Effective Demand Management Research Articles

Application of Phase Change Materials and Conventional Thermal Mass for Control of Roof-Generated Cooling Loads

Among all of the internal fabric and external enclosure components of buildings, sloped roofs and adjacent attics are often the most dynamic areas. Roofs are exposed to high temperature fluctuations and intense solar radiation that are subject to seasonal changes in climatic conditions. Following the currently rising interests in demand-side management, building energy dynamics, and the thermal response characteristics of building components, this paper contains unpublished results from past studies that focused on innovative roof and attic configurations. The authors share unique design strategies that yield significant reduction of daytime roof peak temperatures, thermal-load shavings, and up to a ten-hour shift of the peak load period. Furthermore, advance configurations of the roofs and attics that are discussed in this paper enable over 90% reductions in roof-generated peak-hour cooling loads and sometimes close to 50% reductions in overall roof-generated cooling loads as compared with traditionally constructed roofs with the same or similar levels of thermal insulation. It is expected that the proposed new roof design schemes could support the effective management of dynamic energy demand in future buildings.

The Management of Fiat Money or Gold Standard in the Financial System

In recent years, several prominent figures in Islamic Economics have called for a reform of the financial system from the concept of fiat money to return to the gold standard system (dinar dirham). The main reason for financial system reform is the perspective that fiat money is considered not resistant to inflation. However, the gold standard system is considered to have various weaknesses namely; physical size is relatively heavy, is not proven to be anti-inflation based on historical records, high production costs, inefficient utilization of production resources, only benefits certain groups, and is vulnerable to payment deficits. In theory, the problem of inflation is caused by the imbalance between supply and demand for money itself. If the money supply is greater than the demand, it will cause inflation. Conversely, if the money supply is less than the demand, then the economic activity will not be smooth. Therefore, this article believes that inflation is not due to the physical form of money (paper or gold ), but is caused by effective money supply and demand management. Therefore, government policies are needed including (a) deregulation of the banking system by nationalizing all banks, (b) playing an active role in intervening in the surge in prices of goods and services (c) promoting Ziswaf institutions.

A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service

As app-based ride-hailing services have been widely adopted within existing traditional taxi markets, researchers have been devoted to understand the important factors that influence the demand of the new mobility. Econometric models (EMs) are mainly utilized to interpret the significant factors of the demand, and deep neural networks (DNNs) have been recently used to improve the forecasting performance by capturing complex patterns in the large datasets. However, to mitigate possible (induced) traffic congestion and balance utilization rates for the current taxi drivers, an effective strategy of proactively managing a quota system for both emerging services and regular taxis is still critically needed. This paper aims to systematically design an explainable deep learning model capable of assessing the quota system balancing the demand volumes between two modes. A two-stage interpretable machine learning modeling framework was developed by a linear regression (LR) model, coupled with a neural network layered by long short-term memory (LSTM). The first stage investigates the correlation between the existing taxis and on-demand ride-hailing services while controlling for other explanatory variables. The second stage fulfills the long short-term memory (LSTM) network structure, capturing the residuals from the first estimation stage in order to enhance the forecasting performance. The proposed stepwise modeling approach (LR-LSTM) forecasts the demand of taxi rides, and it is implemented in the application of pick-up demand prediction using New York City (NYC) taxi data. The experiment result indicates that the integrated model can capture the inter-relationships between existing taxis and ride-hailing services as well as identify the influence of additional factors, namely, the day of the week, weather, and holidays. Overall, this modeling approach can be applied to construct an effective active demand management (ADM) for the short-term period as well as a quota control strategy between on-demand ride-hailing services and traditional taxis.

Current Status and Future Trend of Urban Low-Carbon Transportation in China

<p>The concept and connotation of urban low-carbon transportation are put forward and expounded. The status of energy consumption and carbon emission of urban transportation since 2000 in China is analyzed. The analysis shows that the total energy consumption and CO2 emission of urban transportation has increased rapidly, and public transportation has the lowest carbon emission intensity among all kinds of motor vehicles. The construction of urban low-carbon transportation in China faces the following problems: the development speed of public transportation lags behind; the non-motorization degree of residents’ travel structure has decreased greatly; the lack of effective demand management, weak awareness of energy conservation and emission reduction; and the urban transportation management system is incompatible with the construction of urban low-carbon transportation Based on the analysis of the present situation, this article puts forward three strategic directions and five main measures for the construction of low-carbon transportation in Chinese cities. Transportation industry is a basic service industry and a key area of urban energy conservation and emission reduction. Effective measures should be taken to accelerate the development of urban low-carbon transportation from the aspects of urban transportation planning, construction and operation.</p>

Technological enablers and confidence building in end-users for effective non-domestic water demand management

ABSTRACT By 2060, 70% of Singapore’s total water demand will be used for non-domestic purposes. Effective management of this demand will allow the national water agency, PUB, to achieve significant leverage and take strides in water sustainability as land use nears capacity limits in Singapore. Uptake of water conservation projects was sluggish in the initial years due to the high upfront capital and manpower resources required for implementation, and perceived incremental benefit to business margins. Through policy reform, technological innovation and a rigorous industrial engagement strategy, PUB has since completed 45 water conservation projects and is set to achieve 14.3 million gallons per day in non-domestic water savings by the end of 2020.

Ridesharing user equilibrium problem under OD-based surge pricing strategy

Ridesharing is one of the effective urban traffic supply and demand management policies to reduce car ownership and mitigate traffic congestion. The origin-destination (OD) based surge pricing strategy is widely adopted by ridesharing service operators in practice due to its fairness and effectiveness. In this study, we aim to investigate the ridesharing user equilibrium (RUE) problem for an urban transportation network under the OD-based surge pricing strategy. We first build a variational inequality (VI) model for the proposed RUE problem. In particular, we explicitly formulate the necessary ride-matching constraints for the participants of multiple ridesharing services and rigorously demonstrate the existence and uniqueness of the RUE solution under some mild conditions. A parallel self-adaptive projection method (PSPM) incorporating column generation is developed to find an RUE solution for the large-scale problems. Finally, numerical experiments are conducted to provide valuable insights and examine the effectiveness of the proposed solution method. The results quantitatively show that the ridesharing under the OD-based surge pricing strategy reduces not only the travel cost for travelers but also the deliberate detours. Traffic congestion is significantly mitigated by ridesharing. Moreover, the proposed solution method has satisfactory computational efficiency for solving the large-scale problems.

Social Distancing in Public Transport: Mobilising New Technologies for Demand Management under the COVID-19 Crisis

Dense urban areas are especially hardly hit by the COVID-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the COVID-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with the most recent epidemiological advice, and thus ensuring safe and reliable operations with limited capacities. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

The effect of travel time information on day-to-day route choice behaviour: evidence from a real-world experiment

Route choice behaviour in response to travel information receives increasing attention within travel behaviour research. This study contributes to the literature by generating insights into the effect of travel information on day-to-day route choice behaviour based on largely explorative analyses using route choice data obtained from a real-world experiment. As such, our study complements confirmatory stated preference and laboratory experiments. We find that the provision of travel information leads to a decline in switching propensity and a higher probability that the shortest route is chosen. Furthermore, we identified six behavioural profiles, varying from switch-averse to switch-prone. Travel time information seems to influence travellers’ propensity to shift from one profile to another across different OD-pairs. Our results contribute to understanding of the effect of travel information on route choice behaviour, and as such help inform the design of effective information-based demand management measures.

Developing a Model for Decision-Makers in Dynamic Modeling of Urban Water System Management

Water managers may modify many components of urban water systems to minimize water shortage. Since each modification activity has its own positive and negative effects, it is necessary to define an appropriate procedure to predict the consequences of each action. As the parameters of urban water supply and demand system have internal relationships in the time domain, a dynamic model is needed to forecast the result of changes and select the best modification activity. Here the Vensim® is applied as a modeling tool to choose the most effective water management activities in Tehran province. It has been found that the annual increase rate of water tariff by 16.4% and assigning 4.5% of revenue on reducing non-revenue water may be the most effective demand management activity to reduce water shortage in Tehran province. It has also been revealed that, even by implementing the most effective demand management activities in Tehran, the amount of required water in the next 10 years is more than the sustainable capacity of its resources and activities like seawater desalination are inevitable to prevent unsustainable use of water sources.

Bulk water extraction charge calculator: a tool for sustainable water management in Ontario

Given growing anthropogenic pressures on water resources and uncertain climatic conditions, sustainable water management using effective demand management strategies will be crucial to tread the path towards sustainable development. This paper makes a contribution to the interdisciplinary realm of water management by designing a flexible tool that overcomes the weaknesses of current extraction charges and reliance on general taxes to finance water management initiatives. These dynamic water extraction charges are envisioned to be important policy instruments to increase water security in Ontario. To ensure sustainable use of provincial water resources, the province of Ontario not only extended the moratorium imposed in 2016 on new groundwater extraction permits for water bottling until 1 January 2020, but is also reviewing its water taking policies. While the moratorium and the extraction charge hike of $500/Million liters is focused on water bottlers, the need for more long-term comprehensive policy approaches to sustainable management is apparent. Economic theory suggests that pricing a resource by reflecting its economic value and scarcity can incentivize optimal consumption. While economic instruments like extraction charges exist in Ontario, a methodological calculation linking the level of these charges to the actual costs and hydrological water risks is largely lacking. Addressing this gap, this paper designs a calculator that operationalizes the conceptual pricing framework for Ontario to transparently arrive at water extraction charges. The public cost data for water resource management initiatives are considered along with extreme water contamination events as contingency environmental costs. Using data from Statistics Canada for annual volume of water withdrawn, the base provincial water extraction charge for all permit liable sectors is calculated to be $23.08/Million liters. Moreover, price multipliers reflecting the sub-watershed scale water quantity risks (identified in sub-watershed source protection assessment reports) along with sector and source specific risks were embedded in the calculator.

Drivers of households’ electricity expenditure in Ghana

Worldwide, residential electricity consumption accounts for 30% of total electricity usage. In Ghana, household electricity consumption accounts for over 50% of total electricity generation. Moreover, problems faced in mobilising resources for electricity generation and the need for environmental sustainability calls for effective management of electricity demand by consumers. This requires a thorough understanding of the drivers of household electricity expenditure. The present study examines the drivers of household electricity expenditure in Ghana. Data for the analysis were obtained from the 2005/2006 and 2012/2013 waves of the Ghana Living Standards Survey. Results based on ordinary least squares regression analysis indicate that variation in the log of household annual electricity expenditure is explained by income, stock of electrical appliances, number of rooms, and years of education completed by household head. Other factors such as rural/urban location, employment status of household head, tenancy status, ownership of agricultural land, and ecological zone also exert significant influence on household electricity expenditure. The policy implications of the findings are indicated.

Investigating factors affecting the mode choices of commuters in Kuwait city & surrounding urban areas: Strategies for a higher quality and more sustainable public transport system

Abstract This paper reports on the social, cultural, and demographic factors affecting Kuwaiti commuters. The objectives were to 1) investigate the awareness of Kuwaitis of transportation problems, 2) examine the perceptions of Kuwaitis of daily traffic congestion and how it affects them emotionally and physically, and the main objective 3) study the attitudes of Kuwaitis towards using public buses. An online survey was used to examine these factors, and a sample of five hundred transportation system users was obtained. The primary findings showed significant associations between the use of public transport buses and the user’s nationality, gender, age, education, and income level. Men are 2.6 times more likely to use buses, and non-Kuwaiti residents are 6.4 times more likely to use them. In relation to the perceptions of daily traffic congestion, findings indicate that with increase in travel time, commuters, in general, developed more negative feelings, such as exhaustion and stress. A large proportion of the sample population is aware of current local transportation problems and future transportation projects. The results of this study fill a gap in the knowledge of the socioeconomic and cultural factors that influence the success of sustainable public transportation solutions to the traffic challenges found in Kuwait. This knowledge is also crucial to foreign consultants working on planning and transportation projects in the region. It is recommended that officials use this new knowledge on cultural factors to develop integrated land use and transportation plans of the urban areas in Kuwait and to develop more effective and sustainable transportation demand management policies in support of UN Sustainable Development Goals that Kuwait has signed up to pursue.

Parking prices and the decision to drive to work: Evidence from California

This paper explores the impact of parking prices on the decision to drive to work using a California household travel survey dataset and a discrete choice model. The paper tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1–2 percentage point decline in the probability of driving to work. This range varies with initial parking prices, where the lower end of the range increases at a decreasing rate, and the higher end peaks at $2.5 and decreases with higher prices. Moreover, there seems to be no evidence of sample selection bias. The evidence confirms that parking pricing can indeed be an effective transportation demand management tool.

Attitude, behaviour and choice: the role of psychosocial drivers in water demand management in Singapore

ABSTRACT The integration of environmental psychology into the broader domain of water demand research is a growing aspect of water policy. This article contends that effective water demand management policies can only be developed through an identification of the key psychosocial drivers of water use and conservation. By situating goal-framing theory in the context of residential water consumption in Singapore, the article analyzes the determinants of water conservation behaviour. Appealing for a shift away from policies designed to gratify people’s hedonic and gain goals, it asserts that greater priority should be placed on strengthening people’s normative goals towards water conservation.

Optimizing the release of passenger flow guidance information in urban rail transit network via agent-based simulation

• A agent-based simulation optimization model is proposed to optimize the release of passenger guidance information in a rail network. • A hybrid heuristic solution algorithm , integrated with passenger simulator and genetic algorithm , is developed. • A case study of Beijing subway is carried out with the large-scale smart card data. The passenger flow guidance is an effective demand management strategy to alleviate the excessive congestion in the urban rail transit network. In order to determine the scope and the timing, a simulation-based optimization model is proposed to optimize the release of passenger flow guidance information in the rail transit network in this paper. In the optimization model, we mainly focus on three aspects namely; where, when and what type of the guidance information should be released to the passengers. In the simulation model, the passenger choice behavior is captured by the agent-based simulation method, which responses to the congestion and the guidance information. Based on this, the dynamic passenger flow distribution can be derived. Furthermore, the adoption rate of the displayed guidance information on passenger information system as well as its impact on passenger travel behavior are also considered in the model. A hybrid heuristic solution algorithm, integrated with passenger simulator and genetic algorithm, is developed to solve the proposed simulation-based optimization model. Finally, a case study of Beijing subway is carried out with the large-scale smart card data. The numerical study shows that the passenger flow demand affects the guidance effect significantly and the best guidance effect can be met with sufficiently high passenger flow demand. And the guidance rate is also found to affect the guidance results. The results also show that the proposed model can provide a detailed guidance scheme for every station at selected time intervals. The results show that the dynamic releasing scheme can save up to a total of 46,319 min in passenger travel time during a single guidance period.

On the integration of the energy storage in smart grids: Technologies and applications

AbstractSmart grids are one of the major challenges of the energy sector for both the energy demand and energy supply in smart communities and cities. Grid connected energy storage systems are regarded as promising solutions for providing ancillary services to electricity networks and to play an important role in the development of smart grids. The aim of the present article is to analyze the role of storage systems in the development of smart grids. The article includes an analysis and a list of energy storage systems that are applied in smart grids. Various energy storage systems are examined raging from electrical, electrochemical, thermal, and mechanical systems. Two case studies are presented that show the role of energy storage in effective management of energy demand and supply.

WATER DEMAND MANAGEMENT OPTIONS IN MASVINGO CITY, ZIMBABWE

With most cities around the world, especially in less economically developed countries, facing a growing challenge in meeting rapidly escalating demand for water, water demand management has emerged as a potentially powerful instrument with which to enhance socio-economic development amidst limited water supplies. The study sought to assess water demand management strategies in the City of Masvingo in south-eastern Zimbabwe, and suggests some options towards a more effective demand management regime. The need for water demand management in Masvingo City arises due to water supply challenges that have further been compounded by rapidly growing demand. A household questionnaire, structured interviews and observation were the tools used in collecting data for the study. Descriptive statistics were used in analysing quantitative data gathered through the questionnaire, while qualitative data were analysed thematically. Among the water demand management strategies in Masvingo City identified by the study include water pricing, water rationing and prohibited/restricted water use for certain activities. Water pricing as a demand management strategy was, however, being hindered by political interference which has seen government occasionally coercing municipalities across the country, including Masvingo City, to cancel outstanding bills of residents, and especially when elections are around the corner. Such politically motivated moves render water supply services ineffective by depriving them of much needed revenue. Through the study, it was also noted that a lot of the restricted or prohibited water uses such as the use of horse pipes for watering, were not being heeded by most of the residents of Masvingo City. High non-revenue water loss through leakages in the reticulation network has also made water demand management in Masvingo City to be ineffective. The study recommends that water users should pay for the water they would have used, which will force them to use water wisely. The populist and politically motivated interference by government in the affairs of municipalities, such as the coercive cancellation of outstanding water bills of residents, should therefore stop. Masvingo City Council should take measures aimed at reducing non-revenue water loss due to leakages in the reticulation network. Last, but not least, the City of Masvingo should expand its current water supply infrastructure so that it reflects the reality of a rapidly growing water demand. Article visualizations:

Short-term load forecasting using machine learning and periodicity decomposition

The accuracy of electricity consumption forecasts is of paramount importance in energy planning, it provides strong support for the effective energy demand management. In this work, we proposed a load forecast through the decomposition of the historical time series in relation to the historical evolution of each hour of the day. The output of these decomposition were served as input to different algorithms of machine learning. We tested our model by five machines learning methods, the achieved results are examined with three of the most commonly used evaluation measures in forecasting. The obtained results were very satisfactory.

Tradable Parking Permits as a Transportation Demand Management Strategy: A Behavioural Investigation

Tradable parking permit schemes can be an effective transportation demand management strategy by allocating parking spaces efficiently and encouraging the use of non-single occupancy modes of transport. We investigate the feasibility and effectiveness of tradable parking permit schemes in high density cities with parking constraints. We focus on the effect of four behavioural factors, namely, reference dependence, endowment effect, social nudge, and environmental consciousness. Online experiments are conducted via Amazon Turk Prime platform, with participants recruited from eight Chinese cities. Our investigation on reference dependence and endowment effect suggest that tradable parking permit schemes is feasible and efficient. Environmental consciousness can improve the schemes’ effectiveness in encouraging participants to switch to public transport, but social nudge shows no significant effect. Our study provides much-needed empirical findings about tradable credits for transportation management on the individual level.

Optimal Cordon-Based Congestion Pricing Considering Feasible Cordons and Congestion Transfer

As one of the most effective traffic demand management measures, cordon-based congestion pricing can alleviate recurrent traffic congestion. Existing methods of determining the toll cordon require an excessive investigation and calculation, and the model assumptions are too idealistic. This article attempts to reduce the amount of required calculation by introducing feasible cordons. Through GPS data of floating cars, an accurate identification of the research scope and a feasible cordon for congestion pricing can be achieved. Further, considering multi-user choices and congestion transfer when pricing is imposed, this article establishes a joint bi-level model to determine the toll cordons and rate simultaneously. The upper level of the model aims at maximizing social welfare with the restriction of congestion transfer. The lower level of the model is a road network equilibrium model in which the value of time is considered. A combined algorithm is adopted to solve the bi-level model. Finally, the effectiveness of the method is verified by taking Changchun as an example. This article provides a methodological support for designing a feasible and an effective cordon-based congestion pricing scheme.