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1
|
Programming
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Sep 25
|
Oct 4
|
8d
|
|
|
|
|
|
1.1
|
Job distribution
|
Sep 25
|
Sep 25
|
1d
|
|
0%
|
|
|
|
1.2
|
Residence distribution
|
Sep 26
|
Sep 26
|
1d
|
|
0%
|
|
|
|
1.3
|
Optimal solution reuse
|
Sep 27
|
Sep 27
|
1d
|
|
0%
|
|
|
|
1.4
|
Multi monitor / plotting framework
|
Sep 28
|
Sep 28
|
1d
|
|
0%
|
|
|
|
1.5
|
Link utilization plot
|
Sep 29
|
Sep 29
|
1d
|
|
0%
|
|
Add colors to graphML
|
|
1.6
|
Trade study visualization
|
Oct 2
|
Oct 2
|
1d
|
|
0%
|
|
Plot / graph output comparison / flipbook
|
|
1.7
|
Express parameters as ratios
|
Oct 3
|
Oct 3
|
1d
|
|
0%
|
|
|
|
1.8
|
Adjust weighting to serve passengers with long distances
|
Oct 4
|
Oct 4
|
1d
|
|
0%
|
|
|
|
2
|
Scenarios
|
Oct 5
|
Oct 6
|
2d
|
|
|
|
|
|
2.1
|
Rail lines w/ express routes
|
Oct 5
|
Oct 5
|
1d
|
|
0%
|
|
Include bus / PRT feeder network
|
|
2.2
|
Parametric grid network
|
Oct 6
|
Oct 6
|
1d
|
|
0%
|
|
Construction of generic network for transit network design
Multiple transit layers represented:
bus
rail
PRT
aircraft
|
|
3
|
Research
|
Oct 9
|
Oct 13
|
5d
|
|
|
|
|
|
3.1
|
Strengths / Weaknesses compared to existing conventional optimizations
|
Oct 9
|
Oct 9
|
1d
|
|
0%
|
|
|
|
3.2
|
Hubs emerging naturally in transit networks
|
Oct 10
|
Oct 10
|
1d
|
|
0%
|
|
|
|
3.3
|
Automated / robotic transit systems
|
Oct 11
|
Oct 11
|
1d
|
|
0%
|
|
|
|
3.4
|
Rolling Horizon optimization methodology
|
Oct 12
|
Oct 12
|
1d
|
|
0%
|
|
|
|
3.5
|
Spanning Tree algorithm
|
Oct 13
|
Oct 13
|
1d
|
|
0%
|
|
|
|
4
|
Paper
|
Oct 16
|
Oct 17
|
2d
|
|
|
|
|
|
4.1
|
Diagrams
|
Oct 16
|
Oct 16
|
1d
|
|
0%
|
|
|
|
4.2
|
Preventing long passenger wait times
|
Oct 17
|
Oct 17
|
1d
|
|
0%
|
|
|
|
5
|
Future Enhancements
|
Oct 18
|
Oct 23
|
4d
|
|
|
|
|
|
5.1
|
Constrain number of links constructed
|
Oct 18
|
Oct 18
|
1d
|
|
0%
|
|
Optimization constraint on number of transit network links used
Force utilization of certain (e.g. existing) links
|
|
5.2
|
Use alternate MIP solvers
|
Oct 19
|
Oct 19
|
1d
|
|
0%
|
|
GLPK, CPLEX
|
|
5.3
|
Passthrough waypoints
|
Oct 20
|
Oct 20
|
1d
|
|
0%
|
|
Prevent holding at waypoints
Reduces number of decision variables
Prevents non-station transfers
|
|
5.4
|
Continuous time model to discrete time model translation
|
Oct 23
|
Oct 23
|
1d
|
|
0%
|
|
Variable aliasing used for optimization layer
|