team
Characterise junctions with meaningful metrics and data such that better junctions can be advised/predicted.
| The job | Performance | Components |
|---|---|---|
| Move a number of vehicles, bicycles and pedestrians in 12 directions | Effectiveness and quality of job execution: fast and safe | Lanes (all types), traffic lights (regulation), speed limit, |
junction travelRef lat lon vbm straat1
1 A NA 52.52403 6.095156 399 Zwartewaterallee
2 B 4112 52.51929 6.102451 394 Van Wevelinkhovenstraat
3 C NA 52.52071 6.105885 406 Zerboltstraat
straat2
1 Middelweg
2 Bisschop Willebrandlaan
3 Hogenkampsweg
Traffic Data
Travel time measures
Other
notes:
Example dashboard. With significant data the statistical averages for junctions will be it's signutare. Junctions with higher average velocities of traffic flowing through have a more effective signutare.
Increase (data) quality
-> A larger data set with more parameters would make it very appealing to use machine learning (NN, RandomForest).
Design an APP that people do not have to use actively, but it simply transmits geo-data to Zwolle when on the move. Ask residents leave the APP running in the background while they are driving. (Baltimore example)
Or, possibly even easier:
Order thousands of little bluetooth modules on Alibaba and hand out these unregistered devices for Zwolle's citizens to keep in their car. Any cooperating company can get company registered devices that allows Zwolle to map a significant amount of its commuters traffic.