Recently,the introduction of bus GPS data acquisition devices has brought another way to analyse the trip-chains of passengers based on the huge amounts of smart card data.Instead of other time-consuming surveys like the household travel survey,with the help of these devices,the data fusion method of GPS and smart card data(SCD)can continuously provide information of passengers and buses day to day.However,over these years,algorithms for estimating trip chains are rarely changed and lacking in improvement,which just simply use the theory of a closed trip chain cycle on one day or two adjacent days and focus only on the whole group or office workers in SCD.This paper aims to further increase the success rate of alighting stop identification by using general trip behaviours of resident and non-resident students,and then to analyse students trip chains.The alighting algorithms first use and generalize the conventional methods,and then utilize resident and non-resident students typical trip patterns to further increase the success estimated rate.After that,trip chains and OD matrices can be obtained from the boarding and alighting identification algorithms.Also,valuable data like travel time and transfer time can be gained and its visualization like heat maps can be realized.One-week data of Shaoxing City,China in 2013 is applied to the improved algorithms proposed with success estimated rate of 74.9%,where the rate dramatically increases by 8.6%through trip behaviours of resident and non-resident students.Also,adjustment of bus schedules and routes on Friday afternoon are taken as an example to illustrate the application of students trip chains,where detailed suggestions are put forward.In the future,more applications can be described while metro and shared bikes can be taken into consideration.| Recently,the introduction of bus GPS data acquisition devices has brought another way to analyse the trip-chains of passengers based on the huge amounts of smart card data.Instead of other time-consuming surveys like the household travel survey,with the help of these devices,the data fusion method of GPS and smart card data(SCD)can continuously provide information of passengers and buses day to day.However,over these years,algorithms for estimating trip chains are rarely changed and lacking in improvement,which just simply use the theory of a closed trip chain cycle on one day or two adjacent days and focus only on the whole group or office workers in SCD.This paper aims to further increase the success rate of alighting stop identification by using general trip behaviours of resident and non-resident students,and then to analyse students trip chains.The alighting algorithms first use and generalize the conventional methods,and then utilize resident and non-resident students typical trip patterns to further increase the success estimated rate.After that,trip chains and OD matrices can be obtained from the boarding and alighting identification algorithms.Also,valuable data like travel time and transfer time can be gained and its visualization like heat maps can be realized.One-week data of Shaoxing City,China in 2013 is applied to the improved algorithms proposed with success estimated rate of 74.9%,where the rate dramatically increases by 8.6%through trip behaviours of resident and non-resident students.Also,adjustment of bus schedules and routes on Friday afternoon are taken as an example to illustrate the application of students trip chains,where detailed suggestions are put forward.In the future,more applications can be described while metro and shared bikes can be taken into consideration.