Introduction

Mercer County is located in central New Jersey. The county contains New Jersey’s state capital, Trenton, and has a population of 364,883 according to the US Census Bureau1. Mercer contains a handful of college campuses, including Princeton University in Princeton, Rider University in Lawrenceville, Mercer County Community College in West Windsor, and Thomas Edison State College in Trenton. Below is a map of the county:

2

Mercer ranks 80th our of over 3000 counties the United States in per capita income according to the Bureau of Economic Analysis3. Some of the larger employers include the universities, Bristol-Meyers Squibb, Church & Dwight Company, McGraw Hill Companies, and Educational Testing Services.4 The county has three train stations on the Northeast Corridor Line that runs from Washington D.C. through New Jersey and New York up to Boston: Trenton Transit Center, Hamilton Station, and Princeton Junction.

1 US Census Bureau, Population Division2 Image: http://www.real-estate-maplewood-nj.com/3 “250 Highest Per Capita Personal Incomes of the 3111 Counties in the US”, 20064 Mercer County Economic Profile

General Strategies and Initial Networks

Urban—Trenton

For urban areas within Mercer such as Trenton, my strategy was to build interconnected concentric cycles to create a grid. The natural grid network of streets made the network design relatively simple, with stations at street crossings and guideway along the roads. I think this system worked particularly well in Trenton, as the city contains a diverse mix of productions and attractions, and, in general, the concentric cycles allow for direct trips that only incur an additional “around the block” cost. See below for a picture of the Trenton initial network:

Interchanges played a key role in the urban design, serving as ways to move between the cycles and provide two-way guideway in some areas. The stations’ coverages tend to overlap only because the density of Trenton productions and attractions requires nearly full coverage in order to be effective.

Suburban/University—Princeton University

My main strategy in planning the suburban and university networks was to minimize the invasiveness of the system. In general, schools and neighborhoods pay close attention to atmosphere and PRT guideway all over campus and the surrounding areas would be the last structuring that these places would want in a PRT network. However, by completely avoiding the suburbs and colleges, we neglect an important part of the population and a very high number of trips. Thus, I attempted to balance the issues of serving trips while minimizing invasiveness in planning out these networks.

In order to minimize intrusion, I decided to use two-way guideway in a handful of instances. I think colleges and neighborhoods would be happier with a few instances of two-way guideway versus lines that run all the way through the campus or surrounding areas. See below for a closer look example (from the old Princeton Network):

I chose to create the first initial network at Princeton University and its surrounding areas. The University alone has a student population of around 7,500 and a staff of around 5,000. However, the network design is mainly with the staff in mind, as college students tend to be frugal and everything on Princeton’s campus seems to be an easy walk away. The only semblance of PRT currently exists on the campus of West Virginia University in Morgantown, WV, so another college town seems like an appropriate place to begin. See the next page for a snapshot of the Princeton Network.

I made a few changes to the initial Princeton Network, with the goal of reducing travel time for short line of sight trips across campus. On a second consideration, these changes would be imperative to the creation of a network in Princeton. In my old network, I tried to keep campus guideway as close to zero as possible. However, in doing so, there were a few situations where short line of sight trips looked to be a fair amount longer because the guideway went in a loop around campus. Thus, if adjacent stations were not connected by guideway in the right direction, one would have to ride the entire loop to go from one to another. By not making these trips convenient (across campus for instance), we neglect a large portion of trips within the University that could provide significant revenue

In this new network, I decided to take advantage of the existing on-campus streets Elm and the street south of Dillon Gym, to provide the desired accessibility.

This design would probably not align with the University’s ideal system, but by serving more trips and demonstrating higher profitability of this initial network , we can make the push for expansion earlier. And, with this additional revenue, we might be able to make a profit-sharing agreement with the University such that we would be allowed to place some guideway on roads on campus.

Rural

I did not design an initial rural network, as I mainly used these to interconnect the previous two network designs. My basic strategy for rural areas was to make loops with one-way guideway, and use two-way guideway only to connect loops or minimize invasiveness. For an illustration, see the next page:

General Statistics

Below is a table of the general network statistics. The dataset from last year slotted “recreation trips” under “non-student patron trips”, and for consistency I decided to do the same. One area of improvement that can be made to a proposal for PRT in Mercer county would be to split the non-student patron trips into recreation, dining, shopping, etc. The main concept that I changed since the earlier report had to do with home trip ends. According to the old dataset, only 200,000 people resided in Mercer County, so I took a look at the placemark file, and evenly distributed the population throughout the Census blocks to get the number to the 360,000 threshold. Hopefully the 2010 Census data can be used to get more accurate results. The new results are below:

Total Trip (Ends) Served Serves Possible % Service

Home Trip Ends 1,306,572 1,451,448 90.0%Work Trip Ends 393,840 425,649 92.5%Recreation Trip Ends - - -Transport Trip Ends 39,000 39,000 100.0%Student trip Ends 111,656 113,410 98.5%(Non-Student) Patrons trip Ends 294,020 324,334 90.7%Total 2,145,088 2,353,841 91.1%

On the next page is a table containing the population statistics for Mercer as well as a population distribution for New Jersey as of the 2000 Census. The county has about 364,000 people, so the 425,649 work trips indicates a (total) employment

percentage of 46.4%, assuming that each person works away from home and 2.5 trip ends are at work. The breakdown is between male and female:

New Jersey Population Distribution, 2000 Census0-4 288,085 3.42% 275,700 3.28%5-10 309,563 3.68% 294,966 3.51%10-15 302,708 3.60% 287,869 3.42%15-19 271,020 3.22% 254,196 3.02%20-24 244,628 2.91% 235,451 2.80%25-29 272,873 3.24% 272,044 3.23%30-34 319,031 3.79% 325,092 3.86%35-39 360,230 4.28% 367,694 4.37%40-44 348,061 4.14% 359,121 4.27%45-49 297,845 3.54% 313,512 3.73%50-54 263,357 3.13% 284,184 3.38%55-59 202,559 2.41% 220,779 2.62%60-64 156,073 1.85% 174,573 2.07%65-69 132,558 1.58% 160,638 1.91%70-74 121,639 1.45% 159,834 1.90%75-79 95,560 1.14% 144,571 1.72%80-84 58,291 0.69% 104,046 1.24%85+ 38,732 0.46% 97,267 1.16%

Total under 25 32.9%Total over 65 13.3%Sum 46.1%100%-Sum 53.9%10% Unemployment 48.5%

As it turns out, around 48% of the population should be making work trips, so the Mercer numbers slightly low-ball actual employment. This is with fairly liberal assumptions— everyone from 5-25 makes school trips only and everyone over 65 (retirees) makes no work trips. This result makes a lot of sense, as the employment data in the placemark file is less than complete. For instance, I did a search for a handful of popular fast-food chains (McDonald’s, Wendy’s, and Burger King), and not a single one appeared in the file. The placemarks are fine for the large employers and census data, but if someone could find a way to improve the employment data, that would do a great deal in improving the bid for a Mercer PRT system. Given the spotty data, I think the number of work trips is fairly realistic.

The 0.89 ratio of patrons trip ends per day to the county population seems reasonable, as on the average each person might make an non-work, non-home, outside trip every day or every other day. The trip ends per person per day seems slightly on the low side at 6.45, given that the 0-5 and 75+ populations are assumed

to take no trips. However, once these factors are considered, the average person has about 7.6 trip ends per day, which seems reasonable.

Population Statistics

Population 364,883 Possible # Trip Ends Per Person Per Day 6.45 Persons between 5-75 310,151 Possible # Trip Ends Per 5-75 Person Per Day 7.59 Patrons Trips Per Day : Population 0.89

The interchanges were assumed to incur no construction cost, leading to a close to 1:1 ratio of interchanges to stations.

Network Statistics

Total Stations 491Total Interchanges 456Total Miles of Guideway 478.85

An image of the entire network is included on the following page.

Entire Network

Further Analysis

Below is a chart of the total trips served by station in Mercer County. The chart has a heavy tail, as only a pair of stations serve more than 25,000 individuals per day. By far most stations serve 5,000 people or less. See below the chart for descriptions of the two most populous stations.

1 25 49 73 97 121145169193217241265289313337361385409433457481 -

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

Total Trips Per Station

total

Trenton (serves 35,106 people per day) – This station is located in probably the densest residential and commercial area in Mercer County, as it contains Thomas Edison State College (~7,000 people, counted at three trips each).

Princeton Junction (serves 27,609 people per day) – NJTransit Data indicate that 9,000 boardings (each way, so 18,000 total) occur at the Princeton Junction Transit Center on a typical weekday, and residential/commercial establishments make up the rest.

Finances

In terms of financial feasibility, the Mercer County Network seems reasonable with the base line assumptions. At a fare of $3, average trip length of 5 miles, and using a linear mode split discount factor, the network makes an annual profit of $178 million after paying the annual operating expenses and cost of capital. Because Mercer is a very wealthy county, we could also consider a higher fare than the base $3, in which case Mercer would turn even higher profits.

With the changes made to the Census population data to hit the correct Mercer County population, the profitability swung favorably:

Mercer Networks Statistics

Stations Interchanges

Miles of Guideway

Length

Total Trip ends

served Total TripsPeak hour

TripsFleet size

Average trip

Length

Average Vehicle

Occupancy Fare

Vehicle Operating Costs

(#) (#) (miles) (per day) (per day) (per day) (#) (Miles) (Trips/vehicle)

491 456 478.85 2,046,802 851,997 127,799 14,058 5 2 $3.00 $0.20

Most of these numbers can be tweaked to test what-if scenarios for running a Mercer PRT Network, and there is a fare adjustment graph on the following page. A P&L analysis is included in the table below. After amortizing the capital cost in debt (at an interest of 8%) and including the yearly operating costs, the system attains a profit of $178 million:

Mercer Basic Costs, RevenueCapital Costs Annual Recurring Costs Annual Revenue P&L

Stations Guideway Vehicles Total Cost of Capital Maintenance Operating Total Fare

Station lease and naming rights Total

(M$) (M$) (M$) (M$) (M$) (M$) (M$) (M$) (M$) (M$)

$982 $2,394 $1,406 $4,782 $383 $96 $128 $606 $767 $18 $784 $178

One key item to keep in mind is the size of this network’s coverage. In the attempt to reach 90% coverage, I had to build stations in zones that contained fewer and fewer trips. I would guess that there is most likely an optimum profitability level of coverage for profitability between 30% and 80%. However, most importantly, the network does satisfy the overall goal of the NJ PRT system of profitability and 90%+ service.

Another potential adjustment to the model might be in charging a different fare. We assumed $3 for an average trip length of five miles—the chart below depicts yearly

profit when varying fares and given completely inelastic demand. With the changes made to the population statistics, the break-even fare dropped a dollar to about $2/ride.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

-200

-100

0

100

200

300

400

500

600

700

800

P&L (MM) for various fares

P&L

*break even point ~$2.00

Station Cost Analysis

I ran a station revenue analysis, looking at how many stations would surpass the break-even point. I used the assumption that each station would contribute $0.45 in revenue per trip served. I used 300 as the number of days per year, as weekend trips are reduced. I also discounted the number of trips by 0.9 to account for modal split. To compute the break-even point, I assumed that each station cost $2 million dollars and that we would borrow the money over 20 years at an 8% interest rate. Inserting this number into a mortgage amortizing calculator5 gives a regular payment of $203,000 per year.

As it turns out, most stations fall below this break-even point. This result does make sense for this network though—attaining the 90% threshold in Mercer meant building stations that served less than 1,000 trip ends at first, and then moving on to stations that served even less than 500 trip ends per day. The stations in Princeton and Trenton are the ones that are on the left side of the scale, and, in general, the stations become more and more rural as you move rightward. This graph affirms the proposal that PRT start in denser, trip-heavy areas like Princeton and Trenton, and gradually expand outwards.

0 2 4 6 8 10 120

2

4

6

8

10

12

revenue/yearbreak even point

Construction Evolution Plan

5 http://www.bretwhissel.net/cgi-bin/amortize

On the following page is a table indicating the yearly build statistics for the Mercer County PRT Network. The plan runs 15 years, and starts with initial hubs in Trenton and Princeton University. Unfortunately I was not able to run a profit-by-year analysis, but I would imagine that the initial years networks (years 1-5) offer the best profit margins as the large trip productions and attractions—universities, urban areas, large corporate offices, and dense residential areas—are covered. Years 5-10 start to move into the suburbs and school districts. While these network additions are most likely profitable, they would not be as successful as the previous networks due to less trips and more guideway in general. Years 10-15 start to move into the less profitable stations in rural areas, where some stations serve less than 1,000 trips. This is the sacrifice a county or the state would have to make in order to serve 90% of trips, and, in this case, it turns out that the system maintains profitability. I would not necessarily recommend immediate expansion beyond the 2nd or 3rd year as the modal split numbers indicate that stations built in years 4-5 and beyond do not break even. Though the entire system would be profitable in those years, maximizing profit is a great objective and adding unprofitable stations would not make sense. Of course, a more accurate dataset would aid greatly in this regard. We could also consider adjusting the $0.45 revenue per trip assumption especially for the most populous stations, but with this data and these assumptions, full expansion does not seem reasonable.

Year Built Stations Interchanges Shape PointsGuideway (miles)

1 65 245 460 130.232 58 20 96 45.443 24 6.3 / /4 46 50 7 30.25 19 5.18 / /6 32 9.75 / /7 21 22 60 21.118 30 108 29 33.169 26 15 115 21.3

10 23 22 118 43.9411 22 15 57 21.5812 20 16 60 29.5713 23 18 61 20.4314 32 9 88 24.0215 50 44 29 35.61

*It seems as if the program did not correctly calculate years 3, 5, and 6

Unfortunately, I didn’t save each successive year as a separate file, and could not do a year-by-year profit analysis or trips served analysis. Also, it would not be appropriate to assume that each station serves the same number of trips. However, given the hubs in Mercer and Trenton, the profitability chart would most likely take off in the beginning, as the densest areas are served by the network. Towards years

3-5, the profitability would start to plateau as we start building stations in less dense areas, and in years 5 onwards the profitability would start to gradually drop, as in the attempt to serve 90% of the population, the stations have to be built in progressively less populous areas

Room for Improvement and Conclusion

As with most counties, one of the best ways to improve the reasonability of the network is to increase the accuracy of the data. I think as of this year the Mercer County dataset is fairly reasonable for the large trip productions and attractions, but as the number of trips decreases, the sketchiness of the data increases. For instance, as mentioned earlier, I performed a sanity check by searching for “McDonald’s,” “Burger King,” “Chipotle,” and “Wendy’s,” and not a single placemark showed up, even though a handful of each are in Mercer. While the dataset is very large, I would say it is not quite exhaustive. If next year’s team could spend some time coming up with a new way of collecting datapoints (especially employment ones), that would go far in improving the meaningfulness of the PRT project. As mentioned earlier, categorizing the trips into dining, shopping, recreation, etc. would also add to the models representation of Mercer County transportation. Moreover, the assumption that interchanges incur no cost to build is relatively limiting. In order to improve the systems representation to reality, perhaps a (small) cost should be attached to the interchange, such as $50,000-$100,000. In general, the network succeeds in providing a profit while serving over 92% of the Mercer population. I would definitely recommend Trenton and Princeton as starting points for a Mercer PRT network as many trips ends could be served for a reduced cost of capital. If the majority of area residents start choosing PRT as their mode of transit, then the county can start expanding to server more trip ends and defray costs with the profits from the initial networks. I wouldn’t necessarily recommend building the networks directly sequentially though—I firmly believe that the optimal strategy would be to build the initial networks in year one and see how the networks perform profit-wise before expanding. The switch from automobiles, trains, and buses to PRT would be neither quick nor easy. Many companies and employees rely on the success of these forms transportation for a living, and they will definitely put up a fight by cutting prices and expanding services until they can no longer survive. The PRT system might not turn a profit for the first couple years, but ideally ridership would continually expand as the other modes dwindle. PRT will have to pass this initial test before expanding to fewer-trip zones.