In contrast to the ongoing worldwide uncontrolled expansion of urban development resulting in sprawled cities, compact cities have been argued by planners and researchers to be the more sustainable urban form. However, in compact cities, it has been shown that a low proportion of green spaces jeopardizes the sufficient supply of urban ecosystem services. This suggests that there remains a deficiency in clear visions for operationalizing compact and green cities. To remediate this, this paper introduces a systemic conceptual framework for compact and green cities by combining the concepts of smart growth and green infrastructure. The indicator-based, smartcompact-green city framework includes two aspects: 1) smart compact cities (considering the need to limit urban sprawl through smart growth) and 2) smart green cities (reflecting the preservation and (re-)development of urban green infrastructure). The paper suggests that there is the need to balance these two aspects to develop a systemic approach towards smart-compact-green cities. A hierarchical target system grounded on four characters for smart compact and smart green cities is developed. Smart-compact-green cities can be characterized through a 1) smart environment of compact and green cities, 2) smart multifunctionality of compact and green cities (economic, social, environmental), 3) smart government for compact and green cities and 4) smart governance for compact and green cities. The characters comprise twelve factors defined by 39 indicators for smart compact cities and 44 indicators for smart green cities, respectively. The systemic framework can support researchers and practitioners to develop visions of how existing or future cities can approach smart-compact-green cities in mainstreaming the ecology of and for cities by better understanding the complexity of urban systems and providing a basis for a systematic spatial monitoring.

Urbanization is occurring rapidly in southeastern Asia where streams are increasingly pressured. We assessed the ecological integrity of Ampang River in Kuala Lumpur by comparing structural and functional measures between forested and urban sites. We assessed 4 forested, 1 intermediate (deforested, not channelized), and 5 urban, channelized sites. Urbanization altered substrate (concrete at urban sites), riparian vegetation, light, temperature, O 2 , conductivity, nutrients, and major ion levels, and simplified food webs. Invertebrate composition shifted from pollution-intolerant taxa at forested sites to tolerant taxa at urban sites. Richness in Surber samples was 56 species at forested sites and 27 taxa at urban sites. Basal food sources at forested sites were leaf litter and biofilm, whereas at urban sites they were a sediment mat comprising organic matter, cyanobacteria, iron bacteria, and algae. Organic matter biomass on ceramic tiles was greater at urban than forested sites, but chlorophyll a was not. Faunal abundance was greater in litter bags at urban (mean = 72.6 animals/bag, 68% chironomids) than at forested sites (27.9 animals/bag; 33% chironomids), but species richness was higher in forested sites. Food webs at forested sites were more complex and differed from those at urban sites, which lacked shredders, and grazers differed in taxonomic composition and diet between urban and forested sites. At forested sites, insect larvae grazed biofilm, whereas at urban sites, snails consumed the organic mat. Leaf decomposition was fastest at the most disturbed site indicating rapid microbial decomposition caused by increased temperatures (+4.5°C) and nutrients, and possibly increased physical breakdown caused by rapid flow carrying abrasive sediment and garbage. Urbanization has had severe impacts on ecosystem function in Ampang River that could be alleviated by preventing pollution, restoring riparian vegetation, and providing natural substrate.

No abstract

No abstract

No abstract

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

No abstract

No abstract