CTECH Webinars

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    Data and Methods for Prioritizing User-Centered Outcomes in Transportation
    Shaw, Atiyya (2022-05-20)
    Transportation systems are currently facing unprecedented challenges, compounded by unpredictable technological disruptions, that make it critical to take a fresh look at the approaches and tools used by civil engineers to effectively meet and advance human needs. Historically, we see that transportation development patterns have often compromised users’ wellbeing, due in part to inequitable policies, dependence on outdated and underperforming forecasting models and associated data, and an unawareness/reluctance regarding new methods that focus on the measurement of individuals’ traits and behaviors. Accordingly, given the present conditions of rapid urban growth, coupled with the increasing volume and availability of user-centered passive, big data streams that characterize connected cities, the time is ripe for transportation engineers to refocus our attention on the humans that we serve. In this talk, I outline data driven and methodological avenues for prioritizing user-centered transportation outcomes, followed by select samples of my work within these approaches.
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    Health Co-benefits of Active Transportation for Greenhouse Gas Mitigation
    Maizlish, Neil (2019-11-08)
    Background: The Integrated Transport and Health Impacts Model (ITHIM) is a scenario-based risk assessment tool that quantifies the health benefits and harms of physically active travel (walking and cycling), road traffic injuries, and fine particulate air pollution in urban transportation systems. Methods: Descriptive statistics on travel patterns, physical activity, traffic injuries, and car emissions were derived from statewide travel and health surveys, collision databases, and outputs from regional travel demand and emissions models. The change in disease burden was measured in deaths and disability adjusted life years (DALYs) based on dose–response relationships from meta-analyses and the distributions of physical activity and traffic injuries. Alternative scenarios were measured against baseline travel patterns experienced in each major California region. Alternative scenarios included increases in active travel from baseline to 20 median minutes/person/day, apportioned entirely to walking (“all walk”), cycling (“all cycle”), and or transit-related active travel (“all transit”). The health benefits and greenhouse mitigation of these scenarios were compared to those of the preferred scenarios regional transportation planning agencies. These agencies are mandated to demonstrate greenhouse gas reductions in their transportation plans (“Sustainable Communities Strategies (SB375)”, which emphasize transit expansion to achieve this goal. Results:The preferred scenarios increased statewide active transport from 41 to 54 min/person/week, which was associated with an annual decrease of 890 deaths and 15,053 DALYs. The ambitious, maximal alternatives increased population mean travel duration to 283 min/person/week for walking, bicycling, or transit and were associated a reduction in deaths and DALYs from 2.5 to 10 times greater than the California preferred scenarios. The alternative with the largest health impact was bicycling, which led to 8,349 fewer annual deaths and 141,597 fewer DALYs, despite an increase in bicyclist injuries. With anticipated population growth by 2040, no alternative achieved decreased carbon emissions, but bicycling had the greatest potential for slowing their growth. Conclusions: Expansion of transit confers important health benefits through active transport and meets important societal goals for destination accessibility. However, expansion of walking and cycling, independently of transit, can play a larger role in improving population health.
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    Safety in Numbers for pedestrians and bicyclists: Implications for public policy
    Jacobsen, Peter (2019-11-15)
    That motorists are a lot less likely to hit someone walking or bicycling if more people walk or bicycle surprised researchers. In contrast, the number of car crashes increases proportionally with the number of cars. The evidence of a prevalence effect implies that injury risk is more than just a matter of physics, and that something occurs with human physiology or psychology. Safety in Numbers likely occurs because humans have difficulty detecting rare items. That injury risk decreases with more walking and biking creates opportunity for implementing public policies for reducing damage to the climate and improving health. This non-linear risk also explains why the recent NTSB recommendation for compulsory bicycle helmet laws could increase injury risk.
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    Caltrans’ Pedestrian and Bicyclist Safety Program
    Carpenter, Rachel (2019-11-22)
    As recommended by California’s Strategic Highway Safety Plan (SHSP), Caltrans is working to develop a pedestrian and bicyclist safety improvement program. This presentation by Rachel Carpenter provided an overview of what has been completed since the program’s inception in 2016 as well as next steps. Specifics behind the below listed efforts were shared. the 2016 (Pilot) Pedestrian Collision Monitoring Program, the 2018 (Pilot) Bicyclist Collision Monitoring Program, the 2020 Pedestrian Collision Monitoring Program, pedestrian and bicyclist safety training, and modifications to California Manual on Uniform Traffic Control Devices (CA MUTCD) related to pedestrian and bicyclist safety and operations.
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    Population-based strategies to improve driving safety
    Hill, Linda (2020-02-07)
    Driving behaviors and driving safety involve complex interactions between the driver, their vehicle and the environment. Driving behaviors account for approximately 95% of crashes, but interventions to reduce known risks often don’t reach the target audience. The presenter will discuss strategies employed by the UC San Diego Center for Human and Urban Mobility to improve driving safety.
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    Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach
    Di, Xuan (Sharon) (2021-04-09)
    As this era’s biggest game-changer, autonomous vehicles (AV) are expected to exhibit new driving and travel behaviors, thanks to their sensing, communication, and computational capabilities. However, a majority of studies assume AVs are essentially human drivers but react faster, “see” farther, and “know” the road environment better. We believe AVs’ most disruptive characteristic lies in its intelligent goal-seeking and adapting behavior. Building on this understanding, we propose a dynamic game-based control leveraging the notion of mean-field games (MFG). Prof. Di will first introduce how MFG can be applied to the decision-making process of a large number of AVs. To illustrate the potential advantage that AVs may bring to stabilize traffic, she will then introduce a multi-class game where AVs are modeled as intelligent game-players and HVs are modeled using a classical non-equilibrium traffic flow model. Last but not the least, she will talk about how the MFG-based control is generalized to road networks, in which the optimal controls of both velocity and route choice need to be solved for AVs, by resorting to nonlinear complementarity problems.
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    The Various Silver Linings of the Impacts of the Pandemic on Traffic
    Shilling, Fraser (2021-04-16)
    In US states, mitigation of the spread of COVID-19 has been implemented by cities, counties, and governors’ offices through “shelter-in-place” and “stay-at-home” orders and related actions. The Road Ecology Center carried out three primary types of investigation into the traffic reduction that resulted from these orders and the corresponding “silver linings” that emerged for driver safety, climate change, and nature. Traffic Safety: Using observations of reported traffic incidents in our real-time “California Highway Incident Processing System” (CHIPS), the Road Ecology Center found a ~50% reduction in injury and non-injury crashes, on state highways and rural roads that resulted from Governor Newsom’s “shelter in place” order, from ~1,000 crashes and ~400 injury/fatal crashes per day to 500 and 200 per day, respectively. These reductions have resulted in a savings to the public of about $40 million/day, or $1 billion since the order went into effect. Climate Change: In the US, transportation, including personal vehicles, releases about 29% of the greenhouse gas (GHG) per year. We found that estimated emissions had declined by >50% following the various government stay-at-home orders. This puts the US on track to meet its annual goals for GHG reduction under the Paris Climate Accord. Impacts to High traffic volume is a primary contributor to wildlife-vehicle conflict (WVC) and wildlife mortality on roads. Using traffic and collision data from four US states (California, Idaho, Maine, and Washington), we found a 34% reduction in WVC. This reduction in mortality would potentially equate to 10s of millions fewer vertebrates killed on US roadways during one month of traffic reduction, representing an unintentional conservation action unprecedented in modern times.
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    Deployable Decentralized Routing Strategies using Envy-Free Incentive Mechanisms for Connected and Autonomous Vehicle Environments
    Peeta, Srinivas (2021-04-30)
    Routing strategies using dynamic traffic assignment have been proposed in the literature to optimize system performance. However, challenges have persisted in their deployability and effectiveness due to inherent strong assumptions on traveler behavior and availability of network-level real-time traffic information, and the high computational burden associated with computing network-wide flows in real-time. To address these gaps, this study proposes an incentive-based decentralized routing strategy to nudge the network performance closer to the system optimum in a traffic system with connected and autonomous vehicles (CAVs). The strategy consists of three stages. The first stage incorporates a local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles’ knowledge of local traffic information. This system is decentralized in the sense that it only updates the local route choices of vehicles in this area to circumvent the high computational burden associated with computing the flows on the entire network. The second stage optimizes the route for each CAV by considering individual heterogeneity in traveler preferences (e.g., the value of time) to maximize the utilities of all travelers in the local area. Constraints are also incorporated to ensure that these routes can achieve the approximated local system optimal flow of the first stage. The third stage leverages an expected envy-free incentive mechanism to ensure that travelers in the local area can accept the optimal routes determined in the second stage. They prove that the incentive mechanism is expected individual-rational and budget-balanced. The study analytically shows that the proposed incentive-based decentralized routing strategy can enhance network performance and user satisfaction in a connected and autonomous traffic environment.
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    Traffic Flow Smoothing At Scale
    Work, Daniel B. (2021-05-07)
    The majority of the best-selling cars in the US are now available with SAE level-one automated driving features such as adaptive cruise control. As the penetration rate of these vehicles grows on the roadways, it is now possible to consider controlling the bulk human-piloted traffic flow by carefully designing these driver-assist features. This talk will discuss modeling, simulation, and field demonstration advancements that are needed to control automated vehicles to stabilize traffic flow at scale. Prior work on a closed course established that automated vehicles can eliminate human-generated phantom traffic jams that seemingly occur without cause, reducing fuel consumption by up to 40%. The talk will highlight the research challenges and progress towards demonstrating traffic flow smoothing with a fleet of connected and automated vehicles on the I-24 Smart Corridor in Tennessee, as part of the CIRCLES Consortium.
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    Prediction/Causality Tradeoffs and Data Size Issues in Transportation Modeling: The Example of Highway-Safety Analysis
    Mannering, Fred (2021-05-21)
    The analysis of transportation data is largely dominated by traditional statistical methods (standard regression-based approaches), advanced statistical methods (such as models that account for unobserved heterogeneity), and data-driven methods (machine learning, neural networks, and so on). In the analysis of highway safety data, these methods have been applied mostly using data from observed crashes, but this can create a problem in uncovering causality since individuals that are inherently riskier than the population as a whole may be over-represented in the data. In addition, when and where individuals choose to drive could affect data analyses that use real-time data since the population of observed drivers could change over time. This issue, the size of the data (which can often influence the analysis method), and the implementation target of the analysis imply that analysts must often tradeoff the predictive capability (dominated by data-driven methods) and the ability to uncover the underlying causal nature of crash-contributing factors (dominated by statistical and econometric methods). However, the selection of the data-analysis method is often made without full consideration of this tradeoff, even though there are potentially important implications for the development of safety countermeasures and policies. This talk provides a discussion of the issues involved in this tradeoff with regard to specific methodological alternatives, and presents researchers with a better understanding of the trade-offs often being inherently made in their analysis.