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Safety issues associated with transporting crude oil by rail have been a concern since the boom of the U.S. domestic shale oil production in 2012. During the last decade, over 300 crude-oil-by-rail incidents have occurred in the United States. Some of them have caused adverse consequences including fire and hazardous materials leakage. However, only limited information on crude-on-rail routes and their associated risks is available to the public. To this end, this study proposed an unconventional way to reconstruct crude-on-rail routes using geotagged photos harvested from the Flickr website. The proposed method linked the geotagged photos of crude oil trains posted online with national railway networks to identify potential railway segments that those crude oil trains were traveling on. A shortest path-based method was applied to infer the complete crude-on-rail routes, by utilizing the confirmed railway segments as well as their directional information. Validation of the inferred routes was performed using a public map and official crude oil incident data. The results suggested that the inferred routes based on geotagged photos had high coverage, with approximately 96% of the documented crude oil incidents aligned with the reconstructed crude-on-rail network. The inferred crude oil train routes were found to pass through several metropolitan areas of high population density, who were exposed to potential risk. These findings could improve situational awareness for policy makers and transportation planners. In addition, with the inferred routes, this study has established a good foundation for future crude oil train risk-analyses along the rail route.

Despite the plethora of research devoted to analyzing the impact of disability on travel behavior, not enough studies have investigated the varying impact of social and environmental factors on the mode choice of people with disabilities that restrict their ability to use transportation modes efficiently. This research gap can be addressed by investigating the factors influencing the mode choice behavior of people with travel-limiting disabilities, which can inform the development of accessible and sustainable transportation systems. Additionally, such studies can provide insights into the social and economic barriers faced by this population group, which can help policymakers to promote social inclusion and equity.

Short-term traffic forecasting accuracy is related closely to the use of neighboring traffic information. Multivariate forecasting methods are becoming more popular because of their ability to capture both temporal and spatial evolvement in traffic. However, little attention has been given to quantify the effect of upstream and downstream traffic information, and the vast majority of published studies assume that the spatiotemporal relationship is specified in advance. Thus, the selection of surrounding traffic information as input parameters is somewhat arbitrary. To address that issue, this study investigated spatiotemporal relationships of speed series from consecutive segments under different traffic conditions by using the link speeds for nine segments extending over 12 mi on I-24 in Nashville, Tennessee. A prewhitened cross-correlation technique was proposed first to clarify the cross correlations between two speed series. The prewhitened cross-correlation function was performed on speed series for consecutive freeway segments for periods including the morning peak, midday off-peak, and evening peak. The analysis results showed that the correlations for consecutive segments were highly related to traffic conditions and that the effect of downstream traffic increased with the severity of congestion. Influences of upstream and downstream locations on current traffic were also found to be not symmetric in regard to the current site. The algorithm on properly choosing neighboring traffic information was proposed, and the lagged regression model with correctly identified input parameters (segments) outperformed others.

Short-term traffic forecasting accuracy is related closely to the use of neighboring traffic information. Multivariate forecasting methods are becoming more popular because of their ability to capture both temporal and spatial evolvement in traffic. However, little attention has been given to quantify the effect of upstream and downstream traffic information, and the vast majority of published studies assume that the spatiotemporal relationship is specified in advance. Thus, the selection of surrounding traffic information as input parameters is somewhat arbitrary. To address that issue, this study investigated spatiotemporal relationships of speed series from consecutive segments under different traffic conditions by using the link speeds for nine segments extending over 12 mi on I-24 in Nashville, Tennessee. A prewhitened cross-correlation technique was proposed first to clarify the cross correlations between two speed series. The prewhitened cross-correlation function was performed on speed series for consecutive freeway segments for periods including the morning peak, midday off-peak, and evening peak. The analysis results showed that the correlations for consecutive segments were highly related to traffic conditions and that the effect of downstream traffic increased with the severity of congestion. Influences of upstream and downstream locations on current traffic were also found to be not symmetric in regard to the current site. The algorithm on properly choosing neighboring traffic information was proposed, and the lagged regression model with correctly identified input parameters (segments) outperformed others.

The most comprehensive publicly available freight databases are the Commodity Flow Survey and FHWA's Freight Analysis Framework. These two sources contain the dollar value and weight of freight movements at high geographic levels, such as states or metropolitan areas. Because of the difficulty of obtaining freight data at lower geographic levels, various practitioners and researchers have suggested estimating freight models on the basis of aggregate data. Following recent practice, a methodology is presented to estimate a nationwide production and attraction model for U.S. domestic trade of goods. To this end, a Commodity Flow Survey data set provided by the U.S. Census Bureau and composed of a 2-nonconsecutive-year period (2002 and 2007) of goods movement between U.S. states for 27 industry sectors was used. The state payroll by industry sector, obtained from the County Business Patterns of the U.S. census, was the variable used to estimate freight generation models. The main objective of this study was to analyze the temporal stability and predictability of the proposed aggregate models. The results indicate that payroll alone explains a significant portion of the freight production and attraction at the state level. However, such simplification in the model process did not result in reasonable predictions of freight for a future-year horizon. It is recommended that time-dependent factors (e.g., variables related to changes in industry productivity) affecting freight demand be considered in the modeling process.

The most comprehensive publicly available freight databases are the Commodity Flow Survey and FHWA's Freight Analysis Framework. These two sources contain the dollar value and weight of freight movements at high geographic levels, such as states or metropolitan areas. Because of the difficulty of obtaining freight data at lower geographic levels, various practitioners and researchers have suggested estimating freight models on the basis of aggregate data. Following recent practice, a methodology is presented to estimate a nationwide production and attraction model for U.S. domestic trade of goods. To this end, a Commodity Flow Survey data set provided by the U.S. Census Bureau and composed of a 2-nonconsecutive-year period (2002 and 2007) of goods movement between U.S. states for 27 industry sectors was used. The state payroll by industry sector, obtained from the County Business Patterns of the U.S. census, was the variable used to estimate freight generation models. The main objective of this study was to analyze the temporal stability and predictability of the proposed aggregate models. The results indicate that payroll alone explains a significant portion of the freight production and attraction at the state level. However, such simplification in the model process did not result in reasonable predictions of freight for a future-year horizon. It is recommended that time-dependent factors (e.g., variables related to changes in industry productivity) affecting freight demand be considered in the modeling process.

A study was conducted to compare distance estimates derived from great circle distances (GCD) with distance estimates derived from a network-based model. The study used a sample of shipments from the 1993 commodity flow survey (CFS). For each shipment in the sample, the distance from the zip code of origin to the zip code of destination was calculated by using the Oak Ridge National Laboratories National Highway Network and assuming that the minimum impedance path was utilized. For each of these origin–destination pairs, the GCD and several variations of the GCD also were estimated. Finally, the network-based estimates and the GCD-based estimates were statistically compared. As expected, distance estimates based on GCD were found to be different from network-based estimates. However, applying a constant circuity factor of 1.22 or using variable circuity factors based on distance category did not result in a statistical bias in these distance estimates. Examination of distance estimates at the level of origin–destination pair revealed that distance estimates could vary as much as 75%. A comparison of published values for the 1997 CFS with values derived from GCD-based distance estimates shows that approximately 5% to 35% of the GCD-based values for the 1997 CFS would fall outside a two standard error interval. Although GCD-based estimates, under some conditions, may produce unbiased estimates of the mean distance, this does not eliminate the need for network-based estimates.

A study was conducted to compare distance estimates derived from great circle distances (GCD) with distance estimates derived from a network-based model. The study used a sample of shipments from the 1993 commodity flow survey (CFS). For each shipment in the sample, the distance from the zip code of origin to the zip code of destination was calculated by using the Oak Ridge National Laboratories National Highway Network and assuming that the minimum impedance path was utilized. For each of these origin-destination pairs, the GCD and several variations of the GCD also were estimated. Finally, the network-based estimates and the GCD-based estimates were statistically compared. As expected, distance estimates based on GCD were found to be different from network-based estimates. However, applying a constant circuity factor of 1.22 or using variable circuity factors based on distance category did not result in a statistical bias in these distance estimates. Examination of distance estimates at the level of origin-destination pair revealed that distance estimates could vary as much as 75%. A comparison of published values for the 1997 CFS with values derived from GCD-based distance estimates shows that approximately 5% to 35% of the GCD-based values for the 1997 CFS would fall outside a two standard error interval. Although GCD-based estimates, under some conditions, may produce unbiased estimates of the mean distance, this does not eliminate the need for network-based estimates.

The 1993 Commodity Flow Survey(CFS) proved that most freight transport on U.S. highways is interstate. The nation’s highway system functions principally to link the economies of states to form an integrated national economy rather than to serve the economies of individual states. Data from the 1997 CFS allow comparisons with the 1993 data to determine (a) whether the predominant pattern of interstate shipments continues as a stable phenomenon; (b) how fast the within-, from-, to-, and through-state components of truck freight are growing; and (c) whether important regional trends are evident. The shipment of freight by truck in the United States was even more predominantly interstate in 1997 than in 1993. In 1997, interstate shipments made up 76 percent of the total tonmiles shipped by truck; in 1993 it was 73 percent. The proportions of truck freight shipments originating from, destined to, passing through, or occurring entirely within a state vary significantly from state to state. Individual state distributions remained relatively stable from 1993 to 1997, with the exceptions of dramatic increases in interstate truck ton-miles in small New England states and substantial but less-dramatic increases in border states, such as New York, Michigan, Washington, California, Texas, and Florida. These findings could have important implications for highway revenue allocations because trucks carrying freight play a significant role in damage to highway pavements and structures.

The 1993 Commodity Flow Survey (CFS) proved that most freight transport on U.S. highways is interstate. The nation's highway system functions principally to link the economies of states to form an integrated national economy rather than to serve the economies of individual states. Data from the 1997 CFS allow comparisons with the 1993 data to determine (a) whether the predominant pattern of interstate shipments continues as a stable phenomenon; (b) how fast the within-, from-, to-, and through-state components of truck freight are growing; and (c) whether important regional trends are evident. The shipment of freight by truck in the United States was even more predominantly interstate in 1997 than in 1993. In 1997, interstate shipments made up 76 percent of the total tonmiles shipped by truck; in 1993 it was 73 percent. The proportions of truck freight shipments originating from, destined to, passing through, or occurring entirely within a state vary significantly from state to state. Individual state distributions remained relatively stable from 1993 to 1997, with the exceptions of dramatic increases in interstate truck ton-miles in small New England states and substantial but less-dramatic increases in border states, such as New York, Michigan, Washington, California, Texas, and Florida. These findings could have important implications for highway revenue allocations because trucks carrying freight play a significant role in damage to highway pavements and structures.

Automated traffic data posted on the Internet by four cities have been continuously downloaded, processed, and archived for more than 1 year by an automated system developed by Oak Ridge National Laboratory and funded by the U.S. Bureau of Transportation Statistics. Although the experimental system is far from national in scale and scope, it has shown that automated collection and processing of local traffic data via the Internet for national purposes is feasible and practical. Strong seasonal patterns make it too early to estimate statistical models of traffic growth, but comparisons of the same months in 1998 and 1999 indicate changes ranging from 1 percent to 3 percent for the monitored systems. Direct measurements of delay on the monitored systems are lower than published estimates for previous years. Although some progress in the input of missing data has been made, missing data are still a major problem, and better methods are needed.

Automated traffic data posted on the Internet by four cities have been continuously downloaded, processed, and archived for more than 1 year by an automated system developed by Oak Ridge National Laboratory and funded by the U.S. Bureau of Transportation Statistics. Although the experimental system is far from national in scale and scope, it has shown that automated collection and processing of local traffic data via the Internet for national purposes is feasible and practical. Strong seasonal patterns make it too early to estimate statistical models of traffic growth, but comparisons of the same months in 1998 and 1999 indicate changes ranging from 1 percent to 3 percent for the monitored systems. Direct measurements of delay on the monitored systems are lower than published estimates for previous years. Although some progress in the input of missing data has been made, missing data are still a major problem, and better methods are needed.

Automated traffic data being posted on the Internet by an increasing number of cities are a potentially invaluable source of information on national vehicle travel and highway system performance. An experimental system is presented that demonstrates the feasibility of using these data to create national indicators of vehicle travel and traffic congestion. The experiment raises a number of critical institutional and technical issues that must be resolved before a complete national system can be realized. This paper represents a progress report on the initial phase of an ongoing study.

Automated traffic data being posted on the Internet by an increasing number of cities are a potentially invaluable source of information on national vehicle travel and highway system performance. An experimental system is presented that demonstrates the feasibility of using these data to create national indicators of vehicle travel and traffic congestion. The experiment raises a number of critical institutional and technical issues that must be resolved before a complete national system can be realized. This paper represents a progress report on the initial phase of an ongoing study.

Broken appointments have been a major problem at the Anthony L. Jordan Health Center since it opened in 1968. In an earlier 6-month study of broken appointments at the center the authors found 48% of pediatric appointments and 43% of medicine appointments to be broken. Daily broken appointment rates were correlated between the two departments and were associated with day and time scheduled and with certain weather conditions. Specifically, highest broken appointment rates were noted for Mondays, for afternoon and evening hours, and for days with precipitation.