scholarly journals Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 493
Author(s):  
Amir Mousavi ◽  
Jonathan Bunker ◽  
Jinwoo (Brian) Lee
Keyword(s):  
Urban Form ◽  
Demand Forecasting ◽  
Model Performance ◽  
Metropolitan Region ◽  
Regression Equations ◽  
Trip Generation ◽  
Long Day ◽  
Private Car ◽  
Demand Forecasting Model

This study investigated whether indices for socioeconomic, demographic and urban form characteristics can reflect the overall effect of each category in a demand forecasting model. Regression equations were developed for trip generation of the land use of long day care centres (LDCC) in the metropolitan region of Hobart, Australia, to estimate the morning peak hourly private car trip generation of the centres. The independent variables for the model were functions of socioeconomic, demographic and urban form related indices, while the dependent variable was private car trip generation per number of staff or children. Findings show that using indices for socioeconomic, demographic and urban form characteristics enhances overall model performance, while the models based on the commonly used method for estimating trip generation present acceptable results in just some specific sites. The use of socioeconomic, demographic and urban form indices can reflect differences in these characteristics across suburbs when estimating trip generation.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

2017 ◽  
Vol 2668 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Venu M. Garikapati ◽  
Daehyun You ◽  
Wenwen Zhang ◽  
Ram M. Pendyala ◽  
Subhrajit Guhathakurta ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Form ◽  
Travel Demand ◽  
Demand Forecasting ◽  
The United States ◽  
Metropolitan Region ◽  
Demand Model ◽  
Awareness Campaigns ◽  
Energy Footprint ◽  
Household Travel

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

scholarly journals Charging for Scarce Rail Capacity in Britain: A Case Study

2008 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Johnson ◽  
Chris Nash
Keyword(s):  
Opportunity Cost ◽  
East Coast ◽  
Demand Forecasting ◽  
Main Line ◽  
Forecasting Model ◽  
Passenger Demand ◽  
Demand Forecasting Model ◽  
The Way

The aim of this paper is to examine the feasibility of identifying an appropriate rail scarcity charge which would make operators pay for their use of rail capacity in line with the opportunity cost of the use of these slots and to give some idea of the likely effects of such charges. The way in which we do this is to use a passenger demand forecasting model, PRAISE, to consider a situation on the East Coast Main Line which is characterized by scarce capacity and a degree of competition.

scholarly journals Trip Generations at “Polyclinic” Land Use Type in Johor Bahru, Malaysia

2014 ◽  
Vol 26 (6) ◽  
pp. 467-473
Author(s):  
Ishtiaque Ahmed ◽  
Suleiman Abdulrahman ◽  
Mohd Rosli Hainin ◽  
Sitti Asmah Hassan
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Demand Forecasting ◽  
Regression Equations ◽  
Land Use Type ◽  
Trip Generation ◽  
Travel Demand Forecasting ◽  
Floor Area ◽  
Land Use Types ◽  
Best Fit

Transportation planners need to estimate the trip generations of different land use types in the travel demand forecasting process. The Trip Generation Manual of Malaysia, similar to the Trip Generation Manual of the Institute of Transportation Engineers, USA, provides the trip generation rate at “Polyclinics” as a function of the Gross Floor Area. However, the data for this rate have no line of best fit resulting in the lack of confidence in the prediction. This study considered ten locations in Malaysia and verified the significance of different parameters, i.e. Number of Doctors, Number of Staff, Gross Floor Area and Density of Similar Clinics within 0.5 kilometre radius in Johor Bahru, Malaysia. The study developed regression equations for estimating the peak hours and daily trips at polyclinics in terms of “Number of Doctors”. The developed models can be used in estimating the number of trips generated by the polyclinics in Johor Bahru, Malaysia.

Commercial greenhouse water demand sensitivity analysis: single crop case study

2016 ◽  
Vol 16 (5) ◽  
pp. 1185-1197 ◽  
Author(s):  
Dean C. J. Rice ◽  
Rupp Carriveau ◽  
David S.-K. Ting
Keyword(s):  
Sensitivity Analysis ◽  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Time Of Day ◽  
Bell Peppers ◽  
Demand Forecasting Model ◽  
Large Water ◽  
Demand Sensitivity

Today water distribution utilities are trying to improve operational efficiency through increased demand intelligence from their largest customers. Moving to prognostic operations allows utilities to optimally schedule and scale resources to meet demand more reliably and economically. Commercial greenhouses are large water consumers. In order to produce effective forecasting models for greenhouse water demand, the factors that drive demand must be enumerated and prioritized. In this study greenhouse water demand was modeled using artificial neural networks trained with a dataset containing eight input factors for a commercial greenhouse growing bell peppers. The dataset contained water usage, climatic and temporal data for the years 2012–2014. This model was then evaluated using the Extended Fourier Amplitude Sensitivity Test, a global sensitivity analysis, in order to determine the importance, or sensitivity, of each input factor. It was found that time of day, solar radiation, and outdoor temperature (°C) had the largest effects on the model output. These outputs could be used to contribute to the generation of a simplified demand-forecasting model.

scholarly journals Mode Choice Analysis for Commuters in Metropolitan Region A-Case Study of Bogor City, Indonesia

2019 ◽  
pp. 153-162
Author(s):  
Hardiyani Puspita Sari ◽  
Lukytawati Anggraeni ◽  
Yeti Lis Purnamadewi
Keyword(s):  
Public Transportation ◽  
Multinomial Logistic Regression ◽  
Metropolitan Region ◽  
Choice Analysis ◽  
Transportation Modes ◽  
Private Car ◽  
Work Commute ◽  
The Government

The congestion of Bogor City is increasingly alarming that it urgently needs policies on transportation system. This study used crosstab analysis and multinomial logistic regression to analyze the behavior of choice of commuter modes in Bogor City. This study had 588 respondents. The selected-by-subdistrict results showed that gender, total income, private car ownership, motorcycle ownership, trip cost, distance traveled, work commute and distance to the terminal affect the choices of Bogor’s public transportation modes. As for the implications given in this study, the government is expected to add and renew infrastructure such as stations and shelters. The government is also expected to develop inexpensive public transportation that offers good quality of security and convenience.

scholarly journals Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Qiong Bao ◽  
Yongjun Shen ◽  
Lieve Creemers ◽  
Bruno Kochan ◽  
Tom Bellemans ◽  
...  
Keyword(s):  
Travel Demand ◽  
Demand Forecasting ◽  
Computation Time ◽  
Minimum Size ◽  
Modeling Framework ◽  
Travel Demand Forecasting ◽  
Geographical Unit ◽  
Microsimulation Modeling ◽  
Demand Forecasting Model

Nowadays, considerable attention has been paid to the activity-based approach for transportation planning and forecasting by both researchers and practitioners. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time, especially when large amount of population and detailed geographical unit level are taken into account. In this research, we investigated the possibility of restraining the size of the study area in order to reduce the computation time when applying an activity-based model, as it is often the case that only a small territory rather than the whole region is the focus of a specific study. By introducing an accuracy level of the model, we proposed in this research an iteration approach to determine the minimum size of the study area required for a target territory. In the application, we investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders, Belgium, with regard to two different transport modes, that is, car as driver and public transport. Afterwards, a validation analysis and a case study were conducted. All the experiments were carried out by using the FEATHERS, an activity-based microsimulation modeling framework currently implemented for the Flanders region of Belgium.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

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