Sustainable Parking

It’s high time to have a paradigm shift to multi modal transport system and also move towards sustainable parking. All the three pillars of sustainability have to be kept in mind for any policy. The climate change is affecting the human life, it’s essential to have a modal shift so as to achieve a smarter and sustainable travel. It’s essential to have parking policy that supplements this concept of sustainability. On-Street, Off-Street and Peripheral parking have to be designed in such a way so as to supplement each other and should follow a particular policy. Parking price has to be designed in the same manner. It’s essential to assimilate the disabled people in any policy. Sustainable Parking is an important step towards sustainable travel and it should take into account the land use pattern and the local environment/surroundings

AI-based cyclist safety hybrid modelling for future transport network

A cyclist is a vulnerable road user whose safety is affected by several externalities. The global aim of the research is to investigate the effect of critically identified variables of rider attributes of age, gender, varied environmental condition of lighting, meteorology, and micro-infrastructure variables on the safe usage of the infrastructure for a cyclist. Presently, very few works have attempted to undertake such modelling. A novel methodological framework is developed, consisting of descriptive, statistical, artificial intelligence and mathematical approaches. Accurate prediction models are developed, and in-depth knowledge of how different variables affect cyclist safety are identified, modelled, and quantified. It is found that the variables of age, gender, varied environmental conditions, and micro-infrastructure variable are critical variables affecting the safe usage of infrastructure. These variables, both individually and in combination, impact cyclist safety. Cycling safety is a dynamic variable that varies temporally and spatially. The spatial and environmental variables have a significantly varied effect on safety depending upon the rider personal attribute. As the number of safety variables that the cyclist must conform to grows, so does the risk. The riskiest environmental conditions are exacerbated by the prevailing traffic flow regime, posing a significant safety risk to cyclists. The modelling requirement of a cyclist is significantly different from motorists. A hybrid intelligent modelling paradigm is required, as demonstrated in this research. The study results can significantly impact the route choice, modelling, and planning of infrastructure. A shift in the road safety analysis towards nanoscopic modelling can help achieve zero-vision road traffic fatality. The research reinforces a need for planning and design of infrastructure to move towards a more holistic approach while considering the limitations of this vulnerable road user

An Analysis of the Garment sector of Pakistan within a Global Value Chain Framework

The textile industry is Pakistans largest and one of the oldest manufacturing industries. Widely available local cotton and continuous public support have been important factors in the growth of the textile industry. However, the garment sector in Pakistan is trapped at a low-equilibrium in a high value-added categoryproducing low-price items for mass retailers. The objective of this paper is to identify the main reasons for the relative stagnation and lack of competitiveness of Pakistans garments sector in light of survey data collected form 234 garments manufacturers. We use Global Value Chain (GVC) framework to analyze Pakistans garment industry. To come out of low-equilibrium and move up the garments value chain, the sector requires continual investment in state of the art technology, a trained workforce, and agglomeration economies or intra-cluster spill-overs

Regression Analysis for the Forecasting of Production and Yield of Wheat Crop in Sindh and Punjab.

Wheat is an important and major agriculture crop of Pakistan. The purpose of this study is to fit the Simple regression model and to forecast the value of each indicator for planning purpose. The data set of 30 years (i.e. 1988 – 2018) for Punjab Wheat and Sindh Wheat are collected from Pakistan Bureau of Statistics, Agricultural Statistics of Pakistan and internet also investigated. Econometrics techniques (Trend Curves, Lagged Models, Simple Regression, Correlation and Moving Averages) are applied using Minitab software and MS Excel and observed that: The changes in all indicators with respect to time are positive. The changes in Production for Sindh are less than for Punjab which shows better consistency towards Production of Sindh than that of Punjab. The changes in Yield for Punjab Wheat are larger than for Sindh Wheat. Yield of Wheat Sindh is more consistent than Wheat Punjab. For Production, Poly-2(PP), Poly-6(PS) Exponential and lagged-1 models are preferred due to better results. Key words: Simple Regression, Exponential Model, Lagged Model, Data, Production, Forecasting, etc. DOI: 10.7176/JBAH/9-16-06 Publication date: August 31st 2019

HFRAS : design of a high-density feature representation model for effective augmentation of satellite images

Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Intelligent Nanoscopic Cyclist Crash Modelling for Variable Environmental Conditions

A cyclist is a vulnerable road user whose interaction with the road infrastructure depends on several factors, including variable environmental conditions of lighting and meteorological road surface. This paper is concerned with nanoscopic crash modelling under the riskiest environmental conditions. There are very few works in the literature dealing with such modelling. An intelligent methodological framework consisting of the data collection unit and a knowledge processing unit (KPU) is proposed. In the knowledge processing unit, a combination of a) Statistical, b) Data learning and c) Casual inference methods are applied for investigating crashes on the study area of Tyne and Wear county in North-East of England. Three predictive nanoscopic road safety models are constructed (with 86% accuracy) using a) Spatial, b) Personal, and c) Infrastructure input variables. The importance of each of the identified input variable is estimated by deep learning and statistically validated through chi-square test and Cramer's V statistic. It is found that unsafeness of interaction between rider and infrastructure depends on lighting and road surface meteorological conditions. Different environmental conditions present a varying degree of risk to different types of infrastructure. The riskiest environment conditions are significantly affected by rider's gender and age, traffic flow regime, specific riding manoeuvre, and the road hierarchy difference. The increase in the number of variables, a rider encounters during his entire trip, imparts risky riding behaviour, affecting its safe interaction with the infrastructure. A novel infrastructure variable, i.e. `functional road hierarchy level and direction' introduced in this work, is found to be a critical road safety variable. A shift in road safety analysis towards nanoscopic modelling can help achieve zero-vision road traffic fatality. The study reinforces the need to plan and design infrastructure to move towards a more holistic approach while considering this vulnerable road user's limitations

Real-Time Nanoscopic Rider Safety System for Smart and Green Mobility Based upon Varied Infrastructure Parameters

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system

Deep neural network-based hybrid modelling for development of the cyclist infrastructure safety model

This paper is concerned with modelling cyclist road safety by considering various factors including infrastructure, spatial, personal and environmental variables affecting cycling safety. Age is one of the personal attributes, reported to be a significant critical variable affecting safety. However, very few works in the literature deal with such a problem or undertaking modelling of this variable. In this work, we propose a hybrid approach by combining statistical and supervised deep learning with neural network classifier, and gradient descent backpropagation error function for road safety investigation. The study area of Tyne and Wear County in the north-east of England is used as a case study. An accurate dynamic road safety model is constructed, and an understanding of the key parameters affecting the cyclist safety is developed. It is hoped that this research will help in reducing the cyclist crash and contribute towards sustainable integrated cycling transportation system, by making use of cut above methodologies such as deep learning neural network

Development of a Safety System for Intelligent Cyclist modelling

This paper is concerned with the modelling of cyclist road traffic crashes by considering multiple factors affecting the safety of cyclists. There are very few works in the literature dealing with such a problem. The available models in the literature are only based upon the probabilistic function of human error. In this study, we propose an intelligent safety system for modelling cycling infrastructure. The historic crash dataset for the Tyne and Wear County, north-east of England is used as a case study. There are five predictive road safety models develops using the Artificial Neural Network, with the output for the riskiest road type infrastructure. The study demonstrates that infrastructure, spatial variables, personal characteristics, and environmental conditions affect safety, which can also be used for predicting safety. These identified variables are modelled both individually and in combination with each other, and a plausible high accuracy is achieved in all the five models (> 85 accuracy). This demonstrates the benefit of using ANN for effective and efficient modelling of the safety variable for infrastructure design and planning. It is hoped that the proposed model can help in designing better cyclist infrastructure and contribute towards the development of a sustainable transportation system