Autonomous Vehicle Driving Algorithms and Smart Mobility Technologies in Big Data-driven Transportation Planning and Engineering.

• Author: Bennett, Amy

1. Introduction

   Motivation, safety, and risk assessment are instrumental in the real-time and computational performance of self-driving cars' decision-making process. (Wang et al., 2021) Grasping the link between experiencing and intention to use autonomous vehicles is decisive, as unmediated practice configures users' preliminary impressions as regards big geospatial data analytics in networked driverless technologies and smart urban transport systems. (Dai et al., 2021) While modifying the perception concerning smart urban mobility systems, to perform at their foremost capabilities, self-driving cars should undergo a detailed testing and verification operation. (Alnaser et al., 2021)

2. Conceptual Framework and Literature Review

   Attitude, perceived behavioral control, experience satisfaction, and trust shape the intention to use self-driving cars. (Dai et al., 2021) Numerous accidents entirely or to some extent generated by human errors can be decreased in seriousness or prevented completely with autonomous vehicle deployment on the roads. (Alnaser et al., 2021) Behavior decision-making algorithms are instrumental in ensuring the safe operations of self-driving cars. (Yin et al., 2021) In a visionary environment in which autonomous vehicle function unassisted, a significantly enhanced capacity use can be achieved. (Carrone et al., 2021) Self-driving cars can either integrate with or challenge the public transportation system. (Mo et al., 2021) Big data-driven transportation planning and engineering can diminish traffic accidents, safety judgment configuring risk sensitivity and feelings. (Tan et al., 2021) Operational process upgrade to maximize performance is essential in enhancing precision and superior time management in Internet of Things-assisted smart logistics transportation. (Abosuliman and Almagrabi, 2021) Driven by deep learning-based sensing technologies, self-driving cars can diminish vehicle incidents, optimize road efficiency and improve mobility for underserved individuals. (Huang and Qian, 2021) Autonomous vehicles may displace human drivers by enhanced safety and performance. (Gouda et al., 2021)

3. Methodology and Empirical Analysis

   Using and replicating data from ANSYS, Atomik Research, AUDI AG, Brookings, Capgemini, Deloitte, GHSA, Ipsos, Kennedys, McKinsey, Pew Research Center, Schoettle & Sivak (2014), and Statista, I performed analyses and made estimates regarding smart urban transportation systems. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. 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. Any survey which did not reach greater than 50% completion was removed from subsequent analysis to ensure quality. 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. 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%. Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can also 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 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. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling. 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. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. An Internet-based survey software program was utilized for the delivery and collection of responses. All procedures performed in studies involving human participants were in accordance with the ethical standards of the...