Traffic Pattern Analysis of Houston, TXHouston 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-