MOBILE Matrix: Application of Georgia Statewide Multimodal Transportation Planning Tool for Rural Areas

A process under development at the Georgia Institute of Technology to standardize, enhance, and automate planning and project evaluation for transportation projects in rural Georgia is described. The process will incorporate current statewide planning techniques in use at the Georgia Department of Transportation (GDOT) and additional sketch planning techniques that will use existing GDOT data. The purpose of this multimodal transportation planning tool (MMTPT) is to aid GDOT and local agencies in evaluating transportation requirements of rural areas and potential implementation constraints early in the planning process. The tool will include a system-level component to develop a prioritized list of projects by mode for a county and a project-level component to perform more detailed evaluations of roadway project alternatives and multimodal enhancements. The MMTPT will operate in a computerized hybrid system that uses expert system and conventional algorithmic programming techniques. Although organizational, management, and funding constraints that discourage multimodal planning are not overcome, the tool addresses the shortage of analytical planning techniques.

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A Survey of Traditional and Customized Bus Transportation Planning Methodologies

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2022/031012022 ◽

2022 ◽

Vol 10 (1) ◽

pp. 15-19

Keyword(s):

Rural Areas ◽

Transportation Planning ◽

Urban Areas ◽

Cost Effective ◽

Well Being ◽

Transit Planning ◽

Planning Strategy ◽

Bus Transit ◽

Mode Of Transportation ◽

Bus Service

Transportation planning is an area of public policy that is increasingly recognized for having a significant impact on human health and well-being. Passengers all across the world are choosing bus transit as one of the most cost-effective ways of transportation. The number of passengers who use this mode of transportation is steadily increasing. According to statistics, the bus was India's most popular mode of transportation in 2014. A bus was viewed as a mode of transportation by 66 percent of families in rural areas and 62 percent of households in urban areas. With increased demand, there is a concern about efficiently organizing this service. Because a lack of planning can generate major problems in the real world, such as traffic jams and high operating expenses, it is a source of concern for corporate and government entities who provide this service. In this paper, a review on various bus transit planning approaches and stages and methodologies used in each stage of the customized bus planning strategy is presented. This study will assist bus service organizing entities, whether private or public, in efficiently organizing bus service

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Transportation Planning through Pavement Performance Prediction Modeling for Botswana Gravel loss Condition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.256-259.2976 ◽

2012 ◽

Vol 256-259 ◽

pp. 2976-2982

Author(s):

Adewole Oladele ◽

Vera Vokolkova ◽

Jerome Egwurube

Keyword(s):

Performance Prediction ◽

Rural Areas ◽

Transportation Planning ◽

Transportation Network ◽

Road Networks ◽

Small Population ◽

Road Transportation ◽

Feed Forward Neural Network ◽

Pavement Performance Prediction ◽

Gravel Road

Botswana is a Southern African country with an area of about 582,000 sq. km and its small population of about 2 million people. The road transportation network has grown beyond all expectations since independence in 1966. Out of the 18,300 km Botswana Public Highway Networks, gravel road networks are significant in providing access to rural areas where the majority of the population lives. Modelling of gravel loss conditions are required in order to predict their conditions in the future and provide information on the manner in which pavements perform. Such information can be applied to transportation planning, decision making processes and identification of future maintenance interventions. The results of previous attempts to develop gravel loss condition forecasting models using multiple linear regression (MLR) approach have not been reliable. This paper intended to develop accurate and reliable performance models which best capture the effects of gravel loss condition influencing factors using Feed Forward Neural Network (FFNN) modeling technique. As extension of knowledge in unpaved road transportation network, FFNN trained with Levenberg-Marquardt (L-M) method was used to develop gravel loss performance prediction model for Botswana gravel road networks to achieve a reliable result of a higher coefficient of determinant R2 = 0.94 compared with MLR analysis of R2 = 0.74.

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