Traffic Accidents near Mesa Light Rail Extension (between Country Club Dr. and Mesa Dr.)

by Group 13: Brandon Forman, Shelby Leachet, Megan Henriksen, Leslie Araujo, Michelle­Ashley Sciortino Introduction: The Valley Metro Light Rail, which first began operating in 2008, has introduced new features to the urban landscape of the greater metro Phoenix area, including changes to major roads within the city. This study aims to analyze the frequency of traffic accidents along the Valley Metro Mesa light rail extension path (the first Phoenix light rail extension) to determine whether the change in traffic accident frequency is attributed to the light rail. Three stages of the its implementation were studied: before construction, during construction, and during operation. Insight to this type of information will be of great benefit to city planners and other transportation decision­makers involving future light rail extensions or alterations to existing light rail route paths. Background: This study was motivated by public safety, especially as more extensions to the light rail are planned. Examining the effects of implementing such a large transportation network in the midst of some of the densest areas within Mesa will help reveal whether more attention should be paid to the public safety aspect of this project. Despite the relative newness of the light rail in the greater Phoenix metro area, we could not find studies analyzing the impact of the light rail on traffic accidents; other light rail studies have focused on its economic, transit­oriented development, and population density effects instead.

The Mesa Light Rail extension is the first light rail extension implemented. Three more are currently planned east of the current Mesa extension to Gilbert Rd., west of the initial light rail path to 83rd Ave. and north to Peoria Ave., with other potential extension corridors being studied. Studying traffic accident patterns within these extensions should be prioritized, as an increased awareness of the current and potential effects of the light rail on traffic accidents will allow these future extensions to be planned with safety in mind.

Construction for the Mesa Light Rail extension began at the beginning of July 2012 and continued through until August 2015; the official date for beginning operations open to the public was on August 22, 2015 (Holliday). The information was obtained through the Public Information Specialist at Valley Metro and was the most reliable and up to date source available.

Timeline of Mesa Light Rail Extension for this study: Before Construction: Feb 2007* ­ July 2012 During Construction: July 2012 ­ Aug 2015 During Operation: August 22, 2015 to present

*Note: This reflects the earliest available traffic accident data for the Mesa Light Rail Extension Path. The following section will explain more on this study’s data sources and data acquisition method. GIS Data and Acquisition: The bulk of the point data for traffic accidents was acquired through the Mesa Department of Transportation. The data was originally derived from police reports, and because of this, it is important to note that the data set is only as accurate and updated as the amount of police reports submitted for the accidents. The original datum of this data set was North American Datum 1983. The ASU GIS Repository was the source of the line data that was used for adding the streets and roads. For the final map and analysis, the NAD 1983 datum was used with an AZ State Plane Central projection. As the study focuses primarily on a relatively small area within Arizona, this datum and projection pair was deemed as the most accurate option. GIScience Concepts:

This study uses several key GIScience concepts, including spatial analysis, uncertainty, and effective visualization. This analysis directly addresses a spatial problem: how are accidents related to the presence and proximity of the new light rail extension in Mesa. To address this, we used a buffer from the Main Street centerline, corresponding to the location of the light rail. This buffer area was chosen in order to include the entire width of main street and the intersections while excluding accidents further away from the influence of the light rail.

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There exists some uncertainty in our analysis. Most significantly, accident locations reported by law enforcement officials can be inexact. Additionally, our timeline divisions (before construction, during construction, and after opening), as construction projects are not completely divisible into discrete time frames. There were, for example, several test runs of the light rail before it was opened to the public after early June 2015 (Poletta, 2015). We also do not know the specific start and end days for most of the time periods in question. Furthermore, we have only a few months of data representing the time period immediately after the light rail opened, and it is still unclear if the data we have is going to be representative of the number of accidents after a longer period of time, or if the number of accidents will decrease as drivers become accustomed to the presence of the light rail.

Because our time divisions represent very unequal periods of time, the number of accidents cannot be directly compared between time frames. Therefore we needed to normalize the data. Although there are several ways to do this, we chose to pull out only the three months (August through October) that all three periods had in common and average them across the total number of years for each period. This had the added, theoretical benefit of removing seasonal variations from our data.

Effective visualization is another aspect of GIScience. To clearly distinguish between our three time periods of interest, the points representing all the accidents included in our analysis were symbolized with three colors (green for before construction, yellow for during construction, and red for after construction). These colors correlate with our results, as most people in the U.S. associate green with safety (used for our time period with the least accidents) and red with danger (used with the time period with the most accidents); therefore, potential consumers of our map will hopefully have an immediate, instinctual understanding of our results. Methodology: Using our learned knowledge of GISystem operations in ArcGIS software, our group researched and imported crash datasets, set dataset projections, digitized features, and built an evolving code to run model builder three separate times to account for the gaps in our initial data, as well as to match the three time periods of study.

Gathering data was the first step during our operation process. We gathered our cornerstone set of crash data from the Mesa Transportation Planning Department. We imported that crash data as an XY point feature class into ArcMap in the NAD 1983 geographic coordinate system. The “Project” tool in ArcMap was then used to convert to the NAD 1983 Arizona State Plane Central projected coordinate system. Next, we downloaded street centerline feature class from the ASU GIS Repository so we could attribute naming conventions for the streets in our study area. Once the Mesa street centerlines were imported into ArcMap, and digitized the centerline of Main Street and the light rail in order to have a feature for buffering.

The bulk of our analysis was run through Model Builder. In total, we built three models in order to address three spatial objectives. Our first model was built to select crashes within a 60 ft buffer near the light rail on Main Street in order to produce a new map layer to reveal selected features (note that the Main street intersection is about 120 ft across). We then added a new field (WHEN_OCCUR) to distinguish the three time periods of interest.

Our second model was built to assign text values to each crash point, identifying its time period. We divided points by dates before, during, and after construction (July 2012­ August 2015). We used the “Select” tool to select crash points based on those dates. Next, we assigned text values to points under the new field built in Model 1, “WHEN_OCCUR,” and merged the layers back together. Finally, we ran a third model (Figure 1) to select only crashes that occurred from August­October of each year to normalize our data, as described previously.

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Figure 1 ­ Model 3 Final Analysis: The first two models gave us a count of how many accidents occurred within each of the three time frames: 76 accidents occurred before construction started, 93 accidents occurred during construction, and 12 accidents have occurred since the light rail opened in August 2015. It is extremely important to note that the temporal span of each time frame is different; therefore, we cannot yet accurately compare the rate of accidents.

In order to compare the different periods (5 years of data pre­construction, 3 years of data during construction, and only 3 months of data since the light rail opened), we must constrain them to a common interval of time. The months August, September, and October are common in all three time frames. By selecting only the crashes that occurred in these months, then averaging by year for each period, we can show how they interrelate. Model 3 (Figure 1) does this selection for us, and the new counts are inTable 1. Averaging by year to acquire a normalized value suitable for comparison give us the final results in the last column of Table 1. Table 1: Final Analysis Time Period (August ­ October) Total Number of Accidents Average Number of Crashes by Year

Before Light Rail 17 3.4

During Construction 17 5.6

After Light Rail Completion 12 12 Conclusions/Future Directions: It is evident that there was a small increase in crashes during construction, but especially significant is the number of crashes that have occurred since the light rail opened: an increase of more than 350%! The twelve crashes that occurred after construction seemed like a small number before normalization, but we find that it is actually very high when compared to previous time periods. Whether the number of crashes will remain high or level back to the pre­light rail average remains unknown. Studies on older parts of light rail track may hold an answer. Regardless, the results found in this basic study, using geographic information systems methods through ArcGIS, demonstrate that public safety/traffic accidents along the light rail should soon be analyzed more thoroughly.

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Figure 2: Map showing several different image of study area. Top image is entire study area along Main Street in Mesa, Arizona from Country Club Drive to Mesa Drive. Lower four images are some of the intersections where there were a larger cluster of accidents.

Sources Arizona Department of Transportation. (2014) Retrieved from the ASU GIS Repository. Mesa Streets. Holliday, C. Valley Metro Public Information Specialist. (2015). Mesa Department of Transportation Crash Analysis. (2007 ­ 2015). Point data representing crashes in Mesa, AZ. Most recent data not yet publically posted. Polletta, M. (2015, June 4). Mesa light rail opening ahead of schedule in August. The Arizona Republic. Retrieved November 30, 2015.