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Updated: Nov 3, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

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

Mohammad A R Abdeen¹, Ibrahim A Nemer², Tarek R Sheltami²

¹The Faculty of Computers and Information Systems, The Islamic University of Madinah, Al-Madinah 42351, Saudi Arabia.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This study introduces a smart routing and parking algorithm to optimize parking allocation in busy areas. The algorithm reduces traffic congestion and driving time by considering real-time traffic and parking availability.

Keywords:

Madinah city Markov chain balanced parking multi-objective optimization optimal routing

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Area of Science:

Transportation Engineering
Operations Research
Computer Science

Background:

Urban parking scarcity is a major transportation challenge, causing congestion and energy waste.
Drivers face difficulties finding optimal parking due to factors like traffic, distance, fees, and lot availability.
Efficient parking allocation requires algorithms that consider multiple real-time variables.

Purpose of the Study:

To propose a smart routing and parking algorithm for optimal parking space allocation.
To balance parking lot utilization and traffic distribution in urban environments.
To investigate parking lot availability using a queueing model.

Main Methods:

Developed a multi-objective function integrating traffic congestion, trip distance/time, parking availability, waiting time, and fees.
Incorporated real-time traffic data and candidate parking lot information.
Utilized a queueing model to analyze parking lot availability based on arrival/departure rates and capacity.
Conducted simulation scenarios for high and low traffic intensity, with a case study in Al Madinah.

Main Results:

The proposed algorithm effectively balances parking lot utilization.
Demonstrated significant reduction in traffic congestion on routes to parking facilities.
Minimized driving time to the assigned parking spot.
Outperformed the MADM algorithm across key performance metrics.

Conclusions:

The smart routing and parking algorithm provides an efficient solution for urban parking challenges.
The approach leads to reduced congestion, balanced lot usage, and improved driver experience.
The algorithm's effectiveness is validated through simulations and a real-world case study.