The HDM-4 road investment model

Kerali, H R

doi:10.4324/9780203301982_chapter_21

Cited by 10 publications

(10 citation statements)

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“…Although it can be used for the economic evaluation of low volume roads it is not customised for this purpose. HDM models are also more demanding and complex in terms of input requirements (Kerali, 2000:2). HDM models are more applicable to the evaluation of project alternatives where a new road is considered, as the input requirements include engineering specifications that are linked to the life span of the road.…”

Section: Model Developmentmentioning

confidence: 99%

An Economic Appraisal of the Impact of Traffic Diversion – The N1 Toll Road and Its Alternative

Meyer

Breitenbach

Kekana

2008

South African J Economics

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This paper investigates two alternative roads running parallel to one another; one being a double carriageway national road that was tolled four years ago and the other being an inter-city singlecarriageway road. The purpose of the paper is to test the application of the World Bank-developed Road Economic Decision (RED) model for assessing the economic impact of traffic diversion between two existing alternative roads. In order to do so, the RED model is first used to conduct a cost-benefit analysis of each road in isolation. Thereafter, the model is used to do a scenario analysis followed by a sensitivity analysis. The results show that the RED model is a useful tool for evaluating the impact on society of diverted traffic between alternative roads elsewhere in South Africa.

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Section: Model Developmentmentioning

confidence: 99%

An Economic Appraisal of the Impact of Traffic Diversion – The N1 Toll Road and Its Alternative

Meyer

Breitenbach

Kekana

2008

South African J Economics

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“…Its performance prediction models have great flexibility, being able to consider different surface materials, structure types, climates, and traffic characteristics. HDM-4 regression models can forecast surface roughness, cracking, rutting, potholes, and so on ( 4 ), although it is critical to calibrate its models to each network. Considering the robustness of HDM-4 models, but recognizing their limitations in relation to local calibration and to big-data treatment and handling, it is important to develop more current and adaptable solutions.…”

mentioning

confidence: 99%

Effects of Different Training Datasets on Machine Learning Models for Pavement Performance Prediction

Aranha

Bernucci

Vasconcelos

2023

Transportation Research Record: Journal of the Transportation R

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With improvements in data collection, storage, and processing, machine learning (ML) is gaining momentum as a behavior prediction method in the field of engineering. Several studies have evaluated these algorithms’ potential to predict pavement serviceability, however some challenges limit its use. Training data preprocessing has a great impact on the model’s predictive performance, is highly dependent on the modeler’s experience, and is not typically reported in engineering-related literature. The objective of this study was to assess the effects of data preprocessing, hyperparameter selection, and time series size on the model’s evaluation metrics. Therefore, this paper analyzes the performance of three ML algorithms on maximum deflection (D0) and international roughness index (IRI) prediction: support vector machine, random forest (RF), and artificial neural network (ANN). An R2 and mean square error (MSE) analysis was conducted on 12 training datasets, with two sizes of historical data and five stages of data preprocessing. The results indicated that ANN was the most accurate technique with an R2 of 0.99 and MSE of 20 ×10−3 mm on the D0 prediction and an R2 of 0.91 and MSE of 0.03 m/km on the IRI prediction. RF was also identified as an effective technique, generating similar results with less data preprocessing. The addition of structural and traffic categorical features to the training dataset resulted in the most significant improvement of the support vector regression and ANN performance metrics; the hyperparameter selection was effective only on IRI prediction, especially with the ANN algorithm.

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“…It can be basically classified as a pavement management tool, and is the recommended choice of the World Bank and other International Financing Institutions (IFIs) for technical and economic analysis of projects funded by these institutions. [2] HDM-4 simulates physical and economic conditions over the period of analysis, usually a life cycle, for a series of construction and maintenance alternatives and scenarios, either for a given road project on a specific alignment or for groups of links on an entire network. It estimates road user benefits, infrastructure costs and externalities, including accidents, energy consumption and emissions for alternative investment strategies year-by-year for up to 40 years, discounting the future costs, if desired, at different specified discount rates, to search for the alternative with the lowest discounted total cost.…”

Section: Highway Development and Management Modelmentioning

confidence: 99%