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Open Access Publications from the University of California
Cover page of Deep Learning–based Eco-driving System for Battery Electric Vehicles

Deep Learning–based Eco-driving System for Battery Electric Vehicles

(2019)

Eco-driving strategies based on connected and automated vehicles (CAV) technology, such as Eco-Approach and Departure (EAD), have attracted significant worldwide interest due to their potential to save energy and reduce tail-pipe emissions. In this project, the research team developed and tested a deep learning–based trajectory-planning algorithm (DLTPA) for EAD. The DLTPA has two processes: offline (training) and online (implementation), and it is composed of two major modules: 1) a solution feasibility checker that identifies whether there is a feasible trajectory subject to all the system constraints, e.g., maximum acceleration or deceleration; and 2) a regressor to predict the speed of the next time-step. Preliminary simulation with microscopic traffic modeling software PTV VISSIM showed that the proposed DLTPA can achieve the optimal solution in terms of energy savings and a greater balance of energy savings vs. computational efforts when compared to the baseline scenarios where no EAD is implemented and the optimal solution (in terms of energy savings) is provided by a graph-based trajectory planning algorithm.

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Cover page of Identifying Roadway Physical Characteristics that Contribute to Emissions Differences between Hybrid and Conventional Vehicles

Identifying Roadway Physical Characteristics that Contribute to Emissions Differences between Hybrid and Conventional Vehicles

(2019)

In this study, a second-by-second (SbS) data set obtained from monitoring vehicle emissions over a series of 75 test runs from 2 test vehicles (a conventional vehicle (CV) and a hybrid-electric vehicle (HEV)) over an 18-month period in 2010-2011 during real-world on-road operations on a specified 32-mile route in Chittenden County, Vermont was used in an innovative new method of analysis to assess emissions differences between the two propulsion systems and attribute these differences to physical roadway/infrastructure characteristics. The K-S test was used to assess the difference between the cumulative distributions of the CV and HEV emissions samples on each link, and the K-S test statistic was regressed against the full set of roadway link characteristics. The regression results allowed the team to identify specific roadway characteristics that contribute to emissions differences between the vehicle types. Overall, the models that included maximum grade and intersection control type performed best, however speed limit and horizontal curvature were also shown to be important. The performance differences identified in this project confirm that engine controls that are responsive to roadway characteristics are necessary.

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Cover page of Exploring the Relationships Among Travel Multimodality, Driving Behavior, Use of Ridehailing and Energy Consumption

Exploring the Relationships Among Travel Multimodality, Driving Behavior, Use of Ridehailing and Energy Consumption

(2019)

In the last decade, advances in information and communication technologies and the introduction of the shared economy engendered new forms of transportation options and, in particular, shared mobility. Shared mobility services such as carsharing (e.g., Zipcar and Car2go), dynamic ridesharing (e.g., Carma), ridehailing (e.g., Uber and Lyft), and bike/scooter sharing (e.g., CitiBike, Jump Bike, Bird, and Lime) have gained growing popularity especially among subgroups in the population including college-educated or urban-oriented young adults (e.g., millennials). These emerging transportation services have evolved at an unprecedented pace, and new business models and smartphone applications are frequently introduced to the market. However, their fast-changing nature and lack of relevant data have placed difficulties on research projects that aim to gain a better understanding of the adoption/use patterns of such emerging services, not to mention their impacts on various components of travel behavior and transportation policy and planning, and their related environmental impacts.

This report builds on an on-going research effort that investigates emerging mobility patterns and the adoption of new mobility services. In this report, the authors focus on the environmental impacts of various modality styles and the frequency of ridehailing use among a sample of millennials (i.e., born from 1981 to 1997) and members of the preceding Generation X (i.e., born from 1965 to 1980). The total sample for the analysis included in this report includes 1,785 individuals who participated in a survey administered in Fall 2015 in California. In this study, the researchers focus on the vehicle miles traveled, the energy consumption and greenhouse gas (GHG) emissions for transportation purposes of various groups of travelers. They identify four latent classes in the sample based on the respondents’ reported use of various travel modes: drivers, active travelers, transit riders, and car passengers. They further divide each latent class into three groups based on their reported frequency of ridehailing use: non-users, occasional users (who use ridehailing less than once a month), and regular users (who use it at least once a month). The energy consumption and GHG emissions associated with driving a personal vehicle and using ridehailing services are computed for the individuals in each of these groups (12 subgroups), and the authors discuss sociodemographics and economic characteristics, and travel-related and residential choices, of the individuals in each subgroup.

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Cover page of Panel Study of Emerging Transportation Technologies and Trends in California: Phase 2 Data Collection

Panel Study of Emerging Transportation Technologies and Trends in California: Phase 2 Data Collection

(2019)

Individual travel options are quickly shifting due to changes in sociodemographics, individual lifestyles, the increased availability of modern communication devices (smartphones, in particular) and the adoption of emerging transportation technologies and shared-mobility services. These changes are transforming travel-related decision-making in the population at large, and especially among specific groups such as young adults (e.g., “millennials”) and the residents of urban areas.

This panel study improves the understanding of the impacts of emerging technologies and transportation trends through the application of a unique longitudinal approach. The authors build on the research efforts that led to the collection of the 2015 California Millennials Dataset and complement them with a second wave of data collection carried out during 2018, generating a longitudinal study of emerging transportation trends with a rotating panel structure. The use of longitudinal data allows researchers to better assess the impacts of lifecycle, periods and generational effects on travel-related choices, and analyze components of travel behavior such as the use of shared mobility services among various segments of the population and its impact on vehicle ownership over time. Further, it helps researchers evaluate causal relationships between variables, thus supporting the development of better-informed policies to promote transportation sustainability.

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Cover page of Intelligent Parking Assist for Trucks with Prediction

Intelligent Parking Assist for Trucks with Prediction

(2018)

Truck parking has been identified as a major issue both in the USA and E.U. and has been selected by the American Transportation Research Institute as the most important research need for the trucking industry in 2015. The lack of appropriate and convenient parking locations has been the cause of several safety issues over the past years as drivers might be forced to either drive while tired and increase the risk of accidents or park illegally in unsafe locations, which might also pose a safety hazard to them and other drivers. Additionally, the parking shortage also impacts the shipment costs and the environment as the drivers might spend more fuel looking for parking or idling for power when parked in inappropriate locations.

This project’s objective was to study the truck parking problem, generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips. By providing information about parking availability to truck drivers, the researchers expect to induce them to better distribute themselves among existing rest areas. This would decrease the peak demand in the most popular truck stops and attenuate the problems caused by the parking shortage.

In this project, several parking availability prediction algorithms were tested using data from a company’s private truck stops reservation system. The prediction MSE (mean squared error) and classification (full/available) sensitivity and specificity plots were evaluated for different experiments. It is shown that none of the tested algorithms is absolutely better than the others and has superior performance in all situations. The results presented show that a more efficient way would be to combine them and use the most appropriate one according to the situation. A model assignment according to current time of the day and target time for prediction is proposed based on the experiment data.

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Cover page of Modeling for Local Impact Analysis

Modeling for Local Impact Analysis

(2018)

The Los Angeles/Long Beach area is important for freight as it involves the twin ports and warehouses and freight hubs. The way freight is consolidated and distributed affects what is going on within the terminals and roadway and rail networks. The complexity and dynamics of the multimodal transportation networks in Los Angeles/Long Beach region that are also shared by passengers, together with the unpredictability of the effect of incidents, disruptions and demand, in temporal and special coordinates makes the local impact analysis of freight transportation a very challenging task despite recent advances in information technologies.

Under this project, the researchers developed a set of traffic simulation models for the Los Angeles/Long Beach region that allowed them to evaluate the impact of new traffic flow control systems, vehicle routing, policy interventions such as land use changes and other ITS technologies on the efficiency of the transportation system and on the environment. The developed simulation models include: macroscopic simulation model for studying and evaluating large traffic networks, and microscopic simulation model for smaller networks. The macroscopic model focusses on flows and covers a much larger area as it is computationally much more efficient than the microscopic one. The microscopic model models the motion of each truck and vehicle, traffic lights, stop signs, speed limits, traffic rules etc. and resembles the real situation as close as possible.

The developed simulation models have been used to evaluate different systems and application scenarios, including freight priority traffic signal control, multimodal freight routing and the impact analysis of the spatial pattern changes of warehousing and distribution.

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Cover page of Framework for Life Cycle Assessment of Complete Streets Projects

Framework for Life Cycle Assessment of Complete Streets Projects

(2018)

A multitude of goals have been stated for complete streets including non-motorized travel safety, reduced costs and environmental burdens, and creation of more livable communities, or in other words, the creation of livable, sustainable and economically vibrant communities. A number of performance measures have been proposed to address these goals. Environmental life cycle assessment (LCA) quantifies the energy, resource use, and emissions to air, water and land for a product or a system using a systems approach. One gap that has been identified in current LCA impact indicators is lack of socio-economic indicators to complement the existing environmental indicators. To address the gaps in performance metrics, this project developed a framework for LCA of complete streets projects, including the development of socio-economic impact indicators that also consider equity. The environmental impacts of complete streets were evaluated using LCA information for a range of complete street typologies. A parametric sensitivity analysis approach was performed to evaluate the impacts of different levels of mode choice and trip change. Another critical question addressed was what are different social goals (economic, health, safety, etc.) that should be considered and how to consider equity in performance metrics for social goals. This project lays the foundation for the creation of guidelines for social and environmental LCAs for complete streets.

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Cover page of Gentrification Near Rail Transit Areas: A Micro-Data Analysis of Moves into Los Angeles Metro Rail Station Areas

Gentrification Near Rail Transit Areas: A Micro-Data Analysis of Moves into Los Angeles Metro Rail Station Areas

(2018)

Rail transit and neighborhood compositional changes are becoming clearly linked in the public mind. Examples where rail transit has been associated, at least anecdotally, with neighborhood gentrification abound. In Washington, D.C., the Green and Yellow lines are associated with neighborhood transition north and east of downtown. In Los Angeles, the Gold, Expo, and Red/Purple lines have been associated with gentrification concerns (Zuk & Chapple, 2015a), and similar concerns have been raised regarding the soon-to-open Crenshaw Line. On balance, these same concerns are present in most large metropolitan areas that are building or expanding rail transit.

Gentrification is a process of neighborhood change characterized by increasing housing prices and changing demographic and socioeconomic composition of the neighborhood. These components of gentrification are often mutually reinforcing: changing composition can further increase housing prices and vice versa. Prior studies have raised the concern that rail transit expansion catalyzes or exacerbates gentrification (Zuk et al., 2017; Rayle, 2015).

This report seeks to shed light on this latter concern. It begins with a brief summary of the evidence from prior studies on both rail-related housing price increases and changing composition. It then introduces a newly available data source, which the authors use to examine the relationship between new rail transit station opening and neighborhood income composition. This report aims to determine whether a rail station opening in Los Angeles County is associated with the share and income composition of residents who move in and out of neighborhoods near that rail station. Specifically, the researchers address the following questions regarding gentrification and its tie to rail transit stations: (1) Who moves into rail-station neighborhoods and when? (2) Are higher income households growing as a share of station area population relative to lower-income households? (3) Do rail stations cause this phenomenon or is this happening regardless of the transit investment?

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Cover page of Dynamic Scheduling of Chassis Movements with Chassis Processing Facilities in the Loop

Dynamic Scheduling of Chassis Movements with Chassis Processing Facilities in the Loop

(2018)

This work studies the optimization of scheduling of chassis and container movements at the operational level for individual trucking companies when Chassis Processing Facilities (CPFs) are available for use in the vicinity of a container port within a major metropolitan area. A multi-objective optimization problem is formulated in which the weighted combination of the total travel time for the schedules of all vehicles in the company fleet and the maximum work span across all vehicle drivers during the day is minimized. Time-varying dynamic models for the movements of chassis and containers are developed to be used in the optimization process. The optimal solution is obtained through a genetic algorithm, and the effectiveness of the developed methodology is evaluated through a case study which focuses on the Los Angeles/Long Beach port complex. The case study uses a trucking company located in the Los Angeles region, which can utilize three candidate CPFs for exchange of chassis. The company assigns container movement tasks to its fleet of trucks, with warehouse locations spread across the region. In the simulation scenarios developed for the case study, the use of CPFs at the trucking company level, can provide improvements up to 13% (depending upon the specific scenario) over the cases of not using any CPFs. It was found in this work that for typical cases where the number of jobs is much larger than the number of vehicles in the company fleet, the greatest benefit from CPF use would be in the cases where there are some significant job to job differences with respect to chassis usage.

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Cover page of Changing Workforce Development Needs for Regional Transportation Planning Agencies in California

Changing Workforce Development Needs for Regional Transportation Planning Agencies in California

(2018)

The transportation industry faces future workforce challenges, including a lack of trained personnel in fields such as engineering, construction management, and intelligent transportation systems (ITS). The public sector will be particularly hard hit because it faces the threat of attrition at senior levels as skilled workers retire or move to the private sector.

The issue of transportation workforce development has received attention at the national level. Research has been conducted through the United States Department of Transportation (USDOT) National Cooperative Highway Research Program (NCHRP) on workforce needs of the public sector, although these focus exclusively on statewide agencies like DOTs. Little, if any, research has been done on the training and workforce needs at the regional level where Metropolitan Planning Agencies (MPOs), Councils of Government (COG), and transit agencies are engaged in both transportation planning and operations. In California, the workforce capacity of MPOs in particular was challenged by the 2008 passage of Senate Bill (SB) 375. This legislation uses the transportation planning process to achieve reductions in greenhouse gas emissions. It requires MPOs, in partnership with the California Air Resources Board (CARB), to establish greenhouse gas emissions targets. MPOs are also required to include a Sustainable Communities Strategy (SCS) in the regional transportation plan that demonstrates how a given region will meet established targets.

This project aims at understanding how fundamental changes from SB 375 and other legislative mandates have impacted MPOs from a workforce standpoint. Using online surveys, job scans, and in-depth interviews with members of COGs and MPOs in California, we determined the importance of several factors on workforce capacity. These factors include recruitment, available funding for professional development, curriculum content in college and university programs, and the role of in-service training. Results indicate that, for regional transportation planning agencies, there is an increased need for functional modeling expertise to comply with SB 375 mandates and the need to accommodate a shift toward activity-based modeling. The interview participants acknowledged that SB 375 increased responsibilities and changed processes for MPOs, including the need to consider the possible impacts to the agency of litigation over the SCS or the Regional Transportation Plan (RTP). The interviews also indicated that, MPOs hire personnel with diverse skill sets—ranging from engineering to modeling and public outreach—to deliver on SB 375 goals. The report seeks to document the evolving role of MPOs resulting from the kind of mandates enacted by SB 375 and the concurrent demand for both traditional skills sets relating to regional planning processes and those that respond to demands for planners to: (1) Optimize existing projects by making them “smarter” and further ensuring that these projects contribute to environmental sustainability; and (2) link transportation planning to land use patterns with the intention of diminishing vehicle miles travelled (VMTs) and associated pollutants.

These are new inextricable planning synergies that require planning professionals to marry traditional transportation planning skills with climate change assessment and abatement skills, referred to in this report as “sustainable transportation planning skills.” This expectation is tacitly set forth in SB 375 and is impacting employee hiring and retention, and employee salary needs, as well as the need for additional training and skill building. The study’s findings will contribute to the knowledge of workforce development needs as well as the potential for policy responses at the federal, state, and local level.

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