From the review of the transport model development trend in China and other countries，the limitations of both traditional four-step model and activity-based model，especially the shortcomings of the current transport models in explicating and extracting the stability properties of travelers to promote the accuracy of transport model， were summarized． Based on the opportunities and challenges brought with big data to transport model，the feasibility to solve these problems by using location-based services data was discussed． Considering that travel behaviors have stable endpoints-purpose property，this paper defined trip purpose classification which is different from the home-based or none home-based in the traditional four-step model，and developed a simplified activity-based model considering the fact that travelers have fixed house location，job locations ( school locations) and other basic daily live locations and the difference between stable trips and occasional trips． Furthermore，a new four-step model structure combined with trip generation and trip distribution was developed． Taking Huadu District of Guangzhou as an example，the results among a new simplified activity-based model，a traditional optimization model ( i. e. ，the standard gravity model) and a gravity model with K factors was comparatively analyzed． The results show that the optimization model can not reveal the stability attributes of the city，while the simplified activity-based model based on travelers’stability fits urban reality better． Besides，this paper sheds light on the possibility of more stability factors of travelers being introduced to the transport model in the future