EDBT 2026 Demo / reviewers in the wild / expert
Alex Leighton
dblp:33/9923
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Motion planning and robot control · 50% Planning, search and constraint satisfaction · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
path planning |
0.1 | 1 | 2011 | Green Driver: AI in a Microcosm · AAAI 2011 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning under uncertainty
stochastic shortest path |
0.1 | 1 | 2011 | Green Driver: AI in a Microcosm · AAAI 2011 |
Smart cities and intelligent transportation › route planning
eco-routing |
0.1 | 1 | 2011 | Green Driver: AI in a Microcosm · AAAI 2011 |
Smart cities and intelligent transportation
driver behavior analysis |
0.0 | 1 | 2011 | Green Driver: AI in a Microcosm · AAAI 2011 |
Smart cities and intelligent transportation › mobility data analysis
traffic analytics |
0.0 | 1 | 2011 | Green Driver: AI in a Microcosm · AAAI 2011 |
Methods — techniques the papers use, named apart from their topics
hidden markov model · 0.2dynamic programming · 0.2a* search · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Green Driver: AI in a MicrocosmabstractThe Green Driver app is a dynamic routing application for GPS-enabled smartphones. Green Driver combines client GPS data with real-time traffic light information provided by cities to determine optimal routes in response to driver route requests. Routes are optimized with respect to travel time, with the intention of saving the driver both time and fuel, and rerouting can occur if warranted. During a routing session, client phones communicate with a centralized server that both collects GPS data and processes route requests. All relevant data are anonymized and saved to databases for analysis; statistics are calculated from the aggregate data and fed back to the routing engine to improve future routing. Analyses can also be performed to discern driver trends: where do drivers tend to go, how long do they stay, when and where does traffic congestion occur, and so on. The system uses a number of techniques from the field of artificial intelligence. We apply a variant of A* search for solving the stochastic shortest path problem in order to find optimal driving routes through a network of roads given light-status information. We also use dynamic programming and hidden Markov models to determine the progress of a driver through a network of roads from GPS data and light-status data. The Green Driver system is currently deployed for testing in Eugene, Oregon, and is scheduled for large-scale deployment in Portland, Oregon, in Spring 2011. Jim Apple, Aran Clauson, Heidi E. Dixon, Hiba Fakhoury, Matthew L. Ginsberg, Erin Keenan, Alex Leighton, Kevin Scavezze, Bryan Smith |
AAAI | 8 |