As an extension of linear complementary problem, tensor complementary problem has been effectively applied in $ n $-person noncooperative game. And a multitude of researchers have focused on its properties and theories, while the valid algorithms for tensor complementary problem is still deficient. In this paper, stimulated by the potential reduction method for linear complementarity problem, we present a new algorithm for the tensor complementarity problem, which combines the idea of damped Newton method and the interior point method. Utilizing the new algorithm, we settle the tensor complementary problem with the underlying tensor being diagonalizable and positive definite. Furthermore, the global convergence of the iterative scheme is theoretically guaranteed and the given preliminary numerical experiments indicate the efficiency of the method.

To alleviate the problem between parking demand and supply, as well as improving urban traffic environment, shared parking has attracted great interest among researchers, policymakers, and entrepreneurs. Naturally, it is a prerequisite of sharing private parking spaces with public users (P-users) that all of the owners (O-users) providing parking spaces have space to park whenever they come back, with a unified management of all parking resources. However, it remains a challenge both in theory and practice. To solve this problem, firstly, we introduced a management framework of shared parking resource in terms of time and spatial dimension. Under this framework, to control the access to parking spaces of P-users, four phases (preparatory phase, open phase, releasing phase, and reconstructive phase) are divided in time dimension, and two types of parking spaces (the prestored parking spaces, and the shared parking spaces) are classified in spatial dimension. Then, based on the proposed management framework, an intelligent parking management system (IPMS) was developed to simulate the operation of shared parking considering the uncertainties of P-users’ and O-users’ arrival and departure. Furthermore, detailed sensitivity analysis, based on real-world data and simulations, evaluated the proposed framework and the developed IPMS in a case study concerning parking lots in Beijing, China. The results show that the IPMS can not only realize that there will always be enough available parking spaces satisfying O-users’ parking demand, but also bring about vast improvements in both utilization and turnover rate of parking spaces, comparing with the non-shared management strategy.

With the help of accurate parking navigation systems, sharing vacant private parking spaces with public travelers may have the potential of reducing the number of cruising vehicles and contribute to traffic emission reduction. However, the quantitative effects of such a measure are still relatively unknown in traffic management. In this paper, we firstly established an optimal allocation model of shared parking spaces, which is a pure integer linear programming model of maximizing the number of served public vehicles to avoid cruising for parking. Further, the parking space allocation model was expanded to an estimation model that quantifies the effect of emission reduction. Secondly, a branch-and-cut algorithm was introduced as the core algorithm to solve the proposed models. Finally, detailed sensitivity analysis, based on empirical data collected by electronic parking toll collections and questionnaire surveys in Beijing, China, evaluated the proposed model and algorithm. The results indicate that shared parking can both effectively reduce the cruising time and the number of vehicles, but also has significantly a positive effect on emission reduction. This research is helpful to provide theoretical support for alleviating parking pressure and environmental problems.

An understanding of the walking patterns of groups of pedestrians in an evacuation is critical for the establishment of policies, procedures, and organizational structures to respond effectively to emergencies. Groups of pedestrians compose a crowd in which pedestrian motions are significantly constrained to maintain cohesion. On the basis of behavior theory, this paper proposes a multiagent model for the simulation of crowds of pedestrians. The main innovative aspect of this model is the genuine representation of the patterns of movement of groups of pedestrians. Patterns of movement consisting of the line-abreast pattern, the chain pattern, and the mixed pattern were investigated, and their influences on evacuations were evaluated quantitatively by taking into account the discrepant densities, disparate distributions of the proportions of pedestrian groups of different sizes, and heterogeneous velocities of groups of pedestrians. The simulation results show that the walking patterns of groups of pedestrians have a significant influence on the dynamics of pedestrian evacuation. The chain pattern was safer when the time of evacuation under high-density conditions was considered, and the mixed pattern had a better performance under moderate-density conditions. Moreover, the influence of patterns of movement was distinct with different distributions of pedestrian groups of different sizes; the chain pattern had the highest evacuation efficiency among the three patterns of pedestrian movement. In addition, a homogeneous velocity condition had a higher evacuation efficiency than a heterogeneous velocity condition. Thus, a chain pattern with a homogeneous velocity is recommended as the optimal pattern of movement in pedestrian evacuations when the safety and efficiency of plans and designs for the evacuation of pedestrian traffic with the different patterns of movement are considered.

The transport sector accounts for 23% of global carbon emissions, the second largest after electricity and heat generation. Low-carbon travel, such as walking, cycling, and use of public transit, has become an effective way to reduce transportation-related emissions, however, what factors and how they affect low-carbon travel behavior remain unclear. This paper analyzes the key factors that affect the low-carbon travel behavior of urban residents by exploring 26 potential correlation indicators. Low-carbon travel survey data of urban residents in Zhenjiang, China are used as an example. Five factors derived from 26 indicators were identified and named as key factors influencing urban low-carbon travel behavior: traveler personal attributes (contribution rate 31.646%), user satisfaction with travel processes (contribution rate 17.438%), attitude towards low-carbon travel and environmental awareness (16.090%), the level of public transportation system development (10.793%), and overall attributes of travelers’ family (10.561%). The results provide a theoretical basis for the development and implementation of novel urban low-carbon travel concepts in the future.