Development of a Statewide Truck Trip Forecasting Model Based on Commodity Flows and Input-Output Coefficients

2000 ◽  
Vol 1707 (1) ◽  
pp. 49-55 ◽  
Author(s):  
Jose A. Sorratini ◽  
Robert L. Smith
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
State Level ◽  
Census Bureau ◽  
The State ◽  
Input Output ◽  
Commodity Flow ◽  
Demand Models ◽  
Volume Group ◽  
The U.S

This research attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the U.S. Census Bureau, a private freight database (TRANSEARCH), and input-output (I-O) coefficients. The standard urban transportation planning modeling process was applied at the state level to estimate heavy truck trips. Economic-based I-O software was used to derive the I-O direct matrix and the I-O direct coefficients at the state level for developing the trip attraction rates for 28 manufacturing sectors. The Commodity Flow Survey from the U.S. Census Bureau together with a private database developed for Wisconsin were used to develop the trip production rates. Transportation planning software (TRANPLAN) was used to distribute and assign truck trips generated at the zonal level. The selected-link function in TRANPLAN was used to adjust the initial productions and attractions in order to generate link volumes that match the actual ground counts for 40 selected links. The model only required two iterations of the selected link analysis in order to produce an acceptable match with the ground counts, compared with three iterations for two prior similar models. The rapid convergence provides clear evidence that the disaggregate trip generation models give better initial estimates of trip productions and attractions than was possible with the prior studies. A “back forecast” of 15 years to the year 1977 was found to be reasonable both in terms of the percent root mean square error by volume group and the performance measures for five screen lines.

scholarly journals A Panel Evaluation of the Changes in the General Public’s Social-Media-Following of United States’ Public Health Departments during COVID-19 Pandemic

2021 ◽  
Vol 12 ◽  
pp. 215013272199545
Author(s):  
Areej Khokhar ◽  
Aaron Spaulding ◽  
Zuhair Niazi ◽  
Sikander Ailawadhi ◽  
Rami Manochakian ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
State Level ◽  
The State ◽  
Public Health Department ◽  
Health Departments ◽  
The Public ◽  
Public Health Departments ◽  
The Social ◽  
The U.S

Importance: Social media is widely used by various segments of society. Its role as a tool of communication by the Public Health Departments in the U.S. remains unknown. Objective: To determine the impact of the COVID-19 pandemic on social media following of the Public Health Departments of the 50 States of the U.S. Design, Setting, and Participants: Data were collected by visiting the Public Health Department web page for each social media platform. State-level demographics were collected from the U.S. Census Bureau. The Center for Disease Control and Prevention was utilized to collect information regarding the Governance of each State’s Public Health Department. Health rankings were collected from “America’s Health Rankings” 2019 Annual report from the United Health Foundation. The U.S. News and World Report Education Rankings were utilized to provide information regarding the public education of each State. Exposure: Data were pulled on 3 separate dates: first on March 5th (baseline and pre-national emergency declaration (NED) for COVID-19), March 18th (week following NED), and March 25th (2 weeks after NED). In addition, a variable identifying the total change across platforms was also created. All data were collected at the State level. Main Outcome: Overall, the social media following of the state Public Health Departments was very low. There was a significant increase in the public interest in following the Public Health Departments during the early phase of the COVID-19 pandemic. Results: With the declaration of National Emergency, there was a 150% increase in overall public following of the State Public Health Departments in the U.S. The increase was most noted in the Midwest and South regions of the U.S. The overall following in the pandemic “hotspots,” such as New York, California, and Florida, was significantly lower. Interesting correlations were noted between various demographic variables, health, and education ranking of the States and the social media following of their Health Departments. Conclusion and Relevance: Social media following of Public Health Departments across all States of the U.S. was very low. Though, the social media following significantly increased during the early course of the COVID-19 pandemic, but it still remains low. Significant opportunity exists for Public Health Departments to improve social media use to engage the public better.

Forecasting the 2008 U.S. House, Senate and Presidential Elections at the District and State Level

2008 ◽  
Vol 41 (04) ◽  
pp. 723-728 ◽  
Author(s):  
Carl Klarner
Keyword(s):  
House Of Representatives ◽  
Presidential Elections ◽  
State Level ◽  
Barack Obama ◽  
The State ◽  
Popular Vote ◽  
White House ◽  
Forecasting Models ◽  
The U.S

This paper applies the forecasting models of Klarner and Buchanan (2006a) for the U.S. Senate and Klarner and Buchanan (2006b) for the U.S. House of Representatives to the upcoming 2008 elections. Forecasts are also conducted for the 2008 presidential race at the state level. The forecasts presented in this article, made July 28, 2008 (99 days before the election), predicted an 11-seat gain for the Democrats in the House of Representatives, a three-seat gain for the Democrats in the Senate, and that Barack Obama would obtain 53.0% of the popular vote and 346 electoral votes. Furthermore, Obama was forecast to have an 83.6% chance of winning the White House and an 85.9% chance of winning the popular vote.

Legislative Expression: Sanctuary Policymaking in the U.S. States

2019 ◽  
pp. 95-117
Author(s):  
Loren Collingwood ◽  
Benjamin Gonzalez O’Brien
Keyword(s):  
State Level ◽  
The State ◽  
State Legislators ◽  
Minority Population ◽  
Racial Threat ◽  
State Institutions ◽  
The U.S

While sanctuary policies have traditionally been passed by cities and counties rather than states, this situation has shifted in recent years with both California and Oregon embracing their identity as “sanctuary states,” while in Texas SB4 was signed into law, officially banning sanctuary legislation across the state. This chapter examines the factors that increase the likelihood that state legislators will introduce pro- or antisanctuary legislation. We find that racial threat activated by an increasing minority population, the ideology of the state and its voters, and the structure of state institutions all increase the likelihood of pro/anti-sanctuary legislation being introduced at the state level.

Abstract P91: Excess Cerebrovascular Mortality in the U.S. During the Covid-19 Pandemic

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Richa Sharma ◽  
Lindsey Kuohn ◽  
Daniel Weinberger ◽  
Joshua Warren ◽  
Lauren H Sansing ◽  
...  
Keyword(s):  
Negative Binomial ◽  
State Level ◽  
Negative Binomial Regression ◽  
The State ◽  
Social Distancing ◽  
Binomial Regression ◽  
Stroke Death ◽  
The U.S ◽  
Related Mortality ◽  
Health Statistic

Introduction: The magnitude and drivers of excess cerebrovascular-specific mortality during the coronavirus-19 (COVID-19) pandemic are unknown. We aim to quantify excess stroke-related death and characterize its association with psychosocial factors and emerging COVID-19 related mortality. Methods: U.S. and state-level excess cerebrovascular deaths from January-May 2020 were quantified by Poisson regression models built using National Center for Health Statistic (NCHS) data. Weekly excess cerebrovascular deaths in the U.S. were analyzed as functions of time-varying, weekly stroke-related EMS calls and weekly COVID-19 deaths by univariable linear regression. A state-level negative binomial regression analysis was performed to determine the association between excess cerebrovascular deaths and social distancing (degree of change in mobility per Google COVID-19 Community Mobility Reports) during the height of the pandemic after the first COVID-19 death (February 29, 2020), adjusting for cumulative COVID-19 related deaths and completeness of deaths attributable to COVID-19 in NCHS. Findings: There were 918 more cerebrovascular deaths than expected from January 1-May 16 th , 2020 in the U.S. Excess cerebrovascular mortality occurred during every week between March 28-May 2 nd , 2020, up to 7.8% during the week of April 18 th . Decreased stroke-related EMS calls were associated with excess stroke deaths one (β -0.06, 95% CI -0.11, -0.02) and two weeks (β -0.08, 95% CI -0.12, -0.04) later. There was no significant association between weekly excess stroke death and COVID-19 death. Twenty-three states and NYC experienced excess cerebrovascular mortality during the pandemic height. At the state level, a 10% increase in social distancing was associated with a 4.3% increase in stroke deaths (IRR 1.043, 95% CI 1.001–1.085) after adjusting for COVID-19 mortality. Conclusions: Excess U.S. cerebrovascular deaths during the COVID-19 pandemic were observed with decreases in stroke-related EMS calls nationally and less mobility at the state level. Public health measures are needed to identify and counter the reticence to seeking medical care for acute stroke during the COVID-19 pandemic.

It May Cost an Arm and a Leg; Value on Workers and Occupational Fatality Rates in the U.S.

2020 ◽  
Author(s):  
Leah Shely Klos ◽  
Frank B. Giordano ◽  
Stacy A. Stoffregen ◽  
Miki C. Azuma ◽  
Jin Lee
Keyword(s):  
Minimum Wage ◽  
Occupational Safety ◽  
Health And Safety ◽  
State Level ◽  
The State ◽  
Workers Compensation ◽  
Negatively Associated ◽  
Safety And Health ◽  
Workforce Flexibility ◽  
The U.S

Abstract Background The present study aims to observe how societal indicators of workers’ values at the state-level are related to health and safety outcomes, particularly major injuries and fatalities in the U.S. Underscoring workforce flexibility and workability over workforce stability and safety might be indicative of the worth of workers which can be associated with occupational safety and health concerns. Methods Multiple regression analysis was adopted to examine how the state-level indicators of values on workers in terms of 1) minimum wage, using the data from 2015; 2) average of workers’ compensations for the loss of an arm, hand, leg, or foot in 2015 were prospectively associated with occupational fatality rates in 2016 and 2017. Socioeconomic contextual variables such as education level, GDP per capita, income gap, and population at the state-level were controlled for. Results The present study showed that state-level quantitative indicators of how workers are valued at work, namely minimum wage and workers’ compensation benefits, were significantly and negatively associated with fatality rates in the following year. Workers’ compensation benefits were significantly and negatively associated with fatality rates two years later, implying the lasting effect of this particular type of indicator of values on workers. Conclusions The present study illustrates the gap in how workers are valued across the U.S.. The study speaks to the importance of fostering culture where workers are adequately valued, cared about, and protected to prevent and curtail occupational fatality.

scholarly journals TRANSIMS Implementation for a Small Network and Comparison with Enhanced Four-Step Model

2014 ◽  
Author(s):  
Mansoureh Jeihani ◽  
Anam Ardeshiri
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Initial Step ◽  
Demand Analysis ◽  
Analysis Model ◽  
Step Method ◽  
Traffic Pattern ◽  
Step Model ◽  
Demand Models

Travel demand forecasting is a major tool to assist decision makers in transportation planning. While the conventional four-step trip-based approach is the dominant method to perform travel demand analysis, behavioral advances have been made in the past decade. This paper proposes and applies an enhancemnt to the four-step travel demand analysis model called Sub-TAZ. Furthermore, as an initial step toward activity-based models, a TRANSIMS Track-1 approach is implemented utilizing a detailed network developed in Sub-TAZ approach. The conventional four-step, Sub-TAZ, and TRANSIMS models were estimated in a small case study for Fort Meade, Maryland, with zonal trip tables. The models were calibrated and validated for the base year (2005), and the forecasted results for the year (2010) were compared to actual ground counts of traffic volume and speed. The study evaluated the forecasting ability of TRANSIMS versus the conventional and enhanced four-step models and provided critical observations concerning strategies for the further implementation of TRANSIMS.BACKGROUND Traffic pattern prediction is necessary for infrastructure improvement, and travel demand modeling provides tools to forecast travel patterns under various conditions. This modeling involves a series of mathematical equations that represent how people make travel choices. Traditional travel demand models use the four-step method, which was introduced in the 1950s and has been used widely in transportation planning. Although the four-step method has been practical in producing aggregate forecasts, it has some shortcomings. For example, in short-range planning networks, existing and newly constructed roads become congested much faster than forecasted (TRB 2007) and the performance of current four-step models is not always satisfactory. Additionally, these models are not behavioral in nature and as a result they are unable to represent the time chosen for travel, travelers’ responses to demand policies (e.g., toll roads, road pricing, and transit vouchers), non-motorized

Statewide Truck Transportation Planning: Methodology and Case Study

1996 ◽  
Vol 1522 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Ayman Smadi ◽  
T. H. Maze
Keyword(s):  
Transportation Planning ◽  
Transportation Network ◽  
State Level ◽  
Major Elements ◽  
The State ◽  
Truck Traffic ◽  
Economic Sectors ◽  
Industrial Sectors ◽  
Truck Transportation

An alternate approach for truck transportation planning at the state level is presented using a case study application in the State of Iowa. The method was based on some freight modeling concepts and available freight data sets. However, the model takes advantage of two concepts: unconstrained highway capacities and the decomposition of commodities, resulting in manageable data and modeling requirements. Identification of significant economic sectors, selection of appropriate productivity measures, estimation of truck freight volumes for each sector individually, and estimation of routing of truck traffic on major highway routes are major elements of the planning method. The case study used two industrial sectors—food and kindred products, and machinery products—which accounted for the largest portion of state employment in nonservice sectors and the largest truck traffic generated in the state. A simplistic transportation network was used to demonstrate the modeling procedure. The analysis uses county-level employment and population to estimate zonal freight tonnage. The truck share of generated freight was estimated as the total freight generated less the freight tonnage shipped by rail. A gravity model was used to distribute the truck tonnage among origin-destination pairs, using travel time as the impedance on highway links. Estimated truck flows were converted to vehicle trips on least time highway routes using typical vehicle equivalent weights.

Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models

2015 ◽  
Vol 42 (11) ◽  
pp. 854-864
Author(s):  
Jiaqi Ma ◽  
Changju Lee ◽  
Michael J. Demetsky
Keyword(s):  
Transportation Planning ◽  
Travel Demand ◽  
Planning Process ◽  
Methodological Framework ◽  
Demand Model ◽  
Peak Period ◽  
Operational Strategies ◽  
Operational Analysis ◽  
Starting Point ◽  
Demand Models

Recently, limited available resources for physical capacity expansion have generated supports for short-term operational improvements. Yet, only a few studies have dealt with evaluating these operational strategies effectively within the traditional transportation planning process even though suitable operational strategies impact to not only specific corridors or regions but also the whole transportation network. This is because it is generally perceived that integrating travel demand models with operational analysis approaches is quite difficult due to different constraints, modeling structures, and required data sets. In this regard, the concept of methodological framework to evaluate operational strategies with travel demand models is developed and validated by the proper case study (i.e., high occupancy toll lanes deployment in the Hampton Roads area in Virginia, US) in this research. The proposed framework consists of three major components: (i) the selection of an appropriate operational analysis approach, (ii) the disaggregation of daily traffic volumes to peak period volumes, and (iii) the alignment of modeling elements between the travel demand model and operational tool. Key contributions from this research are that (i) the proposed methodology enables the evaluation of travel behavioral changes without microscopic simulation, especially in terms of capturing network flow pattern changes caused by behavioral shifts after operational strategy deployment, (ii) the proposed framework eliminates assumptions required when only operational tools are used to evaluate operational strategies, (iii) the disaggregation method of a daily trip distribution matrix into peak period matrices by using survey data are developed, (iv) specific details influencing integration in terms of data types, peak period link capacity, volume-delay functions, and link impedance are identified. Consequently, even though this research still has some limitations (e.g., inherent weakness of travel demand models), this can be a starting point to develop more detailed guidelines as well as a good reference for practitioners and researchers who wish to evaluate operation strategies within transportation planning process.

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