SimWrapper Dashboard features (matsim-org#3381)

* add explanations to the traffic dashboard, add pt viewer dashboard

* add additional description

* add avg beeline speed to trip dashboard

* added mode share distance distribution

* added plot for detailed distance distribution

* upscale emission dashboard with sample size

contribs/application/src/main/java/org/matsim/application/analysis/emissions/AirPollutionAnalysis.java

@@ -205,11 +205,11 @@ private void writeOutput(Network network, EmissionsOnLinkEventHandler emissionsE
 				if (link2pollutants.get(linkId).get(pollutant) != null) {
 					emissionValue = link2pollutants.get(linkId).get(pollutant);
 				}
-				absolute.print(nf.format(emissionValue));
+				absolute.print(nf.format(emissionValue * sample.getUpscaleFactor()));
 
 				Link link = network.getLinks().get(linkId);
 				double emissionPerM = emissionValue / link.getLength();
-				perMeter.print(nf.format(emissionPerM));
+				perMeter.print(nf.format(emissionPerM * sample.getUpscaleFactor()));
 			}
 
 			absolute.println();
@@ -246,7 +246,7 @@ private void writeTotal(Network network, EmissionsOnLinkEventHandler emissionsEv
 
 			total.printRecord("Pollutant", "kg");
 			for (Pollutant p : Pollutant.values()) {
-				double val = (sum.getDouble(p) / sample.getSample()) / 1000;
+				double val = (sum.getDouble(p) * sample.getUpscaleFactor()) / 1000;
 				total.printRecord(p, val < 100_000 && val > 100 ? simple.format(val) : scientific.format(val));
 			}
 
@@ -286,7 +286,7 @@ private void writeAvroRaster(Network network, Config config, EmissionsOnLinkEven
 		for (int xi = 0; xi < xLength.get(0); xi++) {
 			for (int yi = 0; yi < yLength.get(0); yi++) {
 				Coord coord = raster.getCoordForIndex(xi, yi);
-				double value = rasterMap.get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi);
+				double value = rasterMap.get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi) * sample.getUpscaleFactor();
 				if (xi == 0) yCoords.add((float) coord.getY());
 				if (yi == 0) xCoords.add((float) coord.getX());
 				valuesList.add((float) value);
@@ -349,7 +349,7 @@ private void writeRaster(Network network, Config config, EmissionsOnLinkEventHan
 				for (int yi = 0; yi < yLength.get(0); yi++) {
 
 					Coord coord = raster.getCoordForIndex(xi, yi);
-					double value = rasterMap.get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi);
+					double value = rasterMap.get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi) * sample.getUpscaleFactor();
 
 					if (value == 0)
 						continue;
@@ -406,7 +406,7 @@ private void writeTimeDependentRaster(Network network, Config config, EmissionsO
 					for (TimeBinMap.TimeBin<Map<Pollutant, Raster>> timeBin : timeBinMap.getTimeBins()) {
 
 						Coord coord = raster.getCoordForIndex(xi, yi);
-						double value = timeBin.getValue().get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi);
+						double value = timeBin.getValue().get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi) * sample.getUpscaleFactor();
 
 						if (value == 0)
 							continue;
@@ -467,7 +467,8 @@ private void writeTimeDependentAvroRaster(Network network, Config config, Emissi
 					if (yi == 0 && isFirst)
 						xCoords.add((float) coord.getX());
 
-					valuesList.add((float) timeBin.getValue().get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi));
+					double value = timeBin.getValue().get(Pollutant.CO2_TOTAL).getValueByIndex(xi, yi) * sample.getUpscaleFactor();
+					valuesList.add((float) value);
 				}
 			}
 		}

contribs/application/src/main/java/org/matsim/application/analysis/population/TripAnalysis.java

@@ -8,6 +8,7 @@
 import it.unimi.dsi.fastutil.objects.Object2LongOpenHashMap;
 import org.apache.commons.csv.CSVFormat;
 import org.apache.commons.csv.CSVPrinter;
+import org.apache.commons.math3.analysis.interpolation.LoessInterpolator;
 import org.apache.logging.log4j.LogManager;
 import org.apache.logging.log4j.Logger;
 import org.locationtech.jts.geom.Coordinate;
@@ -32,6 +33,7 @@
 import java.math.RoundingMode;
 import java.nio.file.Files;
 import java.util.*;
+import java.util.stream.IntStream;
 
 import static tech.tablesaw.aggregate.AggregateFunctions.count;
 
@@ -41,6 +43,7 @@
 	produces = {
 		"mode_share.csv", "mode_share_per_dist.csv", "mode_users.csv", "trip_stats.csv",
 		"mode_share_per_%s.csv", "population_trip_stats.csv", "trip_purposes_by_hour.csv",
+		"mode_share_distance_distribution.csv",
 		"mode_choices.csv", "mode_choice_evaluation.csv", "mode_choice_evaluation_per_mode.csv",
 		"mode_confusion_matrix.csv", "mode_prediction_error.csv"
 	}
@@ -109,6 +112,25 @@ private static int durationToSeconds(String d) {
 		return (Integer.parseInt(split[0]) * 60 * 60) + (Integer.parseInt(split[1]) * 60) + Integer.parseInt(split[2]);
 	}
 
+	private static double[] calcHistogram(double[] data, double[] bins) {
+
+		double[] hist = new double[bins.length - 1];
+
+		for (int i = 0; i < bins.length - 1; i++) {
+
+			double binStart = bins[i];
+			double binEnd = bins[i + 1];
+
+			// The last right bin edge is inclusive, which is consistent with the numpy implementation
+			if (i == bins.length - 2)
+				hist[i] = Arrays.stream(data).filter(d -> d >= binStart && d <= binEnd).count();
+			else
+				hist[i] = Arrays.stream(data).filter(d -> d >= binStart && d < binEnd).count();
+		}
+
+		return hist;
+	}
+
 	@Override
 	public Integer call() throws Exception {
 
@@ -247,6 +269,8 @@ public Integer call() throws Exception {
 
 		writeTripPurposes(joined);
 
+		writeTripDistribution(joined);
+
 		return 0;
 	}
 
@@ -293,13 +317,15 @@ private void writeTripStats(Table trips) throws IOException {
 		Object2IntMap<String> n = new Object2IntLinkedOpenHashMap<>();
 		Object2LongMap<String> travelTime = new Object2LongOpenHashMap<>();
 		Object2LongMap<String> travelDistance = new Object2LongOpenHashMap<>();
+		Object2LongMap<String> beelineDistance = new Object2LongOpenHashMap<>();
 
 		for (Row trip : trips) {
 			String mainMode = trip.getString("main_mode");
 
 			n.mergeInt(mainMode, 1, Integer::sum);
 			travelTime.mergeLong(mainMode, durationToSeconds(trip.getString("trav_time")), Long::sum);
 			travelDistance.mergeLong(mainMode, trip.getLong("traveled_distance"), Long::sum);
+			beelineDistance.mergeLong(mainMode, trip.getLong("euclidean_distance"), Long::sum);
 		}
 
 		try (CSVPrinter printer = new CSVPrinter(Files.newBufferedWriter(output.getPath("trip_stats.csv")), CSVFormat.DEFAULT)) {
@@ -338,6 +364,13 @@ private void writeTripStats(Table trips) throws IOException {
 			}
 			printer.println();
 
+			printer.print("Avg. beeline speed [km/h]");
+			for (String m : modeOrder) {
+				double speed = (beelineDistance.getLong(m) / 1000d) / (travelTime.getLong(m) / (60d * 60d));
+				printer.print(new BigDecimal(speed).setScale(2, RoundingMode.HALF_UP));
+			}
+			printer.println();
+
 			printer.print("Avg. distance per trip [km]");
 			for (String m : modeOrder) {
 				double avg = (travelDistance.getLong(m) / 1000d) / (n.getInt(m));
@@ -458,6 +491,55 @@ private void writeTripPurposes(Table trips) {
 
 	}
 
+	private void writeTripDistribution(Table trips) throws IOException {
+
+		Map<String, double[]> dists = new LinkedHashMap<>();
+
+		// Note that the results of this interpolator are consistent with the one performed in matsim-python-tools
+		// This makes the results comparable with reference data, changes here will also require changes in the python package
+		LoessInterpolator inp = new LoessInterpolator(0.05, 0);
+
+		long max = distGroups.get(distGroups.size() - 3) + distGroups.get(distGroups.size() - 2);
+
+		double[] bins = IntStream.range(0, (int) (max / 100)).mapToDouble(i -> i * 100).toArray();
+		double[] x = Arrays.copyOf(bins, bins.length - 1);
+
+		for (String mode : modeOrder) {
+			double[] distances = trips.where(
+					trips.stringColumn("main_mode").equalsIgnoreCase(mode))
+				.numberColumn("traveled_distance").asDoubleArray();
+
+			double[] hist = calcHistogram(distances, bins);
+
+			double[] y = inp.smooth(x, hist);
+			dists.put(mode, y);
+		}
+
+		try (CSVPrinter printer = new CSVPrinter(Files.newBufferedWriter(output.getPath("mode_share_distance_distribution.csv")), CSVFormat.DEFAULT)) {
+
+			printer.print("dist");
+			for (String s : modeOrder) {
+				printer.print(s);
+			}
+			printer.println();
+
+			for (int i = 0; i < x.length; i++) {
+
+				double sum = 0;
+				for (String s : modeOrder) {
+					sum += Math.max(0, dists.get(s)[i]);
+				}
+
+				printer.print(x[i]);
+				for (String s : modeOrder) {
+					double value = Math.max(0, dists.get(s)[i]) / sum;
+					printer.print(value);
+				}
+				printer.println();
+			}
+		}
+	}
+
 	/**
 	 * How shape file filtering should be applied.
 	 */

contribs/application/src/main/java/org/matsim/application/analysis/traffic/TrafficStatsCalculator.java

@@ -13,7 +13,7 @@
 
 /**
  * Class to calculate the traffic congestion index based on the paper
- * "A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index" by Feifei He, Xuedong Yan*, Yang Liu, Lu Ma.
+ * "A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index" by Feifei He, Xuedong Yan, Yang Liu, Lu Ma.
  */
 public final class TrafficStatsCalculator {
 

contribs/application/src/main/java/org/matsim/application/options/SampleOptions.java

@@ -124,6 +124,13 @@ public double getSample() {
 		return sample;
 	}
 
+	/**
+	 * Return factor that is used to upscale the sample size.
+	 */
+	public double getUpscaleFactor() {
+		return 1.0 / sample;
+	}
+
 	private void setSize(double sample) {
 		this.set = true;
 		this.sample = sample;

contribs/simwrapper/src/main/java/org/matsim/simwrapper/DefaultDashboardProvider.java

@@ -19,10 +19,13 @@ public List<Dashboard> getDashboards(Config config, SimWrapper simWrapper) {
 		List<Dashboard> result = new ArrayList<>(List.of(
 			new OverviewDashboard(),
 			new TripDashboard(),
-			new TrafficDashboard(),
-			new StuckAgentDashboard()
+			new TrafficDashboard()
 		));
 
+		if (config.transit().isUseTransit()) {
+			result.add(new PublicTransitDashboard());
+		}
+
 		if (config.counts().getCountsFileName() != null) {
 			result.add(new TrafficCountsDashboard());
 		}
@@ -35,6 +38,8 @@ public List<Dashboard> getDashboards(Config config, SimWrapper simWrapper) {
 			result.add(new NoiseDashboard());
 		}
 
+		result.add(new StuckAgentDashboard());
+
 		return result;
 	}
 

contribs/simwrapper/src/main/java/org/matsim/simwrapper/dashboard/EmissionsDashboard.java

@@ -20,7 +20,7 @@ public class EmissionsDashboard implements Dashboard {
 	public void configure(Header header, Layout layout) {
 
 		header.title = "Emissions";
-		header.description = "Shows the emissions footprint and spatial distribution.";
+		header.description = "Shows the emissions footprint and spatial distribution. Shown values are already upscaled from simulated sample size.";
 
 
 		layout.row("links")

contribs/simwrapper/src/main/java/org/matsim/simwrapper/dashboard/PublicTransitDashboard.java

@@ -0,0 +1,28 @@
+package org.matsim.simwrapper.dashboard;
+
+import org.matsim.simwrapper.Dashboard;
+import org.matsim.simwrapper.Header;
+import org.matsim.simwrapper.Layout;
+import org.matsim.simwrapper.viz.TransitViewer;
+
+/**
+ * Standard dashboard for public transit.
+ */
+public class PublicTransitDashboard implements Dashboard {
+
+	@Override
+	public void configure(Header header, Layout layout) {
+
+		header.title = "Public Transit";
+		header.tab = "PT";
+		header.triggerPattern = "*output_transitSchedule*xml*";
+
+		layout.row("viewer").el(TransitViewer.class, (viz, data) -> {
+			viz.title = "Transit Viewer";
+			viz.height = 12d;
+			viz.description = "Visualize the transit schedule.";
+			viz.network = "*output_network.xml.gz";
+			viz.transitSchedule = data.output("*output_transitSchedule.xml.gz");
+		});
+	}
+}

contribs/simwrapper/src/main/java/org/matsim/simwrapper/dashboard/TrafficCountsDashboard.java

@@ -69,7 +69,7 @@ public TrafficCountsDashboard withQualityLabels(List<Double> limits, List<String
 	public void configure(Header header, Layout layout) {
 
 		header.title = "Traffic Counts";
-		header.description = "Comparison of observed and simulated daily traffic volumes.\nError metrics: ";
+		header.description = "Comparison of observed and simulated daily traffic volumes. Reported volumes are scaled-up according to simulated sample size. \nError metrics based on relative error: ";
 
 		for (int i = 0; i < labels.size(); i++) {
 			if (i == 0)

contribs/simwrapper/src/main/java/org/matsim/simwrapper/dashboard/TrafficDashboard.java

@@ -6,10 +6,7 @@
 import org.matsim.simwrapper.Dashboard;
 import org.matsim.simwrapper.Header;
 import org.matsim.simwrapper.Layout;
-import org.matsim.simwrapper.viz.ColorScheme;
-import org.matsim.simwrapper.viz.MapPlot;
-import org.matsim.simwrapper.viz.Plotly;
-import org.matsim.simwrapper.viz.Table;
+import org.matsim.simwrapper.viz.*;
 import tech.tablesaw.plotly.components.Axis;
 import tech.tablesaw.plotly.traces.ScatterTrace;
 
@@ -93,5 +90,18 @@ public void configure(Header header, Layout layout) {
 			viz.height = 12d;
 		});
 
+
+		layout.row("info").el(TextBlock.class, (viz, data) -> {
+			viz.backgroundColor = "transparent";
+			viz.content = """
+				### Notes
+				- The speed performance index is the ratio of average travel speed and the maximum permissible road speed.
+				A performance index of 0.5, means that the average speed is half of the maximum permissible speed. A road with a performance index below 0.5 is considered to be in a congested state.
+				- The congestion index is the ratio of time a road is in an uncongested state. 0.5 means that a road is congested half of the time. A road with 1.0 is always uncongested.
+
+				cf. *A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index* by Feifei He, Xuedong Yan*, Yang Liu, Lu Ma.
+				""";
+		});
+
 	}
 }