Work

Using big data to analyze long-haul vs regional-short-haul trips for medium- and heavy-duty vehicles

Year: 2025

Type: data-paper

Abstract: This dataset outlines regional short-haul (RSH) and long-haul (LH) trips for medium- and heavy-duty vehicles (MDHDVs) within the Inland Empire, Southern California, collated through collaboration with a big data vendor employing standardized methodologies for traffic data capture. The compilation encompasses detailed records of trip origins, destinations, timestamps, vehicle classifications, and routes. This strategic categorization facilitates a granular analysis, enabling data scientists to differentiate between RSH and LH travel patterns efficiently. The genesis of this dataset was predicated on the necessity to comprehend the operational dynamics of MDHDVs in the context of transitioning to Zero Emission Vehicles. By offering a coherent framework to segregate RSH and LH trips, the dataset stands as a pivotal resource for stakeholders in environmental policy formulation, urban and transportation planning, and logistics management.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.111370

Authors: Kimberly Collins, Raffi Der Wartanian, Yunfei Hou, Sai Kalyan Ayyagari, Bhavik Pankaj Khatri

Institutions: William and Barbara Leonard Transportation Center, California State University, San Bernardino

Language: English

Topic: Urban and Freight Transport Logistics

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,220