DOI: 10.25338/b8qd38
title: Truck idling and parking data for AB 617 disadvantaged communities study

description: 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.
description: 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.
description: 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.<br>- As
 such, users should use caution and perform their own validation checks
 when using this data.<br>- 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.<br>- Users should not use
 this data to make specific operational decisions without consulting
 additional sources of information and/or obtaining expert
 advice.<br>- 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.<br>- Any use of this data should acknowledge
 Geotab as the source in accordance with their data use
 agreement.