road surface database

[AlexisGonin]

2025-m1-project-cerema

github_page

1st year Master degree project, done on behalf of the cerema in the context of reflectivity project and under the supervivion of Valérie MUZET.

In this project, I started with Excel tables describing road surfaces and their photometric properties. I made these data more computationally usable by storing them in a mongoDB database. Then I began some analyses on said data.

Report avaliable in latest release

Things that I have done

Overview

I have created a database containing 855 entries describing road surface characteristics. This comprehensive dataset provides detailed information about various road surface properties.

Database Information

• Entry Count: 855 records
• Last Updated: 2025-05-31
• Author: AlexisGonin

You can find the database created in this project in ./data/export.json
Here is an example of one of its entries:

{
"_id": { "$oid": "6835d9ecf65ea15a534a9863" },
"mesure name": "245.0",
"country of measurement": "denmark",
"type of measure : laboratory / on site": "laboratory",
"apparatus used": "danish lighting laboratory gonioreflectometer",
"date of measurement": 1973,
"contact person name": "kai sørensen",
"contact person email": "kai.sorensen@newmail.dk",
"type of road surface": "asphalt concrete",
"type of sample: manufactured / extracted / on site": "extracted",
"granular : type": "37% granite",
"granular : size": "2/12 mm",
"granular : color": "na",
"granular other": "na",
"type of binder": "na",
"surface treatment": "na",
"surface state: dry / moist / wet": "dry",
"age of road surface when measured": "2 years",
"type of road": "urban road",
"type of road : trafic": "3200 cars/hour",
"type of road: according to cie 115": "na",
"transversal localisation": "6 m from kerbline",
"localisation (city)": "na",
"name of the road": "na",
"is it a largelly used road surface in his type of road today?": "na",
"is it innovative, experimental yet?": "na",
"picture (if available)": "na",
"comments": "na",
"α (observation angle) in degree": 1,
"qd": 0.081,
"q0": 0.096,
"s1": 0.5,
"r-table (sheet)": "rtab245.0",
"general family": "bituminous mixture",
"standard type of road surface": "ac",
"granular gradation": "2/12",
"granular continuity": "na",
"binder": "bituminous",
"type of surface treatment": "raw",
"age of road surface when measured in months": 24,
"reflection indicatrix realistic?": "no"
},

Keys and value

You can find a list of all metadata keys and their values associated in ./data/keys.csv

Example of results

Classic example of spécularity vs age

Verification of the reduction of specularity thanks to surface treatments

Things that you can do

All the source code is avaliable in ./code/notebook.ipynb

Requirements

Run a python environement and install extension in ./requirements.txt

pip install -r requirements.txt

Install MongoDB

Use the notebook.ipynb

Navigate the notebook and take a look at the examples to get familiar with the methods.

Read and manipulate data, quick methods description

To add datas in the database, use the fonction read_data("data_path")

If you want to create a new database with this data, use the argument reset = True

/!\ Make sure that the Excel files you are reading have the correct format, replicate the format in the file ./data/Greek_Database_Formated_Full_Completed.xlsx

Use get_data(query, key) to select datas that matches the query and its associated keys.

Use b_plot_datas(datas, labels, save) to plot the boxplots and plot_datas(datas, categories, save) to plot $f(S1)=Q_0$

Use divide_by_key(data, key) to divide selected data into different list with each lists being associated to one of the key's unique value (use this before a b_plot_datas).

split_data_by_age(data) divides datas in multiple lists each with an preselected age range

Get infos on the database

Use write_key() to write in the .csv file all unique keys and their unique values.

Use database_stats() to get some basic statistics about the database

Navigate the notebook to find more example of usage