Data for Park Blvd

The source of data on this page is publicly available using state or local databases. Much of the data has been obtained directly from Oakland administration by filing public record requests, which by law they must respond to within 10 business days, and must provide all data they have gathered. This allows the citizens of Oakland to hold our administration accountable for the decisions they make and ensure they have used all appropriate means to validate project feasibility, including community outreach and support, before implementing any project. If you feel that projects have been implemented which ignore this data, please contact us and provide the details, so that we may take appropriate action.

Traffic Study

There are several sets of data that have been used for the Park Blvd traffic study:


Kittleson data is captured over multi-year using a combination of manual and automated counts. A map of all counts completed is available here.

Oakland Counts

Annual counts of traffic at key locations are done by OaklandDOT using several agencies including “Metro Traffic Data Inc.”, “Fehr & Peers”,  “Alameda County Transportation Commision” and “Quality Counts”. Those counts are available for 2011 2012 2013 2014 2015 2016

For Park Blvd, the only annual data counts are at the Park Blvd & Monterey Blvd intersection, as follows:

2011 – Metro Traffic Data
Park Blvd & Monterey Blvd – Total Vehicles: 2594, Total Bikes: Not Counted
2013 – Metro Traffic Data
Park Blvd & Monterey Blvd – Total Vehicles: 2231, Total Bikes: 16
2014 – Metro Traffic Data
Park Blvd & Monterey Blvd – Total Vehicles: 2533, Total Bikes: 11
2015 – Metro Traffic Data
Park Blvd & Monterey Blvd – Total Vehicles: 2669, Total Bikes: 17
2016 – Quality Counts
Park Blvd & Monterey Blvd – Total Vehicles: 5106, Total Bikes: 33

For the remaining length of Park Blvd we can use Kittleson data, or data gathered as part of the Park Blvd Bicycle & Pedestrian Safety project to determine vehicle, bike, pedestrian counts as follows:

Kittleseon 2012
Park Blvd & St. James/Leimert – Total Vehicles: 2023, Total Bikes: 23
Park Blvd & El Centro – Total Vehicles: 1626, Total Bikes: 4

Shows a peak number of vehicles ranging from 2023 to 1626 at the intersection of Park Blvd & Leimert. The difference between this number represents vehicles that travel down Trestle Glen, Leimert, St. James, or park on Park Blvd to take casual carpool. The peak number of bikes is 23, which drops to 4 at El Centro, since the majority of bikes use Trestle Glen road.

The Fehr & Peers report did not count traffic, bikes or pedestrians, only assessed the impact of a road-diet and/or traffic signals. Their most optimistic study found that the changes will cause a backup of traffic of upwards of a quarter mile – and this assumes that the flow operates well using their light sequencing. What they do not account for is the irregular flow of vehicles entering from side-roads off Park, or pedestrians wanting to cross – i.e. the very problems they are trying to address. We should expect backups to extend well beyond their optimistic estimate, more likely a half-mile, which will cause traffic to divert off Park Blvd. into other neighborhoods.

Taking all of this data into account and getting an average for the bike versus vehicle traffic, we see that bike volume is just 0.55% of total traffic – once more evidence that it does not justify the removal of 50% of the road area.

Incident Study

California Highway Patrol

The CHP maintains a statewide database of all accident data. There are several tools that consume this data to provide maps, graphs etc. The incident data presented throughout this site is obtained from the CHP source data available here


Caltrans utilizes traffic flow analysis software for longitudinal data analysis. Rather than the more primitive point-in-time studies, this software allows real-time modeling over multi-year, to assess traffic flow across a corridor. Data is continuously gathered from vehicles using in-road detectors, Bluetooth, Fasktrak and other devices, creating a large database which can be analyzed at any time. This is the type of analysis that should be used in preference over point-in-time counts which do not present seasonal, weather variations, and cannot detect how changes such as road-diets change the flow to other neighborhoods. The software used by Caltrans is here.


Below are graphs and maps showing all incident data in Oakland between Jan-1-2006, and Dec-31-2016 where a motor vehicle was involved in an incident with a bike or pedestrian.

All of Oakland

All Motor vehicle and bike or pedestrian incidents, 2006 to 2016, count shows number at that location

Incident Summary


Injury Summary



Pedestrian Incidents

Smartphone Use

Several studies are now considering the effect of SmartPhone use and pedestrian injuries. A 2017 report from the Governors Highway Safety Association correlates cellphone use, and also marijuana use with an increase in pedestrian incidents, so we may expect more distracted pedestrians/drivers/cyclists and injuries with the passing of Proposition 64.


Below is the data analysis for Park Blvd between 2006 and 2016 where a motor vehicle was involved in an incident with either a pedestrian or bike and details of the incident type and who was at fault.

Upper Park Blvd
Upper Park Blvd Car/Bike/Pedestrian Collisions 2006 to 2016

Car driver was at fault

2 bike/car – cause improper passing, turning

1 pedestrian/car – cause pedestrian right of way


Middle Park Blvd
Middle Park Blvd Car/Bike/Pedestrian Collisions 2006 to 2016

Car driver was at fault

1 pedestrian/car – cause pedestrian right of way

1 pedestrian/car – cause unsafe backing or starting

Cyclist was at fault

2 bike/car – cause improper turning

1 bike/car – cause wrong side of road

1 bike solo – cause unsafe speed

Pedestrian was at fault

1 pedestrian/car – cause pedestrian violation


Lower Park Blvd
Lower Park Blvd Car/Bike/Pedestrian Collisions 2006 to 2016

Car driver was at fault

13 pedestrian/car – cause pedestrian right of way – 6 of these are by the school where they plan to keep two lanes uphill so still a problem

1 pedestrian/car – cause unsafe speed

1 pedestrian/car – cause traffic signals

1 car/car – cause following too closely

1 car/car – cause right of way

Cyclist was at fault

2 bike/car – cause wrong side of road

1 bike/car – cause right of way

Pedestrian was at fault

2 pedestrian/car – cause pedestrian violation