Predictive Logistic Models for Off-Street Parking Policy

Controlling Traffic Volume and Movement

Abstract

The land in city centers is typically used for commercial and industrial purposes, leading to increased traffic congestion. To promote more efficient, sustainable, and accessible land use in city centers, it is necessary to manage incoming traffic flow and travel demands effectively. This can be achieved by implementing appropriate parking policies, which should be predicted carefully to avoid adverse effects on human and economic activities. A case study is conducted in Duhok city, Iraq, aims to estimate the potential responses of city center travelers to reasonable off-street parking restriction policies. Real data were gathered through interviews with a quantitative sample of drivers to assess their reactions to two policies: increasing parking fees and reducing available parking spaces. The study examines central parkers’ socio-demographic and travel characteristics, including origin, trip purpose, timing, parking duration, search time, payment, income, age, and car occupancy. The study presents the results of two binary logistic models used to estimate the probability of implementing new parking policies to alleviate traffic congestion and improve movement. The findings suggest that travelers are more inclined to change their mode of transportation or travel time of day rather than altering their destination or canceling their trip. The findings contribute to the ongoing discourse on sustainable urban development and offer practical solutions for addressing the complex challenges associated with traffic volume and movement control in developing cities. This study aims to contribute to the growing body of knowledge on sustainable urban transportation planning and offer practical recommendations for transportation authorities.