Optimization of shared on-demand transportation
Public dissertation presentation by Zahra Ghandeharioun.
![](/en/info/news/2024/03/optimization-sod-transportation/_jcr_content/articleLeadImage/image.imageformat.carousel.1503252701.jpg)
Date, time, and venue
Monday, 18 March 2024, 10:00-11:00
HCI J2, ETH Hönggerberg, Zurich
This is a hybrid event. Seating on-site is limited, however, external pageonline attendance is without restrictions.
Speaker
Gruppe Strassenverkehrstechnik
Stefano-Franscini-Platz 5
8093
Zürich
Switzerland
![Dr. Zahra Ghandeharioun](/en/info/news/2024/03/optimization-sod-transportation.person_image.jpeg?persid=MjUyMTc5)
Abstract
Urban growth globally increases urban commuting, causing congestion, pollution, and health risks. Technology-driven transportation innovations, like on-demand and shared mobility services, aim to address these challenges. Integrating these with public transit could revolutionize transportation. This thesis explores optimizing on-demand transportation in cities through three methods:
- Analyzing historical travel time data improves accuracy, benefiting traffic optimization and congestion identification.
- Implementing a real-time shuttle ridesharing algorithm reduces waiting times and in-car delays for taxi rides in Manhattan.
- Developing precise short-term demand forecasting models, particularly using deep learning techniques, enhances prediction accuracy.
This research provides insights for optimizing urban traffic operations, improving ridesharing services, and efficiently planning fleet dispatching.