BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260313T155620Z UID:D04A46AF-6476-41AB-8766-F6FA5AA377BC DTSTART;TZID=America/New_York:20260114T170000 DTEND;TZID=America/New_York:20260114T180000 DESCRIPTION:Simulations and Optimizations for On-Demand Ride Service System s\nDr. Jintao Ke\, Assistant Professor\,\nDepartment of Civil Engineering\ , University of Hong Kong (HKU)\n\nDate: Wednesday\, January 14\, 2026\nTi me: 5:00 PM – 6:00 PM\nRoom: ENGR 1602\nTeam Meeting ID: 262 088 601 228 93 Passcode: D7XP7kQ6\n\nAbstract:\n\nThis study presents a novel\, open- source simulation platform designed for on-demand ride service operations. Capable of modeling the complex behaviors and movements of diverse agents —including drivers and passengers—on real-world transportation network s\, the platform serves as a robust testbed for operational research. We v alidate the simulator's efficiency and effectiveness through extensive exp eriments using real-world datasets. Furthermore\, we introduce a suite of AI-driven optimization algorithms\, including reinforcement learning appro aches\, to address critical challenges in on-demand ride services\, specif ically order matching\, idle vehicle repositioning\, and dynamic pricing.\ n\nBio:\n\nDr. Jintao Ke is an Assistant Professor in the Department of Ci vil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University\, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong Uni versity of Science and Technology. His research interests include on deman d mobility services\, transportation big data analytics\, multimodal trans portation system optimization\, transportation pricing\, spatiotemporal tr affic prediction\, etc. He has published more than 50 SCI/SSCI indexed res earch papers in top-tier journals in the field of transportation research and data mining\, such as Transportation Research Part A-F\, IEEE Transact ions on Intelligence Transportation System. He has been ranked as the Worl d's Top 2% most-cited scientists by Stanford University since 2023. He is serving as an Editorial Board Member of Transportation Research Part C\, T ransportation Research Part E\, and Travel Behavior and Society.\n\nRoom: 1602\, Bldg: Nguyen Engineering Building\, \, George Mason University\, 45 11 Patriot Circle\, Fairfax\, Virginia\, United States\, 22030 LOCATION:Room: 1602\, Bldg: Nguyen Engineering Building\, \, George Mason U niversity\, 4511 Patriot Circle\, Fairfax\, Virginia\, United States\, 220 30 ORGANIZER:kafi@ieee.org SEQUENCE:106 SUMMARY:Simulations and Optimizations for On-Demand Ride Service Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/532752 X-ALT-DESC:Description: <br /><p><strong>Simulations and Optimizations&nbsp \;for On-Demand Ride Service Systems</strong><br><strong>Dr. Jintao Ke\, A ssistant Professor\,</strong><br><strong>Department of Civil Engineering\, University of Hong Kong (HKU)</strong></p>\n<p>Date: Wednesday\, January 14\, 2026<br>Time: 5:00 PM &ndash\; 6:00 PM<br>Room: ENGR 1602<br>Team Mee ting ID: 262 088 601 228 93&nbsp\;Passcode: D7XP7kQ6</p>\n<p>&nbsp\;</p>\n <p><strong>Abstract: </strong></p>\n<p>This study presents a novel\, open- source simulation platform designed for on-demand ride service operations. Capable of modeling the complex behaviors and movements of diverse agents &mdash\;including drivers and passengers&mdash\;on real-world transportati on networks\, the platform serves as a robust testbed for operational rese arch. We validate the simulator's efficiency and effectiveness through ext ensive experiments using real-world datasets. Furthermore\, we introduce a suite of AI-driven optimization algorithms\, including reinforcement lear ning approaches\, to address critical challenges in on-demand ride service s\, specifically order matching\, idle vehicle repositioning\, and dynamic pricing.</p>\n<p><strong>Bio: </strong></p>\n<p><strong>Dr. Jintao Ke </s trong>is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University\, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. His research interests include on demand mobility services \, transportation big data analytics\, multimodal transportation system op timization\, transportation pricing\, spatiotemporal traffic prediction\, etc. He has published more than 50 SCI/SSCI indexed research papers in top -tier journals in the field of transportation research and data mining\, s uch as Transportation Research Part A-F\, IEEE Transactions on Intelligenc e Transportation System. He has been ranked as the World's Top 2% most-cit ed scientists by Stanford University since 2023. He is serving as an Edito rial Board Member of Transportation Research Part C\, Transportation Resea rch Part E\, and Travel Behavior and Society.</p> END:VEVENT END:VCALENDAR