add reference data and dashboards

Makefile

@@ -70,7 +70,6 @@ input/sumo.net.xml: input/network.osm
 
 input/$V/$N-$V-network.xml.gz: input/sumo.net.xml
 	$(sc) prepare network-from-sumo $< --output $@ --free-speed-factor 0.75
-	$(sc) prepare fix-network $@ --output $@
 	$(sc) prepare clean-network $@ --output $@ --modes car --modes bike
 
 
@@ -129,7 +128,7 @@ input/$V/$N-$V-100pct.plans.xml.gz: input/plans-longHaulFreight.xml.gz input/$V/
 
 	# TODO: set home coordinates attributes
 
-	$(sc) prepare extract-home-coordinates $@ --csv input/$V/$N-$V-homes.csv
+	$(sc) prepare extract-home-coordinates $@ --output $@ --csv input/$V/$N-$V-homes.csv
 
 	$(sc) prepare downsample-population $@\
     	 --sample-size 1\

src/main/java/org/matsim/dashboards/LausitzDashboardProvider.java

@@ -0,0 +1,23 @@
+package org.matsim.dashboards;
+
+import org.matsim.core.config.Config;
+import org.matsim.simwrapper.Dashboard;
+import org.matsim.simwrapper.DashboardProvider;
+import org.matsim.simwrapper.SimWrapper;
+import org.matsim.simwrapper.dashboard.TripDashboard;
+
+import java.util.List;
+
+/**
+ * Dashboards for the Lausitz scenario.
+ */
+public class LausitzDashboardProvider implements DashboardProvider {
+	@Override
+	public List<Dashboard> getDashboards(Config config, SimWrapper simWrapper) {
+		return List.of(new TripDashboard(
+			"lausitz_mode_share.csv",
+			"lausitz_mode_share_per_dist.csv",
+			"lausitz_mode_users.csv")
+		);
+	}
+}

src/main/python/calibrate.py

@@ -3,15 +3,15 @@
 
 import os
 
-import pandas as pd
 import geopandas as gpd
+import pandas as pd
 
 try:
     from matsim import calibration
 except:
     import calibration
 
-#%%
+# %%
 
 if os.path.exists("mid.csv"):
     srv = pd.read_csv("mid.csv")
@@ -26,7 +26,7 @@
 
     adj.to_csv("mid_adj.csv", index=False)
 
-#%%
+# %%
 
 modes = ["walk", "car", "ride", "pt", "bike"]
 fixed_mode = "walk"
@@ -37,29 +37,27 @@
     "ride": -2.22873502992
 }
 
-# FIXME: Adjust
+# Based on MiD 2017, filtered on Lausitz region
 target = {
-    "walk": 0.1,
-    "bike": 0.1,
-    "pt":  0.1,
-    "car": 0.1,
-    "ride": 0.1
+    "walk": 0.199819,
+    "bike": 0.116362,
+    "pt": 0.049501,
+    "car": 0.496881,
+    "ride": 0.137437
 }
 
-region = gpd.read_file("../scenarios/dilutionArea.shp").set_crs("EPSG:25832")
-homes = pd.read_csv("template-v1.0-homes.csv", dtype={"person": "str"})
+region = gpd.read_file("../input/lausitz.shp").to_crs("EPSG:25832")
 
 
 def filter_persons(persons):
-    persons = pd.merge(persons, homes, how="inner", left_on="person", right_on="person")
     persons = gpd.GeoDataFrame(persons, geometry=gpd.points_from_xy(persons.home_x, persons.home_y))
-
-    df = gpd.sjoin(persons.set_crs("EPSG:25832"), city, how="inner", op="intersects")
+    df = gpd.sjoin(persons.set_crs("EPSG:25832"), region, how="inner", op="intersects")
 
     print("Filtered %s persons" % len(df))
 
     return df
 
+
 def filter_modes(df):
     df = df[df.main_mode != "freight"]
     df.loc[df.main_mode.str.startswith("pt_"), "main_mode"] = "pt"
@@ -70,15 +68,15 @@ def filter_modes(df):
 # FIXME: Adjust paths and config
 
 study, obj = calibration.create_mode_share_study("calib", "matsim-template-1.0.jar",
-                                        "../scenarios/metropole-ruhr-v1.0/input/metropole-ruhr-v1.4-3pct.config.xml",
-                                        modes, target, 
-                                        initial_asc=initial,
-                                        args="--10pct",
-                                        jvm_args="-Xmx75G -Xmx75G -XX:+AlwaysPreTouch",
-                                        lr=calibration.linear_lr_scheduler(start=0.5),
-                                        person_filter=filter_persons, map_trips=filter_modes, chain_runs=True)
-
-
-#%%
+                                                 "../scenarios/metropole-ruhr-v1.0/input/metropole-ruhr-v1.4-3pct.config.xml",
+                                                 modes, target,
+                                                 initial_asc=initial,
+                                                 args="--10pct",
+                                                 jvm_args="-Xmx75G -Xmx75G -XX:+AlwaysPreTouch",
+                                                 transform_persons=filter_persons, transform_trips=filter_modes,
+                                                 lr=calibration.linear_lr_scheduler(start=0.3, interval=8),
+                                                 chain_runs=calibration.default_chain_scheduler)
+
+# %%
 
 study.optimize(obj, 10)

src/main/python/create_ref.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import geopandas as gpd
+
+from matsim.scenariogen.data import run_create_ref_data
+
+
+def person_filter(df):
+    df = gpd.GeoDataFrame(df, geometry=gpd.GeoSeries.from_wkt(df.geom, crs="EPSG:4326").to_crs("EPSG:25832"))
+    df = gpd.sjoin(df, region, how="inner", predicate="intersects")
+
+    return df[df.present_on_day & (df.reporting_day <= 5)]
+
+
+def trip_filter(df):
+    # Other modes are ignored in the total share
+    return df[df.main_mode != "other"]
+
+
+if __name__ == "__main__":
+    region = gpd.read_file("../../../../shared-svn/projects/DiTriMo/data/shp/lausitz.shp").to_crs("EPSG:25832")
+
+    persons, trips, share = run_create_ref_data.create("/Volumes/Untitled/B3_Lokal-Datensatzpaket/CSV",
+                                                       person_filter, trip_filter,
+                                                       run_create_ref_data.InvalidHandling.REMOVE_TRIPS)
+
+    print("Filtered %s persons" % len(persons))
+    print("Filtered %s trips" % len(trips))
+
+    print(share)

src/main/resources/META-INF/services/org.matsim.simwrapper.DashboardProvider

@@ -0,0 +1 @@
+org.matsim.dashboards.LausitzDashboardProvider

src/main/resources/lausitz_mode_share.csv

@@ -0,0 +1,31 @@
+dist_group,main_mode,mean_dist,share
+0 - 1000,bike,741.7474152768164,0.05431618817899002
+0 - 1000,car,845.2047065363095,0.031216669310203
+0 - 1000,pt,503.9569413263069,0.0012298417638100443
+0 - 1000,ride,798.8045427222489,0.01929390934129713
+0 - 1000,walk,474.8462693693281,0.14568353330391315
+1000 - 2000,bike,1644.2686306579485,0.01912094472020174
+1000 - 2000,car,1664.6974637289466,0.04206126416523887
+1000 - 2000,pt,1769.9848721595795,0.006125610783078046
+1000 - 2000,ride,1695.6202104840554,0.015018450003780185
+1000 - 2000,walk,1781.56327180136,0.028682645671379056
+2000 - 5000,bike,3415.967452046967,0.025207557865234546
+2000 - 5000,car,3821.2329823444597,0.13695546693420932
+2000 - 5000,pt,3715.77927121702,0.011575801348651667
+2000 - 5000,ride,3336.405564477932,0.03633788006858987
+2000 - 5000,walk,3058.339137223546,0.01911150073233698
+5000 - 10000,bike,6635.65436876184,0.011838556537851376
+5000 - 10000,car,7311.064045237122,0.11269186948089517
+5000 - 10000,pt,6979.744659157714,0.013102503056468777
+5000 - 10000,ride,6898.4939502130765,0.027491496179970917
+5000 - 10000,walk,6657.462194056846,0.006142216096671607
+10000 - 20000,bike,14378.984336238063,0.00281739136837012
+10000 - 20000,car,14576.783406018267,0.0755031637087224
+10000 - 20000,pt,13429.023672194808,0.01383155988361684
+10000 - 20000,ride,14240.788705878334,0.020277335207750966
+10000 - 20000,walk,12940.0,0.0001990871342660644
+20000+,bike,37248.118594095315,0.003061190266223855
+20000+,car,47805.15415602558,0.09845227912097768
+20000+,pt,41088.975762937975,0.0036358034320222465
+20000+,ride,68027.0017296671,0.01901828433527827
+20000+,walk,,0.0

src/main/resources/lausitz_mode_share_per_dist.csv

@@ -0,0 +1,31 @@
+dist_group,main_mode,mean_dist,share
+0 - 1000,bike,741.7474152768164,0.2157629203250064
+0 - 1000,car,845.2047065363095,0.12400354220355093
+0 - 1000,pt,503.9569413263069,0.004885362161698109
+0 - 1000,ride,798.8045427222489,0.07664216439942374
+0 - 1000,walk,474.8462693693281,0.5787060109103207
+1000 - 2000,bike,1644.2686306579485,0.17224692864536398
+1000 - 2000,car,1664.6974637289466,0.3788998751588497
+1000 - 2000,pt,1769.9848721595795,0.05518125066003455
+1000 - 2000,ride,1695.6202104840554,0.1352904850685536
+1000 - 2000,walk,1781.56327180136,0.2583814604671981
+2000 - 5000,bike,3415.967452046967,0.10998627809345088
+2000 - 5000,car,3821.2329823444597,0.5975676879599303
+2000 - 5000,pt,3715.77927121702,0.05050784027132093
+2000 - 5000,ride,3336.405564477932,0.15855039206564583
+2000 - 5000,walk,3058.339137223546,0.08338780160965216
+5000 - 10000,bike,6635.65436876184,0.06912354002160712
+5000 - 10000,car,7311.064045237122,0.6579907715325366
+5000 - 10000,pt,6979.744659157714,0.07650353246287123
+5000 - 10000,ride,6898.4939502130765,0.16051868573455097
+5000 - 10000,walk,6657.462194056846,0.03586347024843422
+10000 - 20000,bike,14378.984336238063,0.025014897963181838
+10000 - 20000,car,14576.783406018267,0.6703732954089843
+10000 - 20000,pt,13429.023672194808,0.12280688549155136
+10000 - 20000,ride,14240.788705878334,0.1800372773487143
+10000 - 20000,walk,12940.0,0.0017676437875682608
+20000+,bike,37248.118594095315,0.024653704529394964
+20000+,car,47805.15415602558,0.7928985749351034
+20000+,pt,41088.975762937975,0.029281428380669564
+20000+,ride,68027.0017296671,0.15316629215483207
+20000+,walk,,0.0

src/main/resources/lausitz_mode_users.csv

@@ -0,0 +1,6 @@
+main_mode,user
+pt,0.07115467203031302
+ride,0.213768422141053
+car,0.49898396490379887
+bike,0.159641024221889
+walk,0.2741758378287667