Lane　change　(LC)　behavior　has　critical　effects　on　traffic　flows　and　safety　due　to　its　complex　interactions　with　surrounding　vehicles.　To　ensure　safe　lane　changes　and　prevent　potential　crashes,　it　is　important　to　recognize　the　potential　crash　risk　of　lane　change　in　real　time.　This　study　proposes　an　improved　risk　estimation　(IRE)　model　to　evaluate　the　potential　collision　risk　of　lane　change　(LCR)　vehicle　groups.　The　safety　margin　is　introduced　to　consider　the　deceleration　capability　of　vehicles　to　measure　the　reaction　time　of　drivers　during　the　LC.　Then　the　IRE　model　is　established,　incorporating　the　collision　probability　and　collision　severity　measured　based　on　the　safety　margin.　The　trajectory　data,　extracted　from　the　highD　dataset,　are　used　and　1536　LC　samples　are　investigated.　We　compare　the　LCR　under　different　contextual　factors,　including　vehicle　types　(cars　and　trucks),　two　lane　change　directions　(left　and　right　lane　change,　LLC　and　RLC),　and　traffic　flows　(low　and　high　traffic).　It　was　found　that　truck　drivers　keep　higher　LCR　compared　with　car　drivers　due　to　limited　brake　capacity,　and　the　left　lane　change　results　in　higher　LCR　compared　with　the　right　lane　change.　Additionally,　lane　change　is　associated　with　higher　crash　risk　in　high　traffic　flow,　as　compared　to　low　traffic　flow.　The　understanding　of　the　crash　risk　of　lane　change　behavior　under　different　contextual　factors,　can　be　useful　for　real-time　crash　prediction　and　devising　traffic　management　strategies.