diff --git a/backend/app/get_travel_time.py b/backend/app/get_travel_time.py
index 73fd06f..8321dfa 100644
--- a/backend/app/get_travel_time.py
+++ b/backend/app/get_travel_time.py
@@ -11,6 +11,10 @@
 import json
 from app.getGitHash import getGitHash
 
+# this configures a setting (globally?) and removes a warning message
+# https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#copy-on-write
+pandas.options.mode.copy_on_write = True
+
 # the way we currently do it
 def mean_daily_mean(obs):
     # group the observations by date
@@ -73,7 +77,7 @@ def get_travel_time(start_node, end_node, start_time, end_time, start_date, end_
         SELECT
             link_dir,
             dt::text,
-            tx,
+            EXTRACT('epoch' FROM tx)::bigint AS tx,
             mean::real AS speed_kmph
         FROM here.ta
         WHERE
@@ -143,16 +147,28 @@ def get_travel_time(start_node, end_node, start_time, end_time, start_date, end_
                 'query_params': query_params
             }
         }
-    # rolling join of bins to data
-    link_speeds_df = pandas.merge_asof(
+    # Pandas is very bad at joining(?), so we start by CROSS joining the bins
+    # to the data, though this is very inefficient
+    link_speeds_df = pandas.merge(
         link_speeds_df,
-        pandas.DataFrame({'tx': bins,'bin':bins}),
-        on='tx',
-        direction='forward'
-    ).set_index('link_dir')
+        pandas.DataFrame({
+            'bin': [ bin['id'] for bin in bins ],
+            'bin_first_tx': [ bin['first'] for bin in bins ],
+            'bin_last_tx': [ bin['last'] for bin in bins ]
+        }),
+        how='cross'
+    )
+    # Now we filter out the records that don't align with their bins
+    link_speeds_df = link_speeds_df.query(
+        'tx >= bin_first_tx & tx <= bin_last_tx'
+    )
     # drop column used only for binning
-    link_speeds_df.drop('tx',axis='columns',inplace=True)
-
+    link_speeds_df.drop(
+        ['tx', 'bin_first_tx', 'bin_last_tx'],
+        axis='columns',
+        inplace=True
+    )
+    link_speeds_df = link_speeds_df.set_index('link_dir')
     # join previously queried link lengths
     link_speeds_df = link_speeds_df.join(links_df)
     # calculate link travel times from speed and length (in seconds)
@@ -229,24 +245,39 @@ def make_bins(links_df, link_speeds_df):
     """Create the smallest temporal bins possible while ensuring at least 80%
     of links, by length, have observations."""
     # start with empty list of bins, defined by their ends
-    bin_ends = list()
+    # TODO: define bin starts as well
+    bins = list()
     minimum_length = 0.8 * links_df['length'].sum()
 
     links_per_5mbin = {}
+    bin_counter = 1
     # iterate over 5-min time bins with data, in chronological order
     for tx in sorted(list(link_speeds_df.tx.unique())):
-        # add links one bin at a time
+        # get the data for this 5min bin
         bin5m = link_speeds_df[link_speeds_df['tx']==tx]
-        # get the distinct links in this 5-minute bin
+        # add the distinct links from this 5-minute bin
         links_per_5mbin[tx] = bin5m.link_dir.unique()
+        # in case data is very sparse, drop observations more than one hour
+        # prior to the current 5min bin - this window is moving!
+        keys_to_drop = []
+        for tx_key in links_per_5mbin.keys():
+            if tx - tx_key >= 3600: # seconds, i.e. 1 hour
+                keys_to_drop.append(tx_key)
+        for tx_key in keys_to_drop:
+            del links_per_5mbin[tx_key]
         # get all the links in all the 5m bins so far
         links = set( link for linklist in links_per_5mbin.values() for link in linklist )
         # measure the length of links in that set
         length_so_far = links_df.loc[list(links),'length'].sum()
-        # compare against length threshold
+        # compare against length threshold; if met, end the bin
         if length_so_far >= minimum_length:
-            bin_ends.append(tx)
+            bins.append({
+                'id': bin_counter,
+                'first': min(links_per_5mbin.keys()),
+                'last': tx # equivalently, max(links_per_5mbin.keys())
+            })
+            bin_counter += 1
             links_per_5mbin = {}
         else:
             pass
-    return bin_ends
+    return bins