CTECH Final Reports

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https://hdl.handle.net/1813/69704

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Assessing the health and environmental benefits associated with changes in transportation activities in near-road communities using low-cost sensors
Chavez, Mayra C.; Williams, Evan; Cheu, Ruey L.; Li, Wen-Whai (2022-05)
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 I10, and another residential site 300 m from the frontage road. The first objective of this study is to assess the suitability of using the spatio-temporal on-road air monitoring data for representing community exposures to transportation-related air pollutants (TRAPs). While TRAP concentrations observed at a central state-operated site appear to be in good agreement with those observed in the near-road community, concentrations in the community may be better represented by spatio-temporal data generated by an on-road monitor. The second objective of evaluating the feasibility of using on-road air monitors instead of near-road monitors is supported by the facts that pollutants primarily emitted from sources other than traffic, such as PM10, display a different pattern than that of the other three pollutants. On-road monitors successfully detected PM10 concentrations near-road as well as in the community that are comparable to the regional background concentrations. PM2.5 and O3 detected by on-road monitors are also comparable to those detected near-road in the community. NO2 concentrations detected by the on-road monitors varied from the near-road monitors due to the complex interactions with ambient temperature, vehicle emissions, and atmospheric chemical reactions. It seems likely that community exposures to TRAPs can be represented by short-term spatio-temporal measurements using on-road monitors. On-road air pollution measurements provide a rapid assessment of the air quality in a community without installing multiple stationary sites.
Assessing the health impact of proposed congestion pricing plan for downtown San Francisco
Wu, Kanglong; Hou, Zenghao; Zhang, Michael (2022-03-20)
Congestion pricing (CP) is seen as a viable solution to urban traffic congestion, but its impact on public health also deserves to be evaluated before implementation. In this study, we assessed several congestion pricing schemes proposed for the San Francisco downtown area from a health perspective. We compare the eight proposed CP schemes with baseline scenario (no-action) to observe the health effects from physical activity (PA), fine inhalable particles matter (PM) exposure, and road traffic injuries (RTI) three pathways using the Integrated Transport and Health Impact Model (ITHIM). The results of the study show that these CP schemes all have a beneficial effect on the public health of San Francisco. Finally, we recommend further research on TNC travel fees in these CP schemes and explore the potential for health improvements on physical activity by encouraging people to use active modes of transport.
Analysisoftheimpactofpavementsurface mixtureontrafficnoiseandrelated public health
Lu, Qing; Li, Mingyang; Alharthai, Mohammad; Uddin, Shihab (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 open-graded asphalt concrete (OGAC) and evaluated the impact of including 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.
Sustainable and Healthy Communities through Integrating Mobility Simulations in the Urban Design Process
Dogan, Timur; Samaranayake, Samitha (2022-01-27)
Rapid urbanization and new global construction estimated to be 250x NYC by 2050 is increasing traffic congestion, pollution, and related health threats. Thus, it is imperative that we develop new modeling capabilities that allow urban designers to quantify the performance of mobility solutions, sustainability, public health impacts, pedestrian thermal comfort and pollution exposure during the earliest stages of a design process. Embedded in a generative, performance-driven design process, such a tool can significantly facilitate the design of healthy and sustainable urban habitats that promote active mobility.
Assess the Mobility and Health Impact of COVID-19 on Diverse Communities
Darr, Justin; Zhang, Michael (2021-12-14)
The COVID-19 pandemic has significantly impacted the lives of communities in many dimensions. In this research, mobility data is 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, is 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 investigates the effects of COVID-19 on travel behavior using a Prais-Winsten model for daily bikeshare ridership over time to determine if ridership is significantly affected by demographics and COVID-19 related temporal data. The results show that trip origins in majority low-income and majority minority census block groups became more statistically significant after March 2020, which supports our hypothesis that different demographics would respond to the pandemic with their travel behavior in different ways. The importance of bikeshare membership on ridership and changes in trip durations during the pandemic are also apparent in the model. Although total ridership has recovered this year, this research aims to provide a better understanding if specific communities are increasing ridership at greater rates than others. Additionally, some groups may not have resumed their bikeshare ridership rates to pre-pandemic levels, and this research may provide insights into these scenarios to aid in future policymaking regarding bikeshare.
Design Autonomous Vehicle Behaviors in Heterogeneous Traffic Flow
Li, Jia; Chen, Di; Zhang, Michael (2022-03-31)
While much attention was paid to the interactions of human-driven and automated vehicles at the microscopic level in recent years, the understanding of the macroscopic properties of mixed autonomy traffic flow still remains limited. In this report, we present an equilibrium model of traffic flow with mixed autonomy based on the theory of two-player games. We consider self-interested traffic agents (i.e., human-driven and automated vehicles) endowed with different speed functions and interacting with each other simultaneously in both longitudinal and lateral dimensions. We propose a two-player game model to encapsulate their interactions and characterize the equilibria the agents may reach. We show that the model admits two types of Nash equilibria, one of which is always Pareto efficient. Based on this equilibrium structure, we propose a speed policy that guarantees the realized equilibria are Pareto efficient in all traffic regimes. We present two examples to illustrate the applications of this model. In one example, we construct flux functions for mixed autonomy traffic based on behavior characteristics of agents. In the other example, we consider a lane policy and show that mixed autonomy traffic may exhibit counterintuitive behaviors even though all the agents are rational. In addition, we present empirical evidence concerning the assumptions made in the model.
Improving Security and User Privacy in Learning-Based Traffic Signal Controllers (TSC)
Haydari, Ammar; Zhang, Michael Z.; Chuah, Chen-Nee (2022-02-28)
The 21st century of transportation systems leverages 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, the associated security and privacy issues start to surface. This project aims to study the security of ML systems and data privacy associated with learning-based traffic signal controllers (TSCs). Preliminary work has demonstrated that deep reinforcement learning (DRL) based TSCs are vulnerable to both white-box and black-box cyber-attacks. Research goals include 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 will be 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 will be evaluated in vehicular simulators using real mobility data from San Francisco and other cities in California. By accomplishing these goals, learning-based transportation systems will be more secure and reliable for real-time implementations.
Development of Framework for Identifying Mobility Desert
Zhang, Yu; Chen, Peng; Guo, Yujie (2021-02-16)
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.
An Agent-based Travel and Charging Behavior Model for Forecasting High-resolution Spatio-temporal Battery Electric Vehicle Charging Demand
Sophia Liu, Yuechen; Tayarani, Mohammad; Gao, H. Oliver (2021-08-20)
The expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro-level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can serve up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public, and it takes the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Comparing to risk-neutral users, high-risk sensitive users require 49% to 91% higher peak power demand of level 2 chargers at work/public. Users' preferences for fast charging rates can change DCFC demand from 36.4% to 53.7% of the total demand. This study helps to qualitatively analyze the factors of charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.
The Impact of Mobility on the Spread of Infectious Diseases to and from High Risk Environments
Luo, Qi; Gee, Marissa; Piccoli, Benedetto; Work, Daniel; Samaranayake, Samitha (2022-01-31)
Transportation flows play a critical role in the propagation of infectious diseases. Mitigating the spread of such diseases requires understanding this dependency and building epidemiological models that explicitly account for transportation flows. In epidemiological studies, compartmental models such as the susceptible, exposed, infectious, and recovered (SEIR) model are an important tool in understanding how infectious diseases propagate through a population. Due to the importance of travel on the dynamics of the disease spread, there has been renewed interest in directly modeling transportation flows through the use of spatial meta-population SEIR models. This project will explore models for explicitly integrating transportation flows in SEIR models with a focus on high risk environments.

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