This project investigates air pollution in California communities disproportionately affected by their proximity to transportation corridors, industrial facilities, and logistics centers, focusing on truck-related activities, including idling, parking search, and parking demand, using comprehensive datasets and robust models employing techniques such as Random Forest, Convolutional Neural Network, Bayesian Ridge Regression, and Spatial Error Model. Key findings reveal factors affecting idling times, parking search times, and parking demand, with heavy-duty trucks having the highest idle times and parking search challenges concentrated around transportation arteries and freight yards. The Spatial Error Model highlights relationships between truck activities, socio-economic variables, and air pollution in AB 617 communities. Based on these findings, preliminary policy recommendations include targeted anti-idling campaigns, improved truck parking facilities, cleaner fuels and technologies, enhanced routing efficiency, stricter emission standards, and strengthened land-use planning.

The data submitted in this dataset originates from various sources, with each source providing unique insights into the study of truck idling and parking in AB 617 Disadvantaged Communities. The dataset submitted here is the result of careful processing and manipulation of the original datasets to create a comprehensive view of truck idling and parking behaviors.

1. Geotab Ignition Platform Data

Though not directly included in this submission due to licensing restrictions, data from the Geotab Ignition platform was instrumental in the creation of this dataset. It includes raw idling data, raw data for searching for parking, and raw truck parking location data. We used these datasets to extract key metrics related to truck idling and parking behaviors.

The Geotab data was processed and aggregated to obtain daily idling times and parking search times. This was done by using the geohash provided to group data by location and then computing the daily metrics. Please note that due to licensing restrictions, the raw Geotab data is not included in this submission. For those interested in using the Geotab data, please refer to the Geotab website to access the data directly.

2. CalEnviroScreen 4.0, Census data, and OpenStreetMap (OSM)

These datasets provided contextual information, such as demographics and infrastructure, which were used to enrich the idling and parking data derived from the Geotab datasets. For example, demographic data from the Census and CalEnviroScreen 4.0 was used to identify disadvantaged communities, while data from OpenStreetMap was used to map idling and parking behavior to specific locations.

3. Kern County Traffic Count Data System (TCDS) Data

The TCDS data was used to provide a count of truck traffic at various locations. This data was integrated with the processed Geotab data to provide a more complete picture of truck activity in the study areas.

4. Final Dataset (The Dataset Used for Modeling)

The final dataset was created by merging the processed Geotab data with the relevant data from the other sources. This process involved spatially joining the Geotab and TCDS data based on location and then appending the relevant demographic and infrastructure data from CalEnviroScreen 4.0, Census, and OSM. The result is a comprehensive dataset that provides a detailed view of truck idling and parking behavior in AB 617 Disadvantaged Communities.

Users are encouraged to cite the original data sources when using the data in their work. Please ensure that the data complies with the licensing requirements of the respective data sources.

Geotab Data Usage Disclaimer: 

- Users should understand that while every effort is made to ensure the accuracy and reliability of this data, it is provided "as is" and we do not make any representations or warranties about its accuracy.
- As such, users should use caution and perform their own validation checks when using this data.
- The analyses and interpretations presented in this project are solely those of the research team and do not represent the views or opinions of Geotab.
- Users should not use this data to make specific operational decisions without consulting additional sources of information and/or obtaining expert advice.
- The authors and contributors to this project are not responsible for any errors or omissions, or for the results obtained from the use of this data.
- Any use of this data should acknowledge Geotab as the source in accordance with their data use agreement.