Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Optimizing Information Values in Smart Mobility

Smart mobility, enabled by advanced sensing, communication, vehicle, and emerging mobility technologies, has transformed transportation systems. Real-time information shared by public and private entities plays a pivotal role in smart mobility, which facilitates informed decision-making, including effective mode choice, dynamic vehicle control, optimized travel routing, and strategic vehicle relocation. While more information is believed to benefit individual decision makers, it is crucial to acknowledge that the effects of information on transportation network performance are contingent; more information may not always benefit the safety and mobility of the whole system. The goal of this dissertation is to investigate the effects of information shared by public and private transportation entities on system-level performance. The challenges are primarily due to the lack of a unified modeling framework to endogenously reflect the decentralized multi-agent interaction involved in the interconnected transportation networks and the resulting computational complexities arising from non-convexity and high dimensionality. To address these challenges, this dissertation proposes novel modeling frameworks and computational solutions for three cutting-edge smart mobility applications. First, to examine the impact of en-route information on a transportation network, we propose a novel two-stage stochastic traffic equilibrium model to characterize the equilibrium traffic patterns considering adaptive routing behavior when locational en-route traffic information is provided through infrastructure-to-vehicles (I2V) communications. This model is formulated as a convex stochastic optimization problem so that efficient stochastic programming algorithms can be directly leveraged to achieve scalability. Second, to achieve optimal control over real-time variable speed limits information sharing and evaluate its impact on the network, we propose a twin-delayed deep deterministic policy gradient model, which converges more reliably than state-of-the-art deep reinforcement learning models. We investigate the transferability of the control algorithm and conduct comparative analyses of different traffic control strategies and spatial distributions of variable speed limit control (VSLC) deployment. Third, to assess the impacts of information provided by private ride-sourcing companies on transportation network congestion, we propose a Stackelberg framework for spatial pricing of ride-sourcing services considering traffic congestion and convex reformulation strategies under mild conditions. We perform numerical experiments on transportation networks of varying scales and with diverse transportation network company (TNC) objectives, aiming to derive policy insights regarding the implications of spatial pricing information on transportation systems

Is the future of urban mobility shared? : modeling ride-hailing adoption and preferences for ownership and sharing of autonomous vehicles

Society is experiencing the initial stages of a technological revolution that promises to disrupt urban transportation as known today and induce behavioral and social changes. The main factors guiding the transformation of urban mobility are the growth of Information and Communication Technology (ICT)-enabled transportation services and the development of autonomous vehicle (AV) technologies. While the use of ICTs and vehicular automation are expected to provide direct road capacity improvements due to the real-time provision of traffic information, crash reductions, and platooning capabilities, these gains may be offset by latent demand effects. That is, the increase in level of service may actually result in the generation of more trips and escalation of vehicle miles traveled. In this sense, proactive planning and policy guided towards promoting the use of shared vehicles and pooled rides are important to minimize possible negative externalities of automation. The current dissertation provides initial guidance to such planning by examining individuals’ preferences toward the adoption of current and future mobility services and technologies. A research framework containing four independent but related analysis components is developed to allow a comprehensive investigation of travelers’ characteristics and behaviors associated with ride-hailing use and preferences regarding AVs. Empirical analyses are conducted using advanced econometric techniques applied to different types of data from three different cities. The results of the empirical analyses are compared and implications to transportation planning and policy are discussed. The results from the analyses undertaken in the dissertation show that, from a behavioral perspective, a service-based transportation future where people predominantly travel using shared vehicles and pooled rides instead of their own vehicles is on its way but still distant. A complex combination of actions is required to promote the use of shared services both today and in an AV future. Among these actions, we identify the need for campaigns to (a) increase technology awareness among older individuals and individuals from lower income households, and (b) reduce privacy-sensitivity among non-Hispanic Whites and millennials. Such efforts should also be complemented by a decrease in service fares and urban densification. The results also suggest the need to promote policies and integrated multi-modal systems that discourage individuals from substituting the use of active and public transit modes by ride-hailing and AV-based services. Finally, we observe that individuals seem to be less sensitive to the presence of strangers in a commute trip than in a leisure trip, but the sensitivity to time is the opposite. The implications of these results are that pooled services may have a large market penetration potential for commute trips as long as operated efficiently with minimal detour and pick-up/drop-off delays.Civil, Architectural, and Environmental Engineerin

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Autonomous Shuttle Transit: An Exploratory Case Study and the Future Impact on TSU Campus

By 2040 the third-largest city in the United States, Houston, Texas, a top global city for traffic congestion, will become a significant metropolis with future growth possibilities of 11 million people passing Chicago (HGAC, 2018). For this purpose, Houston and surrounding growing populations will contribute to gridlock traffic, with highway expansions increasing ozone and inefficient transit systems with longer commutes in underserved, sidelined communities. Above all, historically, persons of color, notably Black Indigenous Persons of Color (BIPOC) in Black and Brown marginalized communities, are deprived of transportation accessibility. Undoubtedly, Driverless Shuttle (DS) rideshare platforms reflect that higher-income whites are admittedly more likely to hold discriminatory attitudes toward fellow passengers of different classes and races (Middleton & Zhao, 2019). At the same time, Environmental Justice (EJ) studies have shown that Black and Brown low-income disenfranchised communities are more exposed to inefficient transit systems. They are characterized by unequal treatment and accessibility to the bus than affluent White commuters (Bullard, Johnson, and Torres, 2004). As a result, systemic racism, an unfair burden of environmental injustice, has plagued the Greater Third Ward transit-dependent population. For this purpose, Houston\u27s Metropolitan Transit Authority (METRO) riddle inequities have shaped public transportation for every minoritized BIPOC within the community (Spieler, 2020). Most importantly, Blacks are twice as likely to experience inferior transportation access as their more affluent counterparts (Sisson, 2019; Bullard, Johnson, and Torres, 2004, p.2). According to Harvard Law (2021), Bullard states, In 1990, Dumping in Dixie: Race, Class and Environmental Quality assuredly documented that environmental vulnerability mapped closely with Jim Crow segregation. This why racial redlining discriminatory zoning, and inefficient land use practices, (Bullard, 2021, p. 245; Bullard, 1990) target Houston\u27s Black and Brown neighborhoods, hindering economic and social advancement in employment, education, and health care (Bullard, 2021, p. 245; Bullard, 1990; Freemark, 2020; Talbott, 2020). The problem of injustice was examined by longitudinal data where an Autonomous Vehicle bus pilot associated with the built environment in this study highlighted 1. Transportation inequality along the TSU Campus Tiger Walk is related to bus stops. 2. Distance between three designated bus stop locations. 3. Safety and critical driving functions fully driverless for an entire trip. 4. First/last mile driverless shuttle connectivity interacting with Metro buses and Light Rail in Houston\u27s Greater Third Ward neighborhood. The methods of research incorporated qualitative and quantitative analysis. The study used a driverless shuttle to compare racial and social economics between bus stops at Texas Southern University, a historically black university, during an Autonomous Vehicle (AV) Shuttle pilot study. For this purpose, Autonomous Shuttle Transit, an additional mode of mobility, will connect Houston\u27s Greater Third Ward transit-dependent population to Metro’s bus and light rail networks. In addition to bus stops along the TSU Campus Tiger Walk. This study made a similar theoretical comparison of the Tiger Tram to AV two years before the TSU Shuttle pilot. The results pointed to a link between income and transit-dependent populations using a driverless shuttle under specific conditions. A Google map determined the half-mile distance along the TSU Campus Tiger Walk. The driverless shuttle and socioeconomics of Political Science, Administrative Justice, and Psychology undergraduate classes were used to measure transportation equity horizontally. A regression analysis was carried out to determine if the socioeconomic factors had statistical significance. Also, linear regression modeling was used to determine which sociodemographic variables strongly predict the transport mode used. The findings revealed that Blacks, people with disabilities, and the TSU AV shuttle working with metro buses were statistically significant at a 95% confidence level. Also, a predictor of respondents walking, and biking will use the Autonomous Shuttle as an additional mode of transportation. Also, the data analysis results indicate a significant negative correlation between the driverless shuttle time intervals along the TSU Tiger Walk and the Metro bus service. This correlation implies that higher percentages of respondents will walk further from the TSU campus Tiger Walk central location to the bus stop connecting Third Ward’s transit-dependent residents to the Metro Light rail. Likewise, in the Third Ward community, low-income transit-dependent populations in the Cuney Homes are disproportionately exposed to inadequate transit access than any other area in the neighborhood. The results also support the Environmental Justice (EJ) claim that minorities and low-income transit-dependent populations are closer to bus stops and farther away from the light rail. Although the results showed that race, income, and disability variations are likely to predict that TSU’s transit-dependent population will use the TSU Autonomous Shuttle connecting the Third Ward community. Comparing the social demographic indicators along the TSU Tiger Walk and the Third Ward area shows that deed restrictions do not address EJ concerns associated with bus stops and transportation modes. The conclusion indicates that despite several decades of EJ policies and transit regulations, institutional racism in the Third Ward neighborhood is embedded. Over the decades, African Americans and other people of color have been disproportionately exposed to transit injustice because they are concentrated in neighborhoods with less transit accessibility. However, the TSU Campus Tiger Walk still has fewer efficient transit options than other Third Ward census tracts that map closer to bus stops with higher income

No woman's land:Feminist approaches to the ride-hailing sector and digital labor platforms in India

In this dissertation, I investigate the concerns, issues and opportunities for platform labor reform with a focus on the ride-hailing sector using Bardzell (2010)’s feminist lens. The feminist viewpoint keeps the marginal user at the center committing to equity, diversity, identity, empowerment, and social justice to improve the work conditions of gig workers in the Global South. By conducting in-depth qualitative interviews with the different stakeholders of the ride-hailing sector, and analysing case studies, media coverage, policy papers, and research reports, I suggest guidelines for redesigning the digital labor platforms

No woman's land:Feminist approaches to the ride-hailing sector and digital labor platforms in India

In this dissertation, I investigate the concerns, issues and opportunities for platform labor reform with a focus on the ride-hailing sector using Bardzell (2010)’s feminist lens. The feminist viewpoint keeps the marginal user at the center committing to equity, diversity, identity, empowerment, and social justice to improve the work conditions of gig workers in the Global South. By conducting in-depth qualitative interviews with the different stakeholders of the ride-hailing sector, and analysing case studies, media coverage, policy papers, and research reports, I suggest guidelines for redesigning the digital labor platforms

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically