In order to evaluate the homogeneity of asphalt mixture quantitatively, the distribution characteristic of internal phases of asphalt mixture were identified based on digital image processing technique and stereology theory, and the homogeneity coefficient (i.e., K) was proposed. At the same time, the trend of variation and reliability of homogeneity of asphalt mixture were analyzed by changing the nominal maximum aggregate size, aggregate gradation and asphalt content. The results suggest that the homogeneity of asphalt mixture could be comprehensively described using DIP technique combined with stereology theory. The smaller the K, the better the distribution homogeneity of the asphalt mixture. An improvement in the homogeneity of an asphalt mixture is achieved with the decrease of the nominal maximum aggregate size and a finer aggregate gradation. The asphalt content corresponding to the optimal homogeneity of the internal structure of asphalt mixture specimen is the optimum asphalt content. According to the experimental study, the suggested values of the homogeneity coefficient were given, which provides theoretical support to control the construction quality of the hot mixture asphalt.

A novel composite framework of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithm called CS-PSO is proposed in this paper. In CS-PSO, initialization is substituted by chaotic system, and then Cuckoo shares optimums in the global best solutions pool with particles in PSO to improve parallel cooperation and social interaction. Furthermore, Cloud Model, famous for its outstanding characteristics of the process of transforming qualitative concepts to a set of quantitative numerical values, is adopted to exploit the surrounding of the local solutions obtained from the global best solution pool. Benchmark test results show that, CS-PSO can converge to the global optimum solution rapidly and accurately, compared with other algorithms, especially in high dimensional problems.

Along with the rapid development of urban social economy, under the influence of sustainable development strategy, urban road traffic engineering design should adhere to the concept of environmental protection and develop towards greening and modernization. Firstly, this method uses the green environmental construction materials, such as hot-melt reflective marking paint, color asphalt concrete pavement and polyurethane elastic isolation column, to build green traffic spaces at intersection; Then on this basis, the motor vehicles signal optimization technology is used to balance the space-time resource allocation of the motor vehicle traffic flow, motor vehicle traffic optimal phase combination analysis method is put forward, and the optimal calculation of motor vehicle traffic time is optimized by the signal timing optimization method that considers the overlapping phase. Finally, through the evaluation and analysis of the VISSIM traffic simulation software, the results show that the method based on green building materials has effectively defined about the green traffic spaces of motor vehicles, and under this premise, the efficiency of motor vehicle traffic is further improved by signal optimization technology, providing the foundation for the research on green traffic on urban roads.

The growth of ride-hailing services has made people’s daily commutes more convenient but has increased traffic on the road. However, the data needed to verify the impact of ride-hailing services on the urban road traffic network are lacking. This study matches data on the trajectories of different kinds of vehicles in Xuancheng city in the urban road network by using vehicle information data, ride-hailing information data, and license plate data recorded by the traffic bayonet system from December 26, 2018, to January 25, 2019. We used two indices, the detecting intensity and the detecting rate, to analyze the characteristics of travel based on ride-hailing services in Xuancheng. The results show that the ride-hailing vehicles have obvious travel characteristics of morning peak and evening peak, and in central urban areas and through the proposed indices of the travel time occupation rate and the travel space occupation rate to further quantitatively analyze the spatial and temporal characteristics of travel of different kinds of vehicles. Following this, we calculated the average ratios of different kinds of vehicles on congested sections of the road network and used simple regression to analyze the relationship between this and the average speed on these sections to quantitatively analyze the impact of ride-hailing on traffic congestion. The work here can provide effective decision-making support to the government for managing travel based on ride-hailing services.