diff --git a/README.md b/README.md
index 7548d71..fdad65d 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ python auto_remove_redlight.py 01.MOV 03.MOV 05.MOV
 | `speed_threshold` | `0` | 時速 ≤ 此值視為停止 |
 | `min_confidence` | `0.5` | OCR 信心低於此值視為雜訊 |
 | `smooth_window` | `5` | 中位數濾波視窗（取樣點數） |
-| `merge_gap` | `3.0` | 相鄰紅燈段間隔小於此秒數即合併 |
+| `depart_seconds` | `5.0` | 起步門檻（秒）：兩段停車間「持續行駛」需超過此秒數才算真正起步而保留；否則（起步 N 秒內又停）視為仍在等待，連同短暫蠕動一起剪掉（處理車陣走走停停），也順便吸收 OCR 雜訊斷點 |
 | `cut_before_stop` | `2.0` | 進入紅燈端（速度到 0）移除起點再往前幾秒，連減速進站一起砍 |
 | `keep_after_stop` | `2.0` | 綠燈起步端（速度從 0 開始跑）移除終點提早幾秒，多留卡達起步畫面 |
 | `min_keep` | `0.8` | 保留片段短於此秒數即丟棄 |
diff --git a/auto_remove_redlight.py b/auto_remove_redlight.py
index 8bf9ded..fce8e50 100644
--- a/auto_remove_redlight.py
+++ b/auto_remove_redlight.py
@@ -101,7 +101,10 @@ MAX_SPEED       = 300               # 合理時速上限,超過視為誤判(雜
 #   - 孤立的雜訊讀數會被周圍的 0 吃掉(中位數)
 #   - 辨識失敗的空值會用前後最近的有效讀數補上
 SMOOTH_WINDOW   = 5                 # 中位數濾波視窗(取樣點數,奇數)。5 = 看前後各 2 秒
-MERGE_GAP       = 3.0               # 兩個紅燈移除段若間隔小於此秒數,視為同一段並合併 (秒)
+DEPART_SECONDS  = 5.0               # 起步門檻(秒): 兩段停車中間「持續行駛(GPS>0)」需超過此
+                                    #   秒數,才算真正起步而保留;否則(起步 N 秒內又停)視為
+                                    #   仍在等待,連同那段短暫蠕動一起剪掉(處理車陣走走停停)。
+                                    #   也順便吸收 OCR 雜訊造成的短暫斷點。
 
 # --- 剪輯 / 輸出參數 ----------------------------------------
 # 紅燈移除段的頭尾「不對稱」微調:
@@ -347,48 +350,59 @@ def find_keep_intervals(samples: List[Sample], duration: float, interval: float
     smoothed = smooth_speeds(samples, SMOOTH_WINDOW)
     stopped = [v <= SPEED_THRESHOLD for v in smoothed]
 
-    removals: List[Tuple[float, float]] = []
+    # 1) 先抓出所有「原始連續停止」區段(不論長短)
+    raw_runs: List[List[float]] = []
     i = 0
     while i < n:
         if stopped[i]:
-            # 找出這一段連續停止的範圍 i..j
             j = i
             while j + 1 < n and stopped[j + 1]:
                 j += 1
-
             run_start = samples[i].t
-            # 視為持續到「下一個取樣點」才結束,所以 +interval
-            run_end = min(samples[j].t + interval, duration)
-            run_dur = run_end - run_start
-
-            # 是否一路停到「影片結尾」(結尾就是 GPS 0)
-            at_end = run_end >= duration - 1e-3
-
-            # 列為移除的條件: 達到紅燈門檻,或停到影片結尾(結尾停車一律砍掉)
-            if run_dur >= STOP_SECONDS or at_end:
-                # 進入紅燈端往前多砍 CUT_BEFORE_STOP 秒(連減速進站一起砍)。
-                rstart = max(0.0, run_start - CUT_BEFORE_STOP)
-                if at_end:
-                    # 停到影片結尾: 沒有起步要留 → 砍到底,不保留 KEEP_AFTER_STOP
-                    rend = run_end
-                else:
-                    # 一般紅燈: 綠燈起步端提早 KEEP_AFTER_STOP 秒結束(多留卡達起步)
-                    rend = run_end - KEEP_AFTER_STOP
-                if rend > rstart:
-                    removals.append((rstart, rend))
+            run_end = min(samples[j].t + interval, duration)   # 持續到下個取樣點
+            raw_runs.append([run_start, run_end])
             i = j + 1
         else:
             i += 1
 
-    # 合併「間隔很短」的相鄰移除段(平滑後仍可能殘留 1~2 格雜訊把一個紅燈切兩半)
-    if removals:
-        merged: List[Tuple[float, float]] = [removals[0]]
-        for (rs, re) in removals[1:]:
-            if rs - merged[-1][1] < MERGE_GAP:
-                merged[-1] = (merged[-1][0], re)   # 接起來
+    # 2) 合併「中間行駛(GPS>0)不超過 DEPART_SECONDS 秒」的相鄰停止段:
+    #    起步後 N 秒內又停 → 視為仍在等待(車陣走走停停),連那段短暫蠕動一起算停止。
+    #    只有行駛「超過」DEPART_SECONDS 才算真正起步,該行駛段才會被保留。
+    merged_runs: List[List[float]] = []
+    for run in raw_runs:
+        if merged_runs and (run[0] - merged_runs[-1][1]) <= DEPART_SECONDS:
+            merged_runs[-1][1] = run[1]            # 行駛 <= 門檻 → 併入前一段停止
+        else:
+            merged_runs.append(list(run))
+
+    # 3) 套用門檻與頭尾偏移 → 產生移除區段
+    removals: List[Tuple[float, float]] = []
+    for (run_start, run_end) in merged_runs:
+        run_dur = run_end - run_start
+        at_end = run_end >= duration - 1e-3        # 是否一路停到影片結尾
+
+        # 列為移除: 達到紅燈門檻,或停到影片結尾(結尾停車一律砍掉)
+        if run_dur >= STOP_SECONDS or at_end:
+            # 進入紅燈端往前多砍 CUT_BEFORE_STOP 秒(連減速進站一起砍)。
+            rstart = max(0.0, run_start - CUT_BEFORE_STOP)
+            if at_end:
+                # 停到影片結尾: 沒有起步要留 → 砍到底,不保留 KEEP_AFTER_STOP
+                rend = run_end
             else:
-                merged.append((rs, re))
-        removals = merged
+                # 一般紅燈: 綠燈起步端提早 KEEP_AFTER_STOP 秒結束(多留卡達起步)
+                rend = run_end - KEEP_AFTER_STOP
+            if rend > rstart:
+                removals.append((rstart, rend))
+
+    # 4) 頭尾偏移後若仍有相鄰移除段重疊/相接,合併之
+    if removals:
+        merged: List[List[float]] = [list(removals[0])]
+        for (rs, re) in removals[1:]:
+            if rs <= merged[-1][1]:
+                merged[-1][1] = max(merged[-1][1], re)
+            else:
+                merged.append([rs, re])
+        removals = [(a, b) for a, b in merged]
 
     # keeps = 全片 [0, duration] 扣掉所有 removals
     keeps: List[Tuple[float, float]] = []
@@ -951,7 +965,7 @@ _CONFIG_KEYS = {
     "speed_threshold": "SPEED_THRESHOLD",
     "min_confidence":  "MIN_CONFIDENCE",
     "smooth_window":   "SMOOTH_WINDOW",
-    "merge_gap":       "MERGE_GAP",
+    "depart_seconds":  "DEPART_SECONDS",
     "cut_before_stop": "CUT_BEFORE_STOP",
     "keep_after_stop": "KEEP_AFTER_STOP",
     "min_keep":        "MIN_KEEP",
diff --git a/config.json b/config.json
index 0ed04f1..0e67464 100644
--- a/config.json
+++ b/config.json
@@ -11,7 +11,7 @@
   "speed_threshold": 0,
   "min_confidence": 0.5,
   "smooth_window": 5,
-  "merge_gap": 3.0,
+  "depart_seconds": 5.0,
   "cut_before_stop": 2.0,
   "keep_after_stop": 2.0,
   "min_keep": 0.8,