Can Public Transit Affect Private Vehicle Use?
The phenomenon of “car society” indeed exist in America. It brought many problems like congestion, air pollution, safety, and environmental issues to people’s daily-life in nowadays. People can benefit a lot from the widespread use of public transportation, because mass transit has many merits as cheap, low-cost, on time, and easy to operate. So can public transit affect private vehicle use? My report tend to use some space analysis methods and four variables like non-vehicle family density, low-income families around stations, people’s travel behavior, distance from transit stations to analyze, trying to find a stronger relationship between mass transit and private vehicle use and make some proposals to the current lines.
Rapid 757 bus line and Green line railway in Los Angeles, California. Bus line has 33 stations and railway has 14 stations. I choose the buffer area which is 0.5 miles from transit stations(figure 1).
Figure 1: Buffer areas of transit stations
Analysis of Non-Vehicle Families Near Transit Stations
The non-vehicle families near bus stops(2192) are much larger than that near rail stops(330) per station. We can see quantities of families along bus line are more concentrative than that in rail line(figure 2).
Figure 2: Analysis of Non-Vehicle Families Near Transit Stations
Analysis with Data of Public Transit and Low-Income Families
low-income families and the places which use public transit are concentrating along the bus and rail line(figure 3).
Figure 3: Analysis with Data of Public Transit and Low-Income Families
Analysis of Distance From Stations
The most walking influencing area of transit stations is limited around 1500 meters(figure 4).
Figure 4: Analysis of Distance From Stations
Hot-Spot Analysis of Transit Stations
For the sake of finding a correct tendency of public transit use near stations, I create a final transit need index through four variables: density of non-vehicle families, %low-income families, %public transit use, distance from transit stations. We can see the high transit need mostly distributing around transit station within 1 miles(figure 5).
Figure 5: Hot-Spot Analysis of Transit Stations
Analyses of Proposed Infill-Stations
From the right figure, there are some uneven distribution of stations in some parts of bus line and rail line within high transit need area(see red area of upper of figure 6). Because it is very hard to change railway’s station, so the proposed infill-stations are focus on bus stations in this report. So I add 10 new stations for rapid 757 bus line(see nether of figure 6).
Figure 6: Analysis of Stations of Transit Line
Now I am going to testify the veracity of these new stations. I choose three degree of distance: 300 meters, 600 meters, and 1000 meters to do the analysis of service area near originally(see upper of figure 7) and new transit stations(see nether of figure 7). Service areas of new and originally stations cover the whole line that seems more reasonable than ever.
Figure 7: Service Area Analysis of Transit Stations
We can see the road densities which around new stations are mostly the same as that around originally stations(figure 8). It is necessary to add some new stations in the original bus route, so the line can service more people.
Figure 8: Road Densities Near New and Originally Stations
- The high transit need mostly distributing around transit station within short distance.
- It is necessary to add some new stations in the original rapid bus 757 route, so the bus line can service more poor people and non-vehicle families, and encourage more people to use public transit rather than private vehicles.
- The method of changing stations costs lower than that of changing line type.
Below figures(figure 9) are using methods of “feature to Raster” and “Reclassify” to create the transit need index.
I use the skill of “Metadata” to describe details of my new adding stations(figure 10).
Figure 10: Metadata Utilized
- Insert Map
- Attribute sub-sets selections
- Aggregating attribute fields
- Graduated Symbols
- Hot-Spot Analysis
- Original Data
- Proximity Analysis
- Network Analyst
- U.S. Census Bureau: 2011 Tiger/Line R Shapefiles
- Metro Developer
- U.S. Census Bureau: American FactFinder