Predictive Control Algorithms, Real-World Connected Vehicle Data, and Smart Mobility Technologies in Intelligent Transportation Planning and Engineering.

AuthorWallace, Susan
  1. Introduction

    Convenient autonomous vehicle requirements should be established for certain persons or self-driving cars should be designed to satisfy demands of specific individuals. (Zhang et al, 2021) Through deep learning technologies, accurate and robust input sensory data (Andronie et al, 2021; Crisan-Mitra et al, 2020; Kliestik et al., 2021; Nica, 2017) assist in autonomous navigation, road anomaly detection, and visual environmental perception. (Wang et al, 2021)

  2. Conceptual Framework and Literature Review

    Motion planning in unpredictable conditions is essential for safe self-driving. (Khaitan et al, 2021) Price sensitivity, social influence, technological trust, perceived risk, creativity, performance expectancy, and hedonic motivation are crucial in acceptance and behavioral intention to use autonomous vehicles in a mixed traffic environment. (Kapser et al., 2021) Route planning and tracking control algorithms are decisive autonomous driving capabilities in intricate traffic scenarios. (Li et al, 2021) Autonomous driving technologies can configure smart and sustainable urban mobility, resulting in social and transportation equity. (Golbabaei et al, 2021) Self-driving car technologies will impact shared urban mobility in terms of consumer travel patterns, preference, acceptance, and intention to use. (Maeng and Cho, 2022) Path planning and formation control are instrumental in multi-vehicle systems, taking into account the nonlinear dynamics and environmental conditions. (Hadi et al, 2021) Socially sensitive vehicles optimize pedestrians' sense of contentment and safety in terms of anthropomorphized emotional expression. (Pazhoohi et al, 2021) Autonomous vehicle diffusion can assist elderly persons in preserving their mobility and independence. (Kadylak et al, 2021) Connected and autonomous vehicles will shape the fabric of road transport risk, integrating categorized decreases in the incidence and seriousness of motor vehicle collisions and leading to further safety upsides generated by heightened levels of car automation. (Shannon et al, 2021)

  3. Methodology and Empirical Analysis

    Building our argument by drawing on data collected from Brookings, Capgemini, Ipsos, Jones Day, Kennedys, KPMG, MRCagney, and Pew Research Center, we performed analyses and made estimates regarding how road anomaly detection, motion planning, and tracking control algorithms (Ginevicius et al, 2020; Lazaroiu et al, 2020) shape behavioral intention to use autonomous vehicles, optimizing smart and sustainable urban mobility by reducing traffic congestion and motor vehicle collisions. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

  4. Study Design, Survey Methods, and Materials

    The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

    Data sources: Brookings, Capgemini, Ipsos, Jones Day, Kennedys, KPMG,

    MRCagney, and Pew Research Center.

    Study participants: 6,500 individuals provided an informed e-consent.

    All data were interrogated by employing graphical and numeric exploratory data analysis methods. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Results are estimates and commonly are dissimilar within a narrow range around the actual value. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process.

    Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

    The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

    Flow diagram of study procedures

  5. Statistical Analysis

    This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling.

    Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

    An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Groundbreaking computing systems and databases enable data gathering and processing, extracting...

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