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May 2019

Home / May 2019
Bike Lanes, Tech

Mapping Bike Lanes Using 3D LiDAR

A safe and effective bike lane is one that connects with the rest of the bike network and that works in parallel with other networks including pedestrian, vehicles, and transit. This asset gives a designated place to safely separate vehicles from the more exposed and often slower bicyclist.

At ActiveAI we are working on better ways to collect accurate and detailed data about assets so that state and local agencies can have a complete summary of the condition and safety. Specializing in field surveys of bike lanes, sidewalks, and trails.

“The best bike lane is the one that is safe and that gets used because it connects you to where you are going!”

There are a variety of types of bike lanes and appropriate installations based on the Level of Traffic Stress (LTS) of the adjacent road and other parallel streets. In every case it is important to have a complete summary of the condition and safety of the entire network so that gaps can be addressed and prioritized.

Bike Lane Survey

The bike lane is an important component of complete streets therefore it is important to know what you have, where it is, and the current condition. Some of these features you need to know about your bike lane infrastructure includes its size, location, and condition:

  • Width of the lane
  • Level of protection
  • Street markings including sharrows
  • Additional green paint
  • Obstructions
  • Proximity to parking
  • Adjacent signs
  • Street lights
  • Condition of reflective paint

How to Survey a Bike Lane

Local and State governments often deploy interns or others to do field surveys. We have noticed they are either done sporadically as needed or in a blitz that is done remotely using images or on the ground often for several months.

We use LiDAR to map the bike lane to check for all the detailed data that help describe the features of the bike lane and the surrounding areas. Using lasers enable us to capture and post process data much faster and more consistent than sending a team of interns or a survey crew.

We are continually experimenting and working on improving our process for data collection. Contact us if you are interested in discussing ideas or a possible project you are considering. We are happy to help.

More Resources about Bike Lanes


Synchronizing Data Collection With GIS IDs To Map Time And Space

Our data collection process involves several different instruments. In order to tie these datasets together into one cohesive whole we rely on highly accurate timestamping all data. The data collection rig is outfitted with a mini computer by Intel called the NUC. It is a lightweight powerhouse that only sips power compared to machines of comparable specifications. The internal clock on the NUC is the master clock that all other instruments synchronize themselves to.

Mapping Everything to Space and Time: coordinate ID

In order for this data to be useful we must be able to accurately pinpoint where in time and space the data were collected. Knowing where the data were collected is important to be able to associate that data with that section of sidewalk and knowing when the data were collected is important to join all data together tightly. We use a single common key to handle this join.

The data are all joined on a common key called coordinate ID. This coordinate ID is an integer 19 digits long that consists of the unix epoch time down to milliseconds concatenated with the unique rig ID of six digits. The coordinate ID gives us a scalar temporospatial value that

is unique among all vehicles in the fleet. The coordinate ID 1556817834000000001 denotes the Unix Epoch time that would convert to Thursday, May 2, 2019 5:23:54 PM GMT and the rig id 001. We can impute the location between two gps coordinates to get a more accurate location for any coordinate ID.

Problem of Aligning Data with What it Actually Represents

The main problem to solve with the data is that there is an important distinction between where the observation was captured and the area the observation represents. The simplest situation to explain this is for the cameras. The camera mounted to the rear of the rig captures images of the sidewalk approximately 5 feet behind the rig. However if we were to simply associate an image with the coordinate ID of when it was captured it would be approximately 5 feet in front of what the image represents. Compounded on this is the fact that the GPS unit is not in the same place as the camera, it is a few inches in front (and a few feet below, but we are unconcerned with altitude for the camera data). To solve this problem we take measurements of the distances between the areas that the data is collecting data of and the GPS unit. Using these measurements we can offset the original coordinate ID of the image, lidar, or other observation by however many inches necessary. Additionally, since the adjusted coordinate IDs found using these offsets are very unlikely to have latitude and longitude points at that exact coordinate ID we will impute the latitude and longitude. For now we are using a simple linear imputation that assumes a constant speed between the two known points. Since these points are already between 0.2 and 0.1 seconds apart (depending on which GPS unit we are using) we think the linear model should be fine for imputing coordinates at any given coordinate ID.


Our New Sidewalk Rig

We have been investing in our data collection capabilities for sidewalks, bike paths, and trails. These are important assets that are used often by the public and take a considerable amount of effort to build and maintain.

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