The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

Two types of typical heavy-haul locomotive coupler models were presented, one is round-pin coupler with shoulder-limit, and another is flat-pin coupler with frictional tail pairs and stop-limit, both of them adapting nonlinear hysteresis buffer models. Utilizing a train model consists of 2 detailed locomotives and 1 simplified freight wagon, curving behaviour of different couplers were studied. The results show that, the actual curving behaviour of different couplers could be accurately simulated by the established coupler dynamics models. This paper will play a promoting role for analyzing actual heavy-haul locomotive running behaviours and safety.

Aiming at accurate predictions of coupler/daft gear systems' dynamic behaviour. Based on the analysis of structural and kinematic relations of two typical systems, hysteretic nonlinear draft gear models and 9DOF (Degree of freedom) detailed coupler/draft gear system models were developed. Frictional pairs on coupling surfaces and coupler tails along with interactive aligning shoulder models were integrated into system models. Utilizing two detailed locomotive models simulations were performed on a section of special track to validate the rationalities of coupler/draft gear system models. Simulation results indicate that all parts in coupler/draft gear system models can fullfill their roles, and the system models themselves can reproduce the run-time behaviour of coupler/draft gear systems with high reliability and accuracy.