Demand Modeling Approach Research Articles

Opportunities and roadblocks in the decarbonization of the global steel sector: a demand and production modelling approach

Opportunities and roadblocks in the decarbonization of the global steel sector: a demand and production modelling approach

Assessment of ethanol and nicotine interactions using a reinforcer demand modeling with grouped and individual levels of analyses in a long-access self-administration model using male rats.

Previous reports have indicated the reciprocal effects of nicotine and ethanol on their rewarding and reinforcing properties, but studies using methodological approaches resembling substance use in vulnerable populations are lacking. In our study, rats first self-administered ethanol, and their sensitivity to ethanol's reinforcing effects was assessed using a reinforcer demand modeling approach. Subsequently, rats were equipped with intravenous catheters to self-administer nicotine, and their sensitivity to nicotine's reinforcing effects was evaluated using the same approach. In the final phase, rats were allowed to self-administer ethanol and nicotine concurrently, investigating the influence of one substance on the rate of responding for the other substance. Group analyses revealed notable differences in demand among sucrose, sweetened ethanol, and ethanol-alone, with sucrose demonstrating the highest demand and ethanol-alone exhibiting greater sensitivity to changes in cost. At the individual level, our study finds significant correlations between rats' demand for sucrose and sweetened ethanol, suggesting parallel efforts for both substances. Our individual data also suggest interconnections in the elasticity of demand for sweetened ethanol and ethanol-alone, as well as a potential relationship in price response patterns between ethanol and nicotine. Furthermore, concurrent self-administration of ethanol and nicotine at the group level displayed reciprocal effects, with reduced responding for nicotine in the presence of ethanol and increased responding for ethanol in the presence of nicotine. This study provides valuable insights into modeling the co-use of ethanol and nicotine and assessing their interaction effects using reinforcer demand modeling and concurrent self-administration or noncontingent administration tests. These findings contribute to our understanding of the complex interplay between ethanol and nicotine and have implications for elucidating the underlying mechanisms involved in polydrug use.

Estimating charging demand by modelling EV drivers' parking patterns and habits

The diffusion of battery electric vehicles (BEVs) requires a proper charging infrastructure to supply users the chance to charge their vehicles according to energy, time, and space needs. Thus, city planners and stakeholders need decision support tools to estimate the impacts of potential charging activities and compare alternative scenarios. The paper proposes a modelling approach to represent parking activities in urban areas and obtain key indicators of the electric energy required. The agent-based model reproduces the dynamics of user parking and assesses the impacts on the electricity grid during the day. Since the focus is on parking activities, no detailed data on vehicle trips are required to apply the standard demand modelling approach, which would require Origin-Destination matrices to simulate traffic flows on the road network. Preliminary results concerning the city of Turin are presented for simulated scenarios to identify zones where charging demand can be critical and peak events in electric power over the day. The model is designed to be scalable for all European cities because, as the case study shows, it uses available data. The results obtained can be used for the design of charging infrastructure (power and type) by zones.

Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS

The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. Doi: 10.28991/CEJ-2022-08-09-013 Full Text: PDF

Does Climate Change Affect Rapeseed Production in Exporting and Importing Countries? Evidence from Market Dynamics Syntheses

Globally, non-cereal crops such as vegetable oils and their associated products will surpass cereals in demand by 2050, according to the World Bank (WB). Despite being considered an energy-efficient food crop, the production and supply capability of rapeseed is mostly influenced by climate conditions. Aiming in this context, the study explored how temperature and rainfall patterns influence rapeseed production, as well as how rapeseed prices in major trading countries may influence production and consumption patterns in developing countries. To do this, a supply and demand model approach has been employed for major exporting (Canada) and importing countries, i.e., China, the United States (U.S.) along with Bangladesh, a developing nation. The baseline study period was considered from 1991 to 2018, and simulations were performed up to 2040. The findings revealed that the most important effect on rapeseed yield is directly related to changes in temperature, which are positively related to the growing season but negatively related to the maturity stages of rapeseed in all studied countries. Rapeseed exports from Canada are expected to rise by 2040, while imports from China and the U.S. will rise simultaneously. In Canada, the per capita consumption of rapeseed oil is expected to increase from 16 to 24 kg per year between 2019 and 2040. Over the projection period, oil per capita consumption has steadily increased in China, the U.S., and Bangladesh. The relative demand for rapeseed oil is projected to increase by 2060, according to representative concentration pathways (RCPs). Therefore, it is necessary to determine market prices considering the probable climatic effect and increasing market demand for rapeseed to sustain the international market access of trading nations.

Fragility of offshore wind turbines variation with pulse-period and amplitude: Directivity and Fling step

This work evaluates the offshore wind turbine structure (OWT) subjected to the forward directivity and fling step types of pulse ground motion. The influence of the pulse period and amplitude of the forward directivity and fling step pulses are ascertained for the ground motion generated from closed-form analysis. The probabilistic seismic demand model (PSDM) approach is used to compute the offshore wind turbine response. The OpenSees tool is used to perform nonlinear time history analysis. Finally, fragility curves are derived for serviceability limits state of wind turbines. The influence of forward directivity and fling step pulse is evaluated and compared for the tower top and pile top rotation. Results shows that the OWT is more sensitive to the directivity pulses than fling step pulse for increased pulse period, while inversed trend observed for the pulse amplitude.

A Novel Cross-Case Electric Vehicle Demand Modeling Based on 3D Convolutional Generative Adversarial Networks

Electric Vehicle (EV) demand modeling constitutes the cornerstone of studies aiming to facilitate the integration of EVs into the power system. The different characteristics of the EV demand (departure time, arrival time, and electric demand), as well as the correlation between thereof, render EV demand modeling a complex task. The Majority of previous methods, which were developed based on the Monte Carlo simulation, are unable to observe and preserve the correlation between EV demand characteristics; because, in these methods, the EV demand characteristics are generated separately in an unsupervised manner. This study proposes a novel semi-supervised EV demand modeling approach by mapping the different EV demand characteristics into a three-dimensional (3D) space as a 3D image. To effectively realize the 3D EV demand modeling, we have employed Generative Adversarial Networks (GANs) with a 3D convolutional structure to develop EV-GANS network—a GANs structure tailored to the needs of EV demand modeling in environments hosting high demand diversity such as EV charging stations. Numerical results confirmed the effectiveness of the proposed EV-GANS in estimating the trend of the actual EV demand on the test day with a small error margin compared to the existing benchmark generation-based methods (Monte Carlo and Copula).

An Analysis of The Shadow Economy in Malta: A Currency Demand and MIMIC Model Approach

The paper applies two commonly used methods in the literature to estimate the shadow economy in Malta, the Currency Demand Approach and the Multiple Indicator Multiple Causes (MIMIC) model. Given the unobservable nature of the shadow economy, estimates are surrounded by a considerable degree of uncertainty. While these two methods differ somewhat on the historical evolution of the size of the Maltese shadow economy, which in turn can be traced back to their different underlying assumptions, both suggest that it has remained relatively stable over the last decade, standing at just below 21% of official GDP in 2019. Where possible, these estimates are compared to other studies on the same subject where we find that the dynamic properties of our variable follow those found in the literature.

Assessing the Impacts of Climate Variations on the Potato Production in Bangladesh: A Supply and Demand Model Approach

From the perspective of nutritional security, we investigated the influence of climate change on potato production in Bangladesh using a supply and demand model by considering the potato as an important non-cereal food crop. To provide an outlook on the variation in potato supplies and market prices under changing climatic factors (temperature, rainfall, and solar-radiation), the yield, area, import, and demand functions were assessed using district-level time-series data of Bangladesh (1988–2013), disaggregated into seven climatic zones. Results suggest that temperatures above or below the optimal range (18–22 °C) lowered yields. Little rainfall and low solar radiation hinder potato cultivation areas during the potato maturity stage. During the simulated period, the annual production was projected to rise from 88 to 111 million metric tons (MT), with an equilibrium farm price of 155 to 215 US dollars MT−1. Between 2014 and 2030, the nation’s per-capita potato intake is expected to increase from 49 to 55 kg year−1 because of changing dietary patterns. According to the estimated equilibrator, scenario simulations that incorporated various dimensions of Intergovernmental Panel on Climate Change (IPCC) scenarios indicate that potato production and consumption can increase in the future.

Direct demand modelling approach to forecast cycling activity for a proposed bike facility

ABSTRACT In the United States, planning and design efforts to generate bike-friendly environments through the greater provision of safe, low-stress bike infrastructure in our cities continue to advance. In Cambridge, Massachusetts, construction of the Grand Junction Pathway – an envisioned shared-use pathway – is at the heart of a citywide effort to enhance its active transportation system. However, a challenge – shared by many public agencies given that data on cycling activity are rarely frequently systematically gathered – is the creation of a baseline estimate of cycling demand for this planned network link. Using short-duration manual data supplemented with long-duration count data, this study employs a state-of-the-practice method for generating annual average daily bicycle trips for current bike network facilities. A statistical modelling strategy is then undertaken to forecast the volume of daily cyclists that the proposed off-street, shared-use path could expect to attract given its physical context and the socioeconomic attributes of nearby residents.

Influence of intensity measure selection on simulation-based regional seismic risk assessment

This study investigates the influence of intensity measure (IM) selection on simulation-based regional seismic risk assessment (RSRA) of spatially distributed structural portfolios. First, a co-simulation method for general spectral averaging vector IMs is derived. Then a portfolio-level surrogate demand modeling approach, which incorporates the seismic demand estimation of the non-collapse and collapse states, is proposed. The derived IM co-simulation method enables the first comparative study of different IMs, including the conventional IMs and some more advanced scalar and vector IMs, in the context of RSRA. The influence of IM selection on the predictive performance of the portfolio-level surrogate demand models, as well as on the regional seismic risk estimates, is explored based on a virtual spatially distributed structural portfolio subjected to a scenario earthquake. The results of this study provide pertinent insights in surrogate demand modeling, IM co-simulation and selection, which can facilitate more accurate and reliable regional seismic risk estimates.

The Impact of Exchange Rate on US Imports of Salmon: A Two-Stage Demand Model Approach

ABSTRACTThis study investigates the impact of the exchange rate on trade flows by using a two-stage demand model. The elasticity of exchange rate pass-through (ERPT) derived from the model is determined by demand elasticity, excess supply elasticity, elasticity of substitution, and market share. The empirical case is the US salmon import market. For the primary export countries, the elasticity of ERPT ranges between 0.37 and 0.62 in the long run. Although the empirical results suggest a partial ERPT in this market, there is no evidence of an asymmetric ERPT, as revealed by the simulated ERPT elasticities.

Examining the Equity Impacts of Autonomous Vehicles: A Travel Demand Model Approach

As investments in autonomous vehicle (AV) technology continue to grow, agencies are beginning to consider how AVs will affect travel behavior within their jurisdictions and how to respond to this new mobility technology. Different autonomous futures could reduce, perpetuate, or exacerbate existing transportation inequities. This paper presents a regional travel demand model used to quantify how transportation outcomes may differ for disadvantaged populations in the Washington, D.C. area under a variety of future scenarios. Transportation performance measures examined included job accessibility, trip duration, trip distance, mode share, and vehicle miles traveled. The model evaluated changes in these indicators for disadvantaged and non-disadvantaged communities under scenarios when AVs were primarily single-occupancy or high-occupancy, and according to whether transit agencies responded to AVs by maintaining the status quo, removing low-performing routes, or applying AV technology to transit vehicles. Across the performance measures, the high-occupancy AV and enhanced transit scenarios provided an equity benefit, either mitigating an existing gap in outcomes between demographic groups or reducing the extent to which that gap was expanded.

A posteriori optimal intensity measures for probabilistic seismic demand modeling

A probabilistic seismic demand modeling approach with the optimal intensity measure (IM) parameters determined a posteriori (POS-PSDM) is employed to examine and compare the full potential of the explanatory power of different IM formulations. A total of six IM formulations adopting the optimal IM parameters determined a posteriori (i.e. POS-IMs) are studied, including two spectral IMs with the optimal period T*, two fractional order IMs with the optimal fractional order α*, as well as two spectral IMs with the optimal period T* and the optimal damping ratio ζ*. A comprehensive IM comparative study is conducted based on hysteretic single-degree-of-freedom systems, considering a wide range of structural parameters. The POS-IMs manifest substantially improved performance (i.e. efficiency and sufficiency) compared with their conventional counterparts, revealing the value of adopting this POS-PSDM approach to ensure the PSDM predictive performance. In particular, the spectral acceleration at the optimal period and damping ratio, Sa(T*,ζ*), which is introduced as an IM candidate for the first time, not only consistently demonstrates superior explanatory power but also exhibits fairly good hazard computability. The POS-PSDM approach in conjunction with Sa(T*,ζ*) exhibits good potential in further improving the accuracy and reliability of probabilistic seismic risk assessment with negligible increase in computation cost.

Assessing the Rebound Effect in Energy Intensive Industries: A Factor Demand Model Approach with Asymmetric Price Response

The purpose of this paper is to analyze the direct rebound effect potentially prevailing in energy intense industries. The rebound effect represents economic mechanisms that will offset energy savings from energy efficiency improvements. For this purpose, a factor demand model is applied incorporating an asymmetric energy price response. Asymmetric prices imply that firms respond more strongly to energy price increases than to energy price decreases. In the empirical model we use a firm level, unbalanced panel covering the years 2001 to 2012 and four major Swedish energy-intensive industries; pulp and paper, iron and steel, chemical, and mining. The result indicates that the rebound effect is considerable in these industries. To mitigate this effect, the results suggest that policies stimulating an increase in energy efficiency should be combined with a raise in energy taxes.

On the choice of the demand and hydraulic modeling approach to WDN real‐time simulation

AbstractThis paper aims to analyze two demand modeling approaches, i.e., top‐down deterministic (TDA) and bottom‐up stochastic (BUA), with particular reference to their impact on the hydraulic modeling of water distribution networks (WDNs). In the applications, the hydraulic modeling is carried out through the extended period simulation (EPS) and unsteady flow modeling (UFM). Taking as benchmark the modeling conditions that are closest to the WDN's real operation (UFM + BUA), the analysis showed that the traditional use of EPS + TDA produces large pressure head and water discharge errors, which can be attenuated only when large temporal steps (up to 1 h in the case study) are used inside EPS. The use of EPS + BUA always yields better results. Indeed, EPS + BUA already gives a good approximation of the WDN's real operation when intermediate temporal steps (larger than 2 min in the case study) are used for the simulation. The trade‐off between consistency of results and computational burden makes EPS + BUA the most suitable tool for real‐time WDN simulation, while benefitting from data acquired through smart meters for the parameterization of demand generation models.

A food demand framework for International Food Security Assessment

We present a parsimonious demand modeling approach developed for the annual USDA-ERS International Food Security Assessment, a large-scale prospective assessment focusing on chronic food insecurity in 76 countries. The approach incorporates price effects, food quality variation across income deciles, and consistent aggregation over income deciles and food qualities. The approach is based on a simple demand approach for four food categories. It relies on data on food availability, complemented by own-price and income elasticities and food price data. Beyond consistent aggregation, the framework exhibits desirable characteristics: food quality is increasing with income; price and income responses become less sensitive with income; and increasing income inequality decreases average per capita food consumption. The proposed approach is illustrated for Tanzania. We assess future food insecurity in Tanzania using the calibrated model and evaluate the impact of safety net policies and their budgetary costs. Food-insecure population is estimated as well as the implied food gap expressed in calorie per day per food-insecure person as well as in total annual food volume in grain equivalent. The food gap measure gauges the depth of the chronic food insecurity.

Cognitive learning approach for travel demand modeling: estimation results

Abstract: The paper reports progress in the development of an agent-based model of cognitive learning, which simulates spatial perception updating in connection with daily travel behavior based on the principle of Bayesian perception updating. This model is embedded in a multi agent-based model of activity-travel scheduling and choice behavior. The aim of this paper is to empirically estimate the proposed model using data on individuals’ landmark recognition in a field survey. The main findings of the study show that the model fits the data satisfactorily and results are reasonable. The comparison between the proposed Bayesian model and a more basic binary logit model shows that the model improves when prior probabilities are taken into account, which provides evidence for the proposed Bayesian model.

Ridership estimation of a new LRT system: Direct demand model approach

The successful introduction of LRT systems is inevitably related to the realistic estimation of their ridership; this is particularly true for cases of no prior experience in the use of such modes in the part of the traveling public. This paper presents a practical approach for developing a direct demand model, for the case of a planned LRT system in Cyprus connecting the three major cities of Nicosia, Larnaca and Limassol. The proposed approach is based on existing traffic demand data and limited roadside surveys. Results indicate that the introduction of the proposed LRT would attract a moderate number of 23,000 passengers daily and shift a small percentage of 3.5% of traffic to the system. It was also found that approximately 33% of these trips correspond to the urban section of the network, while about 62% of the estimated ridership will use the part of the system connecting Nicosia and Larnaca.

The Impact of Climate Variables on Seasonal Variation in Hong Kong Inbound Tourism Demand

In this study, we quantify the link between climate variables, such as temperature, rainfall, humidity, number of tropical cyclones, and number of thunderstorms, and seasonal variation in the Hong Kong inbound tourism demand from mainland China, Taiwan, South Korea, and Japan using the average Euclidean distance (AED) statistics and tourism demand modeling approach. Seasonal variation is measured as the fluctuation in the number of tourists from one season to another. Based on the smallest AED value, results of this study showed that climate variables play a dominant role in shaping seasonal variation relative to season and country. The fluctuation of tourist numbers from one season to another is determined not only by climate variables (e.g., the temperature of Hong Kong vs. the temperature in the country of origin of the tourist) but also by economic factors, including price and income. The variation in the impact of relative temperature depends on the country of origin.