Networked Driverless Technologies, Autonomous Vehicle Algorithms, and Transportation Analytics in Smart Urban Mobility Systems.

• Author: Wells, Robert

1. Introduction

   Connected and autonomous vehicles can network a short distance away to improve link capacity, possibly reducing traffic congestion. (Wang et al., 2021a) Self-driving cars and sustainable and smart urban transport systems are altering individual driving behavior and consequently urban mobility technologies by use of big geospatial data analytics. (Maeng et al., 2021)

2. Conceptual Framework and Literature Review

   Deep learning-based sensing technologies and decision-making self-driving car control algorithms (Dusmanescu et al., 2016; Lazaroiu et al., 2017a, b; Poliak et al., 2021) can improve the safety and mobility related to connected and autonomous vehicle driving operations. (Dong et al., 2021) Shared autonomous vehicles can optimize individuals' spatial fairness in accessibility. (Eppenberger and Richter, 2021) Self-driving cars can advance the current mobility system as intelligent vehicular networks. (Pham and Xiong, 2021; Thomopoulos et al., 2021) Autonomous vehicles can further the capacity of roads and intersections, upgrading mobility to disadvantaged communities and decreasing vehicle crashes, while possibly accelerating travel demand, thus diminishing the potential for self-driving cars to cut down snarl-ups and escalating air pollutants. (Nadafianshahamabadi et al., 2021)

3. Methodology and Empirical Analysis

   Using and replicating data from AAA, Abraham et al. (2017), AUDI AG, AUVSI, Axios, BCG, Capgemini, Ipsos, Kennedys, McKinsey, Perkins Coie, Pew Research Center, SAE, Statista, and YouGov, we performed analyses and made estimates regarding consumer attitudes toward self-driving vehicles. 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

   Onboard sensing equipment can judiciously track the traffic environment surrounding self-driving cars (Bailey, 2021; Kliestik et al., 2020; Meyers et al., 2019; Shaw et al., 2021), but their operations are restricted by their sensor range. (Dong et al., 2021) Adoption of autonomous vehicles impacts travel demand, traffic jams, and vehicle emissions. (Nadafianshahamabadi et al., 2021) Self-driving cars can be deployed to intensify transportation safety, further mobility options, cut down...