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This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

The purpose of this paper is to explore the evolving mechanism of urban roadway network. With the consideration of self-organization effect and planning effect during evolution, the authors try to demonstrate the impact of preferential attachment, module scale and module structure on the evolving network model.

The roadway network is built in the form of abstract network by dual approach. By using the evolving model of modular growth, the authors analyze the effects and mechanism of the evolving process. Then through numerical analysis, the impact of evolving effects on urban roadway network topology structure is discussed from the aspects of preferential attachment, module scale and module structure.

The module structure property, small-world property and scale-free property of roadway network can be affected with various degrees by the change of preferential attachment and module scale. However, the impact of module structure on network properties is small, which can be ignored. Therefore, in practice, the self-organization effect and planning effect of evolving network can be reached by changing the preferential attachment and module scale, so as to generate the network structure with specific properties.

Some local events, such as road extensions, road demolition and intersection rebuilding, exist during the evolving process under real-world situation. While those cases have not been considered in preferential attachment. Therefore, researchers are encouraged to take these factors into consideration in further research.

The paper has implications for practice in urban transportation planning and roadway constructions, which can help to guide the planning of urban roadway and to adjust or restore partial network when broken down according to the evolving law.

The impact of preferential attachment, module scale and module structure on the evolving network model is measured. And the relationship between different network properties can be used to build some patterns of network. From this point of view, the development of urban roadway network can be predicted and intervened.

By aiming at defending cascade failures effectively, the purpose of this paper is to present a strategy of inserting modular topologies into urban road network through reducing the burdens of critical components with too much traffic flow.

Each module is considered as a small-world random network, which is inserted into the initial Barabási-Albert scale-free network. Based on the user-equilibrium assignment, the strategy searches for remote nodes with low betweeness and flow in the network, and sets these nodes to be connected with the modular topologies. In this sense, the inserted modules are supposed to attach to the nodes with lower intensity of shorter path, and avoid bringing more impact to the nodes with higher betweeness and traffic flow. By using efficiency as the measurement of cascading failures, the performance of the networks generated through the strategy is tested.

The results show that the performance of the strategy is sensitive to the average degree of the inserted modular, and the modular size with a better effect on reducing the size of cascading failures or delaying the time of breakdown, while the other factors (e.g. the rewired probability) present few differences among various values. Meanwhile, it is found that the importance-based attachment mechanism has a better effect on preventing the cascading failures, especially delaying the step time of the larger reduction.

The strategy aims at alleviating the burdens in critical components to prevent the cascading failures of the network, and provides practical guidance on the decision of the urban road network evolving process.

An effective strategy for cascade defense in urban road network is proposed in this paper.

The purpose of this paper is to present a distance accuracy-based industrial robot kinematic calibration model. Nowadays, the repeatability of the industrial robot is high, while the absolute positioning accuracy and distance accuracy are low. Many factors affect the absolute positioning accuracy and distance accuracy, and the calibration method of the industrial robot is an important factor. When the traditional calibration methods are applied on the industrial robot, the accumulative error will be involved according to the transformation between the measurement coordinate and the robot base coordinate.

In this manuscript, a distance accuracy-based industrial robot kinematic calibration model is proposed. First, a simplified kinematic model of the robot by using the modified Denavit–Hartenberg (MDH) method is introduced, then the proposed distance error-based calibration model is presented; the experiment is set up in the next section.

The experimental results show that the proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.

The proposed calibration model based on MDH and distance error can improve the distance accuracy and absolute position accuracy dramatically.