diff --git a/pipeline/journey_times/__main__.py b/pipeline/journey_times/__main__.py
index e5f4e97..0951ae4 100644
--- a/pipeline/journey_times/__main__.py
+++ b/pipeline/journey_times/__main__.py
@@ -5,8 +5,8 @@ from datetime import date, timedelta
 import polars as pl
 from tqdm import tqdm
 
-from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR
-from .results import results_to_dataframe, save_results
+from .config import DESTINATIONS, MAX_CONCURRENT, MAX_POSTCODES, OUTPUT_DIR, MAX_DISTANCE_KM
+from .results import CheckpointSaver, results_to_dataframe, save_results
 from .tfl_client import fetch_journey_times
 
 
@@ -28,6 +28,35 @@ def main():
     postcodes_df = pl.read_parquet(OUTPUT_DIR / "postcodes_h3.parquet")
     print(f"Loaded {postcodes_df.height:,} postcodes")
 
+    # Filter to postcodes within 150km of destination using Haversine formula
+    earth_radius_km = 6371
+
+    dest_lat_rad = destination.lat * 3.14159265359 / 180
+    dest_lon_rad = destination.lon * 3.14159265359 / 180
+
+    postcodes_df = postcodes_df.with_columns(
+        (
+            2
+            * earth_radius_km
+            * (
+                (
+                    ((pl.lit(dest_lat_rad) - pl.col("lat") * 3.14159265359 / 180) / 2).sin()
+                    ** 2
+                    + pl.lit(dest_lat_rad).cos()
+                    * (pl.col("lat") * 3.14159265359 / 180).cos()
+                    * (
+                        (pl.lit(dest_lon_rad) - pl.col("long") * 3.14159265359 / 180) / 2
+                    ).sin()
+                    ** 2
+                )
+                .sqrt()
+                .arcsin()
+            )
+        ).alias("distance_km")
+    ).filter(pl.col("distance_km") <= MAX_DISTANCE_KM)
+
+    print(f"Filtered to {postcodes_df.height:,} postcodes within {MAX_DISTANCE_KM}km")
+
     postcode_data = list(
         zip(
             postcodes_df["postcode"].to_list(),
@@ -40,6 +69,15 @@ def main():
         postcode_data = random.sample(postcode_data, MAX_POSTCODES)
         print(f"Randomly sampled {MAX_POSTCODES} postcodes")
 
+    checkpoint_saver = CheckpointSaver(
+        destination_name=destination.name,
+        on_save=lambda path, count: print(f"Checkpoint saved: {count:,} results to {path}"),
+    )
+
+    def on_result(result):
+        pbar.update(1)
+        checkpoint_saver.add_result(result)
+
     with tqdm(total=len(postcode_data), desc="Fetching journeys") as pbar:
         results = asyncio.run(
             fetch_journey_times(
@@ -48,7 +86,7 @@ def main():
                 journey_date.strftime("%Y%m%d"),
                 journey_time,
                 MAX_CONCURRENT,
-                progress_callback=lambda _: pbar.update(1),
+                progress_callback=on_result,
             )
         )
 
@@ -70,6 +108,7 @@ def main():
     print(f"Completed: {successful}/{len(results)} successful")
 
     parquet_path = save_results(results_df, destination.name)
+    checkpoint_saver.cleanup_checkpoint()
     print(f"Saved to {parquet_path}")
 
 
diff --git a/pipeline/journey_times/config.py b/pipeline/journey_times/config.py
index 4741653..f2401cc 100644
--- a/pipeline/journey_times/config.py
+++ b/pipeline/journey_times/config.py
@@ -9,8 +9,11 @@ OUTPUT_DIR = DATA_DIR / "processed"
 
 MAX_DELAY = 10
 REQUESTS_PER_MIN = 500
-MAX_POSTCODES = 100  # Set to None to process all postcodes
-MAX_CONCURRENT = 5
+MAX_POSTCODES = None
+MAX_CONCURRENT = 80
+MAX_DISTANCE_KM = 110
+CHECKPOINT_INTERVAL = 10000
+
 
 DESTINATIONS = {
     "bank": Destination(51.5133, -0.0886, "Bank", "940GZZLUBNK"),
diff --git a/pipeline/journey_times/results.py b/pipeline/journey_times/results.py
index 4d4d54f..2845fb2 100644
--- a/pipeline/journey_times/results.py
+++ b/pipeline/journey_times/results.py
@@ -1,8 +1,9 @@
 from pathlib import Path
+from typing import Callable
 
 import polars as pl
 
-from .config import OUTPUT_DIR
+from .config import CHECKPOINT_INTERVAL, OUTPUT_DIR
 from .models import JourneyResult
 
 
@@ -21,6 +22,54 @@ def results_to_dataframe(results: list[JourneyResult]) -> pl.DataFrame:
     )
 
 
+class CheckpointSaver:
+    """Collects results and saves checkpoints at regular intervals."""
+
+    def __init__(
+        self,
+        destination_name: str,
+        output_dir: Path | None = None,
+        interval: int = CHECKPOINT_INTERVAL,
+        on_save: Callable[[Path, int], None] | None = None,
+    ):
+        self.destination_name = destination_name
+        self.output_dir = output_dir or OUTPUT_DIR
+        self.interval = interval
+        self.on_save = on_save
+        self.results: list[JourneyResult] = []
+        self._last_save_count = 0
+
+    def add_result(self, result: JourneyResult) -> None:
+        """Add a result and save checkpoint if interval is reached."""
+        self.results.append(result)
+        if len(self.results) - self._last_save_count >= self.interval:
+            self.save_checkpoint()
+
+    def save_checkpoint(self) -> Path:
+        """Save current results to checkpoint file."""
+        df = results_to_dataframe(self.results)
+        path = self._checkpoint_path()
+        df.write_parquet(path)
+        self._last_save_count = len(self.results)
+        if self.on_save:
+            self.on_save(path, len(self.results))
+        return path
+
+    def _checkpoint_path(self) -> Path:
+        safe_name = self.destination_name.lower().replace(" ", "-")
+        return self.output_dir / f"journey_times_{safe_name}_checkpoint.parquet"
+
+    def get_results(self) -> list[JourneyResult]:
+        """Return all collected results."""
+        return self.results
+
+    def cleanup_checkpoint(self) -> None:
+        """Remove the checkpoint file after successful completion."""
+        path = self._checkpoint_path()
+        if path.exists():
+            path.unlink()
+
+
 def save_results(
     results: pl.DataFrame,
     destination_name: str,