it, not tomorrow but today!

The Internet Is Rigged, Only Decentralization Can Fix It

When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. With a decentralized system stabilizing supply and demand, energy infrastructure goes from dirty and industrial to clean and welcoming. The Emerging Faults in Energy Infrastructure As data centers are built nationwide to support artificial intelligence and cloud computing technologies, electricity demand increases exponentially. However, energy infrastructure is not keeping pace. In the United States, most transmission lines and transformers are at least 50 years old — they have reached their planned life expectancy. Engineers are beginning to replace them, but the process will cost trillions and take years. In the meantime, peak load — the period when electricity demand is at its highest — is hitting an all-time high. If the grid can’t compensate, blackouts are likely. To avoid this outcome, operators use supplemental gas-fired peaker plants that remain dormant unless consumption is unusually high. Consumers Pay Operators Millions Operators often get millions of dollars to ensure the grid remains operational during peak load. For instance, two Southern England station operators will receive $15.5 million after supplying just three hours of electricity in January 2025. A combination of freezing weather and low wind speeds raised market prices, leading to payouts worth over 50 times the market value. Problems like this will only worsen as the grid ages and power demand increases. While these operators provide an essential service, there are undoubtedly more efficient — and less costly — alternatives. What if people didn’t have to pay surge pricing? What if, instead of relying on a centralized system, they generated their own supply? With virtual power plants (VPPs), this future might be possible. The Role of VPPs in the Clean Energy Future A VPP is a collection of small-scale distributed energy resources like solar panels, plugged-in electric vehicles, and air conditioners. What makes this system virtual if it relies on physical devices? Each electrified, interconnected node wirelessly communicates, constantly sharing information about production, consumption, and storage. When hundreds, thousands, or even millions of small-scale DERs are bundled with a control system, citizens can call on them to supply or store electricity when demand fluctuates. Unlike conventional coal or gas power plants, they can ensure stability without raising prices or contributing to climate change. However, they provide the same reliability and economic value. The Benefits of Leveraging VPPs Virtual plants help stabilize the grid by compensating for fluctuations in generation and consumption. Plus, since they don’t need a centralized physical space, they enable more integration with renewables. A typical solar farm would need around 1.88 million solar panels to produce 1 gigawatt, which takes up a lot of space. Instead of clearing out a local ecosystem to make room, civil engineers can use the existing built environment — homeowners’ rooftops. Moreover, this solution increases durability. Even if existing infrastructure could handle record-breaking peak loads, it is not resilient — few traditional power systems are designed to handle unlikely high-impact events like natural disasters or extreme weather events. Thanks to a VPP’s distributed nature, physical damage isn’t nearly as much of a threat. Can We Count on Consumers for a Solution? Since this technology relies on citizens instead of a centralized operator, some concerns about reliability exist. However, people who are a part of the system don’t get to pick and choose the rate at which electricity is produced, consumed, or stored. Further, communities have risen to the occasion in similar circumstances, proving they can be trusted. In 2022, Californians experienced a scorching, lasting heat wave. The California Independent System Operator’s electricity demandreached a record high of 51,426 megawatts at 4:55 p.m. on September 6. Around one hour later, the operator sent an emergency text alert to appeal for conservation. Consumption immediately dropped off, reaching 35,000 megawatts within six hours. Consumers demonstrated their ability to coordinate and follow through. Using VPPs in Urban and Rural Areas While this solution may be more effective in urban areas due to higher population density, it can still work in rural locations since peak loads will be considerably lower there. As long as enough consumers bundle their devices in a virtual plant, they can improve electricity production, consumption, and storage, eliminating the need for peaker plants and improving the rate of local renewable usage. Although many people don’t understand the inner workings of energy infrastructure, most pay an electricity bill monthly. Even if they have their own solar panels, renewables can’t produce power as consistently as fossil fuels. For this reason, many have to rely on the grid to some extent. If they joined a VPP, they could improve their resilience and sustainability. When Will VPPs Go Beyond Conceptualization? Today, towering smokestacks and fields of photovoltaic panels come to mind when considering energy infrastructure. As VPPs become more prevalent, this decades-old standard may soon change. The U.S. Department of Energy already plans to increase VPP capacity to 160 GW by 2030, up from 80 GW today, according to the MIT Technology Review. That’s the equivalent of eliminating roughly 160 future fossil fuel plants. If the U.S. DOE does this, it would address 10% to 20% of peak demand by the decade's end. While virtual plants have already been successfully deployed, they haven’t become widespread yet. If the DOE follows through, it could become a substantial part of the nation’s modern infrastructure in a few years. The Future of Clean Energy Powered by VPPs With virtual plants, a world where neighbors lend power to each other and dense urban areas prevent rolling blackouts could exist. This technology can incentivize homeowners to install solar panels, drive electric vehicles, and embrace electrification. As smart homes grow smarter and electricity demand rises, this technology may be the most effective solution. When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. When you think of energy infrastructure, you probably imagine towering smokestacks, old power lines, cramped industrial parks, and lengthy coal trains. Once virtual power plants enter the picture, this could change. With a decentralized system stabilizing supply and demand, energy infrastructure goes from dirty and industrial to clean and welcoming. The Emerging Faults in Energy Infrastructure The Emerging Faults in Energy Infrastructure As data centers are built nationwide to support artificial intelligence and cloud computing technologies, electricity demand increases exponentially. However, energy infrastructure is not keeping pace. In the United States, most transmission lines and transformers are at least 50 years old — they have reached their planned life expectancy. Engineers are beginning to replace them, but the process will cost trillions and take years. are at least 50 years old are at least 50 years old In the meantime, peak load — the period when electricity demand is at its highest — is hitting an all-time high. If the grid can’t compensate, blackouts are likely. To avoid this outcome, operators use supplemental gas-fired peaker plants that remain dormant unless consumption is unusually high. Consumers Pay Operators Millions Operators often get millions of dollars to ensure the grid remains operational during peak load. For instance, two Southern England station operators will receive $15.5 million after supplying just three hours of electricity in January 2025. A combination of freezing weather and low wind speeds raised market prices, leading to payouts worth over 50 times the market value. Problems like this will only worsen as the grid ages and power demand increases. will receive $15.5 million will receive $15.5 million While these operators provide an essential service, there are undoubtedly more efficient — and less costly — alternatives. What if people didn’t have to pay surge pricing? What if, instead of relying on a centralized system, they generated their own supply? With virtual power plants (VPPs), this future might be possible. The Role of VPPs in the Clean Energy Future A VPP is a collection of small-scale distributed energy resources like solar panels, plugged-in electric vehicles, and air conditioners. What makes this system virtual if it relies on physical devices? Each electrified, interconnected node wirelessly communicates, constantly sharing information about production, consumption, and storage. When hundreds, thousands, or even millions of small-scale DERs are bundled with a control system, citizens can call on them to supply or store electricity when demand fluctuates. Unlike conventional coal or gas power plants, they can ensure stability without raising prices or contributing to climate change. However, they provide the same reliability and economic value. The Benefits of Leveraging VPPs Virtual plants help stabilize the grid by compensating for fluctuations in generation and consumption. Plus, since they don’t need a centralized physical space, they enable more integration with renewables. A typical solar farm would need around 1.88 million solar panels to produce 1 gigawatt, which takes up a lot of space. Instead of clearing out a local ecosystem to make room, civil engineers can use the existing built environment — homeowners’ rooftops. around 1.88 million solar panels around 1.88 million solar panels Moreover, this solution increases durability. Even if existing infrastructure could handle record-breaking peak loads, it is not resilient — few traditional power systems are designed to handle unlikely high-impact events like natural disasters or extreme weather events. Thanks to a VPP’s distributed nature, physical damage isn’t nearly as much of a threat. few traditional power systems few traditional power systems Can We Count on Consumers for a Solution? Since this technology relies on citizens instead of a centralized operator, some concerns about reliability exist. However, people who are a part of the system don’t get to pick and choose the rate at which electricity is produced, consumed, or stored. Further, communities have risen to the occasion in similar circumstances, proving they can be trusted. In 2022, Californians experienced a scorching, lasting heat wave. The California Independent System Operator’s electricity demand reached a record high of 51,426 megawatts at 4:55 p.m. on September 6. Around one hour later, the operator sent an emergency text alert to appeal for conservation. Consumption immediately dropped off, reaching 35,000 megawatts within six hours. Consumers demonstrated their ability to coordinate and follow through. reached a record high of 51,426 megawatts reached a record high of 51,426 megawatts Using VPPs in Urban and Rural Areas While this solution may be more effective in urban areas due to higher population density, it can still work in rural locations since peak loads will be considerably lower there. As long as enough consumers bundle their devices in a virtual plant, they can improve electricity production, consumption, and storage, eliminating the need for peaker plants and improving the rate of local renewable usage. Although many people don’t understand the inner workings of energy infrastructure, most pay an electricity bill monthly. Even if they have their own solar panels, renewables can’t produce power as consistently as fossil fuels. For this reason, many have to rely on the grid to some extent. If they joined a VPP, they could improve their resilience and sustainability. can’t produce power as consistently can’t produce power as consistently When Will VPPs Go Beyond Conceptualization? Today, towering smokestacks and fields of photovoltaic panels come to mind when considering energy infrastructure. As VPPs become more prevalent, this decades-old standard may soon change. The U.S. Department of Energy already plans to increase VPP capacity to 160 GW by 2030, up from 80 GW today, according to the MIT Technology Review. That’s the equivalent of eliminating roughly 160 future fossil fuel plants. increase VPP capacity to 160 GW increase VPP capacity to 160 GW If the U.S. DOE does this, it would address 10% to 20% of peak demand by the decade's end. While virtual plants have already been successfully deployed, they haven’t become widespread yet. If the DOE follows through, it could become a substantial part of the nation’s modern infrastructure in a few years. 10% to 20% of peak demand 10% to 20% of peak demand The Future of Clean Energy Powered by VPPs With virtual plants, a world where neighbors lend power to each other and dense urban areas prevent rolling blackouts could exist. This technology can incentivize homeowners to install solar panels, drive electric vehicles, and embrace electrification. As smart homes grow smarter and electricity demand rises, this technology may be the most effective solution.

The Role of Virtual Power Plants in the Future of Clean Energy

When Celsius Network fell into bankruptcy in 2022, the headlines focused on a multibillion-dollar implosion of a once-thriving crypto lending empire. But underneath the chaos, one case—buried in the docket—has emerged as the most consequential legal battle to arise from the crypto bankruptcy era. It isn’t a class action or a federal indictment. It’s a single pro se litigant: Jason Voelker, from California, standing alone against a system that may be forced to change because of him. Voelker’s case is not about technicalities. It’s about first principles. It raises constitutional questions about judicial integrity, takings without compensation, and the due process owed to creditors in a post-confirmation world. More than that, it exposes the deep incompatibility between traditional bankruptcy doctrine and the rapidly evolving legal architecture of digital asset custody. This is not just a story about 17.61163 bitcoin. It is the front line of a legal transformation. If Voelker succeeds, the implications will be seismic. If he fails, a precedent may be set that allows bankruptcy estates to defeat enforcement by misrepresenting compliance—and forfeiting the truth before anyone can correct the record. The Spark: A Misrepresentation at the Heart of a Denial In 2024, Voelker filed a motion for leave to bring an adversary proceeding against Celsius. In that complaint, he alleged that 17 bitcoin were held in bailment under Wyoming’s blockchain custody statutes—assets that were never property of the estate and thus not subject to reorganization. Celsius’s Plan Administrator didn’t contest the allegation. They didn’t argue the 17 bitcoin were part of the estate. Instead, they claimed Voelker was already receiving 105% of his claim under the confirmed Plan and that further relief was barred. The bankruptcy court relied on that assertion. The May 8, 2024 order denied Voelker’s motion on the premise that his 17 bitcoin were already being paid, and he was receiving even more than he asked for. But that wasn’t true. He never received a single coin. And the Plan Administrator knew that when they said otherwise. A Doctrinal Time Bomb: Judicial Estoppel Meets the Blockchain This moment is bigger than estoppel. It’s about whether a bankruptcy court, having relied on a factual assertion to deny a litigant access to justice, can allow that same assertion to be quietly abandoned—leaving the creditor without recourse, without remedy, and without rights. It’s about whether a reorganized estate can promise payment to shut the courthouse door—and then walk away, free of obligation, when the dust settles. This is the watershed. Never before has a bankruptcy case so clearly implicated the doctrine of judicial estoppel in the digital asset context. Never before has a plan administrator argued that a creditor is overpaid in order to defeat a lawsuit, then refused to issue the payment and stood silent while a forfeiture order loomed. Why This Case Will Change the Law If Voelker’s motion is granted, the law will shift. If it’s denied, the case will become a constitutional time bomb on appeal. Either way, the impact will be felt across bankruptcy courts and digital custodians nationwide. • State Custody Laws Will Be Elevated: Courts will have to reckon with bailment laws like Wyoming’s that deliberately place digital assets outside bankruptcy estates. Voelker’s claim will force that confrontation. • Judicial Estoppel Will Gain Constitutional Dimensions: The consequences of false or strategic representations to bankruptcy courts will no longer be procedural. They will become constitutional violations. • Forfeiture Orders Will Face National Scrutiny: If a court allows forfeiture of assets it previously acknowledged as owed, it will create a takings problem under the Fifth Amendment. The line between abandoned property and confiscated property will be tested like never before. • Post-Confirmation Rights Will Be Redefined: Once a Plan is confirmed, courts typically wash their hands of contested matters. Voelker’s case shows why that assumption is not only outdated—it’s dangerous. The Clock and the Curtain The forfeiture deadline is March 31, 2025. On that day, any unclaimed property is slated to be returned to the reorganized entity, Ionic Digital, or used to pay administrative professionals. Voelker’s bitcoin will be gone. His appeal will be mooted. The May 8 order, never enforced, will become a judicial relic—honored in text but dishonored in practice. Voelker has filed an emergency motion asking the court to enforce its own ruling—or at least stay the forfeiture long enough to litigate. The court has ordered responses. The hearing will decide whether bankruptcy courts remain courts of equity, or whether plan finality can override fundamental fairness. The First Great Test of Crypto Bankruptcy Integrity This case will be studied for years. It is the first moment where the convergence of blockchain custody, federal bankruptcy law, and constitutional protections have all collided in the same courtroom. Jason Voelker didn’t plan to make history. But by standing firm and forcing the court to reconcile its words with its actions, he has become the plaintiff of record in a legal inflection point. Whether this ends with a victory in bankruptcy court or a groundbreaking decision on appeal, one thing is clear: the old rules are no longer enough. A new era of digital asset insolvency law has begun. And it begins here. - Art Stephan

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