5  Operations_Research

6 EasyPark’s Parking Optimization with Gurobi

Business Context

EasyPark tackles urban traffic congestion and driver frustration by optimizing parking. With 30% of city vehicles searching for parking, EasyPark enhances the urban parking experience using mathematical optimization.

Problem

Cruising for parking worsens congestion, wastes time and fuel, and increases carbon emissions. Traditional parking methods fail to address drivers’ challenges, causing delays and urban gridlock.

Solution

EasyPark’s innovative solution involves a mathematical optimization model powered by Gurobi, focusing on real-time data inputs to efficiently guide users to available parking spots.

How It Works

Data Collection: EasyPark uses machine learning for comprehensive data, including historical transactions, mapping, GIS, user transactions, and routes.

Probability Estimation: Bayesian models estimate the probability of finding parking by time and city block using collected data.

Mixed-Integer Programming (MIP): EasyPark employs an MIP model to find the best route, minimizing travel time based on the user’s destination and spot probabilities.

Personal Commentary

EasyPark’s innovative approach not only reduces parking search time but also promotes a sustainable urban lifestyle. Using advanced technology like Gurobi, it showcases smart solutions in urban planning, impacting individual convenience with dynamic pricing and aiding in optimal capacity planning. This solution acts as a catalyst, enhancing city life, improving traffic flow, and meeting the demands of modern urban living.

Reference

https://www.gurobi.com/case_studies/easy-park-urban-parking-optimization/