Understanding Dhaka City Traffic Intensity and Traffic Expansion Using Gravity Model

Analysis of traffic pattern recognition and traffic congestion expansion in real time are one of the exciting and challenging tasks which help the government to build a robust and sustainable traffic management system specially in a densely populated city like Dhaka. In this paper, we analyze the traffic intensity for small areas which are also known as junction points or corridors. We describe Dhaka city traffic expansion from a congestion point by using gravity model. However, we process real-time traffic data of Dhaka city rather than depend on survey and interview. We exactly show that traffic expansion of Dhaka city exactly follows gravity model. Expansion of traffic from a congestion point spreads out rapidly to its neighbor and impact of congested point decreases as the distance increases from that congested point. This analysis will help the government making a planned urbanized Dhaka city in order to reduce traffic jam.Comment: 6 pages, 10 citation

Annex 21 : scrutiny of electricity billing and supply data as a probable proxy for economic activities : an analysis of power consumption of Dhaka, Bangladesh (draft)

This case study attempts to provide a load forecasting model to help ascertain short-term electricity demand at the regional level in Bangladesh. To assist policy makers in determining how regulatory decisions impact behavior, consumer level billing data, and power satiation level, supply data such as load variability and load shedding is analyzed. Cleaning the dataset and dealing with outlier values includes such problems as lack of exact household addresses in Dhaka city. The impact of changes in appliance use due to weather or price hikes is examined in order to predict future energy needs of consumers

The Influence of Institutional Structure on Regulatory Choices and the Impact of these Choices on the Telecommunication Marketplace

<p>The telecommunications regulatory authorities are separate independent bodies equipped with people with the knowledge of the technology and the economics of the sector and endowed with certain statutory power to oversee the market. In different countries these sector specific organizations are different in terms of structure, scope and level of independence. Nonetheless, these independent regulatory authorities (IRA) take various decisions that are often proved to be vital to change the course of the specific sector. This research aims to understand what influences the regulatory decisions and how the decisions impact the course of the sector. Mobile telephony and Internet revolutionized the telecommunication marketplace since early 1990s. Better technology made transfer of real time voice and video transfer via Internet affordable and ubiquitous. The need for spectrum increased as the use of mobile data increased. Traditional regulatory regimes recognize ‘voice’ and ‘data’ to be distinct services. With Voice over Internet Protocol (VoIP), this regulatory notion is gradually becoming obsolete. Now wired, wireless and Internet based telecom services are competing in the voice telephony market. With competition, customers are migrating to newer, cheaper and better quality services. The barrier to this free movement is the absence of number portability- customer’s ability to keep the phone number when she migrates from the existing service provider to a better alternative. The regulators are now facing new challenges –legalization of VoIP, introduction of mobile number portability, decisions to award electromagnetic spectrum via auction, legalization of reusing specific spectrum band and mandating neutrality of technology for specific technology generations. Broadly, these regulatory choices can be divided into three categories- decision to introduce new technology (legalization of Voice over IP), decision to enhance competition (introduction of mobile number portability) and decision to manage scarce resources efficiently (spectrum related decisions). This thesis is a novel addition to the anthology of telecommunication regulation, technology policy, institutional economics and political science. Theoretical works that explain the dynamics of the regulatory institutions and their decisions are in abundance. However, quantitative scrutiny is scarce. We try to fill the gap through empirical research. We ascertain how over all politicoregulatory environment impact certain regulatory decisions, and how these decisions impact diffusion of a certain technology generation, price of telephony and the country’s economic growth. The dataset is of time-series-cross-sectional form consisting of 145 countries of the world. It contains information regarding political conditions (such as system of the governments, parliament, regulatory design (such as composition of the institution, independence, functions), socio-economic condition (such as income level), and communication-market specific data (such as various regulatory decisions, sector performance indicators). With the help of the dataset, this thesis ascertains firstly the impact of the institutional environment on the regulatory choices and secondly the impact of these choices on the marketplace. Our estimations find that in the telecommunications sector, it is not the over all political condition but the construct of the regulatory structure-independence and scope that enables the regulators to take decisions in favor of technologies and interventions that may be paradigm shifting and disruptive. We find that the decisions related to spectrum management, have positive impact on diffusion. However, the ‘one size fit all solution’ does not work well as in terms of consumer price and GDP growth, influence of these interventions affect the countries of different economic level and geopolitical locations differently. We also find evidence that, whilst the institutions impact various regulatory decisions which in turn impact the diffusion of technologies, the variables of the institutions do not have direct influence on the diffusion process.</p

Impact of spectrum management policy on the penetration of 3G technology

<p>Diffusion of 3G cellular technology varies widely across countries and regions. Past studies have shown that lower levels of diffusion of previous technologies and higher levels of income are significant factors in accelerating the take up of 1st and 2nd generation of mobile telephony. In addition, spectrum management policy plays a significant role in shaping 3G diffusion. Regulatory policies regarding spectrum management include mandating band and technology and decisions to hold spectrum auctions. An econometric analysis over a multi-country panel dataset shows that these spectrum management policies do have significant influence on the take-up of 3G. Findings suggest that the presence of multiple technologies for the previous generation is associated with rollout delay. The estimations indicate that countries that mandated a specific frequency band for 3G saw faster roll out, but in the long run those countries experienced a slower growth rate. Also estimations find that 3G diffusion is not significantly affected by the choice of auctions vs. alternative license award processes. Insights gained from this study of the 2G to 3G transition can provide guidance to regulators now contemplating the transition to newer generations.</p

Understanding the Urban Environment from Satellite Images with New Classification Method&mdash;Focusing on Formality and Informality

Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively

A Proposal for Formulating a Spectrum Usage Fee for 5G Private Networks in Indonesian Industrial Areas

The Indonesian spectrum usage fees&mdash;the so-called Biaya Hak Pengguna Frekuensi Izin Pita Frekuensi Radio (BHP IPFR)&mdash;are currently calculated using a formula determined by the three following main parameters: the frequency band, the country&rsquo;s economic parameter, and the nationwide population. As spectrum usage fees are proportional to the width of the bandwidth, the current formula would result in an extremely high price when applied to 5G-mmWave private networks, with the cost burden being a direct consequence for the service operator. In this paper, we propose the formulation of a new spectrum usage fee for 5G-mmWave private network implementation in Indonesian industrial areas. To do so, we evaluate the current formula, adopt the framework offered by the ITU-R SM.2012-5 (06/2016), and use an industrial reference index&mdash;the Indonesia Industry Readiness Index 4.0 (INDI 4.0) score. We test the proposal by applying the new formula to calculate the 5G-mmWave private network spectrum usage fee for the Jakarta industrial area. The result shows that the new formula gives a lower spectrum usage fee than the current formula, which benefits 5G-mmWave private network service operators. Such savings can be regarded as a government subsidy for the service operators to use in various ways in the industry, providing further economic benefits. Using the input&ndash;output model, we prove that despite the proposed new formula brings a lower spectrum usage fee, resulting in a loss in state income, it will lead to a much greater positive impact on the national economic output. Applying the new formula will eventually have a multiplier effect on various sectors and encourage digital economic growth and national digital transformation, especially for vertical industries in Indonesia. This study may serve as a guideline or initial reference for Indonesian policymakers and service operators for applying the CAPEX and OPEX cost of using the new spectrum for 5G-mmWave private network service implementation and estimating the economic multiplier for 5G-mmWave private network service deployment in industrial areas. It can also be used as a benchmark case for other countries to apply spectrum usage fees for private networks in industrial areas

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing 70% of the urban space was used to train the models, and the remaining 30% was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of 90.0% for Dhaka, 91.5% for Nairobi, 94.75% for Jakarta, 82.0% for Guangzhou city, 94.25% for Mumbai, 91.75% for Cairo, and 96.75% for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces