1. ORF467 Final Presentation
Veenu King, Sylvia Okafor, Sally Yu
TEAM XNJ

2. EXTERNAL NJ
Because the focus of this assignment is NJ, each of our counties is represented by 1 pixel (very much like a centroid, or a station):
Anyone who wants to take a trip must go to these pixels, and take a taxi/train from there.
Is this realistic?

3. BUC
NOR
SOU
WES
NYC*
INTL*
PHL*
*counties/pixels not shown

4. Train Trips

5. AVO

6. SOU

7. SOU
our data is really weird… doesnt necessarily match up… probs because we are an external county and more data would be needed..?

8. BUC

9. BUC

10. WES

11. WES

12. NOR

13. NOR

14. Taxi Optimization Algorithm
For each line of input data, make a trip, and give it it’s own personal taxi
Compare the arrival taxis to departure taxis, and remove the departures that have a corresponding arrival
Cycle remaining departure taxis to reduce fleet size
before going into algorithm, describe data structures; result = rough estimate of how many taxis that would be needed for pixel (depending on existing demand in data)

15. Optimization Constraints
Pixel -> pixel, CD = 3, DD = 300, Max circuity = 20%
Assume an independent aTaxiService Company exists in each county

16. aTaxi Plan
In order to meet demand, we will have garage(s)/lot(s) close to the station that holds the taxis not currently in use / waiting for deployment.
Make sure there is at least one taxi of each size at station at all time, plus the taxis required by given demand distribution

17. SOU Optimization Results
Total Departures from SOU: 6,455
Total Arrivals into SOU: 22,002
Foreign Taxis Used: 64
Min Number of Taxis Needed: 125

18. BUC Optimization Results
Total Departures from BUC: 20,067
Total Arrivals into BUC: 70,597
Foreign Taxis Used: 726
Min Number of Taxis Needed: 790

19. WES Optimization Results
Total Departures from WES: 3,079
Total Arrivals into WES: 10,745
Foreign Taxis Used: 113
Min Number of Taxis Needed: 29

20. NOR Optimization Results
Total Departures from NOR: 2,719
Total Arrivals into NOR: 8,827
Foreign Taxis Used: 2
Min Number of Taxis Needed: 20

21. Room for Improvement...
Unfortunately, because of the structure, there is a large empty mile burden, which could be solved, possibly, with more data (e.g. the taxi could check pixels/surrounding areas for a return trip, instead of coming back empty)

22. Room for Improvement...
Updating the algorithm to be able to wrap over midnight when dealing with time
Enhancing the algorithm’s calculation of taxi return time to be more accurate (which may reduce/increase the fleet size even more)

23. Room for Improvement...
Make the algorithm more dynamic. Currently, algorithm gives a rough estimate of number of taxis needed based on the given demand in the data. However, anything can happen, so there should be (more) taxis to adjust for such cases. Problem is figuring out what said number is...