Final Project Written Report

ORF 467 - Transportation System Analysis

Final Project Written Report

by

Camden 1

Camden 2

Camden 3

Voorhees Town Center -- Biggest AVO…………………………………….page 16

Gloucester 1

ShopRite……………………………………………………………...page 18

Gloucester 2

Deptford Mall………………………………………………………...page 21
Atlantic County………………………………………………………….......page 25

Etess Arena…………………………………………………………...page 27

South Jersey is composed of three very diverse counties: Camden, Gloucester, and Atlantic, each with its own unique characteristics. The area is located directly south of Interstate 95, and boarders Pennsylvania on the Delaware River. Most importantly, it shares a border with the 5^th biggest city in the United States, Philadelphia. In the following report, we will explain our findings over the past two months of our trip analysis on each county.

Camden is the largest county of the three in terms of population (500,000 +), even though it has the smallest square mileage (227 sq. mile). Thus it has the highest density of all three counties. This is due to its proximity to Philadelphia, as it serves as a major suburban hub, containing towns such as Cherry Hill. The county is also home to what is known as the economic hub of South Jersey, the city of Camden. NJ Transit and PATCO have a good amount of stops within Camden, connecting county citizens to Philadelphia and Atlantic City. Due to Camden’s population density, there are almost as many total potential aTaxi trips in Camden as there are in Atlantic and Gloucester combined. In our analysis, the aTaxi trips were broken up into 3 different files (CAM-1, -2, -3). In terms of AVO (Average Vehicle Occupancy), it would make sense if Camden’s values were higher since higher density should correlate with greater AVO.

Gloucester has a similar land area as Camden (337 sq. miles), but with much fewer people (288,000). It is also is a suburb of Philadelphia, located just south of Camden. It is safe to say that at least some portion (probably a big one) of workers in both Gloucester and Camden work and commute to Philadelphia on a daily basis. The two major roadways through Gloucester are Interstate 295 and the New Jersey Turnpike. The aTaxi trips are separated into two different trip files (GLO-1,2).

Atlantic is a very large county (672 sq. miles) with a comparable population to Gloucester (275,000). As a coastal county, its population is much more concentrated on the Atlantic Ocean by cities like Atlantic City, away from state lines. Their biggest form of commerce comes in the form of tourism, concentrated mostly on the shore. The county is also home to an international airport. NJ Transit ends a rail line in Atlantic City that connects the county with Philadelphia. Its major roadways are the Garden State Parkway and the Atlantic City Expressway.

The following report will focus on trips from individual pixels (areas of 0.25 square miles within each county) to superpixels (grids of nine individual pixels arranged in a 3x3 grid, with trips to a superpixel serving all the pixels in that superpixel, with waiting time between taxis set to a maximum of 5 minutes (DD = 300 seconds) and with no stops in the middle for rider transfers between taxis (CD = common destination = 1). We believe that this is the most realistic representation for how an aTaxi system would work most efficiently, since a) pixelpixel representation would require too much specificity in destination, b) no one would want to transfer taxis or stop in the middle to reach their final destination, and c) no one would want to walk the distance of a superpixel to reach the initial taxi stand.

Population: 513,657

Census Blocks: 7,707

Area: 227 sq. miles

Total number of pixels generating at least one oTrip: 772

DD = Departure Delay = 300 sec = 5 minutes. CD = Common Destination.

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  Pixel to Pixel, CD = 1: Average = 1.16, Max = 3.75, Min = 1
  
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  Pixel to Pixel, CD = 3: Average = 1.55, Max = 3.99, Min = 1
  
• 
  Pixel to Super Pixel, CD = 1: Average = 1.16 , Max = 3.75 , Min = 1
  

The overall AVO is still looks low when using 5 minute waiting windows (DD = 300 seconds) and 1 stop (CD = 1). As we are going to find out in graphs below, there is a large disparity in terms of AVO at different times during the day, and a clear solution will be to modify our aTaxi program in terms of different car sizes activated at different times per day, to limit gas consumption. No one in New Jersey is going to wait more than 5 minutes at a time for an aTaxi at a station stop, so it is still our recommendation to keep DD = 300 seconds, thus reducing gas consumption, and saving money without sacrificing ridership.