Autonomous Vehicle Algorithms, Big Geospatial Data Analytics, and Interconnected Sensor Networks in Urban Transportation Systems.

• Author: Mitchell, Ann

1. Introduction

   Deontological assessment, in addition to perceived benefit and risk, anticipates behavioral intention to use and readiness to pay a bonus for self-driving cars. (Liu and Liu, 2021) Historical accident data can assist in the monitoring of the autonomous vehicles while encountering hazardous situations a long time before the real-time sensors supply any associated information, identifying regions of the public road network where the volume of accidents connected with negligent pedestrians or unsatisfactory route surface conditions is considerably more increased than expected. (Szenasi, 2021)

2. Conceptual Framework and Literature Review

   The predictable and safe performance of an urban public transportation system is instrumental in the sustainable development of a metropolitan area. (Ma et al., 2020) Self-driving cars should monitor other road users, determine imaginable similarities, and take consonant actions at exactly the right moment. (Xing et al., 2020) Self-governing mobility-on-demand systems tackle urban mobility challenges by deploying fleets of shared self-driving cars to react to user requirements on adjustable transport network instantaneously. (Javanshour et al., 2021) Autonomous vehicles configured with crash algorithms entail whether self-driving cars should be calibrated with selfish algorithms to ensure the safety of their users sparing no effort or with utilitarian algorithms to curtail social loss in collisions entailing moral dilemmas. (Liu and Liu, 2021) To optimize the public adoption of self-driving cars, grasping the aspects shaping individuals' trust perception is crucial. (Ayoub et al., 2021) Robotics and self-governing systems collecting data (Howard, 2020; Lazaroiu, 2018; Popescu et al., 2021) will alter land use, transport networks, and human-nature networking. (Goddard et al., 2021) Autonomous vehicles may decide to navigate rather than park, possibly complicating traffic jams. (Bahrami et al., 2021) Object detection algorithms are pivotal in driving assistance systems developed on data merging of heterogeneous sensors for autonomous driving. (Yang et al., 2021) Developments in connected autonomous vehicles can assist traffic control in coordinating car operations across urban networks. (Zhao et al., 2021)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from AAA, BCG, Brookings, Capgemini, CivicScience, eMarketer, GenPop, Ipsos, MRCagney, OpinionWay, Statista, Thomas et al. (2015), and World Economic Forum, I performed analyses and made estimates regarding connected and autonomous vehicle mobility. 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...