NYC Bike Routes & Accidents Involving Cyclists

Introduction

As many, I grew up riding bicycles for transportation, as a mean of relaxation or just for exercise. When there’s a nice day out I love to take my bike and go for a relaxing ride, discovering places and enjoying what nature has to offer.

We know of the rules for safety (bike paths, helmets, brakes, speed, lights, reflectors, air pressure and so on). NYC has adopted a wise decision to create and mark bicycle routes across the city as a mean to create a safety corridor for cyclists while improving transportation and traffic conditions.

Study

For this project, I will perform a study analysis for these defined bicycle routes and the NYC accidents database in which a cyclist was involved.

Goal

The goal is to identify how much safety has increased for cyclists since bicycle friendly routes were implemented and possible causes of why and where bicycle accidents happens the most.

Datasets

I will be using real data available as follows:

Open Data NYC Project (rich environment of diverse datasets).

Open Data NYC: https://opendata.cityofnewyork.us/

– Bike Routes: https://data.cityofnewyork.us/Transportation/Bike-Routes/7vsa-caz7

– NYC Accidents: https://data.cityofnewyork.us/Public-Safety/NYC-accidents-heat-map/ehqi-g294

Since these data sets are getting updated frequently, I will be working with their latest reports.

Overview

For this project first I will start by looking and summarizing the given reports and then stripe them down to possible causes and locations in order to identify patterns and possible causes.

This study will be perform in R Studio and many if not all functions and processes will be hidden; only results will be provided.

Gathering data

NYC Bicycle Routes

Obtaining a Bicycle Map

In order to obtain a bicycle map, we can visit the NYC DOT and download a friendly NYC bike friendly map.

This map offers valuable information about paths, NYC Biking Laws, Tips for the Ride, CyclEyes, Bikes on Transit with Subway Tips, Riding and Locking Tips.

Link to download a bicycle map: http://www.nyc.gov/html/dot/downloads/pdf/bikemap-2017.pdf

Link to download a summary listing of NYC Biking laws: http://www.nyc.gov/html/dot/html/bicyclists/biketips.shtml

For this project I will be using the following links in which electronic data related to bicycle routes are provided.

Url: https://data.cityofnewyork.us/Transportation/Bike-Routes/7vsa-caz7

API: https://dev.socrata.com/foundry/data.cityofnewyork.us/cc5c-sm6z

NYC Accidents Report

For this section of the project, I will be using details provided by the Motor Vehicle Collisions in New York City provided by the Police Department (NYPD). The latest update on this database was on May 11, 2017.

Obtaining Bicycle Accidents Reports

For this section of the project, I will be using the following open data links.

Url: https://data.cityofnewyork.us/Public-Safety/NYC-accidents-heat-map/ehqi-g294

API: https://dev.socrata.com/foundry/data.cityofnewyork.us/qiz3-axqb

Geocoding

One of the ideas of this project is to interpolate datasets and find out if extra information is available by combining results given by Geocoding services.

Google

The Google Maps Geocoding API is a service that provides geocoding and reverse geocoding of addresses, this service has a limitation of 2500 daily queries and it might not be of much value in the incipient part of the project but as an extended part of it.

API: https://developers.google.com/maps/

For this project I will be employing provided mechanisms in order to plot a Google Map from R and overlay it with shapes and markers. Also I will retrieve Google Maps info trough APIs, including places, directions, roads, distances, geocoding, elevation and timezone.

MapQuest

With the previous limitation provided by Google of 2500 daily queries, I have then looked into alternatives; that is MapQuest offers extra data that Google does Not provide but also an increase number of 15000 queries a month.

If it is true 15000 queries a month is not a much compared to Google, this 15000 can be performed at one time instead of breaking down into smaller batches.

API: https://developer.mapquest.com/documentation/geocoding-api/reverse/get/

A remarkable programming part about MapQuest is that they offer results as JSON and XML, I have programmed the two options in order to obtain information but I have decided for JSON; due to XML does not provide some information that JSON does such as the speed limit for example.

Physical Addresses

If it is true that our given data has some physical addresses, some present missing entries or typos that could generate errors; this can be easily “fix” by interpolating given coordinates from accidents and routes into reverse geocoding services such as Google or MapQuest as described above in order to obtain more accurate information.

MariaDB

My database server of choice is MariaDB, once downloaded the data I will store the information in it and retrieve it when needed.

By the numbers

Dates

  • First reported accident involving a cyclist: 2012-07-01.

  • Last reported accident involving a cyclist: 2017-05-13.

  • Total number of days passed since first and last reported accident involving a cyclist: 1777.

  • Total number of years passed since first and last reported accident involving a cyclist: 5.

Amount of data collected

  • Total number of records referring to accidents involving cyclists in NYC: 20308.

  • Total number of records referring to bike routes: 25209.

Reported accidents table

  • Grand total number of injuries due to accidents involving cyclists in NYC: 21474.

Yearly summary

Table 1: Summary yearly table of accidents involving cyclists in NYC.
Year No. pedestrians injuried No. cyclist injuried Percentage Monthly Average Daily Average Probability
2017 18 1306 6.08 % 109 4 1 every 6 hours
2016 45 5607 26.11 % 467 15 1 every 2 hours
2015 5 4280 19.93 % 357 12 1 every 2 hours
2014 6 3997 18.61 % 333 11 1 every 2 hours
2013 5 4074 18.97 % 340 11 1 every 2 hours
2012 6 2210 10.29 % 184 6 1 every 4 hours

Observed patterns:

If we analyze the above table, we can quickly figure out that at the current rate of accidents involving cyclists in NYC for this year will be as follows:

  • Forecasted number of accidents involving cyclists in NYC for the current year.
Table 1.1: Poorly forecasted trend based on a current daily average for the current year.
Year Current.Reported Curent.Daily.Avg Forecasted.Month.Avg Forecasted.Yearly.Avg Previous.Year Difference
2017 1306 10 300 3650 5607 -1957

If you noticed, I have described the results as “poorly”; that is due to lack of rigor with the current evaluated data, and many adjustments need to be done, but as an initial point of reference will be a “good” starting point.

From Table 1.1 we can have high hopes that this year’s accident rate will be lower but with the spike of accidents observed during last year alone, it will be difficult to predict.

  • An interesting observation is that 1 accident involving a cyclist in NYC has been reported at an average rate of approximately every two hours for the majority of the recorded years.

Monthly summary

In this section, I have listed our given data into monthly results, with hopes of finding some patterns; one of these patterns could be related to the season of the year for example.

Table 2: Summary monthly table of accidents involving cyclists in NYC.
Month No. pedestrians injuried No. cyclist injuried Percentage Daily Average Probability
May, 2017 3 170 0.79 % 6 1 every 4 hours
Apr, 2017 4 366 1.7 % 12 1 every 2 hours
Mar, 2017 0 234 1.09 % 8 1 every 3 hours
Feb, 2017 6 228 1.06 % 8 1 every 3 hours
Jan, 2017 5 308 1.43 % 10 1 every 2 hours
Dec, 2016 0 260 1.21 % 9 1 every 3 hours
Nov, 2016 1 418 1.95 % 14 1 every 2 hours
Oct, 2016 9 540 2.51 % 18 1 every 1 hours
Sep, 2016 9 780 3.63 % 26 1 every 1 hours
Aug, 2016 19 1234 5.75 % 41 1 every 1 hours
Jul, 2016 2 494 2.3 % 16 1 every 2 hours
Jun, 2016 1 465 2.17 % 16 1 every 2 hours
May, 2016 0 422 1.97 % 14 1 every 2 hours
Apr, 2016 0 350 1.63 % 12 1 every 2 hours
Mar, 2016 3 312 1.45 % 10 1 every 2 hours
Feb, 2016 1 166 0.77 % 6 1 every 4 hours
Jan, 2016 0 166 0.77 % 6 1 every 4 hours
Dec, 2015 0 292 1.36 % 10 1 every 2 hours
Nov, 2015 1 343 1.6 % 11 1 every 2 hours
Oct, 2015 0 408 1.9 % 14 1 every 2 hours
Sep, 2015 0 517 2.41 % 17 1 every 1 hours
Aug, 2015 2 569 2.65 % 19 1 every 1 hours
Jul, 2015 0 574 2.67 % 19 1 every 1 hours
Jun, 2015 1 441 2.05 % 15 1 every 2 hours
May, 2015 1 474 2.21 % 16 1 every 2 hours
Apr, 2015 0 304 1.42 % 10 1 every 2 hours
Mar, 2015 0 161 0.75 % 5 1 every 5 hours
Feb, 2015 0 82 0.38 % 3 1 every 8 hours
Jan, 2015 0 115 0.54 % 4 1 every 6 hours
Dec, 2014 0 186 0.87 % 6 1 every 4 hours
Nov, 2014 1 280 1.3 % 9 1 every 3 hours
Oct, 2014 1 430 2 % 14 1 every 2 hours
Sep, 2014 1 488 2.27 % 16 1 every 2 hours
Aug, 2014 0 501 2.33 % 17 1 every 1 hours
Jul, 2014 1 538 2.51 % 18 1 every 1 hours
Jun, 2014 1 473 2.2 % 16 1 every 2 hours
May, 2014 1 388 1.81 % 13 1 every 2 hours
Apr, 2014 0 309 1.44 % 10 1 every 2 hours
Mar, 2014 0 190 0.88 % 6 1 every 4 hours
Feb, 2014 0 94 0.44 % 3 1 every 8 hours
Jan, 2014 0 120 0.56 % 4 1 every 6 hours
Dec, 2013 1 175 0.81 % 6 1 every 4 hours
Nov, 2013 1 281 1.31 % 9 1 every 3 hours
Oct, 2013 0 429 2 % 14 1 every 2 hours
Sep, 2013 2 479 2.23 % 16 1 every 2 hours
Aug, 2013 0 524 2.44 % 17 1 every 1 hours
Jul, 2013 0 470 2.19 % 16 1 every 2 hours
Jun, 2013 0 508 2.37 % 17 1 every 1 hours
May, 2013 0 400 1.86 % 13 1 every 2 hours
Apr, 2013 0 290 1.35 % 10 1 every 2 hours
Mar, 2013 0 195 0.91 % 6 1 every 4 hours
Feb, 2013 0 131 0.61 % 4 1 every 6 hours
Jan, 2013 1 192 0.89 % 6 1 every 4 hours
Dec, 2012 0 212 0.99 % 7 1 every 3 hours
Nov, 2012 0 276 1.29 % 9 1 every 3 hours
Oct, 2012 0 332 1.55 % 11 1 every 2 hours
Sep, 2012 0 433 2.02 % 14 1 every 2 hours
Aug, 2012 1 507 2.36 % 17 1 every 1 hours
Jul, 2012 5 450 2.1 % 15 1 every 2 hours

If it is true, by looking at the Table 2, it is very difficult to determine some accident patterns in which cyclists were involved, with the below graph we can quickly observe and identify very well defined patterns.

Observed patterns:

  • There is an evident increased number of accidents involving cyclists during the summer months, with a remarkable spike during the months of August and September of 2016.

  • There is a remarkable reduction of accidents during the month of February in previous years but an observed increased in the most current data. This could be correlated due to February being the shortest month in the year and being during the winter season.

  • There seems to be an observed increase of accidents involving cyclist starting in January 2016.

Questions:

Those interesting observations come to wonder the following questions:

  • Are accidents involving cyclists happening in established bicycle friendly paths?

  • Why there seems to be an increased number of accidents involving cyclists if there are more “safe” zones?

  • What happened in the summer months of 2016? Why so many more accidents involving cyclists?

  • What could be the reasons for the observed patterns?

In hopes of responding the above questions, I will keep breaking down the data in hopes of identifying more patterns and possible causes.

By the borough

In hopes of identifying extra patterns and possible, causes and “hot” locations, I will review the data provided and split it into the five boroughs.

Borough yearly summary

Table 3: Borough summary yearly table of accidents involving cyclists in NYC.
borough No. pedestrians injuried No. cyclist injuried Percentage Monthly Average Daily Average Probability
BROOKLYN 27 7339 34.18 % 612 20 1 every 1 hours
MANHATTAN 21 5559 25.89 % 463 15 1 every 2 hours
QUEENS 11 3632 16.91 % 303 10 1 every 2 hours
NA 21 3076 14.32 % 256 8 1 every 3 hours
BRONX 4 1677 7.81 % 140 5 1 every 5 hours
STATEN ISLAND 1 191 0.89 % 16 1 1 every 24 hours

Observed patterns:

From the Table 3, we can quickly identify interesting observations as follows:

  • BROOKLYN heads the count with the highest number of cyclist involved in an accident with a total number of 7339, representing 34.18 % of our given data.

  • MANHATTAN is the second borough with the highest number of cyclist involved in an accident with a total number of 5559, representing 25.89 % of our given data.

  • The difference in between BROOKLYN and MANHATTAN is given by 1780 accidents involving cyclists or 8.29 % difference.

  • STATEN ISLAND heads the count with the lowest number of cyclist involved in an accident with a total number of 191, representing 0.89 % of our given data. Thus, makes it the “safest” borough to ride a bicycle since an average of 1 accident per day has been recorded.

  • BRONX has a significant low number of reported accidents involving cyclists compared to the other boroughs.

  • There’s an important number of accidents involving cyclists not assigned to their respective borough.

With the expectation of identifying extra patterns and identifying extra information, I will break down our data into their respective boroughs.

Borough monthly summary

Table 4: Borough summary monthly table of accidents involving cyclists in NYC.
Month BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND Not Available Total
May, 2017 8 49 40 32 1 40 170
Apr, 2017 22 87 84 43 4 126 366
Mar, 2017 21 61 52 30 1 69 234
Feb, 2017 13 54 59 30 2 70 228
Jan, 2017 24 92 58 57 7 70 308
Dec, 2016 11 74 53 40 1 81 260
Nov, 2016 18 115 80 62 3 140 418
Oct, 2016 19 135 119 85 8 174 540
Sep, 2016 76 207 145 121 2 229 780
Aug, 2016 97 342 246 180 9 360 1234
Jul, 2016 43 155 90 69 6 131 494
Jun, 2016 40 131 106 64 2 122 465
May, 2016 37 122 106 72 6 79 422
Apr, 2016 28 124 116 50 2 30 350
Mar, 2016 29 125 74 48 3 33 312
Feb, 2016 15 59 53 20 1 18 166
Jan, 2016 7 67 49 28 2 13 166
Dec, 2015 26 101 94 42 1 28 292
Nov, 2015 22 125 111 62 0 23 343
Oct, 2015 34 148 129 60 3 34 408
Sep, 2015 49 176 142 95 7 48 517
Aug, 2015 52 194 145 114 2 62 569
Jul, 2015 45 216 123 113 5 72 574
Jun, 2015 48 146 111 89 2 45 441
May, 2015 38 173 133 78 2 50 474
Apr, 2015 25 104 92 54 6 23 304
Mar, 2015 8 61 52 27 2 11 161
Feb, 2015 2 26 24 16 0 14 82
Jan, 2015 14 43 35 14 0 9 115
Dec, 2014 14 65 59 33 2 13 186
Nov, 2014 21 113 60 60 2 24 280
Oct, 2014 41 160 116 75 2 36 430
Sep, 2014 62 167 116 87 7 49 488
Aug, 2014 39 189 143 87 7 36 501
Jul, 2014 46 182 145 113 6 46 538
Jun, 2014 52 170 135 74 5 37 473
May, 2014 39 133 127 64 1 24 388
Apr, 2014 21 107 100 54 4 23 309
Mar, 2014 11 53 70 32 1 23 190
Feb, 2014 4 27 44 11 1 7 94
Jan, 2014 6 42 40 24 1 7 120
Dec, 2013 9 68 55 30 0 13 175
Nov, 2013 10 113 91 46 1 20 281
Oct, 2013 22 182 113 64 6 42 429
Sep, 2013 36 174 128 88 7 46 479
Aug, 2013 46 202 132 88 5 51 524
Jul, 2013 48 180 119 68 1 54 470
Jun, 2013 45 197 125 98 5 38 508
May, 2013 27 154 110 70 4 35 400
Apr, 2013 23 97 86 58 4 22 290
Mar, 2013 13 57 61 48 2 14 195
Feb, 2013 8 42 46 27 0 8 131
Jan, 2013 8 73 64 36 0 11 192
Dec, 2012 16 88 58 34 0 16 212
Nov, 2012 12 108 67 64 3 22 276
Oct, 2012 20 138 85 55 2 32 332
Sep, 2012 33 162 105 88 6 39 433
Aug, 2012 36 208 125 81 8 49 507
Jul, 2012 38 176 113 80 8 35 450

Bronx borough

Brooklyn borough

Manhattan borough

Queens borough

Staten Island borough

Unknown borough

Observed patterns:

  • Normality is observed with the majority of the boroughs as follows:

If we look and visually compare our Graph 2 with their respective boroughs graphs, we quickly identify some sort of “pattern similarity” in regards of all boroughs except Staten Island; that is, Staten Island seems not to follow the norm “observed” with the other boroughs and the General data as displayed in the Graph 2. This could be related to lower number of reported accidents not allowing a sense of normality to be defined with their borough’s data.

By Street and Zip Code

Yearly summary

The below table, display the top 10 streets with the highest number of accidents involving cyclists in NYC. This table is ranked with the top mention being first and the least of the ten mentions being last.

Top 10 by year

Table 5: Top 10 Streets involving accidents with cyclists in NYC by years.
Rank X2017 X2016 X2015 X2014 X2013 X2012
1 [ 5 ] CHRYSTIE STREET, 10002 [ 25 ] BROADWAY, 11206 [ 17 ] GRAND STREET, 11211 [ 18 ] 5 AVENUE, 11215 [ 20 ] GRAND STREET, 11211 [ 13 ] DELANCEY STREET, 10002
2 [ 5 ] EAST HOUSTON STREET, 10002 [ 13 ] OCEAN AVENUE, 11226 [ 15 ] BROADWAY, 11206 [ 18 ] GRAND STREET, 11211 [ 17 ] MYRTLE AVENUE, 11205 [ 12 ] ROOSEVELT AVENUE, 11368
3 [ 4 ] CATON AVENUE, 11226 [ 10 ] 5 AVENUE, 11215 [ 15 ] BUSHWICK AVENUE, 11206 [ 17 ] BROADWAY, 11206 [ 15 ] 5 AVENUE, 11215 [ 11 ] FLUSHING AVENUE, 11205
4 [ 4 ] FLUSHING AVENUE, 11206 [ 10 ] DE KALB AVENUE, 11205 [ 15 ] DELANCEY STREET, 10002 [ 14 ] 5 AVENUE, 11232 [ 15 ] DELANCEY STREET, 10002 [ 11 ] GRAND STREET, 11211
5 [ 3 ] 18 AVENUE, 11204 [ 10 ] FLUSHING AVENUE, 11205 [ 15 ] MYRTLE AVENUE, 11205 [ 13 ] EAST 23 STREET, 10010 [ 14 ] EAST 23 STREET, 10010 [ 11 ] ROOSEVELT AVENUE, 11372
6 [ 3 ] 61 STREET, 11377 [ 10 ] GRAND STREET, 11211 [ 14 ] CHRYSTIE STREET, 10002 [ 13 ] EAST HOUSTON STREET, 10002 [ 14 ] FULTON STREET, 11238 [ 10 ] FLATBUSH AVENUE, 11217
7 [ 3 ] 7 AVENUE, 10019 [ 9 ] BEDFORD AVENUE, 11226 [ 14 ] FLUSHING AVENUE, 11205 [ 13 ] ROOSEVELT AVENUE, 11377 [ 14 ] ROOSEVELT AVENUE, 11372 [ 10 ] MYRTLE AVENUE, 11205
8 [ 3 ] BORINQUEN PLACE, 11211 [ 9 ] DELANCEY STREET, 10002 [ 13 ] 5 AVENUE, 11232 [ 12 ] MYRTLE AVENUE, 11205 [ 14 ] VANDERBILT AVENUE, 11238 [ 9 ] CHRYSTIE STREET, 10002
9 [ 3 ] BROADWAY, 11206 [ 8 ] EAST 138 STREET, 10454 [ 13 ] ROOSEVELT AVENUE, 11368 [ 11 ] AVENUE OF THE AMERICAS, 10011 [ 13 ] 1 AVENUE, 10009 [ 8 ] BEDFORD AVENUE, 11226
10 [ 3 ] CLARENDON ROAD, 11226 [ 8 ] EAST 149 STREET, 10455 [ 12 ] 34 AVENUE, 11372 [ 11 ] FULTON STREET, 11238 [ 13 ] BEDFORD AVENUE, 11225 [ 7 ] BROADWAY, 11206

Observed patterns:

From the table 5, we can identify as follows:

  • EAST HOUSTON STREET, 10002 seems to be a hot spot for accidents involving cyclists in NYC. As we know, that is busy street and the proximity to BROOKLYN which contains the highest number of accidents in the five borough might impact as well.

  • BROADWAY, 11206 was the top street with the most accidents involving cyclists reported in 2016, but also ranks within the top ten in previous years as well; an interesting observation is that BROADWAY, 11206 is opposite located from EAST HOUSTON STREET, 10002 being BROADWAY in BROOKLYN and EAST HOUSTON STREET in MANHATTAN. The two of them are connected by the famous Williamsburg Bridge.

  • GRAND STREET, 11211 is located right off the Williamsburg bridge and same as BROADWAY, 11206 it has a high rate of accidents in the BROOKLYN area.

  • DELANCEY STREET, 10002 has been on the top as well, and a very interesting observation is that this is also, located off the Williamsburg bridge in MANHATTAN.

  • A good observation to make is the high number of accidents involving cyclists around the Williamsburg bridge; there seems to be a great sense of distraction and traffic around that area.

Top 10 of all times

Table 6: Top 10 streets involving accidents with cyclists in NYC of all times.
street Count Percentage
GRAND STREET, 11211 79 0.48 %
BROADWAY, 11206 78 0.47 %
DELANCEY STREET, 10002 63 0.38 %
5 AVENUE, 11215 60 0.36 %
MYRTLE AVENUE, 11205 60 0.36 %
ROOSEVELT AVENUE, 11368 59 0.36 %
FLUSHING AVENUE, 11205 54 0.33 %
EAST 23 STREET, 10010 48 0.29 %
ROOSEVELT AVENUE, 11372 48 0.29 %
CHRYSTIE STREET, 10002 47 0.28 %

From our previous Graph, you can find all the top ten streets by the number of accidents in all reported accidents involving cyclists.

Observed patterns:

  • GRAND STREET, 11211 and BROADWAY, 11206 have the most accidents recorded involving a cyclist.

  • Some of those streets are marked as bicycle friendly zones and others do not; interesting is to find that bike paths present a high number of accidents specially in BROOKLYN.

Borough summary

Table 7: Borough summary table of vehicle types at the time of accidents involving cyclists in NYC.
vehicle.Type BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND Not Available Total Percentage
PASSENGER VEHICLE 889 4078 2219 2018 116 1547 10867 52.54 %
SPORT UTILITY / STATION WAGON 277 1559 762 797 33 522 3950 19.1 %
TAXI 40 150 1240 75 0 135 1640 7.93 %
UNKNOWN 162 539 290 226 22 146 1385 6.7 %
NA 102 219 201 145 9 250 926 4.48 %
VAN 28 165 157 72 1 33 456 2.2 %
LIVERY VEHICLE 56 49 150 31 0 32 318 1.54 %
OTHER 24 110 89 42 5 29 299 1.45 %
BUS 12 70 86 30 1 16 215 1.04 %
PICK-UP TRUCK 12 52 82 33 2 32 213 1.03 %
SMALL COM VEH(4 TIRES) 11 51 77 22 0 19 180 0.87 %
LARGE COM VEH(6 OR MORE TIRES) 3 28 33 13 0 12 89 0.43 %
MOTORCYCLE 7 28 26 11 1 15 88 0.43 %
PEDICAB 0 3 20 0 0 2 25 0.12 %
AMBULANCE 5 7 4 1 0 1 18 0.09 %
SCOOTER 0 2 1 3 0 2 8 0.04 %
FIRE TRUCK 0 3 1 1 0 1 6 0.03 %

Observed patterns:

From table 10, we can quickly observe a few interesting patterns of vehicle types reported during accidents involving cyclists as follows:

  • The main vehicle type involved in accidents with cyclists in all NYC boroughs is PASSENGER VEHICLE with grand total of 10867 accidents involved representing 52.54 % for all years.

  • BROOKLYN has the highest bicycle ratio of accidents with a passenger vehicle and sport utility / station wagon vehicle types, doubling MANHATTAN and QUEENS on each vehicle type.

  • MANHATTAN leads the number of accidents involving cyclists with Taxi cabs, livery vehicles, buses and pick-up trucks.

By contributing factors

Yearly summary

The below table, display the top 10 contributed factors that were mentioned as the cause of accident involving cyclists in NYC. This table is ranked with the top mention being first and the least of the ten mentions being last.

Top 10 by year

Table 8: Top 10 contributing factors involving accidents with cyclists in NYC by years.
Rank X2017 X2016 X2015 X2014 X2013 X2012
1 Distraction Distraction Distraction Distraction Distraction Distraction
2 Driver Inattention Driver Inattention Driver Inattention Driver Inattention Driver Inattention Driver Inattention
3 Failure to Yield Right-of-Way Failure to Yield Right-of-Way Passenger Distraction Passenger Distraction Passenger Distraction Passenger Distraction
4 Bicyclist NA Failure to Yield Right-of-Way Failure to Yield Right-of-Way Failure to Yield Right-of-Way Failure to Yield Right-of-Way
5 Confusion Bicyclist Other Vehicular NA Other Vehicular Physical Disability
6 Other Pedestrian Error Confusion NA Other Vehicular Physical Disability Other Vehicular
7 Pedestrian Other Pedestrian Error Physical Disability Physical Disability Lost Consciousness Lost Consciousness
8 NA Pedestrian Lost Consciousness Lost Consciousness Prescription Medication Limited
9 Passing or Lane Usage Improper Passenger Distraction Drowsy Prescription Medication Drowsy Turning Improperly
10 Traffic Control Disregarded Traffic Control Disregarded Fatigued Traffic Control Disregarded Fatigued View Obstructed

Observed patterns:

From the table 5, we can quickly identify as follows:

  • Distraction and Driver Inattention are the two leading contributing factors for the present year.

  • Distraction and Driver Inattention were also the two most contributing factors in registered accidents involving cyclists in the past years as well.

  • Pedestrians show a significant increase starting in 2016; that is, pedestrians were not listed in the 10 ten in previous years.

  • Failure to obey traffic control signals seems to be a major contributing factor by both drivers and cyclists.

  • Prescription, non prescription medication and physical disability seems to be recurrent factors for all years.

Top 10 of all times

Table 9: Top 10 contributing factors involving accidents with cyclists in NYC of all times.
contributing.Factor Count
Distraction 3928
Driver Inattention 3928
Passenger Distraction 1954
Failure to Yield Right-of-Way 1895
NA 959
Other Vehicular 805
Physical Disability 594
Lost Consciousness 473
Bicyclist 470
Confusion 470

From our previous Graph, you can find all the top ten contributing factors by the number of mentions in all reported accidents involving cyclists. These mentions were taken from all parties involved, that is: drivers, cyclists and pedestrians.

Observed patterns:

  • In this, case we can quickly identify the contributing factor mentions difference in between the top and bottom, these differences are quite significant and large.

  • Distraction and driver inattention have been consistent for all years and really play a big role in the contribution of factors at the time of accidents involving cyclists if we go by the number of mentions.

  • Amazing to see how pedestrian factors are in the rise starting in 2016.

Borough summary

Table 10: Borough summary table of contributing factors at the time of accidents involving cyclists in NYC.
contributing.Factor BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND Not Available Total
Distraction 237 1059 1211 812 38 571 3928
Driver Inattention 237 1059 1211 812 38 571 3928
Passenger Distraction 191 621 600 316 10 216 1954
Failure to Yield Right-of-Way 101 726 447 348 12 261 1895
NA 102 236 250 151 10 210 959
Other Vehicular 58 124 499 47 12 65 805
Physical Disability 50 215 181 88 2 58 594
Lost Consciousness 32 149 189 59 3 41 473
Bicyclist 40 111 110 54 4 151 470
Confusion 40 111 110 54 4 151 470
Other Pedestrian Error 40 111 110 54 4 151 470
Pedestrian 40 111 110 54 4 151 470
Traffic Control Disregarded 25 126 100 60 1 96 408
Prescription Medication 23 117 93 42 1 34 310
Drowsy 13 106 124 30 1 27 301
Fatigued 13 106 124 30 1 27 301
Driver Inexperience 15 70 85 34 0 46 250
Turning Improperly 22 44 132 18 0 32 248
Limited 19 81 46 26 2 23 197
View Obstructed 19 81 46 26 2 23 197
Passing or Lane Usage Improper 4 31 68 22 0 57 182
Illness 7 27 121 10 0 5 170
Alcohol Involvement 11 56 31 25 3 22 148
Backing Unsafely 17 46 32 21 0 18 134
Outside Car Distraction 4 46 41 14 0 9 114
Pavement Slippery 9 34 25 15 2 9 94
Following Too Closely 4 20 20 9 1 29 83
Aggressive Driving 5 28 22 7 0 13 75
Reaction to Other Uninvolved Vehicle 4 33 19 9 1 9 75
Road Rage 5 28 22 7 0 13 75
Unsafe Speed 1 24 14 7 0 27 73
Glare 12 16 3 14 1 5 51
Unsafe Lane Changing 4 5 22 3 1 14 49
Brakes Defective 12 10 8 5 0 11 46
Oversized Vehicle 0 8 17 3 0 3 31
Other Electronic Device 3 13 8 0 0 5 29
Pavement Defective 0 7 8 4 0 9 28
Debris 1 4 6 3 0 4 18
Obstruction 1 4 6 3 0 4 18
Drugs (Illegal) 1 5 7 3 0 1 17
Inadequate 2 4 7 2 0 2 17
Failure to Keep Right 3 3 3 4 0 3 16
Lane Marking Improper 2 3 6 2 0 2 15
Fell Asleep 1 5 4 2 0 1 13
Headlights Defective 0 2 2 2 0 1 7
Other Lighting Defects 0 3 2 1 0 1 7
Cell Phone (hand-held) 0 0 4 1 0 1 6
Steering Failure 0 1 1 3 0 0 5
Cell Phone (hands-free) 0 2 1 1 0 0 4
Non-Working 0 0 3 0 0 1 4
Traffic Control Device Improper 0 0 3 0 0 1 4
Animals Action 0 2 0 0 0 1 3
Driverless 0 0 0 0 0 2 2
Runaway Vehicle 0 0 0 0 0 2 2
Tire Failure 0 1 1 0 0 0 2
Tow Hitch Defective 0 0 0 0 1 0 1
Windshield Inadequate 0 1 0 0 0 0 1

Observed patterns:

From table 7, we can quickly observe a few interesting patterns of contributing factors reported as the cause of accidents involving cyclists as follows:

  • The main contributing factors are problematic all over the 5 boroughs, that is Distraction and Driver inattention are the top contributed factors all over the five boroughs.

  • BROOKLYN seems to present a very high number of accidents reporting Failure to Yield Right-of-Way contrary to the BRONX or STATEN ISLAND for example.

  • BROOKLYN and MANHATTAN stand out due to Passanger distraction, doubling up QUEENS and about three times more than the BRONX boroughs.

  • BROOKLYN present a high number of Alcohol Involvement mentions compared to the other boroughs.

By vehicle type

Yearly summary

The below table, display the top 5 vehicle types that were mentioned in accidents involving cyclists in NYC. This table is ranked with the top mention being first and the least of the ten mentions being last.

Top 5 by year

Table 11: Top 5 vehicle types involving accidents with cyclists in NYC by years.
Rank X2017 X2016 X2015 X2014 X2013 X2012
1 PASSENGER VEHICLE PASSENGER VEHICLE PASSENGER VEHICLE PASSENGER VEHICLE PASSENGER VEHICLE PASSENGER VEHICLE
2 SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON SPORT UTILITY / STATION WAGON
3 NA NA UNKNOWN TAXI TAXI TAXI
4 TAXI TAXI TAXI UNKNOWN UNKNOWN UNKNOWN
5 PICK-UP TRUCK UNKNOWN NA NA VAN VAN

Observed patterns:

From the table 8, we can quickly identify as follows:

  • PASSENGER VEHICLE and SPORT UTILITY / STATION WAGON are the two leading vehicle types registered in accidents for the present year.

  • PASSENGER VEHICLE and SPORT UTILITY / STATION WAGON were also the two most leading vehicle types in registered accidents involving cyclists in the past years as well.

  • NA show as a consistent vehicle type involved in accidents with a cyclist.

  • PICK-UP TRUCK and MOTORCYCLE seems to be on the raise in the last couple years.

Top 10 of all times

Table 12: Top 10 vehicle types involving accidents with cyclists in NYC of all times.
vehicle.Type Count Percentage
PASSENGER VEHICLE 10867 52.54 %
SPORT UTILITY / STATION WAGON 3950 19.1 %
TAXI 1640 7.93 %
UNKNOWN 1385 6.7 %
NA 926 4.48 %
VAN 456 2.2 %
LIVERY VEHICLE 318 1.54 %
OTHER 299 1.45 %
BUS 215 1.04 %
PICK-UP TRUCK 213 1.03 %

From our previous Graph, you can find all the top ten vehicle types by the number of accidents in all reported accidents involving cyclists.

Observed patterns:

  • In this, case we can quickly identify how great of a difference relies in between the top (Passenger Vehicle) and the second to highest (Sport Utility / Station Wagon), these differences are quite significant and large.

  • Public transporation and Commercial vehicles don’t have as many accidents involving cyclists as private owned vehicles do.

  • Small vehicles have the highest number of accidents involving cyclists compared to large vehicles such as buses, pick-up trucks or commercial vehicles.

  • PASSENGER VEHICLE is involved in one of every two accidents when a cyclist gets injured.

Borough summary

Table 13: Borough summary table of vehicle types at the time of accidents involving cyclists in NYC.
vehicle.Type BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND Not Available Total Percentage
PASSENGER VEHICLE 889 4078 2219 2018 116 1547 10867 52.54 %
SPORT UTILITY / STATION WAGON 277 1559 762 797 33 522 3950 19.1 %
TAXI 40 150 1240 75 0 135 1640 7.93 %
UNKNOWN 162 539 290 226 22 146 1385 6.7 %
NA 102 219 201 145 9 250 926 4.48 %
VAN 28 165 157 72 1 33 456 2.2 %
LIVERY VEHICLE 56 49 150 31 0 32 318 1.54 %
OTHER 24 110 89 42 5 29 299 1.45 %
BUS 12 70 86 30 1 16 215 1.04 %
PICK-UP TRUCK 12 52 82 33 2 32 213 1.03 %
SMALL COM VEH(4 TIRES) 11 51 77 22 0 19 180 0.87 %
LARGE COM VEH(6 OR MORE TIRES) 3 28 33 13 0 12 89 0.43 %
MOTORCYCLE 7 28 26 11 1 15 88 0.43 %
PEDICAB 0 3 20 0 0 2 25 0.12 %
AMBULANCE 5 7 4 1 0 1 18 0.09 %
SCOOTER 0 2 1 3 0 2 8 0.04 %
FIRE TRUCK 0 3 1 1 0 1 6 0.03 %

Observed patterns:

From table 10, we can quickly observe a few interesting patterns of vehicle types reported during accidents involving cyclists as follows:

  • The main vehicle type involved in accidents with cyclists in all NYC boroughs is PASSENGER VEHICLE with grand total of 10867 accidents involved representing 52.54 % for all years.

  • BROOKLYN has the highest bicycle ratio of accidents with a passenger vehicle and sport utility / station wagon vehicle types, doubling MANHATTAN and QUEENS on each vehicle type.

  • MANHATTAN leads the number of accidents involving cyclists with Taxi cabs, livery vehicles, buses and pick-up trucks.

Conclusions

Something that caught my interest was something that NYCBikeMaps.com express on their site as follows: “NYCBikeMaps.com makes no guarantees that the bike routes listed will be suitable for all cyclists”. I think this is remarkable true due to the high number of accidents already listed in diverse roads and locations.

  • Avoid Brooklyn as much as possible, pick Staten Island or the Bronx for a safer ride!

  • Be alert and watch for passenger vehicles since one in every two accidents is reported to be one of those types.

  • Follow the greenways and designated bike paths.

  • Respect the transit signals.

Recommendations

  • Follow NYCBikeMaps.com five color legend to classify routes for cyclists.

  • Stay in a Bike Path, Green way, Off-Street bike path or designated path in parks or on-street protected bike paths.

  • Use Special Bike Paths or Lanes with special hours or unique conditions, including Central Park & Prospect Park.

  • Use the Bike Lane on-street striped route.

  • Use Sharrows / Signed Route on street signed route or sharrow signs for bicycle riders.

  • Cyclists must be on high alert if riding on-street routes with traffic; that is no bike signage or separation is present.

  • Since bike lanes & paths sometimes may be blocked by cars or pedestrians, or closed due to construction or maintenance; drivers and cyclists need to enforce extra cautioning order to ride safely and responsibly.