Drawing on household data from Germany, this study econometrically analyzes the determinants of automobile ownership, focusing specifically on the extent to which decreases in family size translate into fewer cars at the national level. Beyond identifying several variables over which policy makers have direct leverage, including the price for fuel, the supply of public transit, and land use features, the analysis uses the estimated coefficients from a multinomial logit model to simulate car ownership rates under alternative scenarios pertaining to demographic change and other socioeconomic variables. Our baseline scenario predicts continued increases in the number of cars despite decreases in population, a trend that could be partially off set by substantial increases in fuel prices.

/pdf/do-fewer-people-mean-fewer-cars-population-decline-and-car-hupvs7if30.pdf

Do Fewer People Mean Fewer Cars? – Population Decline and Car Ownership in Germany

The study is conducted to comprehend the relation of the oil sector with Oman’s economic growth. Oman is an abundant oil country, and the oil sector plays an influential part in its economic develo...

https://www.tandfonline.com/doi/pdf/10.1080/23322039.2020.1838418

An ARDL investigation on the nexus of oil factors and economic growth: A timeseries evidence from Sultanate of Oman

Capturing long-memory properties in road fatality rate series by an autoregressive fractionally integrated moving average model with generalized autoregressive conditional heteroscedasticity: A case study of Florida, the United States, 1975-2018.

Abstract Sub-Saharan suffers from a poor transport system failing to address equity and sustainability. The system can be affected by several factors; however, this study explores how African historical transport infrastructure development and socio-economic changes affect institutional changes and preferences of available transport modes. We used recently published systematic reviews, case studies, analytical cross-sectional studies, policy review papers, and available relevant studies reports and some grey literature to understand the impacts of these factors. Our analysis shows that the transport demand is affected by local socio-economic contexts shaped by historical pretexts, socio-technical changes, economic transitions, and related value systems. These is essential to understand the operational characteristics of the formal and informal transport modalities, their roles and contribution to meet the mobility needs of the people and designing of an effective management system. This paper can provide insights to policy-makers, urban transport planners and researchers in the field on the complementary modalities involving formal and informal transportation. 

/pdf/a-review-on-the-socio-economic-impacts-of-informal-1vtdbjl5.pdf

A review on the socio-economic impacts of informal transportation and its complementarity to address equity and achieve sustainable development goals

Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant average monthly crashes. On the contrary, the accuracy of crash prediction improved in provinces with higher per capita GDP, and higher traffic exposure. A 1% increase in crash variability, average historical crash count, GDP per capita, and traffic exposure, respectively, resulted in a 0.65%, 0.52%, −0.38%, and −0.13% change in the RMSE of forecasting. The addition of traffic exposure and macroeconomic factors significantly enhanced the model fit and improved the adjusted R-squared by 14% compared to the reduced model that only used the historical average and variability of crash count as the independent variables. The findings of this research suggest planners and policymakers should consider the notable influence of macroeconomic factors and traffic indicators on the crash forecasting accuracy. • This research models the accuracy of monthly road crash counts in Iran. • Holt-Winters' method is used to acquire the monthly provincial crash count forecast and accuracy. • Macroeconomic and traffic indicators are used in an OLS regression to model the accuracy of forecast among the provinces. • Provinces with a higher average annual hourly traffic and per capita GDP had more accurate forecasts. • Provinces with a higher historical average crash count and crash count variability had less accurate forecasts. 

Modeling the accuracy of traffic crash prediction models

Exploring the impact of social economic variables on traffic safety performance in Hong Kong: A time series analysis

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Exploring the impact of social economic variables on traffic safety performance in Hong Kong: A time series analysis