Hyperloop USA Network
How can Hyperloop be implented in the USA?
1 Introduction
The USA is a car-centric country, which brings many problems that are more specifically assessed in the following SideCar. Cars and road transportation contribute signficantly to the greenhouse effect, are energy-inefficient and consume much space in cities. This is why we have designed a better solution.
1.1 Problem: Bad Infrastructure Planning
This graph clearly illustrates that the United States lags significantly in international passenger rail service rankings. Surprisingly for an industrialised country, it falls behind nations typically categorized as less affluent, such as Azerbaijan and Argentina.
(We had to draw every line by hand for it to suit the design requirement)
In yellow, we can observe that domestically, rail travel has a small share among the mass transit transportation systems. Even bus travel exceeds rail traffic nearly ten-fold, altough bus travel is slower, more susceptable to traffic and worse for the environment.
The sparse concentration of passenger railroad lines in the USA is clearly noticeable. It is even more impressive in comparison to the cargo rails, which are vastly denser.
[2; f8ff2e] [1.33; 502f93]
In addition, rail roads for commuting trains are also centred around the suburb to city-centre traffic, whereas travel in between suburbs is difficult.
[3.33; 00c8c8]
Cars also take a lot of space in cities, e.g., for parking lots in Houston. This space could be used for shops, residences and public buildings. A decrease of the amount of parking lots, would lead to a drop of land prices because of the greater available space. Ultimately, this could contribute to more financial stability, especially for poorer families.
The shown car-centric design and lack of rail transportation of the USA causes a tremendous environmental impact. Transportation contributes to 28% of the greenhouse gas emissions in the USA, which could be signficantly reduced by expanding the rail traffic or finding a solution, which is faster and more energy-efficient.
Car traffic is also an unsafe mode of transportation. According to NHTSA , road transportation causes 42,795 fatalities per year in the USA, without taking the deaths caused by pollution into account.
Money lost in Billion of 2009 $ due to congestion
In addition, road transportation causes congestion, which causes the average American to lose time. Estimations show, that the average American spends around 36 hours in traffic jams per year. This number rises to around 100 hours per year in densly populated areas like New York, where much of the USA's wealth is concentrated. This is the reason why the productivity and time spent decreased to an extend that the US economy loses 130 Billion USD to congestion every year (graph).
Consequently, a change in American infrastructure is necessary!
1.2 Solution: Hyperloop
Hyperloop is a revolutionizing transportation concept that combines high-speed and efficient travel using elements of magnetic levitation, low-pressure environments, and electric propulsion to accelerate passenger pods through airless tubes at incredible speeds, potentially exceeding 1,100 kilometers per hour. This futuristic mode of transportation promises to offer a better way to transport people over long distances, offering an alternative to domestic air traffic and long-distance car traffic (thus keeping the roads free for short-distance travel). In theory, the Hyperloop tubes are covered by solar panels which allow the Hyperloop pods to be maintained almost carbon-neutrally.
ChatGPT
For this futuristic mode of transport, we designed a network which fits the needs of the American public best.
2 Network Design
2.1 City determination
The most important factors to determine the cities that will host the main stations of the Hyperloop network are population, wealth, existing traffic and security.
To take into account whether the population is sufficient for a main station to be profitable, we culled the postal code areas with under 250'000 permanent residents from a population layer.
[POP.>250000; 1.33; ea5b41; trans. dep. on pop. class]
We filtered the counties with an average annual GDP per capita exceeding $360K from a GDP per capita layer. Because it did not include the southern parts of the USA, we researched which of the regions that would be important due to their population also have a sufficient GDP . Then, we overlapped the sketch and the layer.
[GDP/c.>360K; 1; col. dep. on GDP/c., c9bf00; 50%] + [1; 00b8eb, 0086ab; 75%] 🠖 [1; 00bd91; 0-70%]
Next, we overlapped the income layer and the population layer to get the areas that are home to enough citizens who are wealthy enough for a hyperloop main station to be profitable.
🠖 [5 ( 🠖visible); b60dff; 25%]
To choose from these locations we considered three main factors: existing traffic and infrastructure, geographical position and the risk of natural disasters, as shown in the following slides. The results can be seen on the left.
[20, 7.6; ff0000, 000000]
These are buffers of 60km to each side of the tracks of hurricanes of the last decade (filtered from the original layer), the entirety of which represents the area that has been hit by destructive cyclones. As visible, we tried to avoid the densest zones as well as possible.
[Season>2010; 1.33; bababa, 6e6e6e; blur 15, sepia 20 ( 🠖 windy); 63%]
In the map, we can see clusters of recent earthquakes of magnitude greater than 3.5. Again, we gave our best to avoid these spots but San Francisco, for instance, is so relevant that we had to include it despite the earthquakes.
[clust.>0; 4-40; c3e34f, bababa; shadow 3 (🠖 shaking motion); 40%, 60%]
The layers of recent traffic counts, from which we culled the ones below 8000 in 2020, and the freeway system show us where the most important cities for travel are. Because the traffic layer seems to be broken, we had to use an image.
[traffic count>8000; 4; 6b0099; 20-80%] [60%]
To choose the exact location of each station we had to zoom in and find free space. We respected the following factors: proximity to existing means of transport, such as streets or railway lines, sufficient GDP and population, and an area of approximately 0.25km 2 . The optimal location was often in parks as seen in the example of L.A.
[new col. 4f81bd] [1; ff00ee, ffffff; 75%]
2.2 Connection of Cities
London: Population and Underground
The remaining question is: how should we connect our main stations? To find out more about this, we decided to take a closer look at working systems of public transport, particularly on the lines of the Underground in London and Métro in Paris, with respect to the population density.
In both cities, the densest population is in a circular area around the centre, which is thinly populated, like the outskirts. The tubes are the densest in the centre and spread out gradually. In the area of the highest population density, there is a circular line connecting the others.
Paris: Population and Métro
In Paris, the population is evenly distributed, and so are the lines of the Métro. The lines of the Underground, however, follow the areas with the highest population density.
We used a heatmap and a point density design to illustrate the population layers in London and Paris, respectively. We used different designs because it was the best way to show the distribution of the population with the available layers. Then, we overlayed it with the layers of the public transportation systems that we wanted to analyse.
[38%] [subway: open map to see legend] [1.3; 65%]
Taking our results from above into account, we came to the conclusion that the connections between our main stations to the right are our best match. Despite some differences that derive from the different scales, we adopted the most important features of the subway networks, such as the circular line around most of the USA, and the correlation of network and population density.
[5; 00c8c8] [20; f0321e]
To determine the exact trajectory of each tube, we considered obstacles and guidances, which will be shown in the following slides. Please notice, however, that the ArcGIS sketch tool did not always allow complete precision.
To avoid the first obstacle (mountains), we used a layer containing every spot higher than 500m. Because the East has a lower overall elevation, we had to conduct an outlier analysis. Then, we hid low-low clusters (low spots with low surroundings), leaving back only high-high clusters, high-low, and low-high outliers, which are the areas it may be more difficult and costly to build in.
[20] [Type≠LL/NS; 6.7; f0b8b1, e01b1b, 1b53e0]
Because the ArcGIS tools do not have the capacity to handle all data points at once, we had to do a separate analysis for each region, ending up with 15 different layers.
In the example of Denver, one can see that we had to avoid the adjacent part of the Rocky Mountains.
Using the same earthquake layer we used before, we tried to avoid said clusters as well as possible, as seen in the example of Oklahoma City. Sometimes other factors predominated, though.
The last obstacles are tornadoes and hurricanes. To avoid these, we used a 60m buffer to each side of tornado tracks of magnitude greater than 4 and the same layer for hurricanes that we used before. Although we could not avoid these two phenomena completely, we did not plan anything in the densest hotspots. A great example is the Atlanta-D.C. line.
[Mag.>4; 1.33; ff0000; 50%]
Especially in cities, we used existing streets and tunnels as guidances for two reasons: 1. The space in the tunnels, around or on the streets could be utilized for Hyperloop-tubes, saving the costs of establishing a completely new infrastructure. 2. The proximity to existing infrastrucutre facilitates the transport of building material and equipment.
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The efficiency of building next to (or on top of) existing highways increases in cities such as Chicago because no existing infrastructure or buildings have to be destroyed.
3 Evaluation of Result Quality
Comparison: Hyperloop vs. Railway
To determine how realistic our results are, we decided to compare them to the existing passenger railway lines, which we filtered from a layer containing all railway lines in the USA.
The main difference is the density in the East. This can be explained by the fact that railway lines are used for short-distance travel (which is more common in the East due to the higher population density). The Hyperloop-network, however, is not designated for short distances.
Although there are slight differences, many connections are almost identical: S.D.-Seattle, Denver-Chicago, and Miami-Boston to mention only a few examples.
Therefore, we are proud to say that we, indeed, did a pretty good job.
4 Conclusion
But is our Hyperloop-system really better than the existing infrastructure? And is it realistic?
4.1 Comparison
This Excel-Chart shows that Hyperloop is a liable alternative to common passenger travel between Los Angeles and San Francisco. Note that car pollution is calculated for one-person-driving only as it is the most common usage . ("i": speed of Hyperloop)
Comparison in respect to time and pollution between cars trains, airplanes and the hyperloop
As the graph shows, Hyperloop is more environmentally friendly and faster. But the costs to build the proposed Hyperloop network can make the new transportation system financially unlikely to stem.
Using the map tools in ArcGIS, we measured 241‘000km of Hyperloop tubes that would have to be built. We researched in the internet, where we found out that one kilometre of Hyperloop tube costs around 37 million USD to build. This would conclude that the total cost of building our Hyperloop network would be 8.9 Trillion USD. This number is equivalent to 40% of the annual GDP of the entire USA. However, the costs would decrease, because the Hyperloop tubes are scalable. Also, we didn‘t take the costs of the Hyperloop Hubs and tunnels into account, altough they are crucial to realizing this project.
Due to the huge costs, it is merely impossible that the U.S. Government would execute our proposed network. However, sections of our proposed network are more affordable to build.
