Browsing by Author "Qian, Xinwu"

Now showing 1 - 9 of 9
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    Envisioning Urban Air Mobility in Small and Medium-Sized Urban Areas in the United States
    (University of Alabama Libraries, 2023) Yang, Chenxuan; Liu, Jun
    Urban Air Mobility (UAM) represents a revolutionary innovation that utilizes low-altitude urban space to provide air transportation. While UAM offers numerous advantages and holds promise as a solution to traffic congestion, it faces various constraints, including public acceptance, economic considerations, and management challenges. While extensive research has explored the integration of UAM into mobility systems for large metropolitan areas, the potential benefits it could bring to smaller urban areas with populations under 350,000 have been under-discussed. This dissertation research aims to assess the feasibility and viability of implementing UAM in small and medium-sized urban areas. The first major study in this dissertation involves a national survey to investigate Americans' willingness to pay for URAM services and their preferences when faced with commutes that exceed a certain duration. The second study evaluates the impact of UAM on over 300 small and medium-sized urban areas by comparing the travel accessibility of existing road-based regional transportation systems with hypothetical UAM-integrated systems. These hypothetical UAM networks interconnect vertiports within regions, enabling travelers to reach their destinations via Vertical Take-Off and Landing (VTOL) aircrafts after reaching the nearest vertiport by ground transportation. The third study explores an innovative intermodal mobility system that integrates Shared Autonomous Vehicles (SAVs) and VTOLs within the UAM framework. Agent-based simulations are employed to assess the feasibility and viability of this system in small and medium-sized urban areas. This dissertation sheds light on the benefits and trade-offs associated with UAM in small and medium-sized urban areas. It serves as a valuable resource for evaluating the practicality of intermodal UAM services and informs policies related to the planning and implementation of UAM services in the United States.
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    An Exploratory Approach to Digitizing the Operational Environments for Connected and Automated Vehicles
    (University of Alabama Libraries, 2022) Fu, Xing; Liu, Jun; University of Alabama Tuscaloosa
    The transportation industry is going beyond the traditional areas. The digital innovations operated by the technology companies are reinventing transportation with new business and technologies, such as shared mobility and autonomous driving. Among these, connected and autonomous vehicles (CAV) emerge to be a strike in both the industry and research areas. However, in comparison to the thriving in public discussion, the practice and the market of autonomous driving are not clear yet. To ensure operation safety, the CAVs are equipped with various sensors to perceive the surrounding environments during the operation. The road tests of the CAVs collect comprehensive driving contextual information which is not available in the past. The data conveys the information about the vehicle operation, interaction, and the static road environments. The booming in the CAV data provides extensive sources to investigate the traffic from a microscopic aspect, which brings an evolution to the traditional transportation industry and academics to transform from the planning, design, and operation to the age of data, modeling, and machine learning. Under this trend, this dissertation takes the advantage of the multi-source data to examine the driving environments that the CAVs will encounter. The overarching research goal of this dissertation is to explore a framework or methodology to understand the driving environments from the view of CAVs. This research utilizes the (1) Connected vehicle basic safety message data; (2) Google street image data; (3) Lyft Level 5 perception data and (4) Waymo motion data to explore the driving environments from both the static and dynamic aspects. The methodology of this research incorporates spatiotemporal analysis, statistical modeling, and machine learning. The dissertation research will be unfolded into 4 sections which are targeted at four datasets: (1) Historical driving performance study; (2) Static driving environment classification; (3) Dynamic driving environment characterization; (4) Contextual vehicle lane changing prediction.The research in this dissertation is expected to contribute by providing practical metrics for digitizing the driving environments. The extracted information can be compiled to the High Definition (HD) map for autonomous driving or can be employed as indicators for the Operational Design Domains (ODD) evaluation.
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    Innovative Operations and Network Designs for High-Velocity Intra-City Courier Services
    (University of Alabama Libraries, 2024) Satici, Ozgur; Dayarian, Iman
    This dissertation introduces a novel network design for intra-city courier services, aiming to improve operational efficiency and service quality of courier companies. Traditional hierarchical network models in courier services mandate all shipments be aggregated at centralized distribution centers for sorting, which can create bottlenecks and delays. In contrast, this study proposes an alternative that utilizes the existing infrastructure of courier package drop-off and pick-up stores dispersed throughout urban areas. This alternative network design reorganizes these stores as mini sorting hubs, enabling the decentralization of the sorting process. The research is presented in three separate articles, each addressing different facets of the proposed network design under various conditions of demand, capacity, and operational constraints. In the first article, we examine tactical and operational planning for the proposed network structure. Our tactical approach uses a multi-commodity service network design to maximize consolidation and optimize commodity paths, managed through mixed integer programming. We refine these paths in a secondary model to minimize necessary cycles for service guarantees. In operational planning, we adjust our strategies based on short-term demand deviations, enhancing service levels through plans tailored to daily operational specifics. In the second article, we address the stochastic service network design for an intra-city courier service using a hybrid fleet of contracted and crowdsourced drivers. We strategically acquire capacity at reduced rates considering future demand and adjust dynamically based on actual crowdshipper capacities and spot market conditions. Our modeling approach uses two-stage stochastic programming with advanced decomposition methods, improving efficiency and adaptability to operational data. In the third article, we focus on service network design challenges for an intra-city express delivery system with specific hub capacity constraints. We model and solve the network design on a time-space framework as an integer program, using commercial solvers for smaller scenarios and a constructive metaheuristic approach for larger cases. This strategy separates the problem into freight routing and vehicle scheduling, iteratively solved to create robust solutions suitable for diverse real-world conditions.
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    A knowledge graph-based method for epidemic contact tracing in public transportation
    (Pergamon, 2022) Chen, Tian; Zhang, Yimu; Qian, Xinwu; Li, Jian; Tongji University; University of Alabama Tuscaloosa
    Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
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    New Perspectives on the Role of Transportation Systems in Disaster Resilience
    (University of Alabama Libraries, 2023) Islam, Riffat; Jones, Steven
    Natural disasters such as hurricanes and tornadoes can cause significant disruption to transportation systems, making emergency response efforts challenging. To enhance disaster resilience, transportation systems must be able to effectively respond and assist in emergency situations. This dissertation examined the impact of transportation systems on disaster resilience by exploring various research studies, analyzing public attitudes toward adopting emerging technologies, and assessing the practical implications of these technologies in disaster scenarios. The dissertation is therefore divided into three separate but related studies.In the first part of this dissertation, a systematic literature review was conducted to investigate the critical roles of transportation systems in emergency response during a hurricane. A total of 86 scholarly publications were examined to assess the broader functions of transportation systems concerning hurricanes, with a particular focus on the transportation modes utilized and the research methodologies employed. The second part of this dissertation investigates the willingness to use shared autonomous vehicles (SAVs) during tornadic events. The study used a survey to measure the willingness of Alabama residents to use SAVs to reach a community shelter during tornado events. The third part of this dissertation simulates a dynamic ridesharing operation to explore the potential of SAVs in evacuating vulnerable populations during tornado early warning in Tuscaloosa County, Alabama. Finally, the dissertation delves into how transportation systems can bolster disaster resilience while ensuring equitable access to emerging technologies. The outcomes of this dissertation will offer helpful directions for future research and serve as a valuable resource for emergency management agencies seeking to create effective transportation plans in response to disasters.
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    A Safe Systems Approach to Vulnerable Road User Safety Issues in Ghana
    (University of Alabama Libraries, 2021) Agyemang, William; Jones, Steven; University of Alabama Tuscaloosa
    Approximately, 90% of global road traffic deaths occur in low-and middle-income countries (LMICs), and vulnerable road users (VRUs) constitute 54%, even though these countries have about 60% of the world’s vehicle population. VRUs are particularly prone to injuries and fatalities because they are not protected by any external vehicular body and their vulnerability is higher in mixed traffic conditions. There have been efforts by many countries across the globe to reduce road traffic deaths and injuries, but progress varies significantly between different regions and countries. In Ghana, VRUs account for a high proportion of crashes, with pedestrians and motorcyclists making up about 60% of total crashes every year. Motorcycle-related road safety has become topical due to the recent rapid rise in commercial motorcycle activities attributed to the problem of urban traffic congestion and the general lack of reliable and affordable public transport in rural areas. Additionally, uncontrolled interaction between human and high-speed vehicular activities in and around settlement areas throughout Ghana has resulted in numerous pedestrian deaths and injuries. The phenomenon has been attributed to the land-use and right-of-way planning practices as well as lack of safe crossing facilities for VRUs, comprising pedestrians, bicyclists, and motorcyclists (both two- and three-wheelers). This dissertation is the result of three distinct, but interrelated research efforts addressing VRU safety issues in Ghana. Ghana, like many other countries in sub-Sahara Africa (SSA), is a rapidly developing nation with a rising middle-income population that is driving the urbanization and motorization processes. Nonetheless, a vast majority of the population rely on walking and motorcycles for their daily travel needs. Pedestrians and motorcyclists, both drivers and pillions, make up a significant proportion of VRU fatalities in the country. This dissertation seeks to throw more light on the VRU safety concerns in the country by a) understanding how local transport professionals perceive pedestrian and motorcycle safety issues and the adoption of the Safe Systems approach as a countermeasure tool to address them, and b) conducting data-driven analyses to identify factors contributing to these crashes so that potential countermeasures can be developed based on local conditions and input from local road safety professionals. The dissertation consists of three major areas related to VRU safety in the country. The first part of the study assessed opinions of local transport professionals on the complex safety issues relating to VRUs using the Safe Systems approach to explore how local context could guide the implementation of countermeasures. The Safe Systems approach takes a holistic view of road safety, and this framework is based on the basic premise that humans are prone to errors, mistakes, and mishaps and that as a result are vulnerable to crashes and must therefore be protected systemically. The Safe Systems approach addresses: behavioral issues that may result in crashes (speeding, driving under the influence, aggressive or distracted driving, etc.) under its safer people pillar; issues related to vehicle design and condition under safer vehicles; emphasis on infrastructure designed and constructed to prevent or reduce the severity of crashes through the safer roads pillar; and finally the promulgation of policies that promote safer speeds, especially where vehicular traffic is mixed with VRUs. The study used a Multi-Criteria Decision-Making tool, the Analytic Hierarchy Process (AHP), to develop a framework based on knowledge and opinions gleaned from a survey of local road safety professionals to prioritize countermeasures for VRUs (i.e., pedestrians and motorcyclists) using a Safe Systems approach. This initial work provided a reference frame for two subsequent data-driven analyses of motorcycle and pedestrian crashes throughout the country. The motorcycle crash study emphasized the differences among crashes that occur in rural versus urban areas. The pedestrian study focused explicitly on crashes that occurred on inter-urban highways in Ghana. It is anticipated that the findings of the dissertation research will provide a basis for the development of targeted and appropriate countermeasures to reduce the number of VRU deaths and injuries in Ghana. The recommendation for a localized Safe Systems approach and a data-driven strategy to address VRU safety issues is expected to result in improved overall road safety in the country, and other countries with similar characteristics in the region. The proposed Safe Systems framework developed, and its results can be used by transport professionals to prioritize localized efforts to improve the safety of VRUs.
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    Scaling of contact networks for epidemic spreading in urban transit systems
    (Nature Portfolio, 2021) Qian, Xinwu; Sun, Lijun; Ukkusuri, Satish V.; University of Alabama Tuscaloosa; McGill University; Purdue University; Purdue University West Lafayette Campus
    Improved mobility not only contributes to more intensive human activities but also facilitates the spread of communicable disease, thus constituting a major threat to billions of urban commuters. In this study, we present a multi-city investigation of communicable diseases percolating among metro travelers. We use smart card data from three megacities in China to construct individual-level contact networks, based on which the spread of disease is modeled and studied. We observe that, though differing in urban forms, network layouts, and mobility patterns, the metro systems of the three cities share similar contact network structures. This motivates us to develop a universal generation model that captures the distributions of the number of contacts as well as the contact duration among individual travelers. This model explains how the structural properties of the metro contact network are associated with the risk level of communicable diseases. Our results highlight the vulnerability of urban mass transit systems during disease outbreaks and suggest important planning and operation strategies for mitigating the risk of communicable diseases.
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    Spatiotemporal impacts of human activities and socio-demographics during the COVID-19 outbreak in the US
    (BMC, 2022) Ling, Lu; Qian, Xinwu; Guo, Shuocheng; Ukkusuri, Satish V.; Purdue University; Purdue University West Lafayette Campus; University of Alabama Tuscaloosa
    Background Understanding non-epidemiological factors is essential for the surveillance and prevention of infectious diseases, and the factors are likely to vary spatially and temporally as the disease progresses. However, the impacts of these influencing factors were primarily assumed to be stationary over time and space in the existing literature. The spatiotemporal impacts of mobility-related and social-demographic factors on disease dynamics remain to be explored. Methods Taking daily cases data during the coronavirus disease 2019 (COVID-19) outbreak in the US as a case study, we develop a mobility-augmented geographically and temporally weighted regression (M-GTWR) model to quantify the spatiotemporal impacts of social-demographic factors and human activities on the COVID-19 dynamics. Different from the base GTWR model, the proposed M-GTWR model incorporates a mobility-adjusted distance weight matrix where travel mobility is used in addition to the spatial adjacency to capture the correlations among local observations. Results The results reveal that the impacts of social-demographic and human activity variables present significant spatiotemporal heterogeneity. In particular, a 1% increase in population density may lead to 0.63% more daily cases, and a 1% increase in the mean commuting time may result in 0.22% increases in daily cases. Although increased human activities will, in general, intensify the disease outbreak, we report that the effects of grocery and pharmacy-related activities are insignificant in areas with high population density. And activities at the workplace and public transit are found to either increase or decrease the number of cases, depending on particular locations. Conclusions Through a mobility-augmented spatiotemporal modeling approach, we could quantify the time and space varying impacts of non-epidemiological factors on COVID-19 cases. The results suggest that the effects of population density, socio-demographic attributes, and travel-related attributes will differ significantly depending on the time of the pandemic and the underlying location. Moreover, policy restrictions on human contact are not universally effective in preventing the spread of diseases.
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    Understanding and Optimizing Equity-Of-Travel in Demand-Responsive Transit Services: Exact Algorithm and Learning-Based Method
    (University of Alabama Libraries, 2024) Guo, Shuocheng; Qian, Xinwu
    Demand-Responsive Transit (DRT) is a flexible on-demand transit solution that complements fixed-route transit services by serving door-to-door travel needs with flexible routes and schedules. The DRT commonly takes the form of paratransit and microtransit and is especially useful in lower-density areas, which support the travel needs of disadvantaged populations such as senior citizens, people with disabilities, and low-income households. Considering the diverse travel needs and different levels of service received among heterogeneous populations, this dissertation explores the overlooked aspect of travel equity--Equity of Travel (EoT)--within DRT operations, particularly focusing on static, reservation-based services and contributes to promoting equitable DRT services with mathematical models and solution algorithms. Specifically, this dissertation identifies and addresses equity issues in DRT operations by first mining historical trip data of DRT services to highlight the EoT issues in real-world operations, followed by the development of exact and learning-based optimization models validated by comprehensive real-world instances. The dissertation is organized into three major studies: (1) The first evaluates EoT metrics, using real-world data to analyze service equity during both normal operations and the COVID-19 pandemic. This analysis led to the development of pandemic-specific EoT metrics to better assess and address these exposure risks. (2) The second study introduces the Equitable Dial-a-Ride Problem (EDARP), formulated as a bi-objective optimization model that balances minimizing travel costs with EoT goals. Utilizing the Branch-Cut-and-Price algorithm and novel ride-time-related resource calibration methods, this study advances solution strategies for real-world DRT scenarios, enhancing service equity effectively. (3) The third study presents the Branch-and-Price with Neural Cuts (BP\&NeuCuts) algorithm, integrating Graph Neural Networks with traditional Branch-and-Price techniques to accelerate the optimization process. This approach effectively narrows the decision space and improves scalability and efficiency in handling complex Dial-a-Ride problems within DRT systems, evidenced by computational improvements in test cases. The dissertation concludes by demonstrating the practical impact of these methodologies in refining DRT operations to better serve equitable access, integrating both theoretical advancements and operational applications. This consideration seamlessly connects both planning and operational dimensions, integrating quantitative and qualitative perspectives to pave the way for a more equitable DRT system.