A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Abstract

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

Admission and pricing optimization of on-street parking with delivery bays

“…Xiao et al (2018) prove that the loss queue model performs well in estimating the system parameters and making occupancy predictions. Abdeen, Nemer, & Sheltami (2021) investigate the loss queue model to estimate the availability of parking spots in a parking lot. They derive this performance measure based on the arrival and service rates of vehicles.…”

Solving Hajj and Umrah Challenges Using Information and Communication Technology: A Survey

“…Abdeen et al invented a parking spot allocation algorithm for parking allocation. The proposed system considered real-time street traffic while choosing the optimal parking spot [275]. Al-Shariff et al suggested using an Intelligent Transportation System (ITS) that is based on both Autonomous Electric Vehicles (AEVs) and IoT [276].…”

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

“…Due to massive urbanization, traffic volume in urban areas has grown, making urban life very congested and polluted, leading to many negative impacts on human life, such as higher energy consumption, global warming, and airborne diseases [1]. According to World Resource Institute [2], 74% of CO 2 is produced by greenhouse gas emissions, and 93% of it results from fossil fuel usage, transportation, manufacturing, and consumption.…”

“…The data from occupancy sensors allows us to learn availability patterns and predict probabilities of parking occupancy of the spots. Based on Parking Sensor Data (PSD), various machine learning methods have been used to predict parking occupancy rates [1,11]. The most common ML used for parking prediction are Regression Trees [12][13][14], DTs [14], Support Vector Machine [13,15], Genetic Algorithm [16], Bayesian [17], and Neural Network [13,18].…”