Choice modelling of a car traveler towards park-and-ride services in Putrajaya to create green development

• Main Authors: Ahmed Memon, Irfan, Sahito, Noman, Kalwar, Saima, Hwang, Jinsoo, Napiah, Madzlan, Shah, Muhammad Zaly
• Format: Article
• Language: English
• Published: MDPI AG 2021
• Subjects: HE Transportation and Communications
• Online Access: http://eprints.utm.my/id/eprint/97692/1/MuhammadZalyShah2021_ChoiceModellingOfACarTravelerTowardsParkAndRide.pdf; http://eprints.utm.my/id/eprint/97692/; http://dx.doi.org/10.3390/su13147869
• Institution: Universiti Teknologi Malaysia

Putrajaya is facing an increasing number of private car ownership and its usage. Inte-grated transportation infrastructure connecting the city with suburban areas and comparatively low-cost housing schemes are at the fringes of Putrajaya City. It creates a discrepancy between housing and employment attentiveness. Due to the attractiveness of jobs in the city centre, commuters’ travelling pattern is morning/evening peak hours, and it leads to traffic congestion on a few major artilleries leading to and from the city. In contrast, Putrajaya was designed to achieve a 70:30 modal split ratio. This policy was introduced to target 70% of the commuters towards a sustainable mode of transport as their mode choice. Currently, congestion in Putrajaya is due to the use of single-occupant vehicles (SOV). The SOV users cannot be convinced to use the park-and-ride services (P&RS) without understanding their travel behaviors. Therefore, the mode choice models (MCM) were developed through binary logit regression (BLR) approaches to determine the factors that influence the SOV travelers’ decisions to adopt the P&RS. As a result, several factors, which included the socio-demographic factors, travel time, travel expenses, environmental protection, avoiding stress, parking problems, vehicles sharing, and traveling directly, were found to be significant and will promote green development. Furthermore, the quality of the developed mode choice model was validated through the training and testing approach of logistic regression. Ultimately, this study can help stakeholders to encourage SOV users towards P&RS by overcoming these factors.