Morris & Sussex CountyJeffrey Yan & Jacob Perricone

–Robin Chase

Transportation is the center of the world! It is the glue of our daily lives. When it goes well, we don't see it. When it goes wrong, it negatively colors our day, makes us feel

angry and impotent, curtails our possibilities.

Welcome to Morris

• Population ~ 500,000 people (10th most populous country)

• 481 square miles

• 6th wealthiest county in the United States (median household income $90,000)

AVO & Vehicle Miles Morris

CDF & PDF of Morris Trips

Top Pixel in MorrisParsipanny Park: Pixel {125, 269}

CDF & PDF of Parsippany

Supply & Demand

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Welcome to Sussex

• Population ~ 150,000 people

• 536 square miles

• median household income of $59,000

AVO & Vehicle Miles Sussex

CDF & PDF of Sussex Trips

Top Pixel in SussexTown of Newton: Pixel {92, 298}

CDF & PDF of Newton

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Morris County Taxi Reallocation

• Morris County, before any algorithmic optimization, required 790,459 vehicles, assuming that each departing trip got one vehicle. The majority of the vehicles needed are under 6 passenger. # of vehicles 2 PAX ~ 400,000, 6 PAX~ 350,000. The total number of pixels to which vehicles travel is 1,575

• Total trips into pixel: 849,740

• Number of pixels, to which the taxis depart: 1,575

After Optimization • Two Cases: One where I ran the algorithm including SUS within Morris County, the other where it was considered a separate county.

• Results:

• With SUS

• Total Fleet Size: 342,862

• 2 & 6 passenger vehicles: 328,662

• 6<x<15 vehicles : 12,452

• 15<x<50: 1,019

• Without SUS:

• Total Fleet Size: 328,729

• 2 & 6 Passenger vehicles: 316,202

• 6<x <15 vehicles: 11,517

• 15<x<15: 1,010

So how’s it work?• Rough draft of code, more optimization can be made:

• We start out assuming that any departure that exists will have the right sized taxis. (You could think of this as being spontaneously created)

• First Leg:

• Then we loop through for every pixel and break the cases up to within county and outside county:

• For every departure going outside the county:

• If there is an arrival into THAT pixel in the span of 10 minutes from the departure AND the origin county of the arrival is the same as the destination county of the departure, then we remove that taxi from the list, given the arrival taxi is of the right size

• If the departure is within county:

• If there is an arrival into that pixel at most ten minutes before, then have that taxi take the trip, add the trip to that taxi.

Second Leg

• Each taxi object has a return time to its origin pixel, calculated a lá Kornhauser. (this could be optimized) Every time a trip is added to a taxi, the return time is recalculated.

• Second leg of optimization: if the return time of a previously deployed taxi is earlier than a departure, have the taxi take the trip and update its return time.

Algorithm Analysis

• Our code only looks 10 minutes into the future (5 for travel 5 for wait) to see how the taxi should be repositioned/what other departures it can take (so as to minimize taxis). Arriving taxis only looks at departures from the set of trips within the pixel occurring in the next 10 minutes, which is not optimal. It should be the set of all departures reachable in the next 10 minutes.

SUS RESULTS

• SUS Without Mor AND SUS With Mor is:

• Total Trips into County: 188,502

• Total Trips within and out of pixel: 194,718

• Number of pixels to which the taxis depart: 7,540

• Number of taxis if each trip has its own taxi: 194,718

• Number of taxis taking out returning arrivals: 194,718

• Min number of cycling taxis needed: 117,670

• WOW! Optimization worked terribly on this one.