Excessive dependence on autonomous vehicles (AVs) may exacerbate traffic congestion and increase exhaust emissions in the future. The diffusion of AVs may be significantly affected by the public’s acceptance. A few factors that may affect people’s acceptance of AVs have been researched in the existing studies, one-third of which cited behavioral theories, while the rest did not. A total of seven factors with behavior theories are screened out that significantly affect the acceptance intention, including perceived ease of use, attitude, social norm, trust, perceived usefulness, perceived risk, and compatibility. Six factors without behavior theories are summed up that affect AV acceptance, namely safety, performance-to-price value, mobility, value of travel time, symbolic value, and environmentally friendly. We found that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs by scholars and respondents. Public acceptance of the different types of AVs and consumers’ dynamic preferences for AVs are highlighted in the review too. The quality of literature is systematically assessed based on previously established instruments and tailored for the current review. The results of the assessment show potential opportunities for future research, such as the citation of behavior theories and access to longitudinal data. Additionally, the experimental methods and the utilization of mathematical and theoretical methods could be optimized.

Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers’ intention to use AVs. Perceived behavioral control potentially stymie the travelers’ intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.

Traffic speed estimation is a fundamental task for traffic management centers and a critical element of intelligent transportation systems. For this purpose, various sensors are used to collect traffic speed information. The cellular probe system is gaining market penetration and becomes a newly effective and practical traffic speed measurement technique. In this article, the handoff system, one type of cellular probe technology, is used for speed detection. However, the handoff coverage size is usually variable and consecutive handoff points are usually far apart on a freeway. To improve the accuracy of speed estimation, this article proposes a travel-time-based method to aggregate the estimation results of the cellular probe system and loop detectors. For the purpose of a rigorous analysis, data are generated from microscopic simulation models of virtual one-direction freeway segments under various traffic conditions. Thus, the correlations between estimation accuracy and handoff distance, traffic condition, and the number of loop detectors are evaluated and analyzed. The results show that best performance is achieved with the shortest handoff distance. The aggregation of estimated speeds from cellular probe system and loop detectors can improve the speed estimation accuracy by taking advantage of each sensor if the space of loop detectors is more than 500 m. Also, an increasing number of loop detectors will improve the accuracy. Furthermore, the improvement of integration accuracy is much better under free flow conditions than under congested conditions.

Existing layouts of sensors on freeways usually have room for improvement. The optimal allocation of multi-type sensors could increase the accuracy and coverage of data acquisition for traffic control and management. This paper aims to explore the appropriate method to solve the multi-type sensor allocation problem on freeways. Two types of sensors, the micro-loop sensor and the microwave sensor, are considered in this study. Three sensor allocation methods, the Integer Programming-Genetic Algorithm (IP-GA) method, the cluster method, and the two-stage method, are proposed to identify the optimal sensor locations. To investigate the performances of our proposed methods, a case study of sensor allocations on Ning-Hang freeway in Jiangsu province, China was conducted. The analysis results demonstrated that the IP-GA method has the best performance among the three proposed algorithms in view of the Mean Absolute Relative Errors (MAREs) of link travel time. Besides, this study also found that optimizing the composition of sensors has a far greater impact on data accuracy than just increasing the number of sensors. Within a specific range, data accuracy increases along with the number of sensors. Under the cases of the total number being constant, the greater the proportion of high accuracy sensors, the more accurate of the estimated traffic information.