Predictive positioning and quality of service ridesharing for campus mobility on demand systems
Name
1609.08116.pdf
Size
2.17 MB
Format
Adobe PDF
Checksum (MD5)
caf512800520bc39528c3babee65b27c
Author(s)
Miller, Justin Lee
How, Jonathan P
Date Issued
July 2017
Journal
2017 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Miller, Justin, and Jonathan P. How. “Predictive Positioning and Quality of Service Ridesharing for Campus Mobility on Demand Systems.” 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, Singapore, Singapore, 2017.
Version
Original manuscript
Abstract
Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2017.7989167
