Hassan Chaudhry & Jessica deng ORF467 Final …alaink/Orf467F16/MyCityF16/La V… · Web viewHassan...

Hassan Chaudhry & Jessica deng ORF467 Final Report

La Ville de Kornhauser


I. Land Use CharacteristicsIntroduction: Welcome to the “La Ville de Kornhauser” (loosely based on Pittsburgh, PA)! This is a city that known for not only its steel manufacturing and sports culture, but also for having the highest density of bars per capita and being named the most “livable” city in the US for several years in a row. Our city has an especially long and storied history with football, as the home of the Steelers, Panthers and Heinz Field. It also boasts the country’s oldest golf course in continuous use, the Foxburg Country Club, and the famous suburban Oakmont Country Club, which has hosted the US Open, PGA Championships, and US Women’s Open many times over the course of its history. Our city also has an ever-growing Financial Services and Information Technology industry that is represented in the form of a central business district. In all, La Ville is a lively city with a lot of hustle and bustle, with its fair share of bars and restaurants, multiple lakes, a lush golf resort and huge football stadium as well as an immensely diverse population.

We have 64 zones: each is one of 17 different types:

Basic Summary of Land Use: Open Space: 29.8%We were very cognizant of making sure that the lakes, river, and open space area added up to almost exactly 30% of the total city area. This will allow La Ville to grow and expand as necessary. We placed 3 out of 4 lakes near schools/residential districts, so that students can have a space to have crew or sailing practice, while wealthy residents can enjoy the view.

La Ville de Kornhauser


Four forms of housing: 30%o Assisted Livingo Government o Light Residential (having lower density)o Heavy residential (having higher density)

We determined each zone’s density based on the age and income breakdown of Pittsburgh, PA – we assumed that working class citizens would likely live in government subsidized housing, middle class citizens would live in heavy residential areas, while wealthy citizens would live in light residential areas (more space for a backyard, proximity to schools and lakes etc.) We assumed that 50% of senior citizens over the age of 65 would live in the assisted living area (conveniently located next to the hospital).

One University, a private school, three public schools Pittsburgh is home to many universities and schools, but for the sake of simplicity, we included one University (Penn State), where all students were full-time and lived on campus. We also have three public schools (elementary, middle, high) and one private school which runs from K-12.

Heinz Field and Foxburg Country Club both in prime scenic locations along the riverWe chose to include these landmarks in our city because they represent a large part of Pittsburgh’s culture, they are major attractions for the population, and they also enhance the amount of green space in this metropolitan city.

One hotel on the river (Kornhauser Hotel)

One hospital (note proximity to the assisted living area)

Professional offices and retail/restaurant zones scattered throughout the cityWe were careful to make sure that every residential area, every school (including the University), and every attraction (such as Heinz Field or Foxburg Country Club) was close to a retail/restaurant/professional office zone. This was because we assumed that real estate and business developers would want to build their stores and restaurants near residential areas (where demand would come from the inhabitants) or areas of high traffic (such as schools and attractions like Heinz Field).

Industry – heavy and light industrial (i.e. steel manufacturing, logging, mining, construction)We placed the industrial zones in the bottom left corner of the city, to minimize environmental and pollution impact on the rest of the population.

Recreation areas (parks, playgrounds etc.) We included recreational areas next to the public elementary, middle, and high schools. We also included recreational areas near the assisted living zone and in the busy


professional district so that senior citizens and young working professionals would also have a space to exercise, enjoy the outdoors, or take a break from work.

Government offices

II. Demographics

The first thing we determined were the demographics. There were multiple facets to figuring out the demographics. First, we researched the population breakdown of various cities in the US by age, income levels, gender, employment industry, educational enrollment, ethnical and religious distributions. After extensive deliberating, we decided to base our city, named “La Ville de Kornhauser” (Kornhauser City), after Pittsburgh, Pennsylvania, because it has a population of 300,000, which is very close to our target of 250,000 (and it seemed like a more interesting city than our other close options, which were Newark, NJ or Jersey City, NJ). Moreover, it is a truly American city with a diverse population, vibrant bars, lively residents and lush green golf resorts. We based our numbers for the age, income, and employment breakdowns upon the actual demographics of Pittsburgh, PA.

In our city, we have toddlers (1-4) make up about 5% of the population, the school-age children ages (5-17) make up about 11.5 % of the population. The working population makes up about 70% of the total population and the remaining 14% are the elderly.

For the student population, we estimated that about a little <50% of the adult population aged 18-22 was enrolled in University, so we subtracted that number (7,000) from the working age population. We assumed 5% unemployment.


This is the complete breakdown of all pre-University and University students:

Similarly, we used data from Pittsburgh to estimate the distribution of income across various income levels. The median income range was $50,000 - $74,999 which can be estimated to be $62,000. Similarly, the 10th percentile income was $21,000 and the 90th percentile household income was $120,000. The complete distribution of income is as follows:

The city is also self-sufficient in terms of its manufacturing and other employment opportunities. The city is home to both heavy industrial and light industrial areas as well as modern industries like Fintech and Hi-Tech firms. The city is also home to a modern hospital which is replete with facilities that accommodate every segment of the population ranging from the toddlers to the elderly. The city’s booming sports industry also employs about 5% of the working population. The complete distribution of the main industries is as follows:


In terms of residence, as described before, our city’s plan is cognizant of the needs of the different segments of our population. Therefore, we have a huge variety in terms of our residences. We have government subsidized housing which makes sure that the underprivileged also have access to the basic necessities. Our government subsidized housing is conveniently located near the industrial zones in order to encourage the people from the lower strata to partake in employment activities. The residential areas within the city can be divided into two broad categories, the light and the heavy residential zones. As the name implies, the light residential areas have a much lower population density and therefore, the houses in that zone are generally bigger. Consequently, a large proportion of these houses is home to residents having higher income brackets. Similarly, the heavy residential areas are more centrally located and are home to most of our population. This inner city population enjoys proximity to the business district as well as other social amenities. Lastly, as a city catering to everyone, we also have assisted living. The Assisted Living zone is placed in a convenient spot near the hospital. The residents of this zone are provided with state-of-the-art assistance with constant monitoring by qualified nurses and custom-made facilities for them. In order to cater to our senior citizens’ recreational needs, we have the Foxburg Country Club, a park, and a lake near the Assisted Living Zone. The complete breakdown is given as follows:


III. Demand for TransportationIn accordance with the assignment specifications, we divided our trips into home-based, work-based and shop-based trips. For each one, we produced production and attraction arrays for the three biggest sources of Home-based trips. So, our Home-Based trips consisted of Home to School, Home to Work and Home to Shopping. As instructed, 100 % of our trips to school and to work are Home-Based. I will go on to describe the mechanism that we adopted for trip generation for Home-based trips first and will then later expand upon the work-based and the shopping-based trips.

Home Based Trips: In accordance with the instructions that were given to us in the assignment prompt, when coming up with our Production and Attraction arrays, we modeled our arrays while keeping the constraints given in mind. Therefore, as you can see above, our home based trips cover 100% of our trips to school. The underlying assumption behind this is that trips to school are only made from home. In real case scenarios, we might have some assumptions (some school-age students may not go to school) but in our city, we have a 100% enrollment rate.

Trip GenerationSince we are dealing with home based trips in this case, you can clearly see that the production will have zero trips for all zones other than the residential zones. This can be clearly understood in light of the fact that a home based trip can only originate from a residential zone. Now, as we were deciding upon the number of trips in our production matrix, we realized that we should make our trips proportional to the working population in each residential zone. Therefore, if you closely observe our numbers, you will realize that our total number of morning production trips from home to work equal the number of employable workers that live in these residential zones. In doing so, we made a couple of assumptions. Firstly, we assumed that unemployment in our city is at 5% and that every employable worker lives in either one of the residential zones except the assisted living area. The Assisted Living area does not produce any trips or attract any trips at all because the residents of that zone are retired and too sick to travel much. Moreover, because of superior town planning, these residents have access to all the facilities within close proximity and they do not feel the need to make the trip. Secondly, I also assumed that each trip only involves one person.

Let me begin by explaining the trips from Home to Work. You can go through the numbers and realize that the people are not distributed uniformly across each residential zone. The heavy residential zone is the most densely populated followed by the government subsidized housing while the light residential zone is the least densely populated. By comparing our numbers in the attraction matrix to the number of people who are employed in the sector, we can conclude that each sector’s jobs are filled by the residents and there are no workers from the outside. Therefore, the total in both P and A column equals the number of employable workers i.e. 159, 140.

If we go on to analyze the trips from Home to School, it is important to bear in mind that 100% of our trips to school are home-based. In other words, home to school are the only trips to school and therefore, the sum of the values in both the Production and Attraction matrix for home to


school equals the number of school going children in the city i.e. 28,375. The numbers in our array are in perfect harmony with the demographics of the school going children that I described earlier. Let me now describe the last part of home based trips. The trips from home to shop while including the employable population also includes the college going residents and some of the retired residents.


Trip Distribution:

In order to calculate the trip matrices, we realized that the best way to do so was to use the gravity model. The gravity model assumes that the trips produced at an origin and attracted to a


destination are directly proportional to the total trip productions at the origin and the total attractions at the destination. The Gravity Model formulation states that the number of trips between each zone is equal to:1

The "friction factor" (F) represents the reluctance or impedance of persons to make trips of various duration or distances. In order to calculate our friction factor, we first calculated our distance matrix that was given by D and then in order to calculate F, we divided 1 by D2. Kij is the optional adjustment factor. For our calculations, we assumed that Kij was 1.

Calculating all these matrices and then running the iterations was one task that took most of our time while doing this project. We first had to analyze our demographics and come up with the Production and Attraction matrices for each one of our trips. Since we assumed the trips to be unidirectional, therefore, we have separate matrices for our trips to home to work, shop and school and vice versa. After starting off from the Production and the Attraction distance, we calculated the Cartesian distance for each trip. However, the Cartesian distance was not scaled to the actual length of the zones and therefore, we multiplied it by a factor to make sure that it accurately represented the distances in our city.

Starting off with the P and A matrix, I was then able to calculate the D matrix. The D matrix is the adjusted Cartesian distance. D’: D’ is the D matrix multiplied by a factor of 1.2. We multiplied all entries NOT on the diagonal by a factor of 1.2 in order to simplify our calculations and make our Interzonal friction factor more accurate.

F: F is the matrix which is given by 1/D’^2I: I is the Identity matrix. Sum: In order to calculate the sum matrix, we used the formula that was given in the sample excel sheet and multiply the F matrix with the A matrix. P/Sum: For this matrix, we divided the P by the sum matrix. P[A] Transpose: Product of the P and A Transpose matrix. P/Sum [A] Transpose: Product of the P/Sum and A Transpose matrix.

1 Gravity Model picture has been taken from lecture slides.


Trip matrix: The Trip matrix is finally given by the product of the Distance and P/Sum[A] Transpose. The trip matrix gives the number of trips.

The process for getting the A vectors to converge was iterative. After performing the rigorous calculations, most of which were performed by Jessica, we arrived at the following result. By matching the entries in the TripArray matrix produced to the entries in the Distance matrix, we were able to come up with the following trip distributions for each of the 9 trip types.

On the following pages are the trip distributions for the Home-based trips in the following order: Home to Work, Home to School and Home to Shopping.


IV. Trip Distribution

A few overall home-based trip analysis tables:To From Percentage

Home 370,907 370,907 37.09%Work 159,410 159,410 15.94%School 28,375 28,375 2.84%Shop/Other 441,357 441,357 44.13%

As we can see, only about 37% of total trips are home-based. A significant amount is shop/other-based, which is no doubt due to the fact that we did not increase the friction factor for the intrazonal trips. If we had more time (and we had not done all the calculations in Excel as opposed to MATLAB), I would go back and increase the Intrazonal Distances to be 50*Sqrt(Area), as opposed to 0.5*Sqrt(Area), to discourage intrazonal trips.

From this table, we can also see that the otherother trips are significantly shorter in length than all other trip types, which is also because we did not increase the friction factor for intrazonal trips.

Breakdown by residential zones (zones #1, 20, 30, 31, 35, 36, 37, 48, 51, 52, and 53):

If we look at the map of the city, we can see that zone 37, which is heavy residential and is 5.6 sq miles right in the heart of downtown, has the most home-based trips out of any residential zone.

Home-Based Trips (see links below for TripArray mtrices) Home to Work Home to School Home to Shopping

The next thing that we did after calculating the Trip matrix was to calculate the trip miles. Calculating trip miles was pretty simple since we assumed that each trip took the most efficient path. Therefore, we took the product of the Distance and the Trip Matrices to be the trip miles. Hence, we got our value for the Trip Length Distribution. We were able to plot the trip length distributions for each type of Home – Based trip in terms of a histogram. The choice of a histogram was considered the most appropriate because trip length is a discrete variable and therefore, a line plot would not serve the purpose.

Home to Work: Home to School:

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000

35000

Trip Length Histogram

Trip Length(# of Miles)

# of T

rips

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000



# of T

rips

Home to Shopping:

Hassan Chaudhry &

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000

35000

40000



# of T

rips

The next thing that we had to calculate were the person trip miles. In order to calculate that, we assumed that each trip only includes one person and therefore, calculated the cumulative distribution of the number of person trip miles.

Home to Work:

1 17 33 49 65 81 97 1131291451611771932092252412572732893053213373533693854014170.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Cumulative Distribution of Trips by Volume

Trip Volume

Cum

ulati

ve P

erce

ntag

e

Home to School:

Hassan Chaudhry &

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 530.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Cumulative Distribution of Person-Trip Miles

Person-Trip Miles

Cum

ulati

ve P

erce

ntag

e

Hassan Chaudhry &

Home to Shop:

1 13 25 37 49 61 73 85 97 1091211331451571691811932052172292412532652772893013130.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cum

ulati

ve P

erce

ntag

e

We were also able to calculate the cumulative distribution of trips as a function of trip length:

Home to Work:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100

Cum. Distr. of Trips as Fn of Trip Length

Trip Length (Miles)

Cumu

lative

Perce

ntage

Hassan Chaudhry &

Home to School:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100

Cum. Distribution of Trips as fn of Trip Length

Trip Length (Miles)

Cum

ulati

ve P

erce

ntag

e

Home to Shop:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length

Cum

ulati

ve P

erce

ntag

e

Hassan Chaudhry &

NON – HOME BASED TRIPS

As we mentioned before, not all of our trips originated from home. I will go on to explain our work based trips first and then proceed to describe the shop-based trips.

Work-Based Trips In order to generate the trips, we used the same procedure as that used for the home based trips. One thing that we kept in mind, however, was that 80% of the work based trips were to Home while all the others were to shopping areas. We, resultantly, got the following production and attraction matrices.

Hassan Chaudhry &

Trip Length Distribution

Work to Home: Work to Shop:

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000


Trip Length Range

# of T

rips

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


Trip Length Range# o

f Trips

Person Trip Miles:

Work to Home: Work to Shop:

1 15 29 43 57 71 85 99 1131271411551691831972112252392532672812953093233373513650.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumula

tive Pe

rcenta

ge

1 31 61 91 1211511812112412713013313613914214514815115415716016316616917217517810.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumu

lative

Percen

tage

Hassan Chaudhry &

Cumulative distribution of trips as a function of trip length

Work to Home

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length (Miles)

Cum

ulati

ve P

erce

ntag

e

Work to Shop

Hassan Chaudhry &

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length (Miles)

Cumu

lative

Perce

ntag

e

School-Based TripsSimilar to the work-based trips, the school-based trips originate from school and go to either home or the shopping area. Using the instructions given, we were able to utilize the constraints given as well as the number of school going population in order to produce the Production and the Attraction matrix for the school-based trips. The Production and the Attraction matrix were calculated as follows:

Hassan Chaudhry &

Then using these production and the attraction matrix, we were able to find the Trip matrix from School to Home and School to Shop:

School to Home School to Shop

Then using the values of the trip matrix, I was able to calculate charts as before:

Hassan Chaudhry &

Trip Length Distribution: School to Home: School to Shop:

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000


Trip Length Range

# of T

rips

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

200

400

600

800

1000

1200



f Trip

s

Person Trip Miles:

School to Home: School to Shop:

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 530.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumu

lative

Percen

tage

1 13 25 37 49 61 73 85 97 1091211331451571691811932052172292412532652772893013130.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumu

lative

Percen

tage

Cumulative Distribution of Trips as a function of TripsSchool to Home:

Hassan Chaudhry &

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length

Cumu

lative

Perce

ntag

e

School to Shop:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length

Cumu

lative

Perce

ntag

e

Shop Based

Hassan Chaudhry &

The shop based trips were also calculated in the exact same manner. The Production and Attraction matrix are as follows:

The Trip matrix can be found here.

Hassan Chaudhry &

Trip Length Distribution: Shop to Home: Shop to Shop:

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000


Trip Length Range

# of T

rips

0 1 2 3 4 5 6 7 8 9 10 11 12 13 140

10000

20000

30000

40000

50000

60000

70000

80000

90000



f Trip

s

Person Trip Miles: Shop to Home: Shop to Shop:

1 13 25 37 49 61 73 85 97 1091211331451571691811932052172292412532652772893013130.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumu

lative

Percen

tage

1 25 49 73 97 1211451691932172412652893133373613854094334574815055295535776010.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%


Person-Trip Miles

Cumu

lative

Percen

tage

Hassan Chaudhry &

Cumulative Distribution of Trips as a function of TripsShop to Home:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length

Cum

ulativ

e Per

cent

age

Shop to Shop:

0 2 4 6 8 10 12 14 160

10

20

30

40

50

60

70

80

90

100


Trip Length

Cumu

lative

Perce

ntag

e

V. Analysis & Conclusions

Hassan Chaudhry &

After running through all these iterations, we summed up our total number of trips which amounted to 1,000,049 which was very close to what we estimated our trips to be. We also had the chance to compare the cumulative distribution of the trips to the trip length for the various places of origin i.e. home-based, work-based, school-based and shop-based trips. We plotted all the curves on the same chart and got the following result:

We can deduce several conclusions from this graph. First, because the HW & WH, HS & SH, and HSh & ShH are mirror images of each other (i.e. they have the same distribution across the zones), it makes sense that these pairs of trips would have the same cumulative distribution function. So in the graph above, we can see the orange dots corresponding to WH but not the light blue dots corresponding to HW, we can see the dark blue dots corresponding to SH but not the yellow dots corresponding to HS, and we can see the brown dots corresponding to ShH but not the deep blue dots corresponding to HSh.

Furthermore, we can see that the dark grey dots corresponding to shopshop are on a steeper curve than all the other trip types. This is because many of the shopping/retail/recreation zones are located very close to each other, and since these trips are all going from one shop zone to another, it makes sense that a greater proportion of them would have short trip lengths. If we had more time, we would increase the intrazonal distance to discourage these types of trips, and make the trip distribution flatter/more similar to the other trip types.

The second-steepest curve is schoolhome (or home school since they are the same). This is because we placed every single school right next to residential areas and therefore, many of these trips will have a short distance.

Hassan Chaudhry &

Comparison of Trips Leaving Each Type of Destination

Trips Leaving Home:

As we can see, the homeshop have a comparatively more uniform distribution of trips by trip length, which makes sense because there are more shopping zones than schools or work zones, which explains the wide range of trip lengths.

Trips Leaving School:

The same pattern is seen in this histogram, where the schoolhome trips are much more heavily skewed to the left (because all of our residential zones border schools), whereas the schoolshop trips are more evenly distributed because there are so many shopping/restaurants/other zones so there will be many different trip lengths.

1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000

35000

40000

Trips Leaving Home

Home-->Work Home-->School Home-->Shop

Trip Length

Numb

er of

Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 140

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Trips Leaving School

School-->Home School-->Shop

Trip Length

Numb

er of

Trips

10% 50% 90%

HomeWork 1.52 3.74 8.04

HomeSchool

1.11 2.15 6.84

HomeShop 1.50 3.74 8.04

10% 50% 90%

HomeWork 1.52 3.74 8.04

HomeSchool

1.11 2.15 6.84

HomeShop 1.50 3.74 8.04

10% 50% 90%

SchoolHome

1.50 3.78 7.81

SchoolShop 1.48 3.74 8.07

Hassan Chaudhry &

Trips Leaving Work:

Here we can see that even though the quantity of workhome trips is much higher than the quantity of workshop trips, the overall distribution/skewness of the trip distributions is about the same, and this is because there are many work zones, many residential zones, and many shopping/other zones. However, it is important to note that the average workshop trip length is shorter – this is because we intentionally placed bars, restaurants, retail, and recreational zones near work zones. We assumed that any worker, regardless of whether he/she works at Kornhauser Hotel or Government Office or Heinz Field etc., would probably want to buy lunch or breakfast or dinner at some point.

Trips Leaving Shop:

Again, we see here that because we did not increase the intrazonal distance factor to discourage intrazonal trips, the trip lengths for shopshop are significantly lower than the shophome trips.

1 2 3 4 5 6 7 8 9 10 11 12 13 140

5000

10000

15000

20000

25000

30000

Trips Leaving Work

Work-->Home Work-->Shop

Trip Length

Numb

er o

f Trip

s

1 2 3 4 5 6 7 8 9 10 11 12 13 140

10000

20000

30000

40000

50000

60000

70000

80000

90000

Trips Leaving Shop

Shop-->Home Shop-->Shop

Trip Length

Numb

er of

Trips

10% 50% 90%

WorkHome 1.52 3.74 8.04

WorkShop 0.78 2.66 8.83

10% 50% 90%

ShopHome 1.50 3.74 8.04

ShopShop 1.37 3.60 7.81

Hassan Chaudhry &

A Typical Day in the Life of a La Ville de Kornhauser resident:Morning: 100% of HomeWork and HomeSchool trips happen between 6 – 9 am 5% of HomeShop trips happen between 6 – 9 am 30% of HomeShop trips happen between 9 am – 12 pm 5% of ShopShop trips happen between 9 am – 12 pm

Lunch break: 50% of WorkShop trips happen between 12 pm – 2 pm 20% of ShopShop trips happen between 12 pm – 2 pm 20% of ShopHome trips happen between 12 pm – 2 pm

Afternoon Activities/Extracurriculars: 100% of SchoolShop trips happen between 2 pm – 5 pm 100% of School Home trips happen between 2 pm – 5 pm 30% of Shop Shop trips happen between 2 pm – 5 pm 25% of HomeShop trips happen between 2pm – 5 pm 30% of Shop Home trips happen between 2 pm – 5 pm

Dinner/Post-Work Activities 100% of WorkHome trips happen between 5 – 8 pm 50% of WorkShop trips happen between 5 pm – 8 pm 40% of HomeShop trips happen between 5 pm – 8 pm 45% of ShopShop trips happen between 5 pm – 8 pm 50% of Shop Home trips happen between 5 pm – 8 pm

We built this table to simulate what a typical day for a resident would look like:Resident Type

Time Full-Time Employed Workers Stay-at-Home Soccer Dads/Moms6 am – 9 am(Morning Commute)

159,410 trips Home Work 28,375 trips HomeSchool9,156 trips HomeShop

9 am – 12 pm N/A 54,937 trips HomeShop11,034 trips ShopShop

12 pm – 2 pm(Lunch)

15,940 trips WorkShop 44,136 trips ShopShop 44,136 trips Shop Home

2 pm – 5 pm(After School Activities)

N/A 5,675 trips SchoolShop22,700 trips SchoolHome66,204 trips Shop Shop45,780 trips Home Shop66,204 trips Shop Home

5 pm – 8 pm(Dinner/Post-Work)

127,528 trips Work Home15,940 trips Work Shop36,624 trips Home Shop44,136 trips Shop Shop55,170 trips Shop Home

36,624 trips Home Shop55,170 trips Shop Shop55,170 trips Shop Home

Total 454,748 545,301 1,000,049

Hassan Chaudhry &

Total P&A Generation: People P A

Light Residential 23,287 1 38,655 38,107 Heinz Field/Foxburg Country Club 3,985 2 19,926 19,926 Light Industrial 13,664 3 13,664 13,664 Light Industrial 2,277 4 2,277 2,277 Heavy Industrial 15,941 5 15,941 15,941 Heinz Field/Foxburg Country Club 1,993 6 9,963 9,963 Heinz Field/Foxburg Country Club 1,594 7 7,971 7,971 Heinz Field/Foxburg Country Club 399 8 1,993 1,993 Educational (University) 7,000 9 11,252 11,252 Recreational 0 10 1,000 1,000 Bars and Restaurants 2,571 11 17,998 17,998 Bars and Restaurants 1,286 12 8,999 8,999 Retail 2,869 13 25,824 25,824 Educational (private) 3,165 14 7,225 8,240 Retail 2,869 15 25,824 25,824 Medical 23,912 16 95,646 95,646 Assisted Living 11,081 17 0 0 Assisted Living 4,925 18 0 0 Assisted Living 1,231 19 0 0 Govt Subsidized Housing 34,696 20 56,904 56,226 Bars and Restaurants 2,571 21 17,998 17,998 Retail 3,826 22 34,433 34,433 Recreational 0 23 2,000 2,000 Bars and Restaurants 3,857 24 26,997 26,997 Retail 2,869 25 25,824 25,824 Kornhauser Hotel 3,985 26 19,926 19,926 Government Offices 3,542 27 3,542 3,542 Government Offices 2,657 28 2,657 2,657 Government Offices 1,771 29 1,771 1,771 Heavy Residential 6,166 30 11,269 10,917 Heavy Residential 2,055 31 4,693 4,388 Retail 5,739 32 51,649 51,649 Bars and Restaurants 3,857 33 26,997 26,997 Professional Offices 9,565 34 9,565 9,565 Heavy Residential 24,666 35 40,860 40,296 Heavy Residential 24,666 36 40,860 40,296 Heavy Residential 57,553 37 93,466 92,526 Recreational 0 38 2,000 2,000

Hassan Chaudhry &

Educational 4,220 39 11,460 13,270 Recreational 0 40 1,950 1,950 Bars and Restaurants 1,928 41 13,498 13,498 Recreational 0 42 2,000 2,000 Educational 3,165 43 9,165 10,665 Professional Offices 4,782 44 4,782 4,782 Professional Offices 1,594 45 1,594 1,594 Bars and Restaurants 3,857 46 26,997 26,997 Retail 5,739 47 51,649 51,649 Light Residential 12,198 48 20,917 20,496 Recreational 0 49 2,000 2,000 Educational 2,110 50 7,510 8,860 Light Residential 22,178 51 36,881 36,346 Light Residential 4,990 52 9,387 9,049 Light Residential 13,307 53 22,691 22,257 Water 0 54 0 0 Water 0 55 0 0 Water 0 56 0 0 Water 0 57 0 0 Water 0 58 0 0 Open Space 0 59 0 0 Open Space 0 60 0 0 Open Space 0 61 0 0 Open Space 0 62 0 0 Open Space 0 63 0 0 Open Space 0 64 0 0

Hassan Chaudhry & Jessica deng ORF467 Final …alaink/Orf467F16/MyCityF16/La V… · Web viewHassan...

Documents

Transcript of Hassan Chaudhry & Jessica deng ORF467 Final …alaink/Orf467F16/MyCityF16/La V… · Web viewHassan...