Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded

Inspiration vs. perspiration in economic development of the Former Soviet Union and China (ca. 1920-2010)

Here, we discuss the role of both perspiration factors (physical and human capital) and inspiration factors (Total Factor Productivity) in the economic development of the Former Soviet Union area (FSU) and China, ca. 1920–2010. Using a newly created dataset, we find that during the Socialist central-planning period, economic growth in both countries was largely driven by physical capital accumulation. This finding follows logically from the development policies in place at that time. During their transition periods, (i.e., starting from the late 1970s in China and the late 1980s in the FSU), China managed to keep technical inefficiency of production factors in check, largely by massively increasing its human capital, thereby lowering the physical- to-human capital ratio. In contrast, the FSU accomplished a similar outcome largely through reducing its stock of physical capital. As a result, although there was little difference in technical efficiency between these two economies, China’s emphasis on human capital formation made it easier for this country to improve its general productivity and to increase per capita growth. This changed in the late 1990s and early 2000s, when the FSU began to recover economically, regaining its 1990 levels of output and productivity