Abstract: At present,characteristics of urban transport are gradually changing from regular to sporadic.The demand for early warning of accidents is also increasing.For accidental events in public transport,a SP survey is used to analyze the characteristics from three parts:degree of influence,frequency of occurrence,and behavior of residents.A system-clustering algorithm is used to classify accidental events into three levels:point,local,and global.A list of information requests under different accidental events is established based on analyses of different types,locations,and time of information demand.A framework and an APP of accidental events are developed to obtain the best access to information demands with analyzing characteristics of release and collection at different service terminals in Mobile Internet environment.Results show that the satisfaction rate of mobile terminals is up to 85 %.It can be conducive to deal with the conflicts between the uncertainty of characteristics of urban transport and the reliability and timeliness of travel information,then improve efficiency of urban transport.