policy brief

A New Approach to Calculating Dynamic Pricing of High-Occupancy-Toll (HOT) Lanes Can Improve the Performance of Travel Corridors

Publication Date

August 1, 2024

Associated Project

A New Approach to Calculating Dynamic Pricing of High-Occupancy-Toll (HOT) Lanes

Author(s)

Wenlong Jin

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Transportation Economics, Funding, & Finance

View policy brief

Abstract

As traffic congestion continues to worsen in urban areas, policymakers are seeking innovative solutions to maximize existing road infrastructure and improve travel times. High-occupancy-toll (HOT) lanes offer a promising solution by allowing single-occupancy vehicles (SOVs) to use underutilized carpool lanes for a fee, reducing congestion in regular lanes. Current pricing methods often struggle to set the right toll in real-time, leading to HOT lanes that are either underused or too congested. This reduces their effectiveness in managing traffic and can frustrate drivers. To address this issue, UC Irvine researchers developed more effective ways to set HOT lane prices in real-time, ensuring they are used efficiently and provide reliable travel times for all drivers. Improving HOT lane operation can lead to reduced congestion, shorter commute times, and more efficient use of existing road infrastructure – all without the need for costly new road construction.

Related Publications

published journal article | February 03, 2025

Stable Dynamic Pricing Scheme Independent of Lane-choice Models for High-Occupancy Toll Lanes

published journal article | February 03, 2025

A Control Theoretic Approach to Simultaneously Estimate Average Value of Time and Determine Dynamic Price for High-Occupancy Toll Lanes

Phd Dissertation

Scheduled Individual Vehicle Movements for Efficient Traffic Flow With a New Link-Based Control Paradigm

Publication Date

July 11, 2024

Author(s)

Pengyuan Sun

Areas of Expertise

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

View Phd Dissertation

Abstract

Traditional traffic control has been based on collective stop-and-go movements for over a century. This dissertation explores the potential integration of scheduling for individual vehicle movements as a new paradigm for next-generation traffic control, which can be developed to avoid forced vehicle stoppage and queuing that is inherent in current urban traffic control. Inspired by its proven efficiency and safety in various transportation modes such as railway systems and air traffic controls, individual scheduling shows a promising perspective in urban traffic management to optimize traffic throughput, reduce traffic congestion, and enhance the overall traffic system performance. Supported by the rapid developments in driver assistance technologies and advanced real-time communication systems, such as in-vehicle indication devices and vehicle-to-everything communications, the integration of individual scheduling into urban traffic management holds the potential for improving the traffic efficiency under current traffic control schemes through eco-driving schemes, and ushering in a new paradigm of smart and efficient transportation systems in the future through the Link-based traffic control concept. Furthermore, this dissertation proposes a mathematical model, i.e., the Vehicle Tube Model, for traffic safety analysis under various vehicle behaviours.The individual scheduling is first implemented for various traffic scenarios under traditional urban traffic control management. The scheduled information for individual vehicles includes the speed and time, and each vehicle is guided by an eco-driving vehicle control approach to fulfil its scheduled information. Considering various levels and requirements on vehicle connectivity and control complexity, this dissertation proposes three vehicle control approaches that respectively provide the advisory speed, two-stage advisory speed limits, as well as the optimal acceleration rates to adjust individual vehicle movements. Each approach can be independently implemented for each vehicle to improve the speed and decrease the speed oscillations. Through a set of simulation studies, the dissertation demonstrates significant improvements in vehicle speed, fuel consumption, and emissions reduction, underscoring the benefits of adopting individual scheduling under signalized intersection controls as well as for traffic flows on a freeway after a slowly moving vehicle.With the implementation of scheduled individual vehicle movements, the dissertation introduces the innovative concept of Link-based traffic control, which represents a paradigm in contrast to the traditional node-based control such as the signalized control. The new paradigm further improves travel times and mobility and leads to smoother eco-driving through the development of optimized schemes to schedule movements that use traffic stream gaps. Emphasizing vehicle controls along the traffic links rather than at individual intersections as nodes, the Link-based traffic control schedules each vehicle movements to enable traffic flows from conflicting directions to pass through the intersections within the same period, thereby significantly enhancing the overall traffic throughput and fuel efficiency. This dissertation proposes four Link-based control models to schedule the speed and time for each vehicle when entering the intersection, and the comprehensive simulated results show that the traffic efficiency is dramatically increased with the Link-based control concept.Moreover, the dissertation proposes the Vehicle Tube Model, as a dynamic representation of vehicle movement and theory for analytical traffic safety analysis. By quantifying the risk probabilities associated with potential collisions under current and future traffic scenarios, this framework provides valuable insights into the safety performance of future urban traffic management systems. This dissertation contributes to advancing understanding of the potential benefits and challenges associated with integrating the individual scheduling and innovative traffic management concepts into urban transportation systems, and proposing a way, as a dual perspective of traffic control, for more sustainable, efficient, and safe urban mobility solutions with the intelligent transportation system.

working paper

A Comparison of Time-use for Telecommuters, Potential Telecommuters, and Commuters during the COVID-19 Pandemic

Publication Date

July 9, 2024

Associated Project

Connecting Telework, Travel Behavior, and System Performance During the COVID-19 Pandemic

Author(s)

Rezwana Rafiq, Michael McNally

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View working paper

Abstract

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, we consider how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time-use, telecommuters spent less time on work activity but more time on caring for household members, household chores, eating, socializing and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time-use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

Related Publications

journal article preprint | February 03, 2025

A Comparison of Time-use for Telecommuters, Potential Telecommuters, and Commuters during the COVID-19 Pandemic

policy brief

Perceptions of Neighborhood Change in a Latinx Transit Corridor

Publication Date

July 1, 2024

Associated Project

Social Impacts of Transit Oriented Development on Disadvantaged Communities

Author(s)

Doug Houston, Michelle Zuniga

Areas of Expertise

Safety, Public Health, & Mobility Justice Travel Behavior, Land Use, & the Built Environment

View policy brief

Abstract

Understanding how residents feel about neighborhood changes due to new development along transit corridors (often referred to as transit-oriented development) remains understudied despite growing concerns over displacement and gentrification. Studies that examined these concerns are largely based on analyzing land use, housing values, and socio-economic shifts (i.e., who is moving in and out of neighborhoods), and do not provide conclusive evidence that transit-oriented development (TOD) is linked to neighborhood gentrification and displacement. Prior surveys of residents living near transit indicate a generally positive assessment of TOD in terms of improved walkability and accessibility but also express concerns over pedestrian safety and parking related to increased traffic and new commercial development. However, recent studies counter this relatively positive assessment of TOD, particularly among activists and community organizers in low-income communities of color.

Related Publications

published journal article | February 03, 2025

The Promise and Pitfalls of Early Project Notification Meetings: Illuminating Santa Ana’s Sunshine Ordinance

published journal article | February 03, 2025

Perceptions of Neighborhood Change in a Latinx Transit Corridor

published journal article

The Promise and Pitfalls of Early Project Notification Meetings: Illuminating Santa Ana’s Sunshine Ordinance

Publication Date

March 22, 2024

Associated Project

Social Impacts of Transit Oriented Development on Disadvantaged Communities

Author(s)

Doug Houston, Michelle Zuniga

Areas of Expertise

Safety, Public Health, & Mobility Justice Travel Behavior, Land Use, & the Built Environment

View published journal article

Abstract

Despite the promise that early public participation could enhance transparency and information access, little is known about which public engagement processes and techniques are most effective at the initial stages of plan development and whether development notification meetings enhance inclusion for impacted residents. Responding to these uncertainties, we analyzed the promise and potential pitfalls of early public notification meetings by reviewing posted development information and interviewing resident leaders and planners involved in the City of Santa Ana’s Sunshine Ordinance development notification meetings for proposed residential and mixed-use projects. Findings confirmed early notification increased access to information and created a more transparent process, but indicated the lack of inclusive practices generated community distrust and opposition and spurred residents to take insurgent actions when meetings offered few specifics and limited collaboration. Findings inform efforts of local jurisdictions and advocates seeking to establish or improve early participation initiatives.

Related Publications

policy brief | February 03, 2025

Perceptions of Neighborhood Change in a Latinx Transit Corridor

published journal article | February 03, 2025

Perceptions of Neighborhood Change in a Latinx Transit Corridor

policy brief

How Risky Are Cyber Security Threats Against Autonomous Vehicles?

Publication Date

April 1, 2024

Associated Project

Risk Assessment for Security Threats and Vulnerabilities of Autonomous Vehicles

Author(s)

Qi Alfred Chen

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data Safety, Public Health, & Mobility Justice

View policy brief

Abstract

To operate safely, autonomous vehicles (AVs) rely on external sensors such as cameras, light detection and ranging (LiDAR) technology, and radar. These sensors pair with machine learning-based perception modules that interpret the surrounding environment and enable the AV to act accordingly. Perception modules are the “eyes and ears” of the vehicle and are vulnerable to cybersecurity attacks. The most critical and practical threats, however, arise from physical attacks that do not require access to the AV’s internal systems. The risks of these types of attacks are still unknown. To advance the field in this area, we conducted the first ever quantitative risk assessment for physical adversarial attacks on AVs. First, we identified relevant attack vectors, or types of cyber security attacks, targeting AV perception modules. Next, we conducted an in-depth analysis of the stages of an attack. Finally, we used these exercises to identify risk metrics and perform a subsequent computation of risk scores for different attack vectors. Through this process, we were able to quantitatively rank the real-life risks posed by different attack vectors identified in existing research. This analysis provides a framework for comprehensive risk analysis to ensure the safety of AVs on our roadways.

Related Publications

research report | March 07, 2025

Risk Assessment for Security Threats and Vulnerabilities of Autonomous Vehicles

Phd Dissertation

Investigation of LiDAR for Traffic Monitoring with emphasis on Heavy Duty Trucks

Publication Date

March 14, 2024

Associated Project

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

Author(s)

Koti Allu

Areas of Expertise

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

View Phd Dissertation

Abstract

Traffic Monitoring is at the center of any Intelligent Transport System. Current traffic monitoring sensors are challenged to deliver in the evolving landscape of connected, autonomous and alternative fuel transportation systems. This dissertation explores the feasibility of emerging LiDAR technology for traffic monitoring, in an infrastructure-based, side-fire LiDAR configuration. LiDAR technology was investigated in terms of providing both the core data elements of existing traffic monitoring systems such as vehicle counts and speeds, as well as more high-resolution data elements required for future connected and autonomous vehicles such as relative positions of vehicles on a roadway, vehicle lateral and longitudinal positions within a lane, physical attributes of individual vehicles, and vehicle microscopic trajectories. LiDAR sensors were deployed at both dense urban corridors and rural highway locations. At the urban location, the LiDAR estimate of vehicle counts across lanes was between 87% to 110% of a baseline calibrated sensor’s vehicle counts. At the rural highway location, microscopic trajectories for vehicles were derived at 0.1 second resolution, enabling detection of anomalies in vehicle behavior. In addition, the precise lateral positions of heavy-duty vehicles were derived at the urban corridor location, with a particular interest being future safety assessment for loss of control of autonomous heavy-duty trucks. The high-resolution traffic data elements derived from this research can assist in detecting anomalous behavior of vehicles, whether from impaired driving or loss of effective autonomous control, with road safety assessments, and in providing inputs for microscopic road emission modeling.

Related Publications

research report | February 03, 2025

Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring

working paper | February 03, 2025

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

Phd Dissertation

Exploring Delivery Services Substituting Household Shopping Trips: Implications for Travel, Transportation Networks, and Fleet Optimization, and Insights on the Potential of Autonomous Vehicles

Publication Date

March 11, 2024

Author(s)

Marjan Mosslemi

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

This dissertation delves into the intersection of two critical elements shaping the future of transportation: opportunities and the challenges presented by shopping delivery services, particularly same-day delivery (SDD), and the necessity to anticipate and explore the forthcoming transportation paradigm with the new possibilities offered by Autonomous Vehicles (AVs). This study investigates the transformative potential of SDD services facilitated by a fleet of shared autonomous vehicles (SAVs) to reshape daily shopping trips and activities.With a dual focus on both the network and household layers, the dissertation addresses the viability of SDD services, considering vehicle miles traveled (VMT) savings and operational strategies for efficient fleet management on one side, and the impacts on travel patterns on the other. Leveraging real-world data for the network of Irvine, CA, and employing optimization methodologies, this dissertation (i) investigates the potential VMT savings from SDD compared to the base scenario where households conduct their own shopping activities, (ii) analyzes the optimal fleet size needed to achieve significant VMT reductions, and (iii) evaluates operational strategies for cost-effective and efficient service delivery. In this dissertation, I analyze the optimal fleet size and system design settings needed to achieve significant VMT reductions without losing profitability and I evaluate operational strategies for cost-effective and time-sensitive service delivery.At the network layer, the system is modeled as a multi-Vehicle and Multi-Depot Pickup and Delivery Problem with Time Windows (m-MDPDPTW), which was implemented in Google OR-Tools. The depots are assumed to be at the warehouse locations from where shopping goods deliveries are made. An analysis is presented for a delivery service comprising an AV fleet serving households on their daily shopping trips for the case study of the City of Irvine, CA. The results indicate these services can significantly decrease the distance traveled and the time spent for shopping trips. The dissertation tests several scenarios to determine how varying possible service operation parameters as well as demand characteristics would affect the results. Scenarios involving varying percentage of the service demand, time window for deliveries, loading/unloading time, and warehouse distribution are considered.At the household layer, the dissertation examines how the SDD service influences household travel patterns and savings, using output from the California Statewide Travel Demand Model (CSTDM) for the City of Irvine. The time saved is used as an accessibility measure. Using the Household Activity Travel Pattern Problem (HAPP), formulated as a pickup and delivery problem with time windows for household daily activities, time saved is compared over four distinct scenarios: a base (existing) case with CSTDM patterns, the HAPP-optimized version of the base case, the base case excluding shopping trips, and its HAPP-optimized version. HAPP-based analysis sheds light on new opportunities in travel and activity planning enabled by AVs as well as insights into future activity patterns shaped by subscription services that may lead to more optimized travel patterns. High Performance Computing is used to tackle the NP-Hard computational problem involved in HAPP in the real-world case study with a large set of households.This research is also intended to establish the viability of operationalizing a HAPP-methodology for analyzing realistic travel network contexts, for transportation policies that involve innovative vehicle usage and routing patterns. A HAPP solution is not a model for the actual household-level travel behavior, but rather a constraint-driven optimal version of it. Nonetheless, with the availability of rich individual level activity data now and in the future, HAPP can indeed become an optimizer for households, if computational problems can be surmounted. This dissertation establishes that computational problems are not insurmountable with current cloud and advanced computing options, even for 4-member households with activities substitutable across individuals, which past research had generally avoided. The research illustrated that, for a real-world network that has an individual and household-level activity-based planning model, or at least a synthesized model of that kind, policy analysis for future transportation options can be done using HAPP to find an optimized implementation of the policy when the behavioral response to such policy is not available in the existing activity models or data. The dissertation also points to future research possibilities involving faster optimizations that can be achieved if HAPP can be implemented with starting feasible solutions that may be developed from existing networks.

research report

Natural Gas Vehicle Incentive Program

Publication Date

February 29, 2024

Associated Project

California Natural Gas Vehicle Incentive Program

Author(s)

Craig Rindt, Youngeun Bae, Jun Park, Mariana Sebastiani, Esmaeil (Sina) Dabbagh, Stephen Ritchie, Suman Mitra

Areas of Expertise

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

View research report

Abstract

This report presents the results of the Natural Gas Vehicle Incentive Program administered by the Institute of Transportation Studies at the University of California, Irvine under agreement number 600-14-006 with the California Energy Commission. Program development and administration is described, including discussion of outreach efforts and engagement with stakeholders to improve program operations. Performance statistics for the Natural Gas Vehicle Incentive Program are presented describing the characteristics of the 916 vehicles incentivized under the project, and the 87 distinct entities that received incentive funding. Recommendations are offered for future vehicle incentive programs to resolve some of the problems that arose during the administration of the program that were mostly due to structural characteristics of the voucher process itself. The report also details the findings of two major research efforts conducted under the agreement. The first research thrust targeted developing a better understanding of alternative fuel demand from the perspective of fleet operations. This included both fleet purchase behavior as it relates to alternative fuel heavy duty vehicles and also a detailed study of how heavy-duty vehicle operating cycles impact their emissions and suitability for alternative fuel deployments. The second research thrust addressed the implications of scaling specific features of the California Sustainable Freight Action Plan to statewide operations. Using results from the California Statewide Freight Forecasting Model, a case study on optimizing the deployment of overhead catenary electric highway infrastructure around the state in terms of either maximizing vehicle miles traveled coverage or maximizing the accrued benefits to disadvantaged communities.

Related Publications

Phd Dissertation | February 03, 2025

Impacts of electric highways for heavy-duty trucks

Phd Dissertation | February 03, 2025

Environmental Impacts of Various Heavy-Duty Natural Gas Vehicles Incentivized in California

journal article preprint

A Comparison of Time-use for Telecommuters, Potential Telecommuters, and Commuters during the COVID-19 Pandemic

Publication Date

January 15, 2024

Associated Project

Connecting Telework, Travel Behavior, and System Performance During the COVID-19 Pandemic

Author(s)

Rezwana Rafiq, Michael McNally

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

View journal article preprint

Abstract

Throughout the ongoing COVID-19 pandemic, changes in daily activity-travel routines and time-use behavior, including the widespread adoption of telecommuting, have been manifold. This study considers how telecommuters have responded to the changes in activity-travel scheduling and time allocation. In particular, the research team considers how workers utilized time during the pandemic by comparing workers who telecommuted with workers who continued to commute. Commuters were segmented into those who worked in telecommutable jobs (potential telecommuters) and those who did not (commuters). Our empirical analysis suggested that telecommuters exhibited distinct activity participation and time use patterns from the commuter groups. It also supported the basic hypothesis that telecommuters were more engaged with in-home versus out-of-home activity compared to potential telecommuters and commuters. In terms of activity time use, telecommuters spent less time on work activities but more time on caring for household members, household chores, eating, socializing, and recreation activities than their counterparts. During weekdays, a majority of telecommuters did not travel and in general this group made fewer trips per day compared to the other two groups. Compared to telecommuters, potential telecommuters made more trips on both weekdays and weekends while non-telecommutable workers made more trips only on weekdays. The findings of this study provide initial insights on time use and the associated activity-travel behavior of both telecommuter and commuter groups during the pandemic.

Related Publications

working paper | February 03, 2025