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Bicycle Crash Analysis Marc h 1, 2015 thru February 28, 2018 Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 1 of15 1 City of Santa Fe Police Department Crime 1\nalysis Section Introduction Demographics Gender Age When do Crashes Occur Day of the Week Time of Day Locations Police Beat Area Intersections Railroad Tracks Crash Dynamics Causation Contributing Factors Hit & Run Bicyclist Citations Issued Citations Recipient Conclusions Page 3 4 4 4 4 5 6 7 7 8 8 10 10 10 11 11 13 13 15 Charts I Graphs I Images Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page2 o/15 Charts Chart 1 Chart 2 Chart 3 Chart 4 Chart 5 Chart 6 Bicycle Crashes by Selected Time Frame Day of the W eek of Bicycle Crashes Time of Day for Cyclist Crashes Contributing Factors Citations to Bicyclists Citations to Vehicle Graphs Graph 1 Graph 2 Graph 3 Graph 4 Graph 5 Graph 6 Graph 7 Graph 8 Graph 9 Graph 10 Graph 11 Gender of Cyclists Age of Cyclists Area of Crash Intersections Considered Main Causal for Crash Hit & Run Bicycle Action Bicycle - Roadway v. Other Bicycle Action Roadway Activity Citations Issued Citations Issued To 4 4 Images Image 1 Image 2 Image 3 Image 4 Area Area Area Area 8 8 2666- Saint Francis & Zia Saint Michaels Cordova & Penn Cerrillos & Saint Francis Page 3 5 6 10 14 14 7 8 10 11 11 12 12 13 13 9 9 2 City of Santa Fe Police l)epartn1cnt (~rime Analysis Introduction: A request was r eceived regarding bicycle crashes on railroad tracks. Additionally, the Support Operations Division needed information r egarding bicycles in general. This report, therefore , covers both topics combined into one report. The time frame of March 1, 2015 through February 28, 2018 was chosen for analysis. The reason this time frame was chosen is the availability of data and the capability of that data to be analyzed. The data selected for analysis was crash r eports in which a cyclist was listed as being involved. The vast majority of the information involved a crash in which a cyclist was involved in a crash with a motor vehicle. In review of the data, there were two (2) instances in which the cyclist crashed, for reasons other than a collision with a vehicle, that were found within the data. During the selected time fram e 113 bicycle crashes w ere found to have been documented through the TraCs reporting system. Three (3) cases were discovered to have been improperly classified; therefore 110 incidents are considered verifiable and valid for analysis. It should be noted that the analyst was made aware, by the officers that responded to the incident, of two (2) cases which would qualify for the analysis. However, those cases were not found in the TraCs data. Without being able to obtain the data selected for analysis, these cases could not be included in the analysis. The analysis focuses on the bicyclist information . Who are the people on the bicycles? When did the crash occur? Where w ere they when the crash occurred? And then, when it comes down to it, how did the crash occur? What were they doing when the crash occurred? What w ere the causal factors? Were any citations issued and to whom? Bicycle Crashes by Selected Time Span 45 so V, V 40 "'... u"' ..... 30 ..d 0 ... V .0 20 8 ::l z 10 0 Mar 1 20 15 to Feb 29 20 16 Mar I 20 16 to Feb 29 2017 Mar 1 20 17 to Feb 29 20 18 Chart I Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page3 o/15 Three (3) years of data is a be9innin9 to determining the traffic interaction between the motoring and bicycling community of Santa Fe. There is not three (3) full years of data for a calendar year , however, using March 1'' as the begin date, three (3) full years of data is obtainable. Using this time span we can go forward with an analysis . 3 City of Santa Fe Police Department Crime 1\nalysis A total of 110 cases w ere deemed reliable and valid for the analysis during this time fram e. A 67% increase was seen between 2016 and 2017, with a 16% decrease seen between 2017 and 2018 . With only three (3) years, a one (1) year decrease cannot be consider e d a predictive downward trend . A decrease, however small, may be consider ed positive . Demo9raphics The first question asked is what are the demographics of the bicyclists involve in crashes over the past three (3) years . Gender Gender of Cyclist Male 90 Graph I The demographics of gender for Santa Fe are shown at being 48% m ale and 52% femal e. However, the individuals involved in bicycle crashes are overwhelmingly and disproportionate!y seen to be male riders at 81 % of the data set population. In one ( 1) instance there wer e two (2) riders involved in the collision . A father had his three (3) year old riding with him at the time of the collision . Age Age Group of Cyclist 61 -70 yrs 3% Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page4 of 15 Graph 2 4 City of Santa Fe Police Department Crime Analysis The median age of the population of Santa Fe is shown to be 4 2. 9 years of age. The individuals represented in the data set ranged from 3 years old to 79 years old. The age most represented in bicycle crashes was individuals 59 years of age. The average age of a cyclist involved in a bicycle crash within the data set is slightly younger than the city average however at 35 .6 years of age. When do Crashes Occur The next question that was considered was when the crashes are occurring. The weekends are the days when people are off from work and enjoy time with their families doing recreational activities. The general expectation would therefore be that the weekends, most specifically Saturday and Sunday would be the most likely days for bicycle crashes to occur. The data set however shows a completely different dynamic of bicyclist activities within the city. Day ef Week Day of the Week of Cyclist Crashes 25 "' "'r: ~ 20 ....U0 15 t 10 ..c 6 i 5 0 Chart 2 Over % (85%) of the bicycle crashes occur during the work week. The data set showed a slight tendency for Thursday to be the most likely day of the week for a bicycle crash to occur at 20% of the total. Wednesday was only slightly behind with one ( 1) less crash for that particular day of the week; and a bicycle crash was just as likely to occur, 17% of the time, on Tuesday as it was on Friday. Prepared by: Malissa A ustin-Cordell Crime Intelligence Analyst April 17, 2018 Page5 of 15 5 City of Santa Fe Police Department c:rimc 1\nalysis Time efDay If bicycle crashes are more than likely to occur during a weekday, then perhaps that is to show that people are using bicycle transportation to and from work. Then surely the time frame they are occurring would be consistent with the start and end of the work day. The data set showed a somewhat different story however. First to be analyzed was the individual hourly time frame starting with a standard beginning of the work day for 8am . Of course, the individual needs to be at work at 8, so what does 7am look like? There were a total of six (6) crashes in the data set for this time frame. The same as fort Oam to 11 am, 11 am to 12pm, and 6pm to 7pm. So what about 8am, maybe people go in a little later. There were a total of seven (7) crashes documented for this time frame . However, the 8am to 9am time frame had the exact same amount of crashes as which occurred between 1pm and 3pm with seven (7) each for the individual hour of 1pm to 2pm and 2pm to 3pm. There were only two (2) individual hour time frames that showed double digits - The end of the work day between 5pm and 6pm with 11 crashes and 3pm to 4pm with 15 crashes. Next a group of times was analyzed. The hours of the clay were broken clown into tim e fram es which are typically known as higher traffic flow times - the beginning of the work clay-6am to 9am, the lull before lunch 9am-11 am, lunch time 11 am to 2pm; the midday break of 2pm to 5pm, the evening rush 5pm and 7pm, the late evening hours between 7pm and midnight, and the wee hours of the morning midnight to 6am. Bicyclists were just as likely at the beginning of the work day (6am to 9am) to be involved in a crash as a bicyclist riding in the late hours of the evening between 7pm and midnight. Riders w ere only slightly more likely at 16% of the time to be involved in a crash b etween 5pm and 7pm . The lunch time hours showed a larger increase at 19% of the data set crashes, with the largest portion of crashes being seen between 2pm and 5pm at 27% of the time. Time of Day for Cyclist Crashes Chart 3 Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 6of15 Next the time frames were grouped into eight (8) standard three (3) hour time frames beginning with the midnight hour as shown in Chart 2. Maybe breaking the time frames 6 City of Santa Fe Police Department Crime 1\nalysis down in this manner would provide a clear picture of the key times of crashes. As shown 1 nearly 1 / 3'"' ( 31 % ) of all the bicycl e crashes in the data set occurred between 3pm and 6pm. The time frame of noon to 3pm accounted for 20% of the crashes; and the morning bike ride of 6am to 9am was still just as likely to be involved in a crash as those riding between 6pm and 9pm at 14% of the time. Locations Now that w e know who is involved in the crashes and when the crashes have occurred the next question is wher e are they occurring? For law enforcem ent purposes the City of Santa Fe is broken down into 11 areas. By sectoring of the city this provides the department with the ability to focus r esources in their efforts to combat the crime of the city. Additionally, it provides the department with the ability to see what is happening with other types of events. Recently an analysis was performed on the first year of operation of the Narcan program and in much the sam e way this analysis on bicycle crash data was able to be performed. The cover page of this analysis has an overlay of the bicycle crash data onto the mapping of the city with the beat areas. Police Beat Area Area of Crash Area 6, 33, 30% Area 5, 7, 6% \ Area 4 , 8, 7% _ Area 9, 5, 5% Area 3, 12, 11 % Graph 3 The analysis of the data set r evealed that the majority of the bicycle involved crashes (30%) are occurring in what is known to law enforcem ent as Area 6; with Areas 1 and 7 rounding out the top three (3) locations for bi cycl e crashes with 15% and 12% r espectively . Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 7 of15 Area 6 is what is consider ed the trian9le of the city. It is bordered by Saint Francis Drive, Siringo Road , Camino Carlos Rey, Maes Road , and Agua Fria St reet . Additionally, included is this area is another m ajor arter y of the city , Saint Michaels Drive. While this area is the triangle of the city it is also a cen ter to the city. Three (3) m ajor arteries - Cerrillos Road , Saint Francis Drive , and Saint Michaels Drive are all included within this area. Additionally, 7 City of Santa Fe Police Department c:rime Analysis the Rail Runner tracks run through this area. We say it is a heart of the city because whether an individual is traveling north to Espanola, to the downtown area, or coming from the north and town area towards Las Vegas or Albuquerque - the traveler goes through this area . Additionally, when looking at the other two areas (Areas 1 and 7), Cerrillos Road continues from Area 6 through Area 1; and Area 7 borders Area 6 along Saint Francis Drive . Each of these major artery roads continue all the way to Interstate 25. Intersection Intersections No, 40, 36% Yes, 70, 64% ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ' Graph4 As was expected a majority of crashes (nearly 2/3rds at 64%) involving bicyclists occur at intersections. The causation and actions of the bicyclist regarding these events is analyzed later w ithin this report. Railroad Tracks One of the main purposes of this analysis is to look at the frequency at which bicycle crashes are occurring on the railroad tracks. ,., • 6 2 ...... ... ! II lma9e I - Area 2 - Saint Francis &.. Zia Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 8of15 ... Image 2 - llrea 6 - Saint Michaels As stated earlier in this analysis there were two (2) events which were brought to the attention of the analyst which were not found in the data set from the TraCs system. The officers relayed to the analyst that those two (2) events did occur on the railroad tracks. The 8 City of Santa Fe Police Department c:rimc Analysis first was a fatality in which the cyclist was hit by the Rail Runner train. This fatality was the first in the city with the Rail Runner and occurred in April 2014. The second was a cyclist that was riding on the tracks and the tire of the bicycle became lodged in the track causing the rider to crash. It is unknown if this event occur within or outside the analysis parameters. This is the extent of the information available on these crashes and they are not addressed a:ny further in this analysis. Image 3 - Area 6 - Core/ova &..Penn -lma9e 4 - Area 6 - Cerrillos &..Saint Francis With the verifiable data set available, none of the bicycle crashes within the data set occurred specifically on the railroad tracks that run through the city. (*Note: the one (I) verifiable case occurred outside the analysis parameters). There were six (6) bicycle crashes within the data set that did occur in proximity to the railroad tracks. All of these crashes were bicycle vs. vehicle crashes. The one commonality with all of these crashes was the failure to yield the right of way to each other. The failure to yield was a 50/ 50 split between the bicyclist and the vehicle. Therefore, the current data does not support a concern regarding a disproportionate hazard to bicyclists in relation to the railroad tracks. Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 9of15 9 c=ity of Santa Fe Police Department Crime 1\nalysis Crash Dynamics The next question to be considered is why are the bicycle crashes occurring. Causation Considered Main Causal for Crash Bicyclist 48 Graph 5 When determining causation for the crash, the officer is trying to determine the party most responsible for the crash having occurred. The analysis showed that bicyclists were found only slightly more responsible for the crashes that occurred than their vehicle counterparts. Contributing Factors Contributing Factors Intoxication (ETOH) Pedestrian Error Passed stop Sign Other Improper Driving Other-No driver error None Improper Turn Improper Overtaking Improper Backing Failure to Yield Right of Way Driver Inattention Disregard traffic signal Failure to avoid Contact 0 5 10 15 20 25 30 35 40 Chart4 Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 10 o/15 Why are the crashes occurring? Looking at the contributing factors as a whole, on the part of the motorist and the cyclist, the Failure to Yield the Right of Way (33%) is overwhelmingly the main contributing factor in the crashes. Driver inattention (19%), other improper driving ( 14%) and pedestrian error (9%) are the next most common contributing factors to the crashes. 10 City of Santa Fe Police Department (:rime Analysis Hit &_Run Hit & Run Yes, 26, 24% No, 84, 76% Graph 6 When looking at the data set, the question arose as to how often a crash involving a bicyclist is what is considered a Hit & Run. This is where one party involved in the crash leaves the scene of the accident. Approximately 14 of the crashes were classified as hit and run. This does not necessarily mean the vehicle is always the one that fl ees the scene. There were four (4) instances in which the bicyclist was the individual that left the scene of the crash. Bicyclist Of course when someone is riding a bicycle they are doing the exact same thing as the driver of a vehicle. They are attempting to get from point A to point B. Bicyclists are bound by the same traffic laws as an individual operating a motor vehicle. As the focus of the analysis are the dangers to bicyclists the question is specific to what are the bicyclists doing at the time of the crash. Bicycle Action Sidewalk-Against Traffic 5% RoadwayPassing Tralllc 1% RoadwayAgainst Traffic 5% Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 11 of15 Unknown Bike Lane 2% 7% Bike Lane-Against Traffic 5% Parking Lot 3% Graph 7 11 City of Santa Fe Police Department (=rime Analysis Looking at the bicyclists actions specifically , there are varying situations in which the cyclist becomes involved in a crash. Graph 9 shows the individual categories of location that were established from the data set in which the cyclist was engaged at the time of the crash. These categories were then grouped to show a more concise view of where the cyclist was operating at the time of the crash . Bicycle Roadway vs. Other Crosswalk ,- ---Unknown 2% Bike Lane 10% Graph 8 Over l/2 of the time the cyclist was interacting directly with the motoring traffic of the roadway. And while bicycle lanes are available, only 10% of cyclists involved in crashes were utilizing the designated lane. It should be cautioned, with such a small data set one could immediately jump to saying if bicycles used the bike lanes all the problems would be solved. With the small set that is available nearly 30% of the bicyclist that w ere in a bike lane were using the lane incorrectly. Bicycle Action Roadway Activity Crossing Roadway \ 25% \ 9% I Oa Roodw,y 55% Passing Traffic 2% Against Traffic 9% Graph 9 Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 12 o/15 The vast m ajority of bicycle crashes are occurring when the bicyclist is in some way interacting with roadway traffic. Over 50% are occurring when the bicyclist is inter-mingling with roadway traffic, while an additional 25% is occurring when the bicyclist is crossing the roadway outside of a crosswalk area. This accounts for 80% of the 12 c=ity of Santa Fe Police Department Crime 1\nalysis Bicycle vs. Vehicle crashes. One (1) case was noted as a crash in which the bicyclist lost control without contacting any vehicles. The cyclist in that specific crash was described by witnesses as "showboatin9," or peiformin9 exhibitionist tricks on the roadway. Citations Issued Citations Issued Graph 10 An officer will not issue a citation if he/ she is unable to make a determination which contributing factor, by which operator, was the most contributable to the crash. In just over half of the cases a citation was issued. In one (1) instance the individual, a cyclist, was issued a summons for court as the cyclist was injured and was transported to the hospital. Citation Recipient Citations Issued To Bicyclist, 28, 25% Neither, 47, 4-3% Graph I I As noted previously officers will appoint causes and contributing factors and upon determination of these two (2) factors will only then issue a citation. In nearly half of the cases, no citations were issued. And a vehicle driver was just slightly more likely to be issued a citation than their cyclist counterpart, with both receiving citations 4% of the time. Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 13 o/15 13 City of Santa Fe Police Department c:rime 1\nalysis Bicyclist Citations Citations to Bicyclists I: 100 0 ·..:: ~ u ..... 0 50 0 Chart 5 Citations to cyclists are most frequently for Failure to Yield at 28% of the time. The failure to stop is the next most common citation received at 19%, and a cyclist is just as likely, 16% of the time , to r eceive a citation for Riding on the Sidewalk or Driving on the Right Side of the street. Vehicle Citations Citations to Vehicle Chart 6 Prepared by: Malissa A ustin-Cordell Crime Intelligence Analyst April 17, 2018 Page 14 of 15 As previously noted, the violation most likely to occur for which an individual will receive a citation, on the part of either the cyclist or the motorist, is the failure to yield. The rate at which motorist were more likely to fail to yield the right of way was 37% within the data set . Vehicles w er e also cited 26% of the time for Car eless Driving and 14% of the time for leaving the scene of the crash . 14 c=ity of Santa Fe Police Department c=rimc Analysis Conclusions With fewer than 120 incidents this analysis cannot be considered as being statistically significant as being related to all bicyclists within Santa Fe. With another year of data, however, a re -analysis of the data is expected to produce statistically significant results which can be used to address concerns of the community and law enforcem ent in ensuring bicycle safety within the community. With the data available, the largest factor in bicycle crashes can be currently contributed to the action of failure to yield on the part of both the cyclist and the motorist. Additionally, while bicycle lanes are available only 10% of cyclists are utilizing the designated lane and nearly 30% of those that are using the lane are using the lane incorrectly. The time frame in which the majority of the crashes are occurring may perhaps be associated with individuals of several different possible categories, those being visitors to the city, shift workers, or the unemployed. The location of the crashes is highly concentrated in the trianoie of the city - Area 6. This area is bordered by Saint Francis Drive, Siring Road, Camino Carlos Rey, Maes Street, and Agua Fria. The majority of the crashes in this area are focused along Cerrillos Road, Saint Michaels Drive and near the intersection of Saint Francis Drive and Cerrillos Road. While one of the concerns and purposes of this analysis was to review and analyze the occurrence of bicycle crashes on or near railroad tracks, the data simply does not support a concern, at this time in that area. Of the 110 crashes analyzed, only six (6) of the crashes occurred in the vicinity of railroad tracks. As previously stated, there were two (2) cases which were not included in the analysis due to verification (*one was able to be verified as being outside the parameters ef the analysis). Including these two (2) cases however, brings the number of bicycle crashes to occur on or near the railroad tracks to eight (8). Naturally, the desire would be for there to be absolutely zero crashes of any kind. However, the pure nature of society in general has shown that while efforts may be made in one area to improve safety, a factor which had not previously been considered dangerous suddenly becomes that which is. I hope you will find this analysis helpful in the efforts of bicycle safety and should there be any questions regarding this analysis please contact our offices. Prepared by: Malissa Austin-Cordell Crime Intelligence Analyst April 17, 2018 Page 15 o/15 15