Name
Abstract | ||
|---|---|---|
Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fuel consumption. To acquire information like traffic light phase schedules, our vision is that drivers share their velocity profiles in a digital cloud, and in return benefit from smart algorithms evaluating the collected data. We present one such algorithm, Traffic Light State Estimation (TLSE), that operates on the velocity profiles to backward-estimate phase schedules of traffic light signal groups operating with fixed cycle length (representing about 80% of all traffic lights in the US). We present simulation results showing that phase schedule prediction on the base of TLSE is correct more than 90% of the time. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/NTMS.2012.6208704 | New Technologies, Mobility and Security |
Keywords | Field | DocType |
cloud computing,driver information systems,fuel economy,road traffic,road vehicles,TLSE algorithm,arrival time,backward-estimate phase schedules,digital cloud,fuel consumption reduction,information acquisition,phase schedule prediction,smart algorithms,traffic light phase schedule learning,traffic light signal groups,traffic light state estimation algorithm,vehicle movement,velocity profiles | Traffic wave,Traffic signal,Computer science,Simulation,Floating car data,Vehicle Information and Communication System,Schedule,Traffic congestion reconstruction with Kerner's three-phase theory,Fuel efficiency,Cloud computing | Conference |
ISSN | ISBN | Citations |
2157-4952 E-ISBN : 978-1-4673-0227-2 | 978-1-4673-0227-2 | 14 |
PageRank | References | Authors |
1.11 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Markus Kerper | 1 | 14 | 1.11 |
| Christian Wewetzer | 2 | 14 | 1.11 |
| Andreas Sasse | 3 | 14 | 1.79 |
| Martin Mauve | 4 | 1840 | 153.45 |