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2 2018

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A decision support tool for a walkable integrated neighbourhood design using a multicriteria decision-making method.

Keyvanfar A., Shafaghat A., Lamit H.

Zeszyty Naukowe. Transport / Politechnika Śląska

2018

z. 100

45--68

Growing concern about transportation emissions and energy security has persuaded urban professionals and practitioners to pursue non-motorized urban development. They need an assessment tool to measure the association between the built environment and pedestrians’ walking behaviour more accurately. This research has developed a new assessment tool called the Walkable Integrated Neighbourhood Design (WIND) support tool, which interprets the built environment’s qualitative variables and pedestrians’ perceptual qualities in relation to quantifiable variables. The WIND tool captures and forecasts pedestrians’ mind mapping, as well as sequential decision-making during walking, and then analyses the path walkability through a decision-tree-making (DTM) algorithm on both the segment scale and the neighbourhood scale. The WIND tool measures walkability by variables clustered into five features, 11 criteria and 92 subcriteria. The mind-mapping analysis is presented in the form of a ‘Walkability_DTM-Mind-mapping sheet’ for each destination and the overall neighbourhood. The WIND tool is applicable to any neighbourhood cases, although it was applied to the Taman Universiti neighbourhood in Malaysia. The tool’s outputs aid urban designers to imply adaptability between the neighbourhood environment and residents’ perceptions, preferences and needs.

Green Driver: driving behaviors revisited on safety

Muslim N. H. B., Shafaghat A., Keyvanfar A., Ismail M.

Archives of Transport

2018

Vol. 47, iss. 3

49--78

Interactions between road users, motor vehicles, and environment affect to driver’s travel behavior; however, frailer of proper interaction may lead to ever-increasing road crashes, injuries and fatalities. The current study has generated the green driver concept to evaluate the incorporation of green driver to negative outcomes reduction of road transportation. The study aimed to identify the green driver’s behaviors affecting safe traveling by engaging two research phases. Phase one was to identify the safe driving behaviors using Systematic literature review and Content Analysis methods. Phase one identified twenty-four (24) sub-factors under reckless driving behaviors cluster, and nineteen (19) sub-factors under safe driving practice cluster. Second phase was to establish the actual weight value of the sub-factors using Grounded Group Decision Making (GGDM) and Value Assignment (VA) methods, in order to determine the value impact of each sub-factor to green driving. Phase two resulted that sub-factors Exceeding speed limits (DB f2.2.) and Driver’s cognitive and motor skills (SD f1.2.2.) have received highest actual values, 0.64 and 0.49, respectively; ranked as the High contributor grade. Contrary, the sub-factors Age cognitive decline (DB f1.2.) and Competitive attitude (DB f1.2.), and Avoid gear snatching (SD f1.1.4.) have the lowest actual values; and ranked in low-contribution grade. The rest of the sub-factors have ranked in medium-contribution grade. The research also found out drivers’ personalities (included, physical and psychological characteristics) remains unaccountable and non-measureable yet in driver travel behavior assessment models. The study outputs would be used in development of Green Driver Index Assessment Model.