research report

The Effects of Truck Idling and Searching for Parking on Disadvantaged Communities

Publication Date

June 1, 2023

Associated Project

Addressing the Impacts of Truck Idling and Searching for Parking on Environmental Justice Communities

Author(s)

Miguel Jaller, Ivan Xiao

Areas of Expertise

Freight, Logistics, & Supply Chain Safety, Public Health, & Mobility Justice Travel Behavior, Land Use, & the Built Environment

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Abstract

This project identifies factors that affect three truck-related parameters: idling, searching for parking, and parking demand. These parameters are examined in communities in Kern County California that have high air pollution levels and are located near transportation corridors, industrial facilities, and logistics centers. Daytime truck idling is concentrated in and around commercial and industrial hubs, and nighttime idling is concentrated around major roads and highway entrances, and exits. Truck idling, searching for parking, and parking demand correlate with shorter distances from freight-related points of interest such as warehouses, increased size of nearby industrial or commercial land use, and proximity to areas of dense population or income inequality. Based on these findings, policy recommendations include targeted anti-idling interventions, improved truck parking facilities, parking systems that provide real-time availability information to drivers, provision of alternate power sources in parking facilities to allow trucks to turn off, cleaner fuels and technologies, enhanced routing efficiency, stricter emission standards, and stronger land-use planning with buffer zones around residential areas.

Related Publications

dataset

Truck idling and parking data for AB 617 disadvantaged communities study

conference paper

Identifying Types of Telecommuters Based on Daily Travel and Activity Patterns

Publication Date

June 6, 2022

Associated Project

Identifying Types of Telecommuters based on Daily Travel and Activity Patterns

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

The ongoing health crisis of the COVID-19 pandemic and the imposed social distancing measures have led a significant portion of workers to adopt “working from home” arrangements, which have greater impacts on workers’ daily activity-travel routines. This new-normal arrangement will possibly be sustained in large measure since the pandemic returns at a certain interval with its new variants. This study explores the activity patterns of workers exclusively working from home (telecommuters) after the initial 2020 pandemic year and deemed as “the 2021 post-vaccine” year. The research classified the activity patterns of telecommuters via Latent Class Analysis. The model results suggest that telecommuters’ activity patterns can be split into three distinct classes where each class is associated with several socio-demographics. Class 1 constituted workers from high-income households who tend to have a conventional work schedule but make non-work activities mostly in the evening. Class 2 was composed of workers from low to medium income, non-Asian households whose work is not pre-dominate but with out of home non-work activities spread throughout the day. Last, Class 3 members are workers of middle to older age, living without children, who primarily remain at home during the day with a conventional work schedule. If telecommuting is to continue at levels much greater than prior to the pandemic, then research insights regarding the variations of activity-travel demands of telecommuters could help to make telecommuting a successful travel demand management tool.

conference paper

An Exploratory Analysis of Alternative Travel Behaviors of Ride-hailing Users

Publication Date

June 6, 2021

Associated Project

The Influence of Housing Characteristics on Complex Travel Behavior

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

The emergence of ride-hailing, technology-enabled on-demand services such as Uber and Lyft, has arguably impacted the daily travel behavior of users. This study analyzes the travel behavior of ride-hailing users first from conventional person- and trip-based perspectives and then from an activity-based approach that uses tours and activity patterns as basic units of analysis. While tours by definition are more easily identified and classified, daily patterns theoretically better represent overall travel behavior but are simultaneously more difficult to explain. We thus consider basic descriptive analyses for tours and a more elaborate approach, Latent Class Analysis, to describe pattern behavior. The empirical results for tours using data from the 2017 National Household Travel Survey show that 76% of ride-hailing tours can be represented by five dominant tour types with non-work tours being the most frequent. The Latent Class model suggests that ride-hailing users can be divided into four distinct classes, each with a representative activity-travel pattern defining ride-hailing usage. Class 1 was composed of younger, employed people who used ride-hailing to commute to work. Single, older individuals comprised Class 2 and used ride-hailing for midday maintenance activities. Class 3 represented younger, employed individuals who used ride-hailing for discretionary purposes in the evening. Last, Class 4 members used ride-hailing for mode change purposes. Since each identified class has different activity-travel patterns, they will show different responses to policy directives. The results can assist ride-hailing operators in addressing evolving travel needs as users respond to various policy constraints.

Related Publications

conference paper

Heterogeneity in Activity-travel Patterns of Public Transit Users

research report

Pattern Recognition for Curb Usage

Publication Date

April 1, 2024

Associated Project

Pioneering Methods for Automated Curb Usage Pattern Recognition

Author(s)

Murat Arcak, Alex Kurzhanskiy

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

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Abstract

The increasing use of transportation network companies and delivery services has transformed the utilization of curb space, resulting in a lack of parking and contributing to congestion. No systematic method exists for identifying curb usage patterns, but emerging machine learning technologies and low-tech data sources, such as dashboard cameras mounted on vehicles that routinely travel the area, have the potential to monitor curb usage. To demonstrate how video data can be used to recognize usage patterns, the research team conducted a case study on Bancroft Way in Berkeley, CA. The project collected video footage with GPS data from a dashboard camera installed on a shuttle bus that circles the area. The team trained a machine learning model to recognize different types of delivery vehicles in the data images and then used the model to visualize curbside usage trends. The findings include identifying hot spots, analyzing arrival patterns by delivery vehicle type, detecting bus lane blockage, and assessing the impact of parking on traffic flow. The proof-of-concept study demonstrated that machine learning techniques when coupled with affordable hardware like a dashboard camera, can reveal curb usage patterns. The data can be used to efficiently manage curb space, facilitate goods movement, improve traffic flow, and enhance safety.

Related Publications

policy brief

Dashboard Cameras Combined with AI Provide an Affordable Method for Identifying Curb Usage

policy brief

Dashboard Cameras Combined with AI Provide an Affordable Method for Identifying Curb Usage

Publication Date

April 1, 2024

Associated Project

Pioneering Methods for Automated Curb Usage Pattern Recognition

Author(s)

Murat Arcak, Alex Kurzhanskiy

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

View policy brief

Abstract

The increasing reliance on transportation network companies (TNCs) and delivery services has transformed the use of curb space. The curb space is also an important interface for bikeways, bus lanes, street vendors, and paratransit stops for passengers with disabilities. These various demands are contributing to a lack of parking, resulting in illegal and double-parking and excessive cruising for spaces and causing traffic disturbance, congestion, andhazardous situations.

Related Publications

research report

Pattern Recognition for Curb Usage

conference paper

Heterogeneity in Activity-travel Patterns of Public Transit Users

Publication Date

June 6, 2021

Associated Project

The Influence of Housing Characteristics on Complex Travel Behavior

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View conference paper

Abstract

Public transit is considered a sustainable mode of transport that can address automobile dependency and provide environmental, economic, and societal benefits. However, with typical temporal and spatial constraints such as fixed routes and schedules, transfer requirements, waiting times, and access/egress issues, public transit offers lower accessibility and mobility services than private vehicles and thus it is considered a less attractive mode to many prospective users. To improve the performance of transit and in turn to increase its usage, a broader understanding of the daily activity-travel patterns of transit users is fundamental. In this context, this study analyzed transit-based activity-travel patterns by classifying users via Latent Class Analysis (LCA). Using data from the 2017 National Household Travel Survey, the LCA model suggested that transit users could be split into five distinct classes where each class has a representative activity-travel pattern. Class 1 constituted employed white males who made transit-dominant simple work tours. Class 2 was composed of employed white females who made complex work tours. Employed white millennials comprised Class 3 and made multimodal complex tours. Transit Class 4 were non-white younger or older adult groups who made transit-dominant simple non-work tours. Last, Class 5 members made complex non-work tours with recurrent transit use and comprised single older women. This study provided insights regarding the variations of activity-travel patterns and the associated market segments of transit users in the United States. The results can assist transit agencies in identifying transit user groups with particular activity patterns and considering market strategies that can address their travel needs.

Related Publications

conference paper

An Exploratory Analysis of Alternative Travel Behaviors of Ride-hailing Users

policy brief

A Case Study: Testing Wildfire Evacuation Strategies for Communities in Marin County, California

Publication Date

April 1, 2024

Associated Project

Testing Wildfire Evacuation Strategies and Coordination Plans for Wildland- Urban Interface Communities in California

Author(s)

Kenichi Soga, Louise Comfort, Bingyu Zhao, Pengshun Li, Paola Lorusso

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

View policy brief

Abstract

Many small, resource-strapped communities located in areas vulnerable to wildfire don’t have resources to conduct dedicated evacuation studies and many do not consider the impact of background traffic (i.e., normal traffic rather than evacuating traffic) on evacuation. In response, the researchers explored the performance of several generalizable evacuation strategies with background traffic for representative communities in Marin County, including the Ross Valley, Woodacre Bowl, Tamalpais Valley, and an area near Highway 101 and Ignacio Boulevard in Novato (hereafter referred to as ‘Novato Neighborhood’). The strategies explored include vehicle reduction (i.e., evacuees share a vehicle), phased evacuation (i.e., evacuees in different zones have different departure times), and off-street parking (i.e., street parking is prohibited on a high-fire Red Flag Day to increase overall road capacity in the event of an evacuation). The researchers then tested each strategy using a wildfire-traffic simulation framework.

Related Publications

research report

Testing Wildfire Evacuation Strategies and coordination plans for Wildland-Urban Interface (WUI) Communities in California

research report

Testing Wildfire Evacuation Strategies and coordination plans for Wildland-Urban Interface (WUI) Communities in California

Publication Date

April 1, 2024

Associated Project

Testing Wildfire Evacuation Strategies and Coordination Plans for Wildland- Urban Interface Communities in California

Author(s)

Kenichi Soga, Louise Comfort, Bingyu Zhao, Pengshun Li, Paola Lorusso

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

View research report

Abstract

In the event of a wildfire, government agencies need to make quick, well-informed decisions to safely evacuate people. Small communities, such as in Marin County, with a mix of residences and flammable vegetation in Wildland-Urban Interface zones, tend to lack resources to conduct evacuation studies. Consequently, this study uses a framework of wildfire and traffic simulations to test the performance of potential evacuation strategies, including reducing the volume of evacuating vehicles through car-pooling, phasing evacuations by staggering evacuation times by zone, and prohibiting street parking in four representative areas of Marin County. Results show that reducing vehicle numbers lowers the average travel time by 20%-70% and average exposure time to wildfire by 27%-60% from the baseline. Phased evacuations with suitable time intervals lower the average travel time by 13.5%-70% but may expose more vehicles to fire in some situations. Prohibiting street parking yields varying results due to different numbers of exits and evacuees. In some cases, prohibiting street parking reduces the average travel time by over 50%, while in other cases it only reduces the average travel time by 9%, contributing little to evacuation efficiency. Altogether, Marin County may want to consider developing a communication and parking plan to reduce the number of evacuating vehicles in wildfire situations. Phased evacuation is also highly recommended, but the suitable phasing interval depends on the speed of fire spread and number of evacuees. Further, whether to establish street parking prohibition policies for a certain area depends on the number of exits and the number of vehicles on the streets.

Related Publications

policy brief

A Case Study: Testing Wildfire Evacuation Strategies for Communities in Marin County, California

policy brief

Higher Bus Ridership Unlikely to Increase Community COVID-19 Transmission

Publication Date

April 1, 2021

Associated Project

Exploring the Role of Transit in COVID-19 Transmission

Author(s)

David Brownstone, Henry Bernal

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Safety, Public Health, & Mobility Justice

View policy brief

Abstract

Public transportation has been blamed for facilitating the spread of COVID-19 in dense, urban areas. As a response to the COVID-19 pandemic, transit agencies have reduced service and implemented mask-wearing mandates and social distancing aboard transit. Some prior studies that address public transportation provide some evidence that negative COVID-19 outcomes are linked to high transit use. One early study of COVID-19 transmission on trains in China found that transmission is also affected by the density of passengers, seat spacing, and length of time traveled with other passengers aboard the trains.

published journal article

Important Considerations in Machine Learning-based Landslide Susceptibility Assessment Under Future Climate Conditions

Publication Date

August 3, 2024

Associated Project

Exploring the Use of Machine Learning-Based Landslide Susceptibility Assessments to Protect California’s Transportation Infrastructure

Author(s)

Shabnam Semnani, Yi Han

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

View published journal article

Abstract

Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. This work introduces methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques were adopted to improve the model performance with the imbalanced dataset. Various models were trained and their performances evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model was evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California were generated using the developed model and compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.