City-wide Area Research Articles

Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal Coverage

Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval; moreover, the mean increases by 2% when 30% of communication messages are randomly lost.

Urban correction of global DEMs using building density for Nairobi, Kenya

Urban flood models that use Digital Elevation Models (DEMs) to simulate extent and depth of flood inundation rely on the accuracy of DEMs for predicting flood events. Despite recent advances in developing vegetation corrected DEMs, the effect of building height and density errors in global DEMs in urban areas are still poorly understood, and their correction remains a challenge. In this research we developed a methodology for building error correction that can be applied to any other case study, where building density data and a local reference DEM data are available. This methodology was applied to Nairobi, Kenya using six global DEMs (SRTM, MERIT, ALOS, NASADEM, TanDEM-X 12 m, and TanDEM-X 90 m DEM). Our results show building error at highest building density varying between 1.25 m and 5.07 m for the DEMs used, with the MERIT DEM showing the smallest vertical height error from the reference DEM. The six DEMs were corrected by deriving a linear relationship between building density and DEM error. Our findings show that the removal of building density error resulted in the improvement of the vertical height accuracy of the global DEMs of up to 45% for MERIT and 40% for ALOS. This methodology was also applied to the Central Business District (CBD) area of Nairobi, characterized by taller buildings and high building density. The error parameters in the CBD area resulted to be between 15 to 45% higher than those of the Nairobi city wide area for the six global DEMs, thus providing further insights into the contribution of building heights to errors in global DEMs. Building height data is still unavailable on a global scale and our results show that global DEMs can be usefully corrected for building density errors in urban areas, even where specific building height data are not available.

Impact of the COVID-19 Pandemic on the U.S. Electricity Demand and Supply: An Early View From Data.

After the onset of the recent COVID-19 pandemic, a number of studies reported on possible changes in electricity consumption trends. The overall theme of these reports was that “electricity use has decreased during the pandemic, but the power grid is still reliable”—mostly due to reduced economic activity. In this paper, we analyze electricity data until the end of May 2020, examining both electricity demand and variables that can indicate stress on the power grid. We limit this study to three states in the U.S. California, Florida and New York. The results indicate that the effect of the pandemic on electricity demand is not a simple reduction, and there are noticeable differences among regions analyzed. The variables that can indicate stress on the grid (e.g., daily peak and trough of the hourly demand, demand ramp rate, demand forecast error, and net electricity interchange) also conveyed mixed messages: some indicate an increase in stress, some indicate a decrease, and some do not indicate any clear difference. A positive message is that some of the changes that were observed around the time stay-at-home orders were issued appeared to revert back by May 2020. A key challenge in ascribing any observed change to the pandemic is correcting for weather as it can be challenging to accurately define it for large geographic regions. We provide a weather-correction method, apply it to a small city-wide area in North Central Florida, and discuss the implications of the estimated changes in demand. The results indicate that a 10% (95% CI [2%, 18%]) increase in electricity demand is likely to have occurred due to COVID-19 for the city analyzed.

The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images

This study proposes a workable approach for quantitatively measuring the perceptual-based visual quality of streets, which has often relied on subjective impressions or feelings. With the help of recently emerged street view images and machine learning algorithms, an evaluation model has been trained to assess the perceived visual quality with accuracy similar to that of experienced urban designers, to provide full coverage and detailed results for a citywide area. The town centre of Shanghai was selected for the site. Around 140,000 screenshots from Baidu Street View were processed and a machine learning algorithm, SegNet, was applied to intelligently extract the pixels representing key elements affecting the visual quality of streets, including the building frontage, greenery, sky view, pedestrian space, motorisation, and diversity. A Java-based program was then produced to automatically collect the preferences of experienced urban designers on representative sample images. Another machine learning algorithm, i.e. an artificial neural network, was used to train an evaluation model to achieve a citywide, high-resolution evaluation of the visual quality of the streets. Further validation through different approaches shows this evaluation model obtains a satisfactory accuracy. The results from the artificial neural network also help to explore the high or low effects of various key elements on visual quality. In short, this study contributes to the development of human-centred planning and design by providing continuous measurements of an ‘unmeasurable’ quality across large-scale areas. Meanwhile, insights on the perceptual-based visual quality and detailed mapping of various key elements in streets can assist in more efficient street renewal by providing accurate design guidance.

Enabling Demand Side Management: Heat Demand Forecasting at City Level.

Implementation of new energy efficiency measures for the heating and building sectors is of utmost importance. Demand side management offers means to involve individual buildings in the optimization of the heat demand at city level to improve energy efficiency. In this work, two models were applied to forecast the heat demand from individual buildings up to a city-wide area. District heating data at the city level from more than 4000 different buildings was utilized in the validation of the forecast models. Forecast simulations with the applied models and measured data showed that, during the heating season, the relative error of the city level heat demand forecast for 48 h was 4% on average. In individual buildings, the accuracy of the models varied based on the building type and heat demand pattern. The forecasting accuracy, the limited amount of measurement information and the short time required for model calibration enable the models to be applied to the whole building stock. This should enable demand side management and lead to the predictive optimization of heat demand at city level, leading to increased energy efficiency.

A scheme on multi-tier heterogeneous networks for citywide damage monitoring in an earthquake

Quick, accurate damage monitoring is strongly required for damage assessment in the aftermath of a large natural disaster. Wireless sensor networks are promising technologies to acquire damage information in a citywide area. The wireless sensor networks, however, would be faced with difficulty to collect data in real-time and to expand the scalability of the networks. This paper discusses a scheme of network architecture to cove a whole city in multi-tier heterogeneous networks, which consist of wireless sensor networks, access networks and a backbone network. We first review previous studies for citywide damage monitoring, and then discuss the feature of multi-tier heterogeneous networks to cover a citywide area.

ANALYSIS OF RECYCLE-ORIENTED REGIONAL INDUSTRIAL METABOLISM MODEL WITH RESOURCE CONVERSION

Since Earth Summit, Some concepts about cyclic systems, such as an eco-industrial park, have been appeared in order to solve serious global environmental problem, surplus consumption of resources and problems of solid wastes. Construction of recycle-oriented regional industrial metabolism model with minimum environmental load requires resource conversion facilities. In addition, there are some promotion factors to make the model, which can improve the efficiency and supply and demand balance. In this study, various conversion facilities and sectors are selected by a set of judgement process of cycle formation. The followed environmental performance is evaluated using account matrix including sectors varing from neighborhood of a factory to city-wide area. As a result of the analysis, various important factors were recognized in considering comprehensive efficiency, environmental load by the transportation, and the usefulness of this account matrix towards further progressing the complex.

Virus Survival and Transport in Ground Water

Viruses are a significant cause of waterborne disease in the United States; it has been estimated that they may be responsible for as much as 50% of the reported outbreaks. This fact has led the U.S. Environmental Protection Agency to propose a maximum contaminant level goal (MCLG) for viruses in drinking water. Septic tanks, which contribute over one trillion gallons of waste to the subsurface every year, are a major source of viruses in soils and ground water. The purpose of this research was to develop a model which could be used to estimate safe distances between septic tanks, or other sources of contamination, and drinking-water wells. The model was based on ground-water flow characteristics and the length of time that viruses remain infective in the subsurface environment. Water samples were collected from 71 continuously pumping municipal drinking-water wells. Viruses were inoculated into the water samples, and the rate at which the viruses were inactivated was calculated for each sample. The inactivation rates were determined to be spatially correlated by calculating a semivariogram. Kriging, a geostatistical technique, was used to estimate virus inactivation rates at unsampled locations using the measured values at nearby locations. The measured and kriged virus inactivation rates were used in conjunction with the regional ground-water flow characteristics to calculate septic tank setback distances over a city-wide area. The setback distance was defined as the distance required for a 7-log reduction in virus number in the time that the water traveled from the source of contamination to a drinking-water well. The model has been extended to account for alterations in the flow field caused by the presence of pumping wells. Setback distances of less than 15 m to greater than 300 m have been calculated using these models. The results of this research may be useful for community planning purposes, because areas with higher potentials for viral contamination of ground water may be identified based on the maps generated by the model. In addition, the models may be useful in granting variances from the mandatory ground-water disinfection requirement under consideration by the Environmental Protection Agency.

A comparison of geostatistical methods for estimating virus inactivation rates in ground water

Enteric viruses are responsible for a large number of ground-waterborne disease outbreaks every year. Septic tanks are the most frequently cited causes of ground-water contamination in disease outbreaks, and are probably the major contributors of viruses to the subsurface environment. Controlling the placement of septic tanks such that all viruses would be non-infective by the time the effluent reached drinking water wells would likely reduce the number of waterborne viral disease outbreaks. This study describes the use of and provides a comparison of different geostatistical techniques (kriging, cokriging, and combined kriging and regression) to estimate virus inactivation rates in ground water. These estimates were used with the regional ground-water flow characteristics to estimate septic tank setback distances over a city-wide area. It was found that combined kriging and regression, which eliminates the need for laboratory values of virus inactivation rates by using the linear regression relationship between temperature and inactivation rates, produced comparable results to the other two methods, yet reduced the cost of the analysis considerably.

An Optimal Traffic Control Strategy to Regulate the Street Noise Over a City-Wide Area from the Two Methodological Viewpoints of D.P. and M.P

Generally speaking, the resultant fluctuation patterns of the environmental road traffic noise can be principally described by the physical factors of circumstances. Based on that fact, in this paper, the representative evaluation index based on the dB observation for the street noise over the city-wide area has been initially introduced in an analytical functional form with the physical parameters of the traffic flow. Then, the optimal traffic control strategies, in order to regulate the street noise from many vehicles over the area, can be generally determined by the methods proposed from the two different methodological viewpoints of dynamic programming and maximum principle. Finally, we have confirmed the validity and effectiveness of our theory by means of digital simulation.