Feasibility study considerations for transport infrastructure performance : a desk study
- Okoro, Chioma Sylvia, Musonda, Innocent, Agumba, Justus Ngala
- Authors: Okoro, Chioma Sylvia , Musonda, Innocent , Agumba, Justus Ngala
- Date: 2017
- Subjects: Forecasting , Infrastructure , Performance
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/241679 , uj:24911 , Citation: Okoro, C.S., Musonda, I. & Agumba, J.N. 2017. Feasibility study considerations for transport infrastructure performance : a desk study. The Ninth International Conference on Construction in the 21st Century (CITC-9), March 5th-7th, 2017, Dubai, United Arab Emirates.
- Description: Abstract: Transport infrastructure projects are complex, stochastic and fraught with uncertainties, which if not accurately predicted, can lead to inadequate assessment and management of risksand over time, poor performance in terms of costs, and associated expected benefits from implementation. The objective of this paper is to identify critical factors which should ideally be included in feasibility studies for adequate prediction of performance of road projects while in operation. A thorough in-depth desk study was conducted using extant literature (from conference proceedings and journals) and reports on feasibility and performance of transport infrastructure projects in Africa and world over. Findings revealed that effectiveness of procurement and financing strategies was the most considered factor during feasibility studies, among the sampled studies; followed by public participation, role of national government and traffic demand factors. Other factors included project environment, planning for operations and effectiveness of plans. These findings will be beneficial to investors who need assurance of the worthwhile performance of transport projects in which they intend to invest in. The study will inform selection of worthwhile projects among alternative and competing options which need to be implemented with limited resources.
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- Authors: Okoro, Chioma Sylvia , Musonda, Innocent , Agumba, Justus Ngala
- Date: 2017
- Subjects: Forecasting , Infrastructure , Performance
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/241679 , uj:24911 , Citation: Okoro, C.S., Musonda, I. & Agumba, J.N. 2017. Feasibility study considerations for transport infrastructure performance : a desk study. The Ninth International Conference on Construction in the 21st Century (CITC-9), March 5th-7th, 2017, Dubai, United Arab Emirates.
- Description: Abstract: Transport infrastructure projects are complex, stochastic and fraught with uncertainties, which if not accurately predicted, can lead to inadequate assessment and management of risksand over time, poor performance in terms of costs, and associated expected benefits from implementation. The objective of this paper is to identify critical factors which should ideally be included in feasibility studies for adequate prediction of performance of road projects while in operation. A thorough in-depth desk study was conducted using extant literature (from conference proceedings and journals) and reports on feasibility and performance of transport infrastructure projects in Africa and world over. Findings revealed that effectiveness of procurement and financing strategies was the most considered factor during feasibility studies, among the sampled studies; followed by public participation, role of national government and traffic demand factors. Other factors included project environment, planning for operations and effectiveness of plans. These findings will be beneficial to investors who need assurance of the worthwhile performance of transport projects in which they intend to invest in. The study will inform selection of worthwhile projects among alternative and competing options which need to be implemented with limited resources.
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Critical considerations in transport service demand forecasting : a literature review
- Okoro, Chioma, Musonda, Innocent, Agumba, Justus
- Authors: Okoro, Chioma , Musonda, Innocent , Agumba, Justus
- Date: 2016
- Subjects: Demand , Forecasting , Infrastructure
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/214949 , uj:21345 , Citation: Okoro, C., Musonda, I & Agumba, J. 2016. Critical considerations in transport service demand forecasting : a literature review.
- Description: Abstract: Infrastructure plays important roles in the development of cities, improvement in the quality of lives and overall socio-economic development and growth of economies. Infrastructure projects are, however, fraught with uncertainties regarding costs, benefits and performance. These uncertainties, if not accurately predicted in the planning of projects, could result in undesirable financial, social and economic consequences. The aim of the current paper is to identify critical factors which influence transport infrastructure performance forecasting outcomes and which should essentially be considered in order to minimize or eliminate errors. A review of related literature was conducted from journals, conference proceedings, magazines, theses and dissertations using databases including Science Direct, Emerald, Ebscohost, Academic Search Complete and ASCE library. The studies reviewed were based on international and South African context. Results revealed that project characteristics including size of project, capacity improvement and time lapses between construction life cycle phases, availability and type of data used, methodology used as well as traffic demand factors influence the outcome and validity of transport infrastructure feasibility studies. The study provides invaluable information to built environment professionals and stakeholders as well as infrastructure policymakers in accurately assessing probable outcomes, positive, in terms of benefits and negative, with regard to costs of proposed projects in order to avoid financial and economic risks. In addition, the study will be indispensable to infrastructure financiers and developers in effective allocation of scarce construction/development funds.
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- Authors: Okoro, Chioma , Musonda, Innocent , Agumba, Justus
- Date: 2016
- Subjects: Demand , Forecasting , Infrastructure
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/214949 , uj:21345 , Citation: Okoro, C., Musonda, I & Agumba, J. 2016. Critical considerations in transport service demand forecasting : a literature review.
- Description: Abstract: Infrastructure plays important roles in the development of cities, improvement in the quality of lives and overall socio-economic development and growth of economies. Infrastructure projects are, however, fraught with uncertainties regarding costs, benefits and performance. These uncertainties, if not accurately predicted in the planning of projects, could result in undesirable financial, social and economic consequences. The aim of the current paper is to identify critical factors which influence transport infrastructure performance forecasting outcomes and which should essentially be considered in order to minimize or eliminate errors. A review of related literature was conducted from journals, conference proceedings, magazines, theses and dissertations using databases including Science Direct, Emerald, Ebscohost, Academic Search Complete and ASCE library. The studies reviewed were based on international and South African context. Results revealed that project characteristics including size of project, capacity improvement and time lapses between construction life cycle phases, availability and type of data used, methodology used as well as traffic demand factors influence the outcome and validity of transport infrastructure feasibility studies. The study provides invaluable information to built environment professionals and stakeholders as well as infrastructure policymakers in accurately assessing probable outcomes, positive, in terms of benefits and negative, with regard to costs of proposed projects in order to avoid financial and economic risks. In addition, the study will be indispensable to infrastructure financiers and developers in effective allocation of scarce construction/development funds.
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Traffic demand determinants : a review of long-term scenario effects
- Okoro, Chioma, Musonda, Innocent, Agumba, Justus
- Authors: Okoro, Chioma , Musonda, Innocent , Agumba, Justus
- Date: 2016
- Subjects: Forecasting , Infrastructure , Planning
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/216628 , uj:21532 , Citation: Okoro, C., Musonda, I & Agumba, J. 2016. Traffic demand determinants : a review of long-term scenario effects.
- Description: Abstract: Transportation infrastructure provision is critical to the development of urban areas. Transport infrastructure such as roads, bridges, and ports are increasingly becoming the corner stone in determining the strength of cities, improving the quality of lives and overall socio-economic development and growth of economies. However, these projects are stochastic in nature and fraught with uncertainties which, if not accurately predicted, can lead to inadequate assessment and management of risks. The aim of the present paper is to identify critical factors which moderate traffic demand over a long period of time, and which should ideally be included in transport demand forecasts. A detailed review of literature was conducted from online journals, conference proceedings and theses using databases including Science Direct, Ebscohost, Google, Emerald and ASCE Library. Findings show that socio-economic factors (such as income, age, employment, vehicle operating costs, fuel price and tax polices), sociocultural factors (such as security, comfort, alternative/competing transport modes, leisure time), and environmental factors (such as pollution, traffic congestion, distance from station and frequency of trips) influence traffic demand. These findings would provide valuable evidence for adequate management of risks in infrastructure planning, and for public policy.
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- Authors: Okoro, Chioma , Musonda, Innocent , Agumba, Justus
- Date: 2016
- Subjects: Forecasting , Infrastructure , Planning
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/216628 , uj:21532 , Citation: Okoro, C., Musonda, I & Agumba, J. 2016. Traffic demand determinants : a review of long-term scenario effects.
- Description: Abstract: Transportation infrastructure provision is critical to the development of urban areas. Transport infrastructure such as roads, bridges, and ports are increasingly becoming the corner stone in determining the strength of cities, improving the quality of lives and overall socio-economic development and growth of economies. However, these projects are stochastic in nature and fraught with uncertainties which, if not accurately predicted, can lead to inadequate assessment and management of risks. The aim of the present paper is to identify critical factors which moderate traffic demand over a long period of time, and which should ideally be included in transport demand forecasts. A detailed review of literature was conducted from online journals, conference proceedings and theses using databases including Science Direct, Ebscohost, Google, Emerald and ASCE Library. Findings show that socio-economic factors (such as income, age, employment, vehicle operating costs, fuel price and tax polices), sociocultural factors (such as security, comfort, alternative/competing transport modes, leisure time), and environmental factors (such as pollution, traffic congestion, distance from station and frequency of trips) influence traffic demand. These findings would provide valuable evidence for adequate management of risks in infrastructure planning, and for public policy.
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Municipal solid waste data quality on artificial neural network performance
- Masebinu, S. O., Akinlabi, Esther Titilayo, Muzenda, E., Aboyade, A. O., Mbohwa, Charles, Manyuchi, M., Naidoo, P.
- Authors: Masebinu, S. O. , Akinlabi, Esther Titilayo , Muzenda, E. , Aboyade, A. O. , Mbohwa, Charles , Manyuchi, M. , Naidoo, P.
- Date: 2017
- Subjects: Municipal solid waste , Neural network , Forecasting
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/250701 , uj:26128 , Citation: Masebinu, S.O. et al. 2017. Municipal solid waste data quality on artificial neural network performance. 2nd International Engineering Conference (IEC 2017) Federal University of Technology, Minna, Nigeria.
- Description: Abstract: Short and long-term municipal solid waste (MSW) management requires adequate planning. Understanding the relationship among variables that affect MSW generation and predicting MSW based on them is needed for an effective planning. Methodologies to forecast MSW are numerous and have been implemented at different level of data granularity. Lack of data in many African cities and countries has hampered effective waste management plan. The lack of data has mainly been attributed to insufficient budget and lack of capacity to implement such management structure. In this study, we investigated the impact of data quality on forecasting efficiency using advanced prediction techniques. It was observed that the quality of waste related data variables determines the extent of model reliability and prediction accuracy.
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- Authors: Masebinu, S. O. , Akinlabi, Esther Titilayo , Muzenda, E. , Aboyade, A. O. , Mbohwa, Charles , Manyuchi, M. , Naidoo, P.
- Date: 2017
- Subjects: Municipal solid waste , Neural network , Forecasting
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/250701 , uj:26128 , Citation: Masebinu, S.O. et al. 2017. Municipal solid waste data quality on artificial neural network performance. 2nd International Engineering Conference (IEC 2017) Federal University of Technology, Minna, Nigeria.
- Description: Abstract: Short and long-term municipal solid waste (MSW) management requires adequate planning. Understanding the relationship among variables that affect MSW generation and predicting MSW based on them is needed for an effective planning. Methodologies to forecast MSW are numerous and have been implemented at different level of data granularity. Lack of data in many African cities and countries has hampered effective waste management plan. The lack of data has mainly been attributed to insufficient budget and lack of capacity to implement such management structure. In this study, we investigated the impact of data quality on forecasting efficiency using advanced prediction techniques. It was observed that the quality of waste related data variables determines the extent of model reliability and prediction accuracy.
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Forecasting volatility on the Resources 10, Financial 15 and Industrial 25 FTSE/JSE indices
- Authors: Petja, Albert Pogiso
- Date: 2018
- Subjects: JSE Limited , Forecasting , GARCH model , Investments - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282383 , uj:30415
- Description: M.Com. (Investment Management) , Abstract: The focus of this study is primarily based on the significance of forecasting volatility on the JSE Limited. The study investigates the appropriateness of using volatility models to forecast volatility on the Resource 10 (RESI), Financial 15 (FINI), and Industrial 25 (INDI) FTSE/JSE sector-indices classified according to the Industry Classification Benchmark (ICB). This study uses historical closing values of the three FTSE/JSE indices which are then converted into log returns. Quantitative data are used to investigate whether volatility on the RESI, FINI, and INDI FTSE/JSE indices is correctly specified by ARCH class of models. The data are obtained from McGregor I-NET BFA databases and spans the period from 17 February 2006 to 16 February 2016. The 10 year period is also divided into two 5 year sub-periods and five 2 year sub-periods for each FTSE/JSE index. This study employs the Autoregressive Conditional Heteroscedasticity (ARCH) model, the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, and the Threshold (Generalised) Autoregressive Conditional Heteroscedasticity (TARCH) model. These models are used to generate in-sample forecasts of volatility on the three aforementioned FTSE/JSE indices. The performance of the volatility models used in this study is evaluated based on three statistical loss functions: the root mean squared error, mean absolute error, and the mean absolute percent error. The results of this study evidence the presence of ARCH effects in the data of the three FTSE/JSE indices. The ARCH, GARCH and TARCH specifications are statistically significant for all indices; though there are some sub-periods of each of the FTSE/JSE indices which show no statistical significance in the parameter estimates of the volatility models employed. There is also evidence of volatility asymmetry in all of the FTSE/JSE indices considered in this study. There is no single superior volatility model between all three ARCH models that specifies the volatility of the FTSE/JSE indices over all the others when the forecasts are evaluated based on the statistical loss functions. However, the TARCH model outperforms the ARCH and GARCH models in most cases. This means that accounting for asymmetries in volatility is important in generating reliable volatility forecasts.
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- Authors: Petja, Albert Pogiso
- Date: 2018
- Subjects: JSE Limited , Forecasting , GARCH model , Investments - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/282383 , uj:30415
- Description: M.Com. (Investment Management) , Abstract: The focus of this study is primarily based on the significance of forecasting volatility on the JSE Limited. The study investigates the appropriateness of using volatility models to forecast volatility on the Resource 10 (RESI), Financial 15 (FINI), and Industrial 25 (INDI) FTSE/JSE sector-indices classified according to the Industry Classification Benchmark (ICB). This study uses historical closing values of the three FTSE/JSE indices which are then converted into log returns. Quantitative data are used to investigate whether volatility on the RESI, FINI, and INDI FTSE/JSE indices is correctly specified by ARCH class of models. The data are obtained from McGregor I-NET BFA databases and spans the period from 17 February 2006 to 16 February 2016. The 10 year period is also divided into two 5 year sub-periods and five 2 year sub-periods for each FTSE/JSE index. This study employs the Autoregressive Conditional Heteroscedasticity (ARCH) model, the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, and the Threshold (Generalised) Autoregressive Conditional Heteroscedasticity (TARCH) model. These models are used to generate in-sample forecasts of volatility on the three aforementioned FTSE/JSE indices. The performance of the volatility models used in this study is evaluated based on three statistical loss functions: the root mean squared error, mean absolute error, and the mean absolute percent error. The results of this study evidence the presence of ARCH effects in the data of the three FTSE/JSE indices. The ARCH, GARCH and TARCH specifications are statistically significant for all indices; though there are some sub-periods of each of the FTSE/JSE indices which show no statistical significance in the parameter estimates of the volatility models employed. There is also evidence of volatility asymmetry in all of the FTSE/JSE indices considered in this study. There is no single superior volatility model between all three ARCH models that specifies the volatility of the FTSE/JSE indices over all the others when the forecasts are evaluated based on the statistical loss functions. However, the TARCH model outperforms the ARCH and GARCH models in most cases. This means that accounting for asymmetries in volatility is important in generating reliable volatility forecasts.
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The ability of GARCH models in forecasting stock volatility on the JSE Limited
- Authors: Mokoena, Tholoana
- Date: 2016
- Subjects: GARCH model , Stock exchanges , Forecasting , Johannesburg Stock Exchange
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/124226 , uj:20891
- Description: Abstract: This study compares the fit and forecast performance of a selected group of parametric Generalised Autoregressive Conditional Heteroskedasticity GARCH (1, 1) models using various underlying distributions. The GARCH (1, 1) type models are empirically tested on the returns of the All Share Index (ALSI), a diversified portfolio of all the shares on the South African Johannesburg Stock Exchange (JSE). Estimates and forecasts generated by each model are compared and analysed to establish the validity and performance of the models. Forecasts given by the various GARCH (1, 1) models are bootstrapped and the efficiency of the models is also investigated through Value at Risk backtesting. The data used is composed of the returns of the ALSI from the 30th of September 2003 to the 14th of August 2013 and the data frequency is daily data. The best fitting distribution is the skewed normal distribution. With regards to the best fitting GARCH (1, 1) model, the E-GARCH (1, 1) model using the normal distribution performed best. The forecasting analysis showed the outperformance of the E-GARCH (1, 1) model and the best underlying distribution is the student’s t-distribution followed by the skewed normal distribution. , M.Com. (Financial Economics)
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- Authors: Mokoena, Tholoana
- Date: 2016
- Subjects: GARCH model , Stock exchanges , Forecasting , Johannesburg Stock Exchange
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/124226 , uj:20891
- Description: Abstract: This study compares the fit and forecast performance of a selected group of parametric Generalised Autoregressive Conditional Heteroskedasticity GARCH (1, 1) models using various underlying distributions. The GARCH (1, 1) type models are empirically tested on the returns of the All Share Index (ALSI), a diversified portfolio of all the shares on the South African Johannesburg Stock Exchange (JSE). Estimates and forecasts generated by each model are compared and analysed to establish the validity and performance of the models. Forecasts given by the various GARCH (1, 1) models are bootstrapped and the efficiency of the models is also investigated through Value at Risk backtesting. The data used is composed of the returns of the ALSI from the 30th of September 2003 to the 14th of August 2013 and the data frequency is daily data. The best fitting distribution is the skewed normal distribution. With regards to the best fitting GARCH (1, 1) model, the E-GARCH (1, 1) model using the normal distribution performed best. The forecasting analysis showed the outperformance of the E-GARCH (1, 1) model and the best underlying distribution is the student’s t-distribution followed by the skewed normal distribution. , M.Com. (Financial Economics)
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Forecasting net energy consumption of South Africa using artificial neural network
- Tartibu, L. K., Kabengele, K. T.
- Authors: Tartibu, L. K. , Kabengele, K. T.
- Date: 2018
- Subjects: Artificial Neural Network , Energy demand , Forecasting
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/279830 , uj:30058 , Citation: L.K. & Kabengele, K.T. 2018. Forecasting net energy consumption of South Africa using artificial neural network.
- Description: Abstract: This work proposes the use of Artificial Neural Network (ANN) as a new approach to determine the future level of energy consumption in South Africa. Particle Swarm Optimization (PSO) was used in order to train Artificial Neural Networks. The population size, the percentage losses, the Gross Domestic Product (GDP), the percentage growth forecasts, the expected Final Consumption Expenditure of Households (FCEH) as well as the relevant manufacturing and mining indexes are the “drivers” values used for the forecasts. Three growth scenarios have been considered for the forecasting namely low, moderate and high (less energy intensive) scenarios. These inputs values for the period of 2014 to 2050, from the Council for Scientific and Industrial Research (CSIR), were used to test data and validate the use of this new approach for the prediction of electricity demand. An estimate of the annual electricity demand forecasts per scenario was calculated. Besides the speed of the computation, the proposed ANN approach provides a relatively good prediction of the energy demand within acceptable errors. ANN was found to be flexible enough, as a modelling tool, showing a high degree of accuracy for the prediction of electricity demand. It is expected that this study will contribute meaningfully to the development of highly applicable productive planning for energy policies.
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- Authors: Tartibu, L. K. , Kabengele, K. T.
- Date: 2018
- Subjects: Artificial Neural Network , Energy demand , Forecasting
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/279830 , uj:30058 , Citation: L.K. & Kabengele, K.T. 2018. Forecasting net energy consumption of South Africa using artificial neural network.
- Description: Abstract: This work proposes the use of Artificial Neural Network (ANN) as a new approach to determine the future level of energy consumption in South Africa. Particle Swarm Optimization (PSO) was used in order to train Artificial Neural Networks. The population size, the percentage losses, the Gross Domestic Product (GDP), the percentage growth forecasts, the expected Final Consumption Expenditure of Households (FCEH) as well as the relevant manufacturing and mining indexes are the “drivers” values used for the forecasts. Three growth scenarios have been considered for the forecasting namely low, moderate and high (less energy intensive) scenarios. These inputs values for the period of 2014 to 2050, from the Council for Scientific and Industrial Research (CSIR), were used to test data and validate the use of this new approach for the prediction of electricity demand. An estimate of the annual electricity demand forecasts per scenario was calculated. Besides the speed of the computation, the proposed ANN approach provides a relatively good prediction of the energy demand within acceptable errors. ANN was found to be flexible enough, as a modelling tool, showing a high degree of accuracy for the prediction of electricity demand. It is expected that this study will contribute meaningfully to the development of highly applicable productive planning for energy policies.
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