published journal article

Dynamic Pricing for Maximizing Performance of High-Occupancy Toll Lanes Along a Freeway Corridor

Abstract

Single-occupancy vehicles (SOVs) are charged to use the high-occupancy-toll (HOT) lanes, while high-occupancy-vehicles (HOVs) can drive in them at no cost. The pricing scheme for HOT lanes has been extensively studied at local bottlenecks or at the network level through computationally expensive simulations. However, the HOT lane pricing study on a freeway corridor with multiple origins and destinations as well as multiple interacting bottlenecks is a challenging problem for which no analytical results are available. This paper attempts to fill the gap by proposing to study the traffic dynamics in the corridor based on the relative space paradigm. In this new paradigm, the interaction of multiple bottlenecks and trips can be captured with Vickrey’s bathtub model by a simple ordinary differential equation. The paper considers three types of lane choice behavior and analyze their properties. Then, it proposes a distance-based dynamic pricing scheme based on a linear combination of I-controllers. This closed-loop controller is independent of the model and feeds back the travel time difference between HOT lanes and general-purpose lanes. Given the mathematical tractability of the system model, this study analytically studies the performance of the proposed closed-loop control under constant demand and show the existence and stability of the optimal equilibrium. Finally, the results were verified with numerical simulations considering a typical peak period demand pattern.

published journal article

Peaked Too Soon?: Analyzing the Shifting Patterns of P.M. Peak Period Travel in Southern California

Abstract

Daily vehicle travel collapsed with the onset of the COVID-19 pandemic in early 2020 but largely bounced back by late 2021. The pandemic caused dramatic changes to working, schooling, shopping, and leisure activities, and to the travel associated with them. Several of these changes have, so far, proven enduring. So, while overall vehicle travel had largely returned to pre-pandemic levels by late 2021, the underlying drivers of this travel have likely changed.

This research examines one element of this issue by analyzing whether patterns of daily trip-making shifted temporally between the fall of 2019 and 2021 in the Greater Los Angeles megaregion. The research team used location-based service data to examine vehicle trip originations for each hour of the day at the U.S. census block group level in October 2019 and October 2021. The team observed notable shifts in the timing of post-pandemic PM peak travel, so the researchers examined changes in the ratio of mid-week trips originating in the early afternoon (12–3:59 PM) and the late afternoon/early evening (4–7:59 PM).

The research paper includes a clear shift in the temporal distribution of PM trip-making, with relatively more late PM peak period trip-making prior to the pandemic, and more early PM peak trip-making in 2021. The peak afternoon/evening trip-making hour shifted from 5–5:59 PM to 3–3:59 PM. The researchers also found that afternoon/evening trip-making each year is largely explained by three workplace-area/school-area factors: (1) the number of schoolchildren in a block group (earlier); (2) block groups with large shares of potential remote workers (earlier), and (3) block groups with large shares of low-wage jobs and workers of color (later, except for Black workers in 2021). The team found the earlier shift in PM peak travel between pre- and late-pandemic periods to be explained most by (1) higher shares of potential remote workers and (2) higher shares of low-wage jobs and workers of color. These findings suggest that the rise of working from home has likely led to a shift in PM peak travel earlier in the afternoon when school chauffeuring trips are most common. This is especially true for low-income workers and workers of color.

published journal article

Environmental Impacts of Transportation Network Company (TNC)/Ridehailing Services: Evaluating Net Vehicle Miles Traveled and Greenhouse Gas Emissions Impacts within San Francisco, Los Angeles, and Washington, D.C. Using Survey and Activity Data

Abstract

Transportation Network Companies (TNCs) play a prominent role in mobility within cities across the globe. However, their activity has impacts on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. This study quantifies the change in personal vehicle ownership and total miles driven by TNC drivers in three metropolitan areas: San Francisco, CA; Los Angeles, CA; and Washington, D.C. The data sources for this analysis comprise two surveys, one for TNC passengers (N = 8630) and one for TNC drivers (N = 5034), in addition to data provided by the TNC operators Uber and Lyft. The passenger survey was deployed within the three metropolitan areas in July and August 2016, while the driver survey was deployed from October to November 2016. The TNC operator data corresponded with these time frames and informed the distance driven by vehicles, passenger frequency of use, and fleet level fuel economies. The data from these sources were analyzed to estimate the impact of TNCs on travel behavior, personal vehicle ownership and associated VMT changes, as well as the VMT of TNCs, including app-off driving. These impacts were scaled to the population level and collectively evaluated to determine the net impacts of TNCs on VMT and GHG emissions using fuel economy factors. The results showed that the presence of TNCs led to a net increase of 234 and 242 miles per passenger per year, respectively, in Los Angeles and San Francisco, while yielding a net decrease of 83 miles per passenger per year in Washington, D.C. A sensitivity analysis evaluating net VMT change resulting from vehicle activity and key behavioral impacts revealed the conditions under which TNCs can contribute to transportation sustainability goals.

research report

Estimating Residential Electric Vehicle Electricity Use

Abstract

The widespread adoption of electric vehicles is a centerpiece of California’s strategy to reach net-zero carbon emissions, but it is not fully known how and where electric vehicles are being used, and how and where they are being charged. This report provides the first at-scale estimate of electric vehicle home charging. Previous estimates were based on conflicting surveys or extrapolated from a small, unrepresentative sample of households with dedicated electric vehicle meters. The research team combined billions of hourly electricity meter measurements with address-level electric vehicle registration records from California households, including roughly 40,000 EV owners. The average electric vehicle increases the overall household load by 2.9 kilowatt-hours per day, well under half the amount assumed by state regulators. Results imply that electric vehicles travel less than expected on electric power, raising questions about transportation electrification for climate policy.

published journal article

Impact of Sensing Errors on Headway Design: From α-Fair Group Safety to Traffic Throughput

Abstract

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. This study proposes a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

other

Working Paper: Low Energy: Estimating Electric Vehicle Electricity Use

Abstract

This white paper covers new research providing the first at-scale estimate of electric vehicle home charging. Previous estimates are either based on surveys that reach conflicting conclusions or are extrapolated from a small, unrepresentative sample of households with dedicated electric vehicle meters. The research team combines billions of hourly electricity meter measurements with address-level electric vehicle registration records from California households. The average electric vehicle increases the overall household load by 2.9 kilowatt-hours per day, less than half the amount assumed by state regulators. The results imply that electric vehicles travel 5,300 miles per year, under half of the US fleet average.

policy brief

Transitioning to Electric Drayage Trucks May Help Avoid Adding New Freeway Lanes to Freight Corridors in Southern California

Abstract

Much has been written about the potential benefits of electric and connected vehicles. However, one important, but often overlooked, implication of electrifying trucks is that if they are powerful enough (such as the Tesla semi), they can eliminate the moving bottleneck or queuing effect created by slow-moving conventional heavy-duty trucks because electric trucks are much more responsive compared to conventional diesel trucks because electric motors provide maximum torque from a standstill. This could substantially increase road capacity in areas with high commercial truck traffic, especially around major ports or logistics complexes, thus alleviating the need to add new lanes to local freeways.

policy brief

Challenges and Opportunities Facing App-Based Gig Drivers Extend Beyond Driver Pay

Publication Date

August 1, 2024

Author(s)

Susan Shaheen, Jaquelyn Broader, "Brooke (Schmidt) Wolfe ", Adam Cohen

Abstract

Throughout the U.S., app-based gig drivers provide valuable services for courier network services (CNS) like Instacart, Uber Eats, and DoorDash, and transportation network companies (TNCs) such as Uber and Lyft. In California, gig labor classification is governed, among other things, by Assembly Bill 5 (AB 5) and Proposition 22 (Prop 22). AB 5 established the ABC Test for worker classification in California labor law, which resulted in most app-based drivers being classified as employees. However, Prop 22 exempted gig drivers from the ABC Test. As a result, most CNS and TNC drivers in California are classified as independent contractors. This brief provides insights to evolving CNS and TNC labor policy, featuring key findings from research involving interviews with experts (n=8) across the U.S. representing labor, academia, and regulators between June 2022 and February 2024 and an examination of how other cities and states are approaching this issue.