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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.

Drivers’ adaptive travel behaviors towards green transportation development: a critical review

Shafaghat A., Keyvanfer A., Muslim N. H. B.

Archives of Transport

2016

Vol. 38, iss. 2

49--70

The transportation professionals integrated the concept Green in various dimensions of transportation, such as, green vehicle, green highway. The current study has established a new dimension to green transportation, which is called Green Driver as whom substantially contributes to less emission and fuel consumption, and higher-safety. The research established the driver’s Green Adaptive Travel Behaviors (GATB), in particular, that is referred to voluntary personal and lifestyle behaviors on less energy consumption and emission. The methodology was designed into two phases. Phase one was to investigate driver’s GATBs through systematic literature review process and content analysis method. The second phase was to verify greenery value impact (GVI) of the finalized list of drivers’ GATBs through an expert input study and Grounded Group Decision Making (GGDM) method. Total twenty six (26) GATB factors have been determined. Amongst, the factor ‘F27- Dangerous overtaking’ has received the highest value (97%) followed with ‘F3- Slow once realizing bike lanes for cyclist crossing’ (91%). In contrast, ‘F4- Realize visual Obstacles to manage the speed’ and ‘F21- Brake with smooth deceleration’ has received the lowest value (77%) among other factors. Two of the initial factors;‘F5-Use traffic calming devices’ (55%), and ‘F24- Change highest possible gear’ (69%) could not reach the 70% saturation; hence, they have been dropped from the list of GATB factors. Indeed, the GATB efforts are not limited to technology and practice; but also can include education and enforcement to driving regulations in order to interconnect driver, technology, environment, and vehicle. The research concluded with an innovative technique used as the decision support tool to evaluate the greenery grade of any individual driver on committing to less emission, less fuel consumption, and higher safety in traveling. As future study, the Green driver behaviour index assessment model will be developed based on this study outputs.