In order to solve the social stability risk problem of the adjusting goods vehicle calculation method (AGVCM) in the cancellation of toll stations at expressway provincial boundaries, a social stability risk evaluation model was proposed. Firstly, based on the possible time stages of the AGVCM, we built the social stability risk evaluation index system of the AGVCM including 4 levels, 3 grades, and 60 indicators. Secondly, we used the semantic information of cloud model to transform qualitative description and quantitative evaluation, calculated the risk level for social stability risk evaluation of AGVCM according to an integrated cloud algorithm, and proposed an optimal combination weighting—cloud model for the social stability risk evaluation of AGVCM. Finally, the feasibility of the evaluation model is verified by a case study in Yunnan province in China. The results showed that the cloud model’s numerical characteristic of social stability risk evaluation of AGVCM in Yunnan based on the optimal combination weighting—cloud model is (0.28, 0.073, 0.0008), implying the risk level is “small” and the conclusion is basically consistent with current standards. The model contains rich process information, which is helpful to better effectively eliminate the subjectivity and arbitrariness in the evaluation process of identifying the key factors affecting the project risk level.

For the purpose of determining whether vacant taxis are supposed to use high-occupancy vehicle (HOV) lanes, this paper builds a framework to explore the performances of a network before and after vacant taxis use HOV lanes and to detect the optimal deployment of HOV lanes. We use a variational inequality (VI) to describe travelers’ mode choice following a logit model and the route choice abided by the user equilibrium principle on the network. A simple network is adopted to explore the network performances when vacant taxis are allowed to use/forbidden from using HOV lanes. Then we propose a framework presented by a bi-level model for the optimal deployment of HOV lanes in which the lower level is the variational inequality and the upper level aims at maximization of social welfare. This bi-level programming with a genetic algorithm combined with a surrogate assistant approach is applied to the simple network and a Sioux Falls network to analyze the network performances in the cases allowing/forbidding vacant taxis from using HOV lanes. The results reveal the paradox that social welfare may decrease when allowing vacant taxis to use HOV lanes compared to when forbidding vacant taxis from using HOV lanes. Considering optimal HOV lane deployment, this paradox still exists. However, it does not always happen due to the different parameters, e.g., flag–fall price. Therefore, the qualification of vacant taxis to use HOV lanes depends on the topology, attributes, and travel demand of the network. The results display that the models and the algorithm are effective in reality.

In the context of transportation development, the simultaneous emergence of automated vehicles (AVs) and human-driven vehicles (HDVs) can lead to varied traffic system performance. For the purpose of improving traffic systems, this paper proposes a traffic restriction scheme only for HDVs. We develop a variational inequality (VI) model to describe travel mode and route choices under this restriction scheme and design an algorithm to solve the model. The proposed model and algorithm are applied to a Sioux Falls network example to evaluate the effects of the traffic restriction scheme. Our findings indicate that the scheme improves overall social welfare, with a higher proportion of restricted travelers leading to greater social welfare as well as increased travel demand due to changes in capacity. However, some lower exogenous monetary factors lead to negative social welfare, as the presence of AVs may exacerbate road congestion. Additionally, advancements in technology are needed to adjust the weightings of travel time and congestion level estimates to further enhance social welfare. These results offer valuable insights for traffic demand management in traffic systems with a mix of AVs and HDVs.

For the purpose of optimizing the traffic network in the presence of carpooling, a traffic demand management scheme was proposed with the consideration of traffic restriction, carpooling, and road pricing. We establish a variational inequality describing the travelers’ mode and route choice following the logit mode split and multimodal user equilibrium principle in the condition of elastic demand. A bilevel programming was designed with the variational inequality as the lower level and optimization objective function taking into account traffic congestion and social welfare as the upper level. The results show that this scheme is possible to be effective in improving network performance. The higher average occupancy of carpooling vehicles and the carpooling demand may not bring better network performance due to the topology, parameters of the network, and travel demand. The fare of carpooling travelers is possible to impact the network very slightly with this scheme. Besides, the group of travelers who is able to afford more than one vehicle is hardly affected by this scheme.

This paper investigates the eigenfrequency of the induced current in airborne cables to evaluate the coupling effect of dual-factor on electromagnetic susceptibility of airborne cables. The airborne cable is situated in the scaled fuselage which has lower complexity compared with the aircraft model. Based on the single-factor approach, the simulation results indicate that the electromagnetic environment and the fuselage have little effect on the eigenfrequencies. With the variations of cable length and cable height above the ground, the eigenfrequencies will change significantly. Based on the dual-factor of cable length and height, two improved analytical expressions for the eigenfrequency are proposed.