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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:7dd0c9cf86251a89e48d69c5b4e5563d CATEGORIES:Qualifying Exam CREATED:20190823T084022 SUMMARY:Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration LOCATION:CoRE 305 (B) DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic cong estion and respond to the emergency. However, almost all existing works (e. g., based on cellphones, onboard devices, or traffic cameras) suffer from h igh costs, low penetration rates, or only aggregate results. To address the se drawbacks, we utilize Electric Toll Collection systems (ETC) as a large- scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetrati on rate. Our novelty is how we address uncertainty issues (i.e., unknown ro utes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with c rowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. More importantl y, we evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data a s ground truth. Compared with state-of-the-art benchmark mobility models, V eMo improves the performance by average 10%.
DTSTAMP:20240329T085711Z DTSTART;TZID=America/New_York:20190422T103000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR