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Sponsor: PTA

Transit Investment Impacts on Land Use Beyond the Half-Mile Mark

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Jae Hong Kim

Project Team

Doug Houston, Naila Sharmeen, Wan-Tzu (Ashley) Lo, Jaewoo Cho

Sponsor, Program & Award Number

PTA: 2017-05
(Also see the UC ITS page)

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Travel Behavior, Land Use, & the Built Environment

Team Departmental Affiliation

Urban Planning and Public Policy

Project Summary

This project examines the impacts of light rail transit investments on broader vicinity areas in Los Angeles County. This project found that the land use impacts of public transit investments are not necessarily confined to the half-mile boundary around station areas, although substantial variation exists by transit line. While the areas beyond the half-mile mark were often excluded from conventional transit-oriented planning processes, these areas show a distinct pattern of land use transformation. Areas beyond the half-mile mark had a higher rate of development for several urban purposes, particularly after a few years have elapsed since the opening of nearby transit lines/stations.

Related Publications

policy brief | May 2019

Transit Investments are Having an Impact on Land Use Beyond the Half-Mile Mark

Read more
research report | Jun 2017

Transit Investment Impacts on Land Use Beyond the Half-Mile Mark

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Policy and Literature Review on the Effect Millennials Have on Vehicle Miles Traveled, Greenhouse Gas Emissions, and the Built Environment

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Doug Houston

Project Team

Michelle Zuniga

Sponsor, Program & Award Number

PTA: 2017-25
(Also see the UC ITS page)

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

Team Departmental Affiliation

Urban Planning and Public Policy

Project Summary

Vehicle travel has reduced substantially across all demographics in the 2000s, but millennials or young adults born between 1985-2000 stand out as the group that has reduced vehicle travel the most. This reduction of travel among millennials is known as the millennial effect. This policy and literature review discusses insights from recent policy reports and literature regarding the millennial effect and identifies the prominent themes and gaps in knowledge. The first section reviews existing research on the millennial effect on vehicle miles traveled (VMT). The second section discusses the influence of the built environment on the travel and activities of the millennial generation. The third section highlights scenarios describing the millennial effect’s potential magnitude and identifies topics for consideration in future scenario planning efforts. The final section discusses the uncertainty that exists regarding the future behavior of millennials and their influence on vehicle miles traveled and greenhouse gas emissions.

Related Publications

research report | Jun 2017

Policy and Literature Review on the Effect Millennials Have on Vehicle Miles Traveled, Greenhouse Gas Emissions, and the Built Environment

Read more

Real Options Models for Better Investment Decisions in Road Infrastructure under Demand Uncertainty

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Jean-Daniel Saphores

Sponsor, Program & Award Number

PTA: 2017-02
(Also see the UC ITS page)

Areas of Expertise

Transportation Economics, Funding, & Finance

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

Tools used to evaluate transportation infrastructure investments are typically deterministic and rely on present value calculations, even though it is well-known this approach is likely to result in sub-optimal decisions in the presence of uncertainty, which is pervasive in transportation infrastructure decisions. This dissertation proposes a framework based on real options and advanced numerical methods to make better road infrastructure decisions in the presence of demand uncertainty. A real options framework was developed to find the optimal investment timing, endogenous toll rate, and road capacity of a private inter-city highway under demand uncertainty. Traffic congestion is represented by a BPR function, competition with an existing road is captured by user equilibrium, and travel demand between the two cities follows a geometric Brownian motion with a reflecting upper barrier. The result shows the importance of modeling congestion and an upper demand barrier –features missing from previous studies. The real options framework was extended to study two additional ways of funding an inter-city highway project: with public funds or via a Public-Private Partnership (PPP). Using the Monte Carlo simulation, the value of a non-compete clause was investigated for both local government and private firms involved in public-private partnerships. Since road infrastructure investments are rarely made in isolation, the real options framework was extended to the multi-period Continuous Network Design Problem (CNDP) to analyze the investment timing and capacity of multiple links under demand uncertainty. No algorithm is currently available to solve the multi-period CNDP under uncertainty in a reasonable time. A new algorithm called “Approximate Least Square Monte Carlo simulation” is proposed and tested that dramatically reduces the computing time to solve the CNDP while generating accurate solutions.

Related Publications

Phd Dissertation | Mar 2017

Real Options Models for Better Investment Decisions in Road Infrastructure under Demand Uncertainty

Read more

Modeling Freight Corridor Movements Using a Mass Balance Approach

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Stephen Ritchie

Project Team

Yue (Ethan) Sun

Sponsor, Program & Award Number

PTA: 2017-37
(Also see the UC ITS page)

Areas of Expertise

Freight, Logistics, & Supply Chain

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

With the expansion of population and economic activity in the country, freight transportation has grown significantly over the last two decades and has become one of the key economic drivers and environmental concerns in California. In California, the freight system is responsible for one-third of the State's economic product and jobs, with freight-dependent industries accounting for over $740 billion in gross domestic product and over 5 million jobs in 2014. Thus, improving the efficiency of California's freight transport system is vital to our State economy and understanding this system is the first key step to identify the actions necessary to make this system sustainable in the future. Proposal: As part of this project, ARB staff is interested to better understand freight mass movement across the major freight corridors in the state. With the truck body classification capability of the state's new Truck Activity Monitoring System (TAMS), researchers have shown that they can identify different types of trucks and trailers (e.g., 40-ft container chassis vs. 53-ft box type trailer) that are utilized within the freight system. This system is even capable of identifying where in the freight supply chain these trucks are operating. For example, a 40-ft container chassis is mostly used to transport freight from ship to truck and then to rail. So it can be considered as a freight mass transported from port to rail-yards/distribution centers, whereas, a box-type trailer might be used for delivery to a freight consignee. If this system can be integrated with data from Weigh in Motion (WIM) sensors located throughout the State, and commodity flow information from the Federal Freight Analysis Framework (FAF), one could potentially measure the freight mass flux in/out of origin/destination zones. Such a system would enable 1) tracking freight mass movement (both spatial and temporal) across the state; 2) connecting truck classifications to economic and commodity information; and 3) better model freight transportation and thus emissions in California. Expected Impact and Benefits: The results from this study can help to develop strategies to reduce emissions from California's trucks for use in the State Implementation Plan, Scoping Plan, Short Lived Climate Pollutant Plan, and Sustainable Freight Action Plan. The information from this study can also be used to calibrate and validate the California statewide freight-forecasting model (CSFFM) and can help inform freight models under development by metropolitan planning organizations (MPOs). Further, this can improve the heavy-duty vehicle inventory in the ARB's EMFAC motor vehicle emissions model and to predict the effectiveness of various emissions control programs.

Related Publications

Phd Dissertation | Jan 2018

Commodity Based Freight Demand Modeling Framework using Structural Regression Model

Read more

Reducing Degradation of High-Occupancy Vehicle Lanes

Status

Complete

Project Timeline

October 1, 2016 - October 1, 2017

Principal Investigator

r-jayakrishnanR. (Jay) Jayakrishnan

Project Team

Riju Lavanya, Marjan Mosslemi, Monica Ramirez-Ibarra, Bumsub Park, Lu Xu, Navjyoth Sarma

Sponsor, Program & Award Number

PTA: 2017-47
(Also see the UC ITS page)

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

Problem Statement: In California, many High-Occupancy Vehicle (HOV) lanes meet the federal standard of degradation, which is met if the average traffic speed during the morning or evening weekday peak commute hour is less than 45 miles per hour (mph) for more than 10 percent of the time over a consecutive 180-day period.   In California, HOV lanes are an effective tool to both promote carpooling and transit, and incentive the purchase of zero-emission vehicles by allowing single occupant drivers of ZEVs access to HOV lanes.  Degradation of HOV lanes reduces travel time reliability of the lanes, which then reduces the incentive for carpooling, transit and ZEV purchase.  In the long-term increasing degradation could result in the need to eliminate ZEV green and white sticker programs as a strategy to address the degradation.     Proposal: Researchers will explore best practices to address HOV lane degradation to strengthen the incentives these lanes provide for carpooling, transit and ZEV purchase.  Strategies explored, should include, but not necessarily be limited to: - Lane striping, or other infrastructure approaches that decrease unauthorized HOV lane use, and maintain or improve safety. - Innovative enforcement approaches to decrease unauthorized use, including new technologies. - Traditional enforcement approaches such as increase law enforcement.    Expected Impact and Benefits: This research should inform Caltrans and regions on best strategies to implement to reduce degradation of HOV lanes and help further state policy goals related to greenhouse gas reduction and a more efficient transportation system. 

Related Publications

research report | Nov 2019

Reducing Degradation in High-Occupancy Lanes

Read more

Improving Highway Performance Monitoring Using Advanced Detector Technologies

Status

Complete

Project Timeline

July 1, 2017 - June 30, 2017

Principal Investigator

Stephen Ritchie

Project Team

Andre (Yeow Chern) Tok, Yiqiao Li

Sponsor, Program & Award Number

PTA: 2017-24
(Also see the UC ITS page)

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The California Department of Transportation (Caltrans) along with other State Departments of Transportation (DOTs) are required to submit axle-based classification count reports to the Federal Highway Administration (FHWA) under the Highway Performance Measurement System (HPMS) program. The data are an input to the federal government’s allocation of funds to the states to effectively maintain pavement quality in high priority corridors. However, safety concerns and high costs are associated with existing methods of data collection. For example, setting up road tubes and other temporary data collection devices frequently exposes DOT personnel to safety hazards due to their close proximity to the traveled lanes of high speed corridors. In addition, permanent detection systems such as existing piezo-based vehicle classifiers are expensive to install and are associated with high maintenance costs due to their frequent failures. The statewide Truck Activity Monitoring System (TAMS) developed by UC Irvine currently provides continuous truck counts by vocation at 70 major truck corridors in California, and will be expanded to over 90 locations by the end of 2016. The system is based on relatively inexpensive updating of hardware in roadside cabinets at existing traffic detection sites that equip existing permanent loop sensors with inductive signature technology. Although the current classification models in TAMS are focused on truck vocations, initial investigations have shown that there is excellent potential to successfully develop truck classification models that are capable of classifying vehicles according to the axle-based FHWA HPMS scheme using only inductive loop signature data. In this proposed study, we will partner with advisors from the Caltrans Traffic Census Program (TCP) to develop HPMS-based classification models, and design a streamlined solution within TAMS to process the data for HPMS reporting requirements. Existing data as well new data sources collected at WIM sites will be used to develop and validate the models, while TCP advisors will provide reporting guidelines to ensure that the developed system meets stakeholder needs. We will also investigate establishing a test detection site using only solar power to study the feasibility of implementing this solution at off-grid sites to further extend the applications of this research.

Related Publications

published journal article | Mar 2022

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops
Transportation Research Record

Read more
conference paper | Jan 2021

A Deep Ensemble Neural Network Approach for FHWA Axle-based Vehicle Classification using Advanced Single Inductive Loops
100th Annual Meeting of the Transportation Research Board (TRB)

Read more

Transition Pathways to a More Sustainable Heavy-Duty Vehicle Sector

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Stephen Ritchie

Project Team

Karina Hermawan, Craig Rindt

Sponsor, Program & Award Number

PTA: 2017-45
(Also see the UC ITS page)

Areas of Expertise

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

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

The State of California has made a commitment to transitioning to cleaner and more advanced alternative-fuel-based technologies in order to mitigate the negative impact of transportation-related emissions. Each of these technologies addresses one or more pollutant externalities ranging from volatile organic- compounds, sulfur dioxide, nitrogen oxides and others. The State’s policy-making with respect to transportation fuels will benefit from a more detailed understanding of the relative strengths and weaknesses of each technology for addressing negative externalities as well as the different challenges and barriers each faces in California’s unique market. In order to compare the potential benefits and tradeoffs of each solution this research is developing new metrics to quantify these costs. This includes finding and analyzing the available data sources and developing new survey designs to facilitate the development of more powerful metrics. Additionally, this study is assessing whether and by how much the strategies compromise economic growth via cost and benefit analysis. A literature review of the different potential solutions is underway, and has thus far identified types of costs to consider, the supply chains of fuels and technologies (biomass and biogas, power-to-gas and vehicle-to-grid) and penetration/adoption rates of alternative fuel vehicles. Remaining work on this project includes comparing the different solutions, categorizing them by type of technologies, intended externalities, and by stakeholders involved.

Related Publications

presentation | Nov 2022

Kent Distinguished Lecture, University of Illinois Transportation Center, Nov 2022: "Data, modeling and emerging technologies on the road to sustainable freight transportation."

Read more
presentation | Apr 2021

Invited Expert Testimony in 2021 on the California ”Heavy Duty Vehicle Sector” to the Joint Informational Hearing of the California Senate Committee on Transportation and Senate Budget Subcommittee 2 on Resources, Environmental Protection, and Energy, on The California Energy Commission’s Clean Transportation Program and California’s Zero Emissions Vehicle Deployment Strategy

Read more

Evaluation of the Air Quality and Greenhouse Gas Benefits of an Advanced Low‐NOx Compressed Natural Gas (CNG) Engine in Medium and Heavy‐Duty Vehicles in California

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Scott Samuelsen

Sponsor, Program & Award Number

PTA: 2017-35
(Also see the UC ITS page)

Areas of Expertise

Zero-Emission Vehicles & Low-Carbon Fuels

Team Departmental Affiliation

Mechanical and Aerospace Engineering

Project Summary

The goal of this research is to assess the greenhouse gas (GHG) emissions and air quality (AQ) impacts of transitions to advanced low‐NOx Compressed Natural Gas (CNG) engines in medium-duty vehicle (MDV) and heavy-duty vehicle (HDV) applications in California with a particular emphasis on renewable natural gas (RNG) as a fueling pathway. To evaluate regional air quality impacts in 2035, pollutant emissions from all end-use sectors are projected from current levels and spatially and temporally resolved. Scenarios are constructed beginning with both a conservative (Base Case) and more optimistic (SIP) case regarding advanced vehicle technology and fuel integration to provide a spanning of potential impacts. To capture the impact of seasonal dynamics on pollutant formation and fate, two modeling periods are conducted including a winter and summer episode. To estimate the potential GHG impacts of transitions to advanced CNG engines in HDV and MDV, scenarios are evaluated under various assumptions regarding fuel pathways to meet CNG demand from a life cycle perspective. Scenarios are compared to the baseline cases assuming (1) all CNG is provided from conventional fossil natural gas and (2) under a range of possible resource availabilities associated with renewable natural gas and renewable synthetic natural gas (RSNG) from in-state resources. Key findings include: i) expanding the deployment of advanced CNG MDV and HDV can reduce summer ground-level ozone concentrations and ground-level PM2.5 in key regions of California; ii) the largest AQ benefits are associated with reducing emissions from HDV; iii) in-state renewable natural gas pathways can meet the CNG demand estimated for both baseline cases; iv) in-state resources are unable to entirely meet CNG demand for the high total CNG demand estimated for the majority of Base alternative cases, and v) advanced CNG HDV and MDV can moderately reduce GHG emissions if fossil natural gas is used (14 to 26%).

Related Publications

research report | Jun 2017

Evaluation of the Air Quality and Greenhouse Gas Benefits of an Advanced Low‐NOx Compressed Natural Gas (CNG) Engine in Medium and Heavy‐Duty Vehicles in California

Read more
policy brief | May 2017

Advanced Low-NOx Compressed Natural Gas Engines in Medium- and Heavy-Duty Vehicles Are Poised to Deliver Air Quality Benefits and Advance California’s Climate Goals

Read more

A Literature Review: Improving How Active Transportation Demand is Modeled and Evaluated

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Jean-Daniel Saphores

Project Team

Daniel Chuong, Pierre Auza

Sponsor, Program & Award Number

PTA: 2017-21
(Also see the UC ITS page)

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

Local transportation agencies typically rely on traditional travel demand forecasting models that focus on highway and roadway improvements to optimize vehicular traffic. These models are not equipped to evaluate active transportation strategies which align with current State of California policies such as reducing vehicle miles traveled to cut greenhouse gas emissions and fostering active transportation modes. In this context, ITS at UC Irvine (ITS Irvine) was invited by the Orange County Transportation Authority (OCTA) to propose, develop, and apply an approach to better model active transportation. This report represents the first phase of this work, which is a review of the recent literature on how to model demand for active transportation and an examination of OCTAM’s (OCTA’s own regional travel demand model) Active Transportation (AT) modeling tool to evaluate its potential for modification or incorporation into a new active transportation model. The following observations/suggestions are offered in this report: First, OCTAM Active Transportation does not include variables that could impact people’s decision to leave their vehicles at home in favor of transit. Second, a number of conditions need to be jointly met for people to walk or bike. Third, OCTAM Active Transportation does not capture residential self-selection, which could be important here as people who do not plan to walk/bike self-select into car-oriented neighborhoods.

Related Publications

research report | Jul 2017

A Literature Review: Improving How Active Transportation Demand is Modeled and Evaluated

Read more

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

Status

Complete

Project Timeline

August 1, 2016 - July 31, 2017

Principal Investigator

Stephen Ritchie

Project Team

Andre (Yeow Chern) Tok

Sponsor, Program & Award Number

PTA: 2017-43
(Also see the UC ITS page)

Areas of Expertise

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

Team Departmental Affiliation

Civil and Environmental Engineering

Project Summary

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks.

Related Publications

policy brief | Oct 2019

New Tool from UC Irvine Could Save the State Millions while Providing Better Data on Truck Activity in California

Read more

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