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Sponsor: Caltrans

Investigation of LiDAR sensing technology to Improve Traffic Monitoring along Multilane Freeways

Status

Complete

Project Timeline

September 29, 2021 - September 14, 2022

Principal Investigator

Stephen Ritchie

Project Team

Andre (Yeow Chern) Tok, Guoliang Feng, Yiqiao Li

Sponsor, Program & Award Number

Caltrans // PSR UTC Caltrans Match: SCON-00002662
(Subcontract to University of Southern California)
(Also see this project page)

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

LiDAR is an emerging technology that can provide detailed point-cloud measurements for accurate detection and characterization of objects. The cost of this technology has seen significant reduction with emerging wide-ranging applications such as autonomous vehicles, infrastructure inventory and topographic mapping, to name a few. Within the field of infrastructure-based traffic monitoring, recent studies have investigated the use of this sensor for advanced truck classification applications in side-fire orientation (by the ITS-Irvine research team), as well as for motorized vehicles, bicycle and pedestrian detection at traffic intersections. Because of its high range resolution, traffic surveillance models developed for this sensor technology have the potential to outperform competing technologies such as inductive loops and microwave radar in traffic stream measurements, as well as vehicle count and classification accuracies for traffic monitoring and census applications. This potentially applies to both permanent freeway locations and temporary work zone locations. In particular, our research to date suggests that the potential for LiDAR to be a cost-effective substitute for inductive loop sensors at permanent traffic surveillance and monitoring sites with available overhead mounting infrastructure, is high. This study will therefore investigate the installation of LiDAR sensors at several locations along existing freeway corridors in both side-fire and overhead configurations. Models will be developed from the obtained data to derive conventional (volume, speed and occupancy) as well as novel traffic stream parameters used in Caltrans traffic operations and compared with existing traffic sensors such as inductive loop detectors. Concerns typically associated with side-fire sensors such as occlusions will be investigated and addressed by harnessing the wide field-of-view characteristics of LiDAR.

Related Publications

published journal article | Jun 2023

LiDAR Vehicle Point Cloud Reconstruction Framework for Axle-Based Classification
IEEE Sensors Journal

Read more
research report | Jan 2023

Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

Read more
working paper | Aug 2021

Vehicle Point Cloud Reconstruction Framework for FHWA axle-based Classification using Roadside LiDAR Sensor

Read more
Phd Dissertation | Jan 2024

Investigation of LiDAR sensor for Traffic Monitoring with Emphasis on Heavy Duty Trucks

Read more

Evaluating Mixed Electric Vehicle and Conventional Fueled Vehicle Fleets for Last-mile Package Delivery

Status

Complete

Project Timeline

January 1, 2022 - December 31, 2022

Principal Investigator

Michael HylandMichael Hyland

Project Team

Dingtong Yang

Sponsor, Program & Award Number

Caltrans // PSR UTC Caltrans Match: SCON-00002787
(Subcontract to University of Southern California)
(Also see this project page)

Areas of Expertise

Freight, Logistics, & Supply Chain Zero-Emission Vehicles & Low-Carbon Fuels

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The goal of this research project is to develop a modeling and analysis approach to determine the optimal mix of electric vehicles (EVs) and conventional fueled vehicles (CFVs) fora last-mile package delivery fleet. However, rather than measuring optimality of the EV and CFV mix in terms of profit or total cost, our proposed modeling and analysis approach will maximize social welfare, where social welfare considers vehicle miles traveled (VMT), emissions of harmful local pollutants, greenhouse gas emissions, fleet costs, and service quality. This research is motivated by (i) technological advancements related to EVs and EV battery technology, (ii)the state of California's strong interest in decarbonizing the transportation sector, (iii) the inequitable impacts of harmful local pollutants from freight vehicles on low-income and ethno-racial minority communities in California, and (iv) the substantial growth in ecommerce and package delivery over the past 15 years, and particularly the past 12 months. The modeling and analysis framework developed in this research project will allow policymakers, regulators, and package delivery companies to assess the trade-offs associated with replacing light- and medium duty CFVs with EVs. Clearly, EVs provide substantial societal benefits in terms of eliminating tailpipe emissions and reducing greenhouse gas emission per mile compared to CFVs. However, because the driving range of EVs are considerably shorter than CFVs, an EV cannot serve as many package locations as a CFV over the course of a day. This means that to serve the same demand a package delivery fleet will need more EVs than an all CFV fleet and those EVs will have to travel longer distances (due to the need to return to the depot or visit a recharging station) than an all CFV fleet. Hence, given the flexibility a mixed CFV and EV fleet would provide package delivery companies, we hypothesize that even when optimizing for social welfare, the optimal vehicle fleet will be a mix of EVs and CFVs. The research will also analyze the impact of EV battery technology improvements on the optimal fleet mix. We believe this research project will provide considerable value to policymakers, regulators, logistics providers, and other researchers.

Related Publications

policy brief | Nov 2023

Evaluating Mixed Electric Vehicle and Conventional Fueled Vehicle Fleets for Last-mile Package Delivery

Read more
presentation | Sep 2023

Electric Vehicles in Urban Goods Delivery Fleets: How Far Can They Go?

Read more
Preprint Journal Article | Sep 2023

Electric Vehicles in Urban Delivery Fleets: How Far Can They Go?

Read more
research report | Jan 2022

Evaluating Mixed Electric Vehicle and Conventional Fueled Vehicle Fleets for Lastmile Package Delivery

Read more
published journal article | Apr 2024

Electric vehicles in urban delivery fleets: How far can they go?
Transportation Research Part D: Transport and Environment

Read more
research report | Jan 2022

Evaluating Mixed Electric Vehicle and Conventional Fueled Vehicle Fleets for Last-mile Package Delivery

Read more

Spatial Analysis of bicycling ridership patterns from bias corrected crowdsourced data

Status

Complete

Project Timeline

July 1, 2023 - June 30, 2024

Principal Investigator

Avipsa Roy

Project Team

Ghangshin Lee

Sponsor, Program & Award Number

Caltrans // PSR UTC Caltrans Match: SCON-00005236
(Subcontract to University of Southern California)
(Also see this project page)

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data Public Transit, Shared Mobility, & Active Transportation

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The recent emergence of GPS-enabled smartphone applications (apps) has  facilitated a vast increase in capturing human movement data that is changing the landscape of AT  research. Crowdsourcing platforms that leverage GPS technology by means of fitness tracking  apps (eg: Strava, Streetlight, etc.) have therefore become a tool of interest for collecting data on  pedestrian volumes and/or bicycling ridership. Such high-resolution data may help planners and decision-makers improve bicycling facilities and safety, raise modal share and help realize the  personal fitness and environmental benefits of AT using Artificial Intelligence (AI) technologies  if used cautiously. These types of smart device captured data are emerging as an important data  source for evidence-based city planning, decision-making, and more efficient provision of urban  services. However, the bias in crowdsourced data collected through apps leads to a major hindrance  in utilizing the data directly into AI-driven transportation planning research. Unless corrected at  the early stages of data preprocessing and conveyed to consequent stages of the AI pipeline, the  biased samples can eventually lead to inequitable infrastructure/policy decisions made by  practitioners. My research aims to directly address the fundamental aspects related to sampling  bias in crowdsourced data at all stages of the AI pipeline to ensure fair distribution of transportation  infrastructure in the Southern California region by using bias-adjustment factors that predict  accurate estimates of ridership/pedestrian volumes representative of all populations at the street  segment across multiple cities. The bias-corrected counts will be used to generate street-segment  level maps of pedestrian and bicyclist volumes across multiple cities in Orange County.

Related Publications

research report | Jun 2025

Spatial analysis of bicycling ridership patterns from bias-corrected crowdsourced data

Read more

How to enhance student outcomes while strengthening transit? An analysis of LA Metro’s GoPass Fareless pilot program

Status

In Progress

Project Timeline

July 1, 2023 - June 30, 2024

Principal Investigator

Jean-Daniel Saphores

Project Team

Farzana Khatun

Sponsor, Program & Award Number

Caltrans // PSR UTC Caltrans Match: 65A0674
(Subcontract to University of Southern California)

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

Effective and affordable transportation remains an obstacle for many low income and students of color because high-performing schools are often located in more affluent neighborhoods, away from where they reside. As highlighted in a 2017 Center on Reinventing Public Education (CRPE) report, transporting students is expensive, representing close to 3.5% of all K-12 expenditures for the 2017-2018 school year. As school budgets have shrunk, school districts have tightened eligibility requirements for free transport to school, shifting the transportation burden to parents. As highlighted in Wexler et al. (2021), although a number of cities around the world have adopted programs that provide students free or discounted access to public transit, few academic studies have evaluated these programs (notable exceptions include McDonald et al., 2004; Gase et al., 2014; Vincent et al., 2014; Wexler et al., 2021). LA Metro’s GoPass Fareless Pilot program for K-14 students, which launched on October 1, 2021, offers a golden opportunity to better understand some of the impacts of free transit pass programs on student outcomes. In this context, this research project has four main goals. First, Contractor will analyze how (when and where) students use their pass. Second, Contractor will explore the socio-economic characteristics (e.g., age, gender, household income, race, Hispanic status) of the students who enroll in LA Metro’s K-14 free transit pass program. Third, Contractor will quantify the impacts of free transit passes on various school outcomes, including attendance, and graduation rates. The Contractor will work with selected participating schools in LA county, which includes the Los Angeles Unified School District (LAUSD). And fourth, building on the study conducted in 2019 by Saphores et al. (2020), Contractor will survey California transit agencies (with a focus on members of the California Transit Association) to understand what free or discounted transit pass they offer to K-14 students, the potential role of contactless payment mechanisms to enhance health, and what their plans are to boost ridership following COVID-19. Understanding the contribution that transit can make to school outcomes (particularly graduating rates) via free transit pass programs is critically important. Indeed, a study from the American Council on Education, found that 90% of Americans without a high school diploma never earn more than $40,000 a year (for reference, according to the U.S. Census Bureau, the 2016-2020 median household income in LA County was approximately $71,000). Moreover, data from the Bureau of Labor Statistics show that unemployment rates drop substantially with education.

Related Publications

research report | Jun 2024

Enhancing Student Outcomes While Strengthening Transit: An Analysis of LA Metro’s GoPass Fareless Pilot Program

Read more

Freight logistics and optimization Integrating freight and passenger systems Vehicles and infrastructure

Status

Complete

Project Timeline

January 1, 2024 - December 31, 2024

Principal Investigator

Stephen Ritchie

Project Team

Andre (Yeow Chern) Tok, Mingqi Yao

Sponsor, Program & Award Number

Caltrans // PSR UTC Caltrans Match: SCON-00006419
(Subcontract to University of Southern California)
(Also see this project page)

Areas of Expertise

Freight, Logistics, & Supply Chain Intelligent Transportation Systems, Emerging Technologies, & Big Data

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The objective of this study is to develop and demonstrate a prototype truck tracking system that integrates WIM, ALPR and Inductive Signature-based technologies that can estimate path-based volumes by vehicle categories. The study will leverage several current and upcoming multi-sensor detector testbed locations at I-710 near the Port of Long Beach (ALPR, inductive signature technology), I405 at Van Nuys (WIM, ALPR and inductive signature technology) and SR-60 at Chino (ALPR and inductive signature technology). Trucks may traverse different routes between these locations. Hence, additional standalone inductive signature sites between these multi-sensor sites will serve as intermediate sensor locations to determine route pathways in the development and testing of the proposed truck tracking system. The data from this tracking system will drive a new FML2 dashboard interface that will be developed as a part of this study to display interactive freight activity metrics such as route heatmaps by truck type.

Related Publications

published journal article | Jan 2025

Adaptive Self-Learning Framework for Resilient Vehicle Classification Through the Integration of Inductive Loops and LiDAR Sensors
IEEE Open Journal of Intelligent Transportation Systems

Read more
research report | Dec 2024

Route-based Freight Activity Metrics along the California State Highway System through a Pilot Multi Sensor Fusion System

Read more

Improving Truck Counts in California

Status

Complete

Project Timeline

June 1, 2023 - May 31, 2025

Principal Investigator

Stephen Ritchie

Project Team

Craig Rindt, Andre (Yeow Chern) Tok, Mingqi Yao, Guoliang Feng, Yiqiao Li, Chenyu Yuan

Sponsor & Award Number

Caltrans: 74A1399

Areas of Expertise

Freight, Logistics, & Supply Chain Intelligent Transportation Systems, Emerging Technologies, & Big Data

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The Improving Truck Counts in California Project will expand operations of the Truck Activity Monitoring System (TAMS).  A subset of the existing sites will be selected for full lane-coverage expansion to ensure that all trucks are captured at those locations.  In addition, this study will explore the fusion of advanced traffic sensor technologies to develop a transfer learning framework to enhance the performance of legacy classification models.  This investigation has the potential to provide a better understanding of truck trip patterns by industry, which can further validate the truck trip matrices and route assignment of the California Statewide Freight Forecasting Model (CSFFM). The data generated from this study will be utilized for calibration and validation of the next update of the California Statewide Travel Demand Model (CSTDM) and CSFFM which will be used by Caltrans Headquarters and Districts, Metropolitan Transportation Agencies, Regional Transportation Planning Agencies, other State agencies such as the California Air Resources Board and the California Energy Commission. This data will also be used to enhance and update existing planning databases within Caltrans.

Caltrans Carbon Reduction Strategy

Status

In Progress

Project Timeline

September 3, 2024 - June 29, 2025

Principal Investigator

Jean-Daniel Saphores

Project Team

Michael Hyland, David Brownstone, Craig Rindt, Llorenc Miquel I Solé, Rezwana Rafiq, Ritun Saha, Jooneui Hong

Sponsor & Award Number

Caltrans: A23-3302-S002
(Subcontract to ITS-Davis)

Team Departmental Affiliations

Civil and Environmental Engineering, Economics

Project Summary

The California Department of Transportation (Caltrans) Division of Planning and Modal Programs (P&MP) shapes the future of multimodal transportation across California by envisioning, articulating, and implementing desired outcomes. The federal Infrastructure Investment and Jobs Act (IIJA) established the Carbon Reduction Program, mandating the creation of a statewide Carbon Reduction Strategy that identifies projects and strategies to reduce transportation emissions. This project is developing a comprehensive and evidence-based Carbon Reduction Strategy that will guide the State in its efforts to reduce transportation emissions. This effort, and the Carbon Reduction Strategy itself, will be crucial to ensuring Caltrans achieves its multimodal transportation, climate, and equity goals. 

Pricing Mechanisms to Achieve California’s Climate Goals

Status

In Progress

Project Timeline

September 5, 2024 - June 30, 2026

Principal Investigator

Jean-Daniel Saphores

Project Team

Michael Hyland, David Brownstone, Rezwana Rafiq, Llorenc Miquel I Solé, Ritun Saha, Jooneui Hong, Craig Rindt

Sponsor & Award Number

Caltrans: 2025-28
(Subcontract to ITS-Davis)

Areas of Expertise

Transportation Economics, Funding, & Finance

Team Departmental Affiliations

Civil and Environmental Engineering, Economics

Project Summary

This project will assist Caltrans in developing a comprehensive and evidence-based Carbon Reduction Strategy (CRS) that identifies projects and strategies to reduce transportation emissions. Caltrans is dedicating its current federal Carbon Reduction Program (CRP) funding to road pricing projects on the State Highway System that commit to funding low carbon transportation options with toll revenues. ITS scholars will provide research assistance in understanding how road pricing in general, and priced managed lanes in particular, could reduce transportation emissions in California. The research will identify and summarize different road pricing strategies; how priced managed lanes have been implemented and performed within California, the United States, and globally to address transportation, climate change, and equity issues; provide specific case studies on existing priced managed lanes within California; provide a methodology for prioritizing future projects for investment; and provide a methodology to evaluate future projects’ impact on California’s greenhouse gas emissions. The research will seek to determine how priced managed lanes could simultaneously address Caltrans’ broader transportation, climate, and equity goals as defined in the Caltrans Strategic Plan 2020-2024; and be the focus of successfully implementing equitable roadway pricing statewide.

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