To view this visualization, go here: https://public.tableau.com/app/profile/ian.kruger1361/viz/HighSpeedRailAnalysisFindingPotentialRoutesintheUnitedStatesbasedoffofChinesemetrics_/Story1
For CSCE567, a conglomorate of data visualizations that both determine the standards for which High Speed Rail could be applied throughout a country, and applying those criteria to finds regions in the United States where High Speed Rail could be built.
The first dataset used is a simplemaps dataset depicting chinese cities with their exact lat/long coordinates and population. This is used to get a measurement between two city locations, as well as to gather a minimum population standard for the average city using HSR (adjusted for relativity, of course.)
Originally, we started with dataset, which had lines and points necessary for reconstructing a geographical representation of all railways in China
lines = gpd.read_file("data/hotosm_chn_railways_lines_geojson/hotosm_chn_railways_lines_geojson.geojson") points = gpd.read_file("data/hotosm_chn_railways_points_geojson/hotosm_chn_railways_points_geojson.geojson")
import geopandas as gpd print(lines.columns) print(points.columns) Index(['name', 'name:en', 'railway', 'ele', 'operator:type', 'layer', 'addr:full', 'addr:city', 'source', 'name:zh', 'osm_id', 'osm_type', 'geometry'], dtype='str') Index(['name', 'name:en', 'railway', 'ele', 'operator:type', 'layer', 'addr:full', 'addr:city', 'source', 'name:zh', 'osm_id', 'osm_type', 'geometry'], dtype='str') Filter to find only HSR stations
But, this data set was messy, with no tag indicating a highspeed railway, and only "highspeed" or "high-speed" in the name to indicate. I could've cleaned up the data, but I did some more searching and found a better data set specifically designed for my topic. Some connecting will still be necessary, but it makes it a lot easier to work with (and its not 300k lines long).
Most HSR routes occur between ~150-500 miles, with the highest concentration around ~300 miles. Most HSR connections occur between cities with ~10 million people. However, Chinas population is estimated to be ~1.4 billion. This means, the average city population connected by HSR comes out to around 0.7% of the total population. High-frequency routes are concentrated in the same distance range as well, suggesting this is the most efficient range for HSR.
Distance Distribution
Percentile --> Miles
10% → ~45 miles
50% → ~157 miles
90% → ~514 miles
Weighted Distribution
10% → ~32 miles
50% → ~75 miles
90% → ~285 miles
HSR exists across a wide range (45–514 miles). HSR is MOST USED between ~30–285 miles. While high-speed rail exists across distances of roughly 45–500 miles, the majority of high-demand routes fall within a much narrower range of approximately 30–300 miles, with a median around 75 miles.
Combined Start-->Destination Population.
25% → ~6.2M
50% → ~8.9M
75% → ~13.3M
High-speed rail connections typically occur between cities with combined populations exceeding ~6 million, with many high-frequency routes connecting cities totaling over 10 million residents.
High-speed rail is most effective when connecting large population centers (6M+ combined) over relatively short to medium distances (~30–300 miles), where demand is highest.
However, translating these metrics can be difficult due to the development of each country. China has a high urban concentration due to their population distribution, leading to the development of many mega cities. However, studies have shown that China built the infrastructure FIRST, and the urban density followed.
Because China’s population is significantly larger than that of the United States, population thresholds were normalized proportionally.
High-speed rail in China typically connects cities whose combined populations represent approximately 0.7% of the national population. Applying this proportion to the U.S. results in a threshold of roughly 2–3 million combined population.
For US City Data --> usCity standard is a population threshold of above 150K. To filter out everything but major metropolitain areas, usCities2 uses a 500k threshold.
You'll notice some inconsistencies in the map. For example, there are lines connection Charlotte to Columbia and Charlotte to Charleston, yet there is not a line connection Columbia to Charleston. Some smaller city pairs (e.g., Columbia–Charleston) do not appear as direct connections because their combined population does not meet the demand threshold. However, these cities would likely still be served as intermediate stops along larger corridors (e.g., Charlotte–Charleston). If we lowered our threshold for combined population, these would be included, but we are simply observing data and can infer that these stops would be connected. This model simply approximates behavior. Not all city pairs are directly connected due to insufficient demand. Instead, smaller cities are likely to be served as intermediate stops along major high-demand corridors.