Road Accident Data Collection Systems in Developing and Developed Countries: A Review

The road accidents trigger major financial loss and casualties to the individual as well as the state as a whole. The intelligent safety systems are developed to provide all road users with a safe transport system. This approach acknowledges the sensitivity of individuals to extreme injury in road accidents and recognizes the need for the system for improvement. To establish a proper system for road accident prevention, records from prior accidents play a key role in the evaluation and prediction of the accident, damage, and consequences. Therefore, this study was performed to evaluate and comparing existing practices in developing and developed countries for collecting road accident data. Moreover, the manual and digital approaches of data collection are highlighted. Keeping this in mind, this review provides an overview of how developing countries currently collect their data and their data dissemination methods to extract such useful information, which could prove beneficial in deciding the road safety programs for the well-being of end-users

Road Accident Data Collection Systems in Developing and Developed Countries: A Review

The road accidents trigger major financial loss and casualties to the individual as well as the state as a whole. The intelligent safety systems are developed to provide all road users with a safe transport system. This approach acknowledges the sensitivity of individuals to extreme injury in road accidents and recognizes the need for the system for improvement. To establish a proper system for road accident prevention, records from prior accidents play a key role in the evaluation and prediction of the accident, damage, and consequences. Therefore, this study was performed to evaluate and comparing existing practices in developing and developed countries for collecting road accident data. Moreover, the manual and digital approaches of data collection are highlighted. Keeping this in mind, this review provides an overview of how developing countries currently collect their data and their data dissemination methods to extract such useful information, which could prove beneficial in deciding the road safety programs for the well-being of end-users

Circular Economy - Recent Advances in Sustainable Construction Waste Management

With time, construction waste is increasing massively and its dumping is a serious issue globally. Utilizing the waste in various products and construction projects is boosting, but still, the amount of waste is much higher. Transitions to more sustainable construction can assist in attaining the specific goal of slowing down natural resources depletion, reducing environmental damage by extracting and recycling new materials, and minimizing pollution from the processing, use, and disposal of materials once they complete their useful life period. An important way to do this is to improve efficiency and bring productivity in the utilization of resources. The circular economy is more productive and healthier, where raw materials are stored longer in the production cycle and can be recycled, thus producing less waste. Due to potential benefits through enhanced quality and productivity in the processes, the concept of circular economy is grabbing the attention of construction industry stakeholders to attain sustainable construction waste management. This chapter focuses on the significance of a circular economy for the attainment of sustainable waste management in the construction sector. Moreover, the impact of construction waste and its utilization through recent sustainable solutions which also impact the economy has also been highlighted

Assessment of Economic Sustainability in the Construction Sector: Evidence from Three Developed Countries (the USA, China, and the UK)

The construction sector plays a significant role in contributing to uplifts in economic stability by generating employment and providing standardized social development. Economic sustainability in the construction sector has been less addressed despite its wide applicability in the economy. This study aimed to perform a comparative analysis to determine the application of a circular economy in the construction sector toward economic sustainability, along with its long-term forecasting. A time series analysis was used on the construction sector of the United States of America (USA), China, and the United Kingdom (UK) from 1970 to 2020, by taking into account individual effects to propose a framework with global validity. Statistical analysis was performed to analyze the dependence of the construction sector and determine its short- and long-term contributions. The results revealed that the construction sectors in these countries tend to bounce back to equilibrium in the case of short-term effects; however, the construction sector behaves differently with respect to each sector after experiencing long-term effects. The results show that the explanatory power of the forecasting model (R2) was found to be 0.997, 0.992, and 0.996 for the USA, China, and the UK. Based on the concept of the circular economy, it was concluded that the USA will become a leader in attaining sustainability in construction owing to its ability to recover quickly from shocks, and that the USA will become the largest construction sector in terms of GDP, with a USD 0.3 trillion higher GDP than that of the Chinese sector. Meanwhile, there will be no significant change in the construction GDP of the UK up to the end of 2050. Moreover, the speeds of the construction sector toward equilibrium in the long run in the USA, China, and the UK, and regaining of their original positions, is 0.267%, 1.04%, and 0.41% of their original positions, respectively. This study has a significance in acting as a guideline for introducing economic and environmental sustainability in construction policies, because of the potential of the construction sectors to recover from possible recessions in their respective countries

Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level