Determinants and the Moderating Effects of Individual Characteristics on Autonomous Vehicle Adoption in China.

Along with the increasing popularity of autonomous vehicles (AVs), urban livability and public health will be enhanced due to ecofriendly issues: alleviated traffic congestion, lower car ownership, and reduced transport emissions. However, some emerging issues, including public safety, trust, privacy, reliability, underdeveloped legislation, and liability, may deter user intentions to adopt an AV. This study introduces an extensive factor, playfulness, into the technology acceptance model (TAM) to quantify the impacts of psychological factors (perceived usefulness, perceived ease of use, and perceived playfulness) on AV adoption intention. This study proposes four AV-related policy measures (financial incentivization, information dissemination, convenience, and legal normalization) and examines how policy measures motivate users to adopt an AV to facilitate public safety. Furthermore, this study investigated the moderating effects of demographic factors on the relationships between independent variables and AV adoption intention. Two models were proposed and estimated using a total of 1831 survey responses in China. The psychology-related and policy-related models explained 62.2% and 33.6% of the variance in AV adoption intention, respectively. The results suggest that perceived playfulness (ß = 0.524, p < 0.001) and information dissemination (ß = 0.348, p < 0.001) are the most important influencing factors of AV adoption intention. In addition, demographic factors (gender, education, income, the number of private cars owned by a family, and types of cities) can moderate the effects of psychological factors and policy measures on user intentions to adopt an AV. These insights can be employed to design more cost-effective policies and strategies for subgroups of the population to maximize the AV adoption intention.

A Bayesian Method for Dynamic Origin-Destination Demand Estimation Synthesizing Multiple Sources of Data.

In this paper a Bayesian method is proposed to estimate dynamic origin-destination (O-D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O-D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O-D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O-D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O-D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen-Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O-D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O-D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and "true" O-D demands is relatively small, and the O-D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O-D demands with fine accuracy.

Understanding the Interaction between Cyclists' Traffic Violations and Enforcement Strategies: An Evolutionary Game-Theoretic Analysis.

An evolutionary game-theoretic analysis method is developed in this study to understand the interactions between cyclists' traffic violations and the enforcement strategies. The evolutionary equilibrium stabilities were analysed under a fixed (FPS) and a dynamic penalty strategy (DPS). The simulation-based numerical experiments show that: (i) the proposed method can be used to study the interactions between traffic violations and the enforcement strategies; (ii) FPS and DPS can reduce cyclists' probability of committing traffic violations when the perceived traffic violations' relative benefit is less than the traffic violation penalty and the enforcement cost is less than the enforcement benefit, and using DPS can yield a stable enforcement outcome for law enforcement compared to using FPS; and (iii) strategy-related (penalty amount, enforcement effectiveness, and enforcement cost) and attitudinal factors (perceived relative benefit, relative public image cost, and cyclists' attitude towards risk) can affect the enforcement strategy's impacts on reducing cyclists' traffic violations.

A Combined Modal and Route Choice Behavioral Complementarity Equilibrium Model with Users of Vehicles and Electric Bicycles.

The popularity of electric bicycles in China makes them a common transportation mode for people to commute and move around. However, with the increase in traffic volumes for both vehicles and electric bicycles, urban traffic safety and congestion problems are rising due to traffic conflicts between these two modes. To regulate travel behavior, it is essential to analyze the mode choice and route choice behaviors of travelers. This study proposes a combined modal split and multiclass traffic user equilibrium model formulated as a complementarity problem (CP) to simultaneously characterize the mode choice behavior and route choice behavior of both vehicle and electric bicycle users. This model captures the impacts of route travel time and out-of-pocket cost on travelers' route choice behaviors. Further, modified Bureau of Public Roads (BPR) functions are developed to model the travel times of links with and without physical separation between vehicle lanes and bicycle lanes. This study also analyzes the conditions for uniqueness of the equilibrium solution. A Newton method is developed to solve the proposed model. Numerical examples with different scales are used to validate the proposed model. The results show that electric bicycles are more favored by travelers during times of high network congestion. In addition, total system travel time can be reduced significantly through physical separation of vehicle lanes from electric bicycle lanes to minimize their mutual interference.

Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method.

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts' judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015⁻2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

Production of three types of krill oils from krill meal by a three-step solvent extraction procedure.

In this study, a three-step extraction method (separately use acetone, hexane, and ethanol as extraction solvent in each step) was conducted to selectively extract three types of krill oils with different compositions. The lipid yields were 5.08% in step 1, 4.80% in step 2, and 9.11% in step 3, with a total of 18.99%. The krill oil extracted with acetone in step 1 (A-KO) contained the lowest contents of phospholipids (PL) (2.32%) and n-3 polyunsaturated fatty acids (PUFA) (16.63%), but the highest levels of minor components (505.00 mg/kg of astaxanthin, 29.39 mg/100 g of tocopherols, 34.32 mg/100 g of vitamin A and 27.95 mg/g of cholesterol). By contrast, despite having traces of minor components, the krill oil extracted using ethanol in step 3 (E-KO) was the most abundant in PL (59.52%) and n-3 PUFA (41.74%). The krill oil extracted using hexane in step 2 (H-KO) expressed medium contents of all the testing indices. The oils showed significant differences in the antioxidant capacity (E-KO > H-KO > A-KO) which exhibited positive correlation with the PL content. These results could be used for further development of a wide range of krill oil products with tailor-made functions.