GIS and Transportation Planning (Public Transportation)
Submitted By: Avinash Shrivastava, Geog 660 – Literature Review Course Instructor: Dr. Andrew Klein
GIS and Transportation Planning (Public Transportation)
Table of Contents Introduction .................................................................................................................................................. 2 Thesis statement ........................................................................................................................................... 2 Literature Review .......................................................................................................................................... 3 Research Methods ........................................................................................................................................ 5 Bibliography ................................................................................................................................................ 16 INDEX .......................................................................................................................................................... 17
Table of Figures Figure 1: Buffer (ArcGIS 9 ArcMap Version 9.3.1)…………………………………………………………………………………..07 Figure 2: Target Area……………………………………………………………………………………………………………………………..07 Figure 3: Segmentation Variables for Consumer Markets (© 2010 South‐Western, Cengage Learning. All rights reserved.)…………………………………………………………………………………………………………………………….………08 Figure 4: Mapping TAZ data segment‐wise along the proposed commuter light rail line……………………..10 Figure 5: Combinations………………………………………………………………………………………………………………………….10 Figure 6: New Layer using function ‘cartographic overlay’ or ‘intersect’………………………………………………..11 Figure 7: Layer showing hierarchy of areas based or ridership and redevelopment potential……………….11 Figure 8: Overlapping pattern of layers ‐ 'ridership and redevelopment' and 'household/population density'………………………………………………………………………………………………………………………………………….………12
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
Introduction
The last decade of the twentieth century brought rapid evolution of Geographic Information System (GIS), thus making GIS an essential technology for land‐use and transportation planning. Maps have always “enriched everyone’s understanding of existing and planned development, locations for new facilities, and accessibility provided by transport.” (Slavin, 2004)To perform analysis in a variety of disciplines, including transportation, the introduction of GIS has helped significantly in the integration of data with geographic elements. (Schweiger, 1992)The most significant and widespread application of GIS in the field of transportation planning, is analysis and networking of public transportation/transit services in an area. Application of GIS in the form of spatial analysis plays a very important role in the transportation planning and decision‐ making process. This paper “describes how planning transit routes can be transformed into a more proactive process and how GIS technologies can be used to reach that goal.” (Azar, Ferreira, & Wiggins, 1994)It mainly focuses on the application of GIS in model‐based transportation planning rather than on other potential uses of GIS. The key features are public transportation, ridership analysis based on market segmentation, identification or alignment of transit routes and identification or re‐allocation of transit stops.
Thesis statement To plan a transit route and potential stops based on market segmentation and ridership analysis.
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
Literature Review
The growing needs imposed on our transportation infrastructure require state and local transportation agencies to approach towards capable and efficient solutions which would in turn enable them to resolve problems and make informed decisions with confidence. To meet these demands, transportation administrators highly depend on the unmatched potential of GIS and its ability to organize, display and facilitate complete analysis of large amounts of information (Marlow, Nov/Dec,2007). Since 1980, geographic information system (GIS) technology and its application have evolved as an important tool in the field of land use and, transportation planning and design (Slavin, 2004) (Sholly, Fall 2008). Databases desired by decision makers and maps (today’s digital maps) have always played a central role in planning; query and visualization of geographic information rather than formal analysis direct most GIS applications, but in transportation planning and modeling, analysis is more common (Slavin, 2004). In order to offer more transportation options, the Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991 and the Transportation Equity Act (TEA‐21) of 1998 wanted transportation agencies to use information technologies to integrate transportation with land use more directly (Sholly, Fall 2008). “Traditionally, transportation modeling of traffic impacts, calculation of vehicle emissions, and consideration of wider transportation planning effects, including transit, has employed different modeling techniques, often in different agencies or in separate sections within an agency” (Sutton, 2007). But the application of GIS in transportation planning is classically seen as, modeling scenarios to predict the effects of potential policy changes.
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
Thus, GIS with its ability to perform all of these functions has become a very important tool to transportation planners (Sholly, Fall 2008). The unique ability of GIS to handle complex spatial relationships makes it a natural tool to use in the planning and analysis of transportation systems, specifically public transportation systems. The current use of GIS technology in public transit agencies and metropolitan planning organizations (MPOs) for transportation planning and analysis has become more popular and demanding (Schweiger, 1992). One of the most important and accepted application of GIS in transportation planning, is public transit networking and routing. Transit modeling uses a variety of GIS spatial analysis functions for identifying the route, potential location of stops on a transit route, calculating distances between stops or between origin and destination points, calculation of temporal values over those distances, etc. (Choi & Jang, 2000). The transit route location, stop identification and analysis problem requires the estimation of a population within the service area of a route. “A route's service area is defined using walking distance or travel time. A procedure for performing service area analysis on transit routes using a geographic information system (GIS) is compared with the more common technique of buffering.” (O'Neill, Chou, & Ramsey, 1992) Another potential application of Geographic Information System (GIS) in transit management and operation is ridership analysis on transit routes. In order to reduce traffic congestion and minimize the need for parking spaces, it is essential to serve the large employment and community centers conveniently by transit routes. (Azar, Ferreira, & Wiggins, 1994)
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
Ridership analysis on a transit route also provides an alternative way, the transit planners, to identify the route and potential location of stops on the transit route based on market segmentation variables (demographic, geographic, psychographic and behavioristic). Transit planners, while designing transit routes, consider various demographic variables such as the number of employers, the number of households, age‐cohort, residents' income levels, household density, travel time, etc. in order to determine the type and characteristics of the service to be provided. (Azar, Ferreira, & Wiggins, 1994) Application of GIS to get the ridership data, based on Market Segmentation, can be performed and analyzed in the following way: (Biswas, 2007)
Collecting consumer information at the TAZ/parcel level.
Segmenting the consumer in each geographic area using significant variables.
Mapping the above information to geographic areas.
Analysis
Hence, the advance of computer technologies, and specifically GIS, coupled with rapidly increasing availability of detailed data, provide new tools for designing better transit routes. (Azar, Ferreira, & Wiggins, 1994)
Research Methods
Overview of the project: The Union Pacific Railroad is the Class 1 railroad company that operates freight mainline through Bryan‐College Station between Houston and Dallas‐Fort Worth. The Union Pacific enters Bryan from the south through College Station.
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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The tracks diverge in Bryan with one branch continuing north to Waco and the other continuing northwest through Hearne. (A Resource Guide of the2000‐2020 Bryan Comprehensive Plan, City of Bryan, Texas). In this project, we are proposing a commuter light rail transit service on the existing freight rail line, in the Bryan‐College Station district. This proposed commuter light rail would run from Wellborn to Bryan downtown and vice‐versa. We are also recommending realignment for the existing freight rail. The purpose of this project is not only to provide public transit to the existing riders but also to attract choice riders to use the public transit over single‐ occupancy cars. It also aims to bring new transit‐oriented development along the proposed rail line (i.e. in the central city or urban core) and trim down on developments inducing sprawl. This paper evaluates the spatial characteristics of the district’s (Bryan‐College Station) ridership and commuting patterns; the accessibility of the Bryan‐College Station’s proposed transit route (i.e. light rail line) to the areas along the line; the number and location of stops and rail‐stations. The methodology includes using the ‘buffer’ and ‘cartographic overlay’ features of GIS to mark the study area and to identify the potential location of consumers that would generate ridership along the proposed light rail line respectively. Based on this information, we can assess the potential of the proposed transit route and the identified stops on that route. Also, if the desired outcome is not achieved, the route should be realigned or the stops should be reallocated, based on the study and analysis, to get the preferred result. Using 2006‐2008 American Community Survey (3 year estimates) public transportation to work data, the study employs Geographic Information Systems (GIS), specifically ESRI ArcGIS, to analyze public transit accessibility in terms of how well proposed public transit light rail line serves a sample of area’s commuting patterns in comparison to automobile travel. Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
Target Area: In this project, the areas along the proposed B‐CS (Bryan‐College Station) commuter transit light rail line are supposed to be the target areas. However, this transit line would primarily serve those living at quarter‐mile distance (comfortably walk‐able distance) from the proposed B‐CS commuter transit light rail line. This commuter light rail will also cater to those areas which are beyond quarter‐mile distance from the transit line. Hence, we consider areas which lie within the half‐mile buffer of the B‐CS commuter transit light rail line (half‐mile on either side). We use ArcGIS 9 (ArcMap Version 9.3.1) to create this buffer zone along the proposed commuter rail line (refer Map‐1 in the index). Firstly, the required layers, shapefiles are added to the map. To create a buffer for a specific point, line or polygon, we go to:
Arc Toolbox > Analysis Tools > Proximity > Buffer
Select ‘Input Features’, Distance (Linear Unit) and Units
Create a separate layer and name it “Buffer_” Figure 1: Buffer (ArcGIS 9 ArcMap Version 9.3.1)
Figure 2: Target Area
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
The variables that we focus chiefly, to get the ridership data of the district (Bryan‐College Station) and the ‘target area’ (areas along the proposed rail line), are ‘Market Segmentation’ variables.
Figure 3: Segmentation Variables for Consumer Markets (© 2010 South‐Western, Cengage Learning. All rights reserved.)
According to the need of the project, we limit our scope and focus only on selected Demographic variables. These are: 1) Age Cohort 2) Income 3) Travel Time 4) Industry 5) Occupation 6) Gender 7) Class of worker 8) Tenure 9) Citizenship Status. According to the Transit Ridership Survey on Buses and at the Transfer Point, 2008 (Source: BCSMPO 2010‐2035 MTP) the following were the results:
•
69% of transit passengers are female.
•
Largest group to use transit is in the age bracket of 25‐34.
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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•
For trip purpose, •
42% used transit for work trips
•
25% for shopping
•
16% for medical
•
15% for other personal
•
2% for school
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53% surveyed use transit 5 days per week
•
Vehicle availability shows that 74% are without the use of a personal vehicle.
Since the highest percentage (42%) of total population in the district uses public transportation for work trips, we collected some ridership data for the variables mentioned above. After analyzing the data, we observed that these demographic variables can be divided into three categories based on the total percentage of transit ridership generated, to work trips, by each variable. In this paper, let us just talk about the first and foremost category i.e. primary category which is the most significant one of all the three, and strongest as an indicator. The variables in this category are: •
Age Cohort: 20‐44 years – contributes 80.91% of total transit ridership to work
•
Income: Focus more on Low Income (> $25,000 ) – 81.15%
•
Travel Time: Origin to Destination – 0 to 25 minutes – 86.01%
•
Industry: Educational services, and health care and social assistance. Arts, entertainment, and recreation, and accommodation and food services – 76.83%
•
Occupation: 1) Management, professional, and related occupations.
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
2) Service occupations. 3) Sales and office occupations – 89.77% (Data Set: 2006‐2008 American Community Survey 3‐Year Estimates, Survey: American Community Survey)
Purpose: To observe and analyze different market segmentation variables at the District level, and evaluate its ‘application and feasibility’ on the Target Area. As we deduced from the analysis that the above mentioned five variables are the most important ones (at district level), thus we try and gather maximum information about these variables, at TAZ (traffic analysis zones) level, for the ‘target area’. After collecting the data, using ArcGIS 9 ArcMap Version 9.3.1 we map this TAZ data, segment wise, along the proposed B‐CS (Bryan‐College Station) commuter transit light rail line, Figure 4: Mapping TAZ data segment‐wise along the proposed commuter light rail line
Then, using the ‘cartographic overlay’ function in ArcGIS 9 (ArcMap Version 9.3.1), we overlap all the five layers ‘or’ intersect all the layers with each other to get a single desired layer which has all the parcels segregated and colored according to the preferred combinations. (Fig.5) Figure 5: Combinations Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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The layer created after using the ‘cartographic overlay’ or ‘intersect’ function over all the five layers, is illustrated below: Figure 6: New Layer using function ‘cartographic overlay’ or ‘intersect’
These combinations are then ranked, based on their ridership and redevelopment potential. Here, first three combinations (Fig.5) are ranked and demonstrated in the Fig. 07 as follows: 1) High, 2) Medium, 3) Low where, High: High Ridership and redevelopment potential Medium: Medium Ridership and redevelopment potential Low: Low Ridership and redevelopment potential
Figure 7: Layer showing hierarchy of areas based or ridership and redevelopment potential
In this layer, we can clearly differentiate between the areas having high, medium and low ridership and development potential based on five primary variables discussed above. However, there is another most important variable which would make the scenario more clear and would be of greatest help in the decision making process. The variable is ‘household/population density’. Hence, we collect the household/population density data for all the TAZ’s along the proposed B‐CS (Bryan‐College Station) commuter transit light rail line, Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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and map it using ArcGIS 9 ArcMap Version 9.3.1. By doing this, we create another new shapefile which contain spatial data on household/population density. Now, by using ‘cartographic overlay’ or ‘intersect’ function, we overlap the layer (shown in Fig. 7) and new layer created (household/population density layer) to see what we get next.
Figure 8: Overlapping pattern of layers ‐ 'ridership and redevelopment' and 'household/population density'
The result we achieved by overlapping the two layers (i.e. ‘ridership and redevelopment potential’ and ‘household/population density’) clearly illustrates that the whole proposed B‐CS commuter transit light rail corridor can be divided into eight different zones, based on the overlapping pattern of these two layers. (as shown in Fig. 8). These zones are,
High Ridership, High Density
High Ridership, Medium Density
High Ridership, Low Density
Medium Ridership, High Density
Medium Ridership, Medium Density
Medium Ridership, Low Density
Low Ridership, High Density
Low Ridership, Low Density
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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GIS and Transportation Planning (Public Transportation)
From the above listed eight zones, it is evident that ‘high ridership, high density’ areas are the existing locations with highest potential for ‘commuter light rail stations/stops’. Also, it is clear that the zones ‐ ‘high ridership, medium density’ and ‘high ridership, low density’ are the existing locations with greatest redevelopment potential. According to the analysis of market segmentation variables, done previously, it is noticeable that these areas (zones) already have high ridership potential. Hence, by redeveloping these zones and adding (increasing) density in these ‘high ridership, medium density’ and ‘high ridership, low density’ areas, would also become ‘high ridership, high density’ zones and subsequently, locations with highest potential for ‘commuter light rail stations/stops’. Now, for ‘medium ridership, high density’ and ‘low ridership, high density’ zones, the ridership need to be increased based on analysis of market segmentation variables. One of the reasons for medium/low ridership in high density areas is that the residents in these regions are more dependent on automobiles i.e. choice riders. Hence, the objective here is to attract those choice riders, by offering them some benefits to use public transit over single‐occupancy automobiles, to convert these zones into ‘high ridership, high density’ zones. As of now, all the four zones, discussed in the above two paragraphs, are locations with future potential for ‘commuter light rail stations/stops’. The other three zones i.e. ‘medium ridership, medium density’, ‘medium ridership, low density’ and ‘low ridership, low density’, currently, has the least potential for ‘commuter light rail stations/stops’. Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Also, by using ArcGIS 9 ArcMap Version 9.3.1, we can map and analyze the existing and proposed land‐use, development/growth pattern, existing and proposed population and employment projection of the ‘target area’. Next step would be to map and assess the land values of all the land parcels in the ‘target area’ (refer Map‐2 in the index) and based on the spatial analysis, we can propose transit oriented developments (TODs), with desired density, on the cheap vacant parcels of land. This is done in order to support the proposed transit system by increasing ridership; raising the value and potential of the urban core by developing the cheap under/undeveloped urban land and by bringing in new developments (TODs, etc.). This would also reduce people’s dependency on single‐occupancy vehicles, thus reducing cars on the streets and subsequently vehicular congestion to at least some extent. Also, bringing in new developments (residential, commercial, business, industrial, TODs, etc.) would completely change the projected development/growth pattern, thus trimming down on developments inducing sprawl. Based on these new developments on the cheap vacant parcels of land, we can propose locations for ‘commuter light rail stations/stops’ with future potential in these areas. Finally, when analyzed and assessed the whole project comprehensively, we found that the there is a need for public transit system along this corridor, for both transit dependent riders and the choice riders, hence proposed B‐CS commuter transit light rail line has tremendous potential along that stretch i.e. from Wellborn to Bryan. Also, there is a need to re‐align the South Pacific freight line, not only because of the commuter rail line, but also for safety and health concerns. Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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As studied in the literature review and also mentioned before, Geographic Information System (GIS) completely facilitates the study, analysis and decision making process in transit (public transportation) related projects. In this paper, we testified that the potential application of Geographic Information System (GIS) in transit management and operation is ridership analysis on transit routes. After all the data collection, study and analysis for the project, we were able to use a variety of GIS spatial analysis functions for identifying the transit route and potential location of stops on proposed B‐CS commuter transit light rail line. (refer Map‐0 in the index).
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Bibliography Azar, K. T., Ferreira, J. J., & Wiggins, L. (1994). Using GIS tools to improve transit ridership on routes serving large employment centers: The Boston South End Medical Area case study. Computers,Environment and Urban Systems‐Volume18 , 205‐231. Biswas, A. (2007). Geo market segmentation and GIS. New Delhi, New Delhi, India. Retrieved from http://www.gisdevelopment.net/application/business/bus0016.htm Choi, K., & Jang, W. (2000). Development of a transit network from a street map database with spatial analysis and dynamic segmentation. Transportation Research‐Part C 8 , 129‐146. Marlow, S. (Nov/Dec,2007). Transportation Projects Simplified with GIS Integration. Right of Way Magazine . O'Neill, W. A., Chou, J., & Ramsey, R. D. (1992). Analysis of Transit service Areas using Geographical Information System. Transportation Research Record , 131‐138. Schweiger, C. L. (1992). Current Use Of Geographical Information Systems In Transit Planning. Transportation Research Record , 93‐106. Sholly, R. (Fall 2008). GIS and Transportation Planning. Urban and Regional Information Systems Association Journal 12:2 , 51‐59. Slavin, H. L. (2004). The role of GIS in land use and transport planning. In D. A. Hensher, Handbook of transport geography and spatial systems, Volume 5 (pp. Chapter 19: 329‐356). Caliper,Newton,MA: Elsevier Ltd. Sutton, J. C. (2007). Role of Geographic Information Systems in Regional Transportation Planning. Transportation Research Record , 25‐31. Another Reference: Khitha, Vikas; Govil,Sanjay (2001). GIS in Public Transportation. New Delhi, New Delhi, India. Retrieved from http://www.gisdevelopment.net/application/Utility/transport/mi03194.htm Figures and Maps: Done by Avinash Shrivastava and Matt Sandidge
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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INDEX Map – 0: Bryan‐College Station Commuter Rail Transit Map
Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 1: Half Mile Rail Line Buffer on either side Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 2: Land Value Map for Bryan‐College Station District Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 3: Segment 1 ‐ Bryan Rail Corridor Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 4: Segment 2 & 3 – North College Station/South Bryan Rail Corridor Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 5: Segment 4 & 5 – South College Station Rail Corridor Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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Map – 6: Segment 6 – Wellborn Rail Corridor Submitted By: Avinash Shrivastava, Geog 660: Literature Review, Course Instructor: Dr. Andrew Klein
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