Sensing and Computing Technologies, Intelligent Vehicular Networks, and Big Data-driven Algorithmic Decision-Making in Smart Sustainable Urbanism.

• Author: Konecny, Vladimir

1. Introduction

   Data-generating smart technologies employed on self-driving car trials and assisted by algorithm-driven sensing devices configure network connectivity systems articulating the urban environment and shaping decisions concerning urban performance management. (Wigley, 2021) Autonomous vehicles can self-position to diminish the deficiency of parking spots at trip destinations by use of big geospatial data analytics. (Zhao et al., 2021)

2. Conceptual Framework and Literature Review

   Experience satisfaction represents an essential determinant that can relevantly influence users' trust, attitude, subjective norm, and perceived behavioral control, shaping their intention to use self-driving cars. (Dai et al., 2021) Ride-sharing and carpooling are more systematized for autonomous vehicles due to their unmanned character and long-established self-governance. (Malik et al., 2021) Self-driving cars can collect sizeable road traffic data and network by harnessing sensing and computing technologies across smart urban transport systems. (Ma et al., 2021) Autonomous vehicles are pivotal in accident reduction and decrease of travel time, with important socio-economic consequences. (Rokonuzzaman et al., 2021) Deep learning-based sensing technologies, big data-driven algorithmic decision-making, and transportation analytics (Andronie et al., 2021; Edwards, 2021; Kliestik et al., 2020; Lazaroiu et al., 2020) are extensively deployed in self-driving cars, especially as regards their perception subsystem. (Yang et al., 2021)

3. Methodology and Empirical Analysis

   We inspected, used, and replicated survey data from AAA, ANSYS, Atomik Research, AUDI AG, Deloitte, eMarketer, Gallup, Ipsos, KPMG, McKinsey, Morning Consult, Pew Research Center, SAE, Schoettle & Sivak (2014), and Statista, performing analyses and making estimates regarding public acceptance of and intention to use self-driving cars. 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 institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. An informed e-consent was obtained from individual participants. Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so.

5. Results and Discussion

   Trust can indirectly impact intention to accept self-driving cars by use of the mediating...