CTECH Project Descriptions

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https://hdl.handle.net/1813/69703

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Assessing the health and environmental benefits associated with changes in transportation activities in near-road communities using low-cost sensors
Li, Wen-Whai; Cheu, Kelvin (2022-05-31)
On-road measurements of four pollutants (PM2.5, PM10, NO2, and O3) were continuously recorded by three U.S. EPA-certified FEM air pollution monitoring devices installed inside a vehicle traveling repeatedly on the same route in a near-road community. Spatio-temporal on-road air quality data were aggregated and compared to data collected at two fixed stations, one residence located 15 m from the frontage road adjacent to Interstate Highway I-10, and another residential site 300 m from the frontage road. The first objective of this study was to assess the suitability of using the spatio-temporal on-road air monitoring data for representing community exposures to transportation-related air pollutants (TRAPs). The second objective evaluated the feasibility of using on-road air monitors instead of near-road monitors.
Assessing the health impact of proposed congestion pricing plan for downtown San Francisco
Zhang, Michael (2022-03-31)
In an effort to reduce worsening traffic congestion in downtown San Francisco, the San Francisco County Transportation Authority (SFCTA) is considering congestion charging in the downtown area, as was done in the City of London. Our research examined how different pricing alternatives considered by SFCTA impacted the health of residents in different communities in the county, paying particular attention to disadvantaged communities. The research leveraged the data obtained from an on-going ITS-Davis research project that is studying the non-health related impact of congestion pricing for the city of San Francisco, where detailed travel data were produced by the city’s transportation modeling team. It also made use of the health assessment procedure developed in a previous CTECH project. We believe that our work adds a health dimension to the assessment of congestion pricing and sheds light on how different communities are impacted by this traffic management tool.
Assessing the health and environmental benefits associated with changes in transportation activities in near-road communities using low-cost sensors
Li, Wen-Whai; Cheu, Kelvin (2022-05-31)
On-road measurements of four pollutants (PM2.5, PM10, NO2, and O3) were continuously recorded by three U.S. EPA-certified FEM air pollution monitoring devices installed inside a vehicle traveling repeatedly on the same route in a near-road community. Spatio-temporal on-road air quality data were aggregated and compared to data collected at two fixed stations, one residence located 15 m from the frontage road adjacent to Interstate Highway I-10, and another residential site 300 m from the frontage road. The first objective of this study was to assess the suitability of using the spatio-temporal on-road air monitoring data for representing community exposures to transportation-related air pollutants (TRAPs). The second objective evaluated the feasibility of using on-road air monitors instead of near-road monitors.
Analysis of the impact of pavement surface mixture on traffic noise and related public health
Lu, Qing; Li, Mingyang (2022-06-30)
Road traffic noise is a harmful environmental pollutant that affects public health. Reducing the tire-pavement noise by appropriate design of a sustainable pavement may reduce the road traffic noise. This study developed and applied a procedure to predict the road traffic noise and resulting health impact from the design parameters of a commonly used pavement surface mixture type, open-graded asphalt concrete (OGAC), and evaluated the impact of including a renewable material (seashell) in OGAC on its mechanical and acoustic performance. A series of empirical models were combined to correlate the mixture design parameters to the perceived road traffic noise and health indicators. Case study results showed that reducing the nominal maximum aggregate size (NMAS) from 19.0 mm to a smaller value had a noticeable impact on the perceived noise from car traffic and the resulting public health. For a given NMAS, variations in the OGAC design parameters did not cause significant change in the perceived noise. The laboratory evaluation of the incorporation of seashell in OGAC showed that coarse aggregates may be replaced with seashell up to a certain percentage without causing statistically significant changes in most mixture properties. The inclusion of seashell, however, reduced the permeability, acoustic absorption, and macrotexture of OGAC, which suggested that seashell in OGAC may increase the tire-pavement noise at high frequencies but reduce the tire-pavement noise at low frequencies.
Mobility in Post-Pandemic under Social Distancing Guidelines: Congestion, Emission, and Transit Contact Network
Gao, H. Oliver; Ozby, Kaan; Chow, Joseph (2021-09-30)
COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. This project proposed a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19, including social distancing and working from home. Open-source agent-based simulation models were used to evaluate transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation was used to evaluate the emissions impacts. Finally, system-wide contact exposure on the subway was estimated from the traffic simulation output. The social distancing requirement in public transit was found to be effective in reducing contact exposure, but it had negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the post-COVID negative impacts were higher due to behavioral inertia and social distancing. The findings suggested that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are following. The proposed integrated traffic simulation models and emission estimation model can help policy makers evaluate the impact of policies on traffic congestion and emissions, as well as identify hot spots, both temporally and spatially.
Sustainable and Healthy Communities through Integrating Mobility Simulations in the Urban Design Process
Dogan, Timur (2021-09-30)
A design focused active mobility simulation tool called Urbano.io that facilitates the design of healthy and sustainable urban habitats was developed and validated. This project added the ability to 1) adapt statistical and behavioral models from the transportation literature to incorporate street quality and thermal comfort-aware active mobility mode choices over others, and 2) validate the new behavioral models with urban data from NYC. More specifically, an hourly outdoor comfort map for NYC was created that was correlated with CitiBike usage and pedestrian count data to investigate the link of urban form, microclimate, and life in the streets. In addition, the most requested features from the community to remove a number of limitations that were revealed during intensive use in practice, online workshops, and conference calls with practitioners and researchers over the last four months were implemented. Key limitations that were addressed were: 1) accelerated algorithms and data structures to speed up analysis to allow larger analysis domains, 2) support for multimodal trips and other travel modes (such as biking, transit, and shared mobility), 3) support for customizable choice models to determine which modes would be used for different mobility needs, and 4) 3D terrain support to accurately model effort of sloped pathways.
Design autonomous vehicle behaviors in heterogeneous traffic flow
Li, Jia; Zhang, H. Michael (2022-03-31)
The benefits of autonomous vehicles (AVs) not only depend on the maturity of technologies, but also on how AVs behave and interact with their peers and human-driven vehicles (HVs). Similar to many other systems, individual and collective dynamics of traffic flow are not always aligned with each other (for instance, aggressive driving may benefit an individual driver but disrupts the overall traffic). It is therefore imperative to consider behavior design for AVs such that the benefits of AVs can be realized at both individual and collective levels, especially when centralized control is absent and all involved agents are self-interested. This research explored behavior designs for AVs in mixed autonomy traffic using a game-theoretic approach. In this new framework, we defined agent utilities and casted interactions of heterogeneous agents (i.e., AVs and HVs) as a collective bargaining game. We analytically characterized the equilibria of mixed autonomy traffic, including equilibria types and conditions when each type of equilibria can be reached. We found that in general, mixed autonomy traffic may reach two types of Nash equilibria, namely one-pipe and two-pipe equilibrium. Depending on traffic regimes, mixed autonomy traffic can always reach one of the equilibria that is Pareto-efficient, and this is known as the collective rationality of self-interested agents. Based on the theoretical characterization, we proposed a class of lane use policies that determine the capacity allocation between AVs and HVs. We then developed a computing algorithm to construct the equilibria numerically and conducted simulation experiments to investigate different AV behavior scenarios when they interact with HVs. We showed that with the proposed lane use policy, mixed autonomy traffic can always reach collective rationality and attain uniform speed and flow improvements over non-Pareto-efficient equilbria. We also showed a capacity drop phenomenon for HVs in mixed autonomy traffic when exclusive AV lanes are introduced, which pinpointed a potential subtlety of similar lane policies.
Assess the Mobility and Health Impact of COVID-19 on Diverse Communities
Zhang, Michael (2021-09-30)
The COVID-19 pandemic has significantly impacted the lives of communities in many dimensions. In this research, mobility data was collected for before and during the pandemic to assess how transportation, a critical service to the community for both daily lives and the response to the pandemic, was affected while paying particular attention to equity using San Francisco, California, as a case study. San Francisco was chosen for being a diverse city comprised of communities from various racial backgrounds and economic standings and for the availability of public data. This study investigated the effects of COVID-19 on travel behavior using a Prais-Winsten model for daily bikeshare ridership over time to determine if ridership was significantly affected by demographics and COVID-19 related temporal data.
Improving Security and User Privacy in Learning-Based Traffic Signal Controllers (TSC)
Chuah, Chen-Nee (2022-03-31)
21st century transportation systems leverage intelligent learning agents and data-centric approaches to analyze information gathered with sensing (both vehicles and roadsides) or shared by users to improve transportation efficiency and safety. Numerous machine learning (ML) models have been incorporated to make control decisions (e.g., traffic light control schedules) based on mining mobility data sets and real-time input from vehicles via vehicle-to-vehicle and vehicle-to-infrastructure communications. However, in such situations, where ML models are used for automation by leveraging external inputs, associated security and privacy issues start to surface. This project studied the security of ML systems and data privacy associated with learning-based traffic signal controllers (TSCs). Preliminary work had demonstrated that deep reinforcement learning (DRL) based TSCs are vulnerable to both white-box and black-box cyber-attacks. Research goals included 1) quantifying the impact of such security vulnerabilities on the safety and efficiency of the TSC operation, and 2) developing effective detection and mitigation mechanisms for such attacks. In learning based TSCs, vehicles share their messages with the DRL agents at TSCs, which will then analyze the data and take action. Sharing vehicular mobility data with a network of TSCs may cause privacy leakage. To address this problem, differential privacy techniques were applied to the mobility datasets to protect user privacy while preserving the effectiveness of the prediction outcomes of traffic-actuated or learning-based TSC algorithms. Approaches were evaluated in vehicular simulators using real mobility data from San Francisco and other cities in California. By accomplishing these goals, learning-based transportation systems are more secure and reliable for real-time implementations.
Development of Framework for Identifying Mobility Desert
Zhang, Yu; Chen, Peng (2021-12-31)
Providing all means of travel facilitates people’s access to jobs, healthcare, critical activities, and other services. To enable equal multi-modal mobility services to the public, it is important to evaluate equity in accessing different travel modes. In this study, we proposed a concept called “multi-modal deserts” and developed an approach to identify them. Multi-modal deserts refer to areas with limited mobility services that constrain people from accessing services and opportunities. Framed under multi-modality, multivariate outlier detection was applied to identify areas’ mobility services that significantly deviate from other areas by analyzing road network factors and travel modes. Downtown Tampa, Florida, was selected as an empirical case to demonstrate the proposed method, and 11 multi-modal deserts were identified among 182 Census Block Groups. In addition, spider charts were used to illustrate and compare the features of these multi-modal deserts. The results show that two multi-modal deserts in central Downtown Tampa have the highest poverty ratios and have very limited access to all travel modes. For such multi-modal deserts, transit and shared micromobility need to be better served in a way to enrich the travel mode choices for low-income residents. Other multi-modal deserts are at the edge of Downtown Tampa, which has no access to shared micromobility and limited access to transit. The results will help local authorities identify mobility gaps by better allocating resources and improving equal access to opportunities for all citizens.

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