SlideShare a Scribd company logo
Traffic Pattern Analysis of Houston, TX
Houston Resident planning Resource for University of Houston Commute
Mandana Merrikh, Greg App, Muhammad Omar Ahmed, Firoozeh Roointan, Chad Humphries
Traffic Patterns 7 am. Introduction
Traffic Patterns for 12 pm.
(010)
Benefits and Recommendations
Traffic Patterns for 10 am.
Traffic Patterns for 3 pm.
Traffic Patterns for 5 pm.
References
Abstract:
Ranking among the most traffic congested cities, Houston’s traffic has continued to plague
Houstonians throughout the years. Through the use of traffic data from houstontranstar and the
ARCGIS software, our group successfully depicted the average amount of traffic congestion to
University of Houston from several different routes at various different times of days. These areas
studied include routes to and from the University of Houston. The various routes were at times 7:00
AM, 10:00 AM, 12:00 AM, 3:00 PM, and 5:00 PM. These timings were chosen in order to visualize the
difference between the early and late rush hour times, the lunch hour, and two time spans in between
to take random variables (such as traffic accidents and bad weather) into account.
Analysis of Significant Variables:
Initially, it was thought that the primary variables affecting traffic speeds were population
density and time of day. After visual careful analysis, it appears that there is a positive correlation
between traffic speed and population density. This relationship is readily apparent from observing the
general increase in population density as one moves closer to the center of the city (especially from
the west side). This positive correlation is best observed at 5:00 pm (afternoon rush hour traffic). This
correlation coincides strongly with our preliminary research, which showed that 5:00 is a common
“let-out” time for the majority of corporate jobs, but the “entrance time” varies widely among such
jobs.
Regarding these “let-out” times and “entrance” times, corporate oil and gas jobs were or
primary source of research. Virtually all employees working in such positions drive a personal motor
vehicle of some sort, thus contributing to traffic congestion. Additionally, surveying oil and gas
companies provides a reasonable sample representation, seeing that a large number of corporate jobs
are related to oil and gas in some capacity.
Not surprisingly, traffic patterns at noon were worse than those at 7:00am, and towards the end
of the day (5:00pm). This is because, like 5:00pm, 12:00pm is almost universally considered “lunch
time” and people are more likely to get in their car and drive to lunch.
Given the project’s analysis of Houston traffic patterns , we are able to provide students commuting
to the University of Houston a method of scheduling when to embark (and perhaps what areas to avoid!)
on their daily commute. In terms of departure times, leaving closer to 7:00 am is better than leaving
closer to 10:00 am. This project also reveals that students and faculty living on the west side of Houston
need to be more cautious than those living on the east side of town if they are using main highways to get
to the university (assuming they do not wish to sit in traffic jams). Additionally, commuters from the east
side can travel through areas without a large population density if they wish to err on the side of caution
in terms of traffic congestion.
Information such as this is especially useful for University of Houston students as a whole. Houston
is a geographically large city, and the student body and the University of Houston is spread out across a
vast majority of it.
An unexpected benefit of this project is the provision of useful traffic information for those not
attending the University of Houston. Since UH is located near the center of the city, pathways to the
university provide a tremendous sample of general traffic patterns throughout the entire city.
-Houston Transtar
-Personal Interviews
-

More Related Content

Similar to GIS Traffic Analysis

Project survey
Project surveyProject survey
Project survey
mmrojas2
 
Limited Public Transit Systems
Limited Public Transit SystemsLimited Public Transit Systems
Limited Public Transit Systems
Jonathan Lloyd
 
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
Emmanuel Eliya
 
J1076773
J1076773J1076773
J1076773
IJERD Editor
 
M-1 Summary
M-1 SummaryM-1 Summary
M-1 Summary
Amanda Hengel
 
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTIONMODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
Ijripublishers Ijri
 
Budget Analysis Paper
Budget Analysis PaperBudget Analysis Paper
Budget Analysis Paper
Jessica Lopez
 
ES135Assignment2
ES135Assignment2ES135Assignment2
ES135Assignment2
Jerel Constantino
 
Destination station.pdf
Destination station.pdfDestination station.pdf
Destination station.pdf
maderikcanales1
 
Travel: Moving Nearer to Normal
Travel: Moving Nearer to NormalTravel: Moving Nearer to Normal
Travel: Moving Nearer to Normal
Posterscope
 
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
Amie Campbell
 
Cooke 2016 MSc thesis
Cooke 2016 MSc thesisCooke 2016 MSc thesis
Cooke 2016 MSc thesis
Sean Cooke
 
Session 54 Petter Næss
Session 54 Petter NæssSession 54 Petter Næss
Session 54 Petter Næss
Transportforum (VTI)
 
FinalReport
FinalReportFinalReport
FinalReport
Ethan Lagarde
 
Urbanization And Development Of Urbanization
Urbanization And Development Of UrbanizationUrbanization And Development Of Urbanization
Urbanization And Development Of Urbanization
Help Writing A College Paper Florida Gateway College
 
AGILE 2012 Poster
AGILE 2012 PosterAGILE 2012 Poster
AGILE 2012 Poster
urbanmovements
 
theinsandoutsofthenewyorkcitysubwaysystem
theinsandoutsofthenewyorkcitysubwaysystemtheinsandoutsofthenewyorkcitysubwaysystem
theinsandoutsofthenewyorkcitysubwaysystem
Riva Tropp
 
TheInsAndOutsOfTheNewYorkCitySubwaySystem
TheInsAndOutsOfTheNewYorkCitySubwaySystemTheInsAndOutsOfTheNewYorkCitySubwaySystem
TheInsAndOutsOfTheNewYorkCitySubwaySystem
Eiman Ahmed
 

Similar to GIS Traffic Analysis (18)

Project survey
Project surveyProject survey
Project survey
 
Limited Public Transit Systems
Limited Public Transit SystemsLimited Public Transit Systems
Limited Public Transit Systems
 
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
2006 - ASSESSMENT OF ITS MEASURES FOR SOUTH AFRICA.pdf
 
J1076773
J1076773J1076773
J1076773
 
M-1 Summary
M-1 SummaryM-1 Summary
M-1 Summary
 
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTIONMODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
MODEL ON CARPOOLING TECHNIQUE TO REDUCE CONGESTION
 
Budget Analysis Paper
Budget Analysis PaperBudget Analysis Paper
Budget Analysis Paper
 
ES135Assignment2
ES135Assignment2ES135Assignment2
ES135Assignment2
 
Destination station.pdf
Destination station.pdfDestination station.pdf
Destination station.pdf
 
Travel: Moving Nearer to Normal
Travel: Moving Nearer to NormalTravel: Moving Nearer to Normal
Travel: Moving Nearer to Normal
 
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
Essays On Race. I knew this was a race I had to win short essay - GCSE Englis...
 
Cooke 2016 MSc thesis
Cooke 2016 MSc thesisCooke 2016 MSc thesis
Cooke 2016 MSc thesis
 
Session 54 Petter Næss
Session 54 Petter NæssSession 54 Petter Næss
Session 54 Petter Næss
 
FinalReport
FinalReportFinalReport
FinalReport
 
Urbanization And Development Of Urbanization
Urbanization And Development Of UrbanizationUrbanization And Development Of Urbanization
Urbanization And Development Of Urbanization
 
AGILE 2012 Poster
AGILE 2012 PosterAGILE 2012 Poster
AGILE 2012 Poster
 
theinsandoutsofthenewyorkcitysubwaysystem
theinsandoutsofthenewyorkcitysubwaysystemtheinsandoutsofthenewyorkcitysubwaysystem
theinsandoutsofthenewyorkcitysubwaysystem
 
TheInsAndOutsOfTheNewYorkCitySubwaySystem
TheInsAndOutsOfTheNewYorkCitySubwaySystemTheInsAndOutsOfTheNewYorkCitySubwaySystem
TheInsAndOutsOfTheNewYorkCitySubwaySystem
 

GIS Traffic Analysis

  • 1. Traffic Pattern Analysis of Houston, TX Houston Resident planning Resource for University of Houston Commute Mandana Merrikh, Greg App, Muhammad Omar Ahmed, Firoozeh Roointan, Chad Humphries Traffic Patterns 7 am. Introduction Traffic Patterns for 12 pm. (010) Benefits and Recommendations Traffic Patterns for 10 am. Traffic Patterns for 3 pm. Traffic Patterns for 5 pm. References Abstract: Ranking among the most traffic congested cities, Houston’s traffic has continued to plague Houstonians throughout the years. Through the use of traffic data from houstontranstar and the ARCGIS software, our group successfully depicted the average amount of traffic congestion to University of Houston from several different routes at various different times of days. These areas studied include routes to and from the University of Houston. The various routes were at times 7:00 AM, 10:00 AM, 12:00 AM, 3:00 PM, and 5:00 PM. These timings were chosen in order to visualize the difference between the early and late rush hour times, the lunch hour, and two time spans in between to take random variables (such as traffic accidents and bad weather) into account. Analysis of Significant Variables: Initially, it was thought that the primary variables affecting traffic speeds were population density and time of day. After visual careful analysis, it appears that there is a positive correlation between traffic speed and population density. This relationship is readily apparent from observing the general increase in population density as one moves closer to the center of the city (especially from the west side). This positive correlation is best observed at 5:00 pm (afternoon rush hour traffic). This correlation coincides strongly with our preliminary research, which showed that 5:00 is a common “let-out” time for the majority of corporate jobs, but the “entrance time” varies widely among such jobs. Regarding these “let-out” times and “entrance” times, corporate oil and gas jobs were or primary source of research. Virtually all employees working in such positions drive a personal motor vehicle of some sort, thus contributing to traffic congestion. Additionally, surveying oil and gas companies provides a reasonable sample representation, seeing that a large number of corporate jobs are related to oil and gas in some capacity. Not surprisingly, traffic patterns at noon were worse than those at 7:00am, and towards the end of the day (5:00pm). This is because, like 5:00pm, 12:00pm is almost universally considered “lunch time” and people are more likely to get in their car and drive to lunch. Given the project’s analysis of Houston traffic patterns , we are able to provide students commuting to the University of Houston a method of scheduling when to embark (and perhaps what areas to avoid!) on their daily commute. In terms of departure times, leaving closer to 7:00 am is better than leaving closer to 10:00 am. This project also reveals that students and faculty living on the west side of Houston need to be more cautious than those living on the east side of town if they are using main highways to get to the university (assuming they do not wish to sit in traffic jams). Additionally, commuters from the east side can travel through areas without a large population density if they wish to err on the side of caution in terms of traffic congestion. Information such as this is especially useful for University of Houston students as a whole. Houston is a geographically large city, and the student body and the University of Houston is spread out across a vast majority of it. An unexpected benefit of this project is the provision of useful traffic information for those not attending the University of Houston. Since UH is located near the center of the city, pathways to the university provide a tremendous sample of general traffic patterns throughout the entire city. -Houston Transtar -Personal Interviews -