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package org.opentripplanner.profile;
import org.locationtech.jts.geom.Coordinate;
import gnu.trove.list.TIntList;
import gnu.trove.list.array.TIntArrayList;
import org.apache.commons.math3.util.FastMath;
import org.opentripplanner.analyst.ResultSet;
import org.opentripplanner.analyst.SampleSet;
import org.opentripplanner.analyst.TimeSurface;
import org.opentripplanner.analyst.cluster.ResultEnvelope;
import org.opentripplanner.analyst.core.IsochroneData;
import org.opentripplanner.analyst.request.SampleGridRenderer;
import org.opentripplanner.analyst.request.SampleGridRenderer.WTWD;
import org.opentripplanner.analyst.request.SampleGridRenderer.WTWDAccumulativeMetric;
import org.opentripplanner.common.geometry.AccumulativeGridSampler;
import org.opentripplanner.common.geometry.DelaunayIsolineBuilder;
import org.opentripplanner.common.geometry.SparseMatrixZSampleGrid;
import org.opentripplanner.common.geometry.SphericalDistanceLibrary;
import org.opentripplanner.routing.graph.Graph;
import org.opentripplanner.routing.graph.Vertex;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.stream.IntStream;
import java.util.stream.Stream;
import static org.apache.commons.math3.util.FastMath.toRadians;
/**
* Stores travel times propagated to the search targets (all vertices in the street network or a set of points of
* interest) in one-to-many repeated raptor profile routing.
*
* Each raptor call finds minimum travel times to each transit stop. Those must be propagated out to the final targets,
* giving minimum travel times to each street vertex (or each destination opportunity in accessibility analysis).
* Those results are then merged into the summary statistics per target over the whole time window
* (over many RAPTOR calls). This class handles the storage and merging of those summary statistics.
*
* Currently this includes minimum, maximum, and average earliest-arrival travel time for each target.
* We could conceivably retain all the propagated times for every departure minute instead of collapsing them down into
* three numbers. If they were all sorted, we could read off any quantile, including the median travel time.
* Leaving them in departure time order would also be interesting, since you could then see visually how the travel time
* varies as a function of departure time.
* We could also conceivably store travel time histograms per destination, but this entails a loss of information due
* to binning into minutes. These binned times could not be used to apply a smooth sigmoid cutoff which we usually
* do at one-second resolution.
*
* When exploring single-point (one-to-many) query results it would be great to have all these stored or produced on
* demand for visualization.
*/
public class PropagatedTimesStore {
private static final Logger LOG = LoggerFactory.getLogger(PropagatedTimesStore.class);
// Four parallel arrays has worse locality than one big 4|V|-length flat array, but merging per-raptor-call values
// into this summary statistics storage is not the slow part of the algorithm. Optimization should concentrate on
// the propagation of minimum travel times from transit stops to the street vertices.
Graph graph;
int size;
int[] mins, maxs, avgs;
ProfileRequest req;
// number of times to bootstrap the mean.
public final int N_BOOTSTRAPS = 400;
private static final Random random = new Random();
public PropagatedTimesStore(Graph graph, ProfileRequest req) {
this(graph, req, Vertex.getMaxIndex());
}
public PropagatedTimesStore(Graph graph, ProfileRequest req, int size) {
this.graph = graph;
this.req = req;
this.size = size;
mins = new int[size];
maxs = new int[size];
avgs = new int[size];
Arrays.fill(avgs, Integer.MAX_VALUE);
Arrays.fill(mins, Integer.MAX_VALUE);
Arrays.fill(maxs, Integer.MAX_VALUE);
}
/**
* @param times for search (varying departure time), an array of travel times to each destination.
* @param includeInAverages for each iteration, whether that iteration should be included in average calculations.
* In RaptorWorker's Monte Carlo code we also include minima and maxima, which should
* not be included in averages.
* Iterations that are not included in averages are still used to determine extrema.
*/
public void setFromArray(int[][] times, boolean[] includeInAverages, ConfidenceCalculationMethod confidenceCalculationMethod) {
if (times.length == 0)
// nothing to do
return;
// assume array is rectangular
int nTargets = times[0].length;
// cache random numbers. This should be fine as we're mixing it with the number of minutes
// at which each destination is accessible, which is sometimes not 120, as well as the stop
// position in the list (note that we have cleverly chosen a number which is a prime
// so is not divisible by the number of iterations on the bootstrap). Finally recall that
// the maximum number of times we're sampling from is generally 120 and we modulo this,
// so the pigeonhole principle applies.
// this is effectively a "random number generator" with phase 10007
int[] randomNumbers = random.ints().limit(10007).map(Math::abs).toArray();
int nextRandom = 0;
int effectiveIterations = 0;
for (int i = 0; i < includeInAverages.length; i++) {
if (includeInAverages[i]) effectiveIterations++;
}
// loop over targets on the outside so we can bootstrap
TARGETS: for (int target = 0; target < nTargets; target++) {
// compute the average
int sum = 0;
int count = 0;
TIntList timeList = new TIntArrayList();
TIntList avgList = new TIntArrayList();
ITERATIONS: for (int i = 0; i < times.length; i++) {
if (times[i][target] == RaptorWorker.UNREACHED)
continue ITERATIONS;
if (includeInAverages[i]) {
avgList.add(times[i][target]);
sum += times[i][target];
count++;
}
timeList.add(times[i][target]);
}
// never reachable
if (count == 0)
continue TARGETS;
// if the destination is reachable less than half the time, consider it unreachable "on average".
// This avoids issues where destinations are reachable for some very small percentage of the time, either because
// there is a single departure near the start of the time window, or because they take approximately 2 hours
// (the default maximum cutoff) to reach.
// Consider a search run with time window 7AM to 9AM, and an origin and destination connected by an express
// bus that runs once at 7:05. For the first five minutes of the time window, accessibility is very good.
// For the rest, there is no accessibility; if we didn't have this rule in place, the average would be the average
// of the time the destination is reachable, and the time it is unreachable would be excluded from the calculation
// (see issue 2148)
// There is another issue that this rule does not completely address. Consider a trip that takes 1:45
// exclusive of wait time and runs every half-hour. Half the time it takes less than two hours and is considered
// and half the time it takes more than two hours and is excluded, so the average is biased low on very long trips.
// This rule catches the most egregious cases (say where we average only the best four minutes out of a two-hour
// span) but does not completely address the issue. However if you're looking at a time cutoff significantly
// less than two hours, it's not a big problem. Significantly less is half the headway of your least-frequent service, because
// if there is a trip on your least-frequent service that takes on average the time cutoff plus one minute
// it will be unbiased and considered unreachable iff the longest trip is less than two hours, which it has
// to be if the time cutoff plus half the headway is less than two hours, assuming a symmetric travel time
// distribution.
// TODO: due to multiple paths to a target the distribution is not symmetrical though - evaluate the
// effect of this. Also, transfers muddy the concept of "worst frequency" since there is variation in mid-trip
// wait times as well.
if (count >= effectiveIterations * req.reachabilityThreshold)
avgs[target] = sum / count;
// TODO: correctly handle partial accessibility for bootstrap and percentile options.
switch (confidenceCalculationMethod) {
case BOOTSTRAP:
// now bootstrap out a 95% confidence interval on the time
int[] bootMeans = new int[N_BOOTSTRAPS];
nextRandom += N_BOOTSTRAPS * count % randomNumbers.length; // prevent overflow
final int randOff = nextRandom;
final int finalCount = count;
IntStream.range(0, N_BOOTSTRAPS).parallel().forEach(boot -> {
int bsum = 0;
// sample from the Monte Carlo distribution with replacement
for (int iter = 0; iter < finalCount; iter++) {
bsum += avgList
.get(randomNumbers[(randOff + boot * iter) % randomNumbers.length] % avgList.size());
//bsum += timeList.get(random.nextInt(count));
}
bootMeans[boot] = bsum / finalCount;
});
Arrays.sort(bootMeans);
// 2.5 percentile of distribution of means
mins[target] = bootMeans[N_BOOTSTRAPS / 40];
// 97.5 percentile of distribution of means
maxs[target] = bootMeans[N_BOOTSTRAPS - N_BOOTSTRAPS / 40];
break;
case PERCENTILE:
timeList.sort();
mins[target] = timeList.get(timeList.size() / 40);
maxs[target] = timeList.get(39 * timeList.size() / 40);
break;
case NONE:
mins[target] = maxs[target] = avgs[target];
break;
case MIN_MAX:
default:
mins[target] = timeList.min();
// worst case: if it is sometimes unreachable, worst case is unreachable; otherwise use the max from the
// time list.
// NB not using count here as it doesn't count iterations that are not included in averages
if (timeList.size() == times.length)
maxs[target] = timeList.max();
break;
}
}
}
/**
* Make a ResultEnvelope directly from a given SampleSet.
* The RaptorWorkerData must have been constructed from the same SampleSet.
*/
public ResultEnvelope makeResults(SampleSet ss, boolean includeTimes, boolean includeHistograms, boolean includeIsochrones) {
ResultEnvelope envelope = new ResultEnvelope();
// max times == worst case accessibility
envelope.worstCase = new ResultSet(maxs, ss.pset, includeTimes, includeHistograms, includeIsochrones);
envelope.avgCase = new ResultSet(avgs, ss.pset, includeTimes, includeHistograms, includeIsochrones);
envelope.bestCase = new ResultSet(mins, ss.pset, includeTimes, includeHistograms, includeIsochrones);
return envelope;
}
public TimeSurface.RangeSet makeSurfaces(RepeatedRaptorProfileRouter repeatedRaptorProfileRouter) {
TimeSurface.RangeSet rangeSet = new TimeSurface.RangeSet();
rangeSet.min = new TimeSurface(repeatedRaptorProfileRouter);
rangeSet.avg = new TimeSurface(repeatedRaptorProfileRouter);
rangeSet.max = new TimeSurface(repeatedRaptorProfileRouter);
for (Vertex vertex : graph.index.vertexForId.values()) {
int min = mins[vertex.getIndex()];
int max = maxs[vertex.getIndex()];
int avg = avgs[vertex.getIndex()];
if (avg == Integer.MAX_VALUE)
continue;
// Count is positive, extrema and sum must also be present
rangeSet.min.times.put(vertex, min);
rangeSet.max.times.put(vertex, max);
rangeSet.avg.times.put(vertex, avg);
}
return rangeSet;
}
/**
* This bypasses a bunch of conversion and copy steps and just makes the isochrones.
* This assumes that the target indexes in this router/propagatedTimesStore are vertex indexes, not pointset indexes.
* TODO parameter for a pointset or a vertex lookup table, so we can handle both.
*/
public ResultEnvelope makeIsochronesForVertices () {
ResultEnvelope envelope = new ResultEnvelope();
envelope.bestCase = makeIsochroneForVertices(mins);
envelope.avgCase = makeIsochroneForVertices(avgs);
envelope.worstCase = makeIsochroneForVertices(maxs);
return envelope;
}
/**
* This bypasses a bunch of TimeSurface conversion/copy steps we were going though and makes the isochrones directly.
* This assumes that the target indexes in this router/propagatedTimesStore are vertex indexes, not pointset indexes.
* Called three times on min/avg/max to create the three elements of a ResultEnvelope.
*/
private ResultSet makeIsochroneForVertices (int[] times) {
final int spacing = 5;
final int nMax = 24;
final int cutoffMinutes = 120;
final double gridSize = IsochroneGenerator.GRID_SIZE_METERS;
final double offroadDistanceMeters = gridSize * IsochroneGenerator.WALK_DISTANCE_GRID_SIZE_RATIO;
SparseMatrixZSampleGrid grid = makeSampleGridForVertices(times, gridSize);
long t0 = System.currentTimeMillis();
DelaunayIsolineBuilder isolineBuilder =
new DelaunayIsolineBuilder<>(grid.delaunayTriangulate(), new WTWD.IsolineMetric());
List isoData = new ArrayList();
for (int minutes = spacing, n = 0; minutes <= cutoffMinutes && n < nMax; minutes += spacing, n++) {
int seconds = minutes * 60;
WTWD z0 = new WTWD();
z0.w = 1.0;
z0.wTime = seconds;
z0.d = offroadDistanceMeters;
IsochroneData isochrone = new IsochroneData(seconds, isolineBuilder.computeIsoline(z0));
isoData.add(isochrone);
}
long t1 = System.currentTimeMillis();
ResultSet resultSet = new ResultSet();
resultSet.isochrones = new IsochroneData[isoData.size()];
isoData.toArray(resultSet.isochrones);
LOG.debug("Computed {} isochrones in {} msec", isoData.size(), (int) (t1 - t0));
return resultSet;
}
/**
* Create a SampleGrid from only the times stored in this PropagatedTimesStore.
* This assumes that the target indexes in this router/propagatedTimesStore are vertex indexes, not pointset indexes.
* This is not really ideal since it includes only intersection nodes, and no points along the road segments.
* FIXME this may be why we're getting hole-punching failures.
* TODO: rewrite the isoline code to use only primitive collections and operate on a scalar field.
*/
public SparseMatrixZSampleGrid makeSampleGridForVertices (int[] times, final double gridSizeMeters) {
SparseMatrixZSampleGrid grid;
long t0 = System.currentTimeMillis();
// Off-road max distance MUST be APPROX EQUALS to the grid precision
// TODO: Loosen this restriction (by adding more closing sample).
// Change the 0.8 magic factor here with caution. Should be roughly grid size.
final double offroadWalkDistance = 0.8 * gridSizeMeters;
final double offroadWalkSpeed = 1.00; // in m/sec
Coordinate coordinateOrigin = graph.getCenter().get();
final double cosLat = FastMath.cos(toRadians(coordinateOrigin.y));
double dY = Math.toDegrees(gridSizeMeters / SphericalDistanceLibrary.RADIUS_OF_EARTH_IN_M);
double dX = dY / cosLat;
grid = new SparseMatrixZSampleGrid(16, times.length, dX, dY, coordinateOrigin);
AccumulativeGridSampler.AccumulativeMetric metric =
new WTWDAccumulativeMetric(cosLat, offroadWalkDistance, offroadWalkSpeed, gridSizeMeters);
AccumulativeGridSampler sampler =
new AccumulativeGridSampler<>(grid, metric);
// Iterate over every vertex, adding it to the ZSampleGrid if it was reached.
for (int v = 0; v < times.length; v++) {
int time = times[v];
if (time == Integer.MAX_VALUE) {
continue; // MAX_VALUE is the "unreached" value
}
WTWD z = new WTWD();
z.w = 1.0;
z.d = 0.0;
z.wTime = time;
z.wBoardings = 0; // unused
z.wWalkDist = 0; // unused
Vertex vertex = graph.getVertexById(v); // FIXME ack, this uses a hashtable and autoboxing!
// FIXME we should propagate along street geometries here
if (vertex != null) {
sampler.addSamplingPoint(vertex.getCoordinate(), z, offroadWalkSpeed);
}
}
sampler.close();
long t1 = System.currentTimeMillis();
LOG.info("Made scalar SampleGrid from TimeSurface in {} msec.", (int) (t1 - t0));
return grid;
}
public int countTargetsReached() {
int count = 0;
for (int min : mins) {
if (min != RaptorWorker.UNREACHED) {
count++;
}
}
return count;
}
public static enum ConfidenceCalculationMethod {
/** Do not calculate confidence intervals */
NONE,
/**
* Calculate confidence intervals around the mean using the bootstrap. Note that this calculates
* the confidence that the mean is in fact the mean of all possible schedules, not the confidence
* that a particular but unknown schedule will behave a certain way.
*
* This is absolutely the correct approach in systems that are specified as frequencies both
* in the model and operationally, because the parameter of interest is the average accessibility
* afforded by every realization of transfer and wait time. This yields nice tiny confidence
* intervals around the mean, and allows us easily to measure changes in average accessibility.
*
* However, when you have a system that will eventually be scheduled, you are interested not
* in the distribution of the average accessibility over all possible schedules, but rather
* the distribution of the accessibility afforded by a particular but unknown schedule. This
* does not require bootstrapping; it's just taking percentiles on the output of the Monte
* Carlo simulation. Unfortunately this requires a lot more Monte Carlo samples, as of
* course the middle of the distribution will stabilize long before the extrema.
*/
BOOTSTRAP,
/**
* Calculate confidence intervals based on percentiles, which is what you want to do when
* you have a scheduled network.
*/
PERCENTILE,
/**
* Take the min and the max of the experienced of the experienced times. Monte Carlo simulations also include
* one run with worst-case and one run with best-case boarding, so this is valid even for frequency service.
*/
MIN_MAX
}
}
