| 英文摘要 |
Microscopic traffic models show the basis for driver behavior. The behaviordata is quite difficult to collect, especially when large samples are required.Macroscopic traffic models manipulate the data that is easy to collect but lack ofthe behavior basis of drivers. Mesoscopic traffic models take advantage of theabove models; mesoscopic models can build the velocity distribution functionfrom the behavior basis and obtain the macroscopic parameters through integrationof the distribution function. Mesoscopic models are first proposed byPrigogine and Herman. We construct a new model to relax some poor assumptionsof Prigogine and Herman’s – lack of consideration of finite space and instantaneousvelocity-changing. Therefore, our model considers a successiveslowing-down process and finite baking space. This study also provides the requiredmathematical definition and the deduction in the proof of model from basictraffic patterns, traffic velocity distribution model, expected velocity distributionmodel, driver interactive impact model, driver following behavior velocitydistribution model, relaxation time and singularity analysis, etc. studies. And wesimulate the Prigogine and Hermann model. With these, we might assist researchersto have a better understanding of the model and thus have the abilityof further research. |