Search

While traffic crash duration on freeways and its association with crash characteristics have been studied extensively, limited research exists regarding the characteristics of crash duration in urb...

The emergence of rapid transit, primarily represented by high-speed railway (HSR), while reshaping the regional traffic patterns, leads to the reconstruction and redistribution of population and industry. This leads to either shrinkage or expansion of urban scale. However, research on the influence mechanisms of the urban scale has mostly concentrated on historical, economic and social factors. The influence of traffic factors is rarely mentioned in current research. Therefore, this study examines Northeast China, where the change in urban scale is most significant, to discuss the spatial impact of high-speed railway on the urban scale. This is of great significance in terms of enriching current understanding of the factors affecting the urban scale. The results included the following: 1) The high-speed railway produced considerable space-time convergence effects, however, simultaneously aggravated the imbalance in traffic development in Northeast China. The increase in accessibility presents attenuation characteristics from the high-speed railway. Additionally, the high-speed railway has changed the mode of cooperation between cities in the provinces, inter-regional and inter-provincial cooperation models gradually become popular. 2) The change rate of accessibility and the urban scale present significant spatial coupling phenomena, with the change rate of the Harbin-Dalian trunk lines and its surroundings being more significant. 3) There are predominantly four modes of the influence of high-speed railway on the urban scale, which make difference city present expansion or shrinkage.

This study aims to investigate the effects of built environment factors on pedestrian crash frequency during the morning, noon-afternoon, night, and during peak hours. To accomplish this, micro-cra...

Transportation disadvantaged (TD) populations, which include elderly people, people with disabilities, and people who do not own a vehicle, face mobility challenges because alternative transportati...

Coastal regions worldwide are during the process of rapid urban expansion. However, expanded urban settlements in land-sea interfaces have been faced with unprecedented threats from climate change related hazards. Adaptation to coastal hazards has received increasing attention from city managers and planners. Adaptation and land management practices are largely informed by remote sensing and land change modeling. This paper establishes a framework that integrates land change analysis, coastal flooding, and sea level rise adaptation. Multilayer perceptron neural network, similarity learning, and binary logistic regression were applied to analyze spatiotemporal changes of residential, commercial, and other built-up areas in Bay County, Florida, USA. The prediction maps of 2030 were produced by three models under four policy scenarios that included the population relocation strategy. Validation results reveal that three models return overall acceptable accuracies but generate distinct landscape patterns. Predictions indicate that planned retreat of residents can greatly reduce urban vulnerability to sea level rise induced flooding. While managed realignment of the coast brings large benefits, the paper recommends different mixes of adaptation strategies for different parts of the globe, and advocates the application of reflective land use planning to foster a more disaster resilient coastal community.