published journal article

Will COVID-19 Jump-Start Telecommuting? Evidence from California

Publication Date

September 25, 2023

Associated Project

Telework Trends in California: Before, During, and Possibly After the Pandemic

Author(s)

Jean-Daniel Saphores, Md Rabiul Islam

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View published journal article

Abstract

Health concerns and government restrictions have caused a surge in work from home during the COVID-19 pandemic, resulting in a sharp increase in telecommuting. However, it is not clear if it will perdure after the pandemic, and what socio-economic groups will be most affected. To investigate the impact of the pandemic on telecommuting, we analyzed a dataset collected for us at the end of May 2021 by Ipsos via a random survey of Californians in KnowledgePanel©, the largest and oldest probability-based panel in the US. Structural equation models used in this research account for car ownership and housing costs to explain telecommuting frequency before, during, and possibly after the pandemic. Research findings point to an additional 4.2% of California workers expect to engage in some level of telecommuting post-pandemic, which is substantial but possibly less than suggested in other studies. Some likely durable gains can be expected for Californians who work in management, business / finance / administration, and engineering / architecture / law / social sciences. Workers with more education started telecommuting more during the pandemic, a trend expected to continue post-pandemic. Full time work status has a negative impact on telecommuting frequency, and so does household size during and after the pandemic.

Related Publications

policy brief | February 03, 2025

Did COVID-19 Fundamentally Reshape Telecommuting in California?

policy brief

Connected Vehicle Technology and AI Could Help Reduce Highway Congestion through Better Utilization of Park and Ride Facilitie

Publication Date

September 7, 2023

Associated Project

Reducing Congestion by Using Integrated Corridor Management Technology to Divert Vehicles to Park-and-Ride Facilities

Author(s)

Mohammad Al Faruque

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

View policy brief

Abstract

Considerable advancements have been made in traffic management strategies to address highway congestion over the past decades; however, the continuous growth of metropolitan regions has impeded such progress. In response, transportation planners have given special attention to integrated corridor management (ICM), an approach that coordinates various traffic control units (e.g., ramp metering) to optimize their operations along the entire freeway. Emerging connected vehicle (CV) technology is expected to substantially benefit ICM, where vehicles can communicate with each other and surrounding roadway infrastructure. The combined potential of ICM strategies and CVs could be even greater if combined with strategies that leverage underutilized infrastructure (specifically park-and-ride facilities) to reduce the total number of vehicles on the roadway.

Related Publications

research report | February 03, 2025

Reducing Congestion by Using Integrated Corridor Management Technology to Divert Vehicles to Park-and-Ride Facilities

research report | February 03, 2025

An Integrated Corridor Management for Connected Vehicles and Park and Ride Structures using Deep Reinforcement Learning

Phd Dissertation

Restaurant meals consumption in California: channel shifts during COVID-19, food justice, and efficient delivery

Publication Date

September 4, 2023

Author(s)

Bumsub Park

Areas of Expertise

Freight, Logistics, & Supply Chain Safety, Public Health, & Mobility Justice

View Phd Dissertation

Abstract

This dissertation explores changes in the channels used for consuming prepared food (restaurant meals) and proposes optimization approaches for better managing a fleet of delivery vehicles. In the context of the COVID-19 pandemic, Chapter 1 examines how the consumption of prepared meals has evolved in California, with meal delivery gaining in popularity, dine-in experiences shrinking, and takeout witnessing marginal growth. I estimated heterogeneous ordered logit models to explain the frequency of consumption of restaurant meals before, during, and possibly after the pandemic for dine-in, takeout, and online orders with delivery using a broad range of explanatory variables, including components of the Social Vulnerability Index (SVI). My results show disparities in dine-in, takeout, and delivery frequencies, which have implications for equitable access to prepared meals.Chapter 2 extends my investigation to meal delivery in California and contributes to the traditional Food-Away-From-Home (FAFH) literature. I estimate spatial Durbin models to explain the demand for monthly meal delivery at the census tract level in three major MSAs (Metropolitan statistical areas) in California before and during the pandemic. Unique dynamics in meal delivery behavior emerge across regions and time, with accessibility proving pivotal in driving demand. In particular, I find that meal deliveries benefitted marginalized communities, which underscores the role of meal deliveries in enhancing food access. This chapter presents a holistic perspective, which encompasses business strategies and discusses policy implications. Chapter 3 explores a fleet management framework for meal delivery platforms based on graph theory optimization algorithms. I identified critical parameters for meal delivery operations and measured platform performance metrics such as Vehicle Hours Traveled (VHT), Vehicle Miles Traveled (VMT), and fleet size by adjusting the parameters. The comparative analysis of the Hopcroft-Karp and Karp algorithms reveals trade-offs between cost minimization and computational complex based on the algorithmic objects. My evaluation of Proposition 22’s impact on platform costs underscores the importance of modeling legal constraints. This chapter provides practical insights for platform operators to optimize service efficiency. It also provides directions for future research for more realistic simulations, including a dynamic approach, vehicle repositioning strategy, and consideration of different modes. Overall, this dissertation helps understand dynamic shifts in prepared meal consumption and delivery, and shows the importance of modeling legal constraints when optimizing the size of a delivery fleet. Findings could guide equitable policy interventions by highlighting the influence of demographic, regional, and economic factors on the frequency of restaurant meal consumption. My research bridges academia and practices through its interdisciplinary approach, which helps promote informed decision-making for platform managers, restaurant owners, and equity-conscious urban planners.

policy brief

What Drives Shared Micromobility Ridership?

Publication Date

August 30, 2023

Associated Project

What Drives Success in Public Shared Micromobility Programs?

Author(s)

Arash Ghaffar, Michael Hyland, Jean-Daniel Saphores

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View policy brief

Abstract

Shared micromobility (e.g., e-scooters, bikes, e-bikes) offers moderate-speed, space-efficient, and “carbon-light” mobility, promoting environmental sustainability and healthy travel. While the popularity and use of shared micromobility has grown significantly over the past decade, it represents a small share of total trips in urban areas. To better understand shared micromobility ridership, researchers from across the U.S. and the world have analyzed statistical associations between shared micromobility usage and various explanatory factors, including socio-demographic and -economic attributes, land use and built environment characteristics, surrounding transportation options (e.g., public transit stations), geography (e.g., elevation), and micromobility system characteristics (e.g., station capacity). To understand what these studies collectively mean in terms of expanding shared micromobility usage, we conducted a meta-analysis of 30 empirical studies and then developed robust estimates of factors that encourage ridership across different markets.

Related Publications

published journal article | February 03, 2025

Meta-analysis of shared micromobility ridership determinants

research report

Improved California Truck Traffic Census Reporting and Spatial Activity Measurement

Publication Date

August 29, 2023

Associated Project

Improved California Truck Traffic Census Reporting and Spatial Activity Measurement

Author(s)

Andre (Yeow Chern) Tok, Stephen Ritchie, Yiqiao Li

Areas of Expertise

Freight, Logistics, & Supply Chain

View research report

Abstract

The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation operational and planning needs. Many transportation agencies rely on Weigh-In-Motion and automatic vehicle classification sites to collect vehicle classification count data. However, these systems are not widely deployed due to high installation and operations costs. One cost-effective approach investigated by researchers has been the use of single inductive loop sensors as an alternative to obtain FHWA vehicle classification data. However, most models do not accurately classify under-represented classes, even though many of these minority classes pose disproportionally adverse impacts on pavement infrastructure and the environment. As a consequence, previous models have not been able to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of the majority classes, such as passenger vehicles and five-axle tractor-trailers. This project developed a bootstrap aggregating (bagging) deep neural network (DNN) model on a truck-focused dataset obtained from Truck Activity Monitoring System (TAMS) sites, which leverage existing inductive loop sensor infrastructure coupled with deployed inductive loop signature technology and already deployed statewide at over ninety locations across all Caltrans Districts. The proposed method significantly improved the model performance on truck-related classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research studies. Remarkably, the proposed model is also capable of distinguishing classes with overlapping axle configurations, which is generally a challenge for axle-based sensor systems.

Related Publications

working paper | February 03, 2025

A Deep Ensemble Neural Network Approach for FHWA Axle-based Vehicle Classification using Advanced Single Inductive Loops

Phd Dissertation

Quantifying Sharing Potential in Transportation Networks and the Benefits of Mobility-on-Demand Services with Virtual Stops

Publication Date

August 17, 2023

Author(s)

Navjyoth Sarma

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View Phd Dissertation

Abstract

Cities around the world vary in terms of their transportation network structure and travel demand patterns, with implications for the viability of shared mobility services. Recently, the urban mobility sector has witnessed a significant transformation with the introduction of several new types of Mobility-on-Demand (MOD) services that vary in terms of their capacity and flexibility of routes, schedules, and user Pickup and Dropoff (PUDO) locations. This dissertation proposes models and algorithms to analyze sharing in transportation networks and Mobility-on-Demand (MOD) services in two comprehensive studies.

The first study aims to quantify the sharing potential of travelers within a city or region’s transportation network. The second study aims to measure trade-offs in user and operator costs when MOD services operate with Virtual Stops which refer to flexible PUDO locations requiring travelers to walk the first/last mile of their trip.The first study addresses the lack of metrics that jointly characterize a region’s travel demand patterns and its transportation network in terms of the potential for travelers to share trips. I define sharing potential in the form of person-trip shareability and introduce and conceptualize ‘flow overlap’ as the fundamental metric to capture shareability. The study formulates the Maximum Network Flow Overlap Problem (MNFLOP), a math program that assigns person-trips to network paths that maximize network-wide flow overlap. The results reveal that the shareability metrics can (i) meaningfully differentiate between different Origin-Destination trip matrices in terms of flow overlap, and (ii) quantify demand dispersion of trips from a single location considering the underlying road network. Finally, I validate MNFLOP’s ability to quantify shareability by showing that demand patterns with higher flow overlap are strongly associated with lower mileage routes for a last-mile microtransit service.

The second study proposes a scalable algorithm for operating shared-ride MOD services with flexible and dynamic PUDO locations—called C2C (Corner-to-Corner) services—in a congestible network. I compare four MOD service types: Door-to-Door (D2D) Ride-hailing, D2D Ride-pooling, C2C Ride-hailing, and C2C Ride-pooling by evaluating operator and user costs. The results show that Ride-pooling reduces operator costs while slightly increasing user costs, whereas C2C reduces operator costs but significantly increases user costs. Combining Ride-pooling and C2C appears promising to reduce operator costs and to reduce vehicles miles traveled (VMT) in MOD systems.

other

Story Map: Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

Publication Date

August 26, 2023

Associated Project

Examining Spatial Disparities in Access to Electric Vehicle Charging Stations in Southern California

Author(s)

Avipsa Roy

Areas of Expertise

Zero-Emission Vehicles & Low-Carbon Fuels

View other

Related Publications

published journal article | February 03, 2025

Examining Spatial Disparities in Electric Vehicle Charging Station Placements in Orange County

published journal article | February 03, 2025

Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

research report

An Integrated Corridor Management for Connected Vehicles and Park and Ride Structures using Deep Reinforcement Learning

Publication Date

August 16, 2023

Associated Project

Reducing Congestion by Using Integrated Corridor Management Technology to Divert Vehicles to Park-and-Ride Facilities

Author(s)

Mohammad Al Faruque, Mohanad Odema

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

View research report

Abstract

The upcoming Connected Vehicles (CV) technology shows great promise in effectively managing traffic congestion and enhancing mobility for users along transportation corridors. Data analysis powered by sensors in Connected Vehicles allows us to implement optimized traffic management strategies optimizing the efficiency of transportation infrastructure resources. In this study, the research team introduces a novel Integrated Corridor Management (ICM) methodology, which integrates underutilized Park-And-Ride (PAR) facilities into the global optimization strategy. To achieve this, the team uses vehicle-to-infrastructure (V2I) communication protocols, namely basic safety messages (BSM) and traveler information messages (TIM) to help gather downstream traffic information and share park and ride advisories with upstream traffic, respectively. Next, the team develops a model that assesses potential delays experienced by vehicles in the corridor. Based on this model, the research team employs a novel centralized deep reinforcement learning (DRL) solution to control the timing and content of these messages. The ultimate goal is to maximize throughput, minimize carbon emissions, and reduce travel time effectively. To evaluate the Integrated Corridor Management strategy, the paper includes simulations on a realistic model of Interstate 5 using the Veins simulation software. The deep reinforcement learning agent converges to a strategy that marginally improves throughput, travel speed, and freeway travel time, at the cost of a slightly higher carbon footprint.

Related Publications

research report | February 03, 2025

Reducing Congestion by Using Integrated Corridor Management Technology to Divert Vehicles to Park-and-Ride Facilities

policy brief | February 03, 2025

Connected Vehicle Technology and AI Could Help Reduce Highway Congestion through Better Utilization of Park and Ride Facilitie

published journal article

Examining Spatial Disparities in Electric Vehicle Charging Station Placements in Orange County

Publication Date

August 8, 2023

Associated Project

Examining Spatial Disparities in Access to Electric Vehicle Charging Stations in Southern California

Author(s)

Avipsa Roy

Areas of Expertise

Zero-Emission Vehicles & Low-Carbon Fuels

View published journal article

Abstract

Electric vehicles (EVs) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors that may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 11.04% of predicted EVCS placements in Orange County will lie within a high spatial inequity zone – indicating populations with the lowest accessibility may require greater investments in EVCS placements. 69.52% of the study area experience moderate accessibility issues and the remaining 19.11% face the least accessibility issues w.r.t EV charging stations. Within the least accessible areas, 7.8% of the area will require a low density of predicted EVCS placements, 3.4% will require a medium density of predicted EVCS placements and 0.55% will require a high density of EVCS placements. The moderately accessible areas would require the highest placements of EVCS but mostly with low-density placements covering 54.42% of the area. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.

Related Publications

other | February 03, 2025

Story Map: Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

published journal article | February 03, 2025

Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

published journal article

Meta-analysis of shared micromobility ridership determinants

Publication Date

July 26, 2023

Associated Project

What Drives Success in Public Shared Micromobility Programs?

Author(s)

Arash Ghaffar, Michael Hyland, Jean-Daniel Saphores

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View published journal article

Abstract

Shared micromobility (SμM)—shared e-scooters and (e-)bikes—offer moderate-speed, space-efficient, and carbon-light mobility, promoting environmental sustainability and healthy travel. SμM benefits and SμM data availability have fueled a growing literature that analyses SμM ridership. We present a meta-analysis of 29 studies that estimate statistical models of zone- or station-based SμM trip counts, including 22 that examine station-based bikeshare systems. The meta-analysis reveals positive elasticities between SμM usage and population density (median elasticity of 0.16), employment density (0.07), median household income (0.33), bus stops (0.12), metro stations (0.17), bike infrastructure (0.09), and nearby station capacity (0.32). In contrast, station elevation has a negative elasticity. These magnitudes can inform SμM providers and transportation planners seeking to plan/design SμM systems to promote environmentally sustainable travel. Additionally, we critique the existing literature’s failure to (i) capture spatial dependencies, and (ii) discuss the practical implications of model parameters. Finally, we identify themes for future research.

Related Publications

policy brief | February 03, 2025