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package org.opentripplanner.analyst.scenario;
import com.google.common.collect.HashMultimap;
import com.google.common.collect.Multimap;
import com.google.common.primitives.Ints;
import gnu.trove.iterator.TObjectIntIterator;
import gnu.trove.map.TObjectIntMap;
import gnu.trove.map.hash.TObjectIntHashMap;
import gnu.trove.set.TIntSet;
import gnu.trove.set.hash.TIntHashSet;
import org.opentripplanner.model.Stop;
import org.opentripplanner.profile.RaptorWorkerTimetable;
import org.opentripplanner.routing.edgetype.TripPattern;
import org.opentripplanner.routing.graph.Graph;
import org.opentripplanner.routing.trippattern.FrequencyEntry;
import org.opentripplanner.routing.trippattern.TripTimes;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.*;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
import java.util.stream.Stream;
/**
* Convert scheduled trips to frequencies. Will partition trips by service day.
*/
public class ConvertToFrequency extends Modification {
private static final long serialVersionUID = 1L;
private static final Logger LOG = LoggerFactory.getLogger(ConvertToFrequency.class);
public List scheduledTrips = new ArrayList<>();
public List frequencyEntries = new ArrayList<>();
private Multimap tripsToConvert = HashMultimap.create();
public String[] routeId;
@Override public String getType() {
return "convert-to-frequency";
}
/** Windows in which to do the conversion, array of int[2] of startTimeSecs, endTimeSecs */
public int windowStart;
public int windowEnd;
/** How to group trips for conversion to frequencies: by route, route and direction, or by trip pattern. */
public ConversionGroup groupBy;
public void apply (List frequencyEntries, List scheduledTrips, Graph graph, BitSet servicesRunning, RaptorWorkerTimetable.BoardingAssumption assumption) {
// preserve existing frequency entries
this.frequencyEntries.addAll(frequencyEntries);
Set routeIds = new HashSet<>();
if (routeId != null)
Stream.of(routeId).forEach(routeIds::add);
// loop over scheduled trips and figure out what to do with them
for (TripTimes tt : scheduledTrips) {
if (routeId == null || routeIds.contains(tt.trip.getRoute().getId().getId())) {
// put this in the appropriate group for frequency conversion
String key;
switch (groupBy) {
case ROUTE_DIRECTION:
key = tt.trip.getRoute().getId().getId() + "_" + tt.trip.getDirectionId();
break;
case ROUTE:
key = tt.trip.getRoute().getId().getId();
break;
case PATTERN:
key = graph.index.patternForTrip.get(tt.trip).getExemplar().getId().getId();
break;
default:
throw new RuntimeException("Unrecognized group by value");
}
tripsToConvert.put(key, tt);
} else {
// don't touch this trip
this.scheduledTrips.add(tt);
}
}
// loop over all the groups and create frequency entries
GROUPS: for (Map.Entry> e: tripsToConvert.asMap().entrySet()) {
// get just the running services
List group = e.getValue().stream()
.filter(tt -> servicesRunning.get(tt.serviceCode))
.filter(tt -> windowStart < tt.getDepartureTime(0) && tt.getDepartureTime(0) < windowEnd)
.collect(Collectors.toList());
if (group.isEmpty())
continue GROUPS;
if (group.size() == 1) {
group.stream().forEach(scheduledTrips::add);
continue GROUPS;
}
// find the dominant pattern
TObjectIntMap patternCount = new TObjectIntHashMap<>(5, 0.75f, 0);
group.forEach(tt -> patternCount.adjustOrPutValue(graph.index.patternForTrip.get(tt.trip), 1, 1));
int maxCount = 0;
TripPattern tripPattern = null;
for (TObjectIntIterator it = patternCount.iterator(); it.hasNext();) {
it.advance();
if (it.value() > maxCount) {
maxCount = it.value();
tripPattern = it.key();
}
}
// find a stop that is common to all trip patterns. Sort the list so that the same common stop is always returned
NavigableSet stops = new TreeSet<>((s1, s2) -> s1.getId().compareTo(s2.getId()));
stops.addAll(tripPattern.getStops());
patternCount.keySet().stream().forEach(p -> stops.retainAll(p.getStops()));
if (stops.isEmpty()) {
LOG.warn("Unable to find common stop for key {}, not converting to frequencies", e.getKey());
scheduledTrips.addAll(e.getValue());
continue GROUPS;
}
Stop stop = stops.stream().findFirst().get();
// determine the median frequency at this stop
// use a set to handle duplicated trips
TIntSet arrivalTimes = new TIntHashSet();
for (boolean filter : new boolean[] { true, false }) {
for (TripTimes tt : group) {
TripPattern tp = graph.index.patternForTrip.get(tt.trip);
int arrivalTime = tt.getArrivalTime(tp.getStops().indexOf(stop));
// filter again so we don't have issues where one pattern has gone out of the time window but a longer
// one hasn't (i.e. there are two patterns, one has the common stop an hour past the start and the other
// five minutes after the start. The longer pattern will have times past the window, the
// shorter will not.
// however, if we apply the filter and end up with no trips at this stop, re-run with the filter disabled
if (windowStart < arrivalTime && arrivalTime < windowEnd || !filter)
arrivalTimes.add(arrivalTime);
}
// if we didn't find stops, continue, which will turn off the filter
if (arrivalTimes.size() > 1)
break;
}
// now convert to elapsed times
int[] arrivalTimeArray = arrivalTimes.toArray();
Arrays.sort(arrivalTimeArray);
int[] headway = new int[arrivalTimeArray.length - 1];
for (int i = 1; i < arrivalTimeArray.length; i++) {
headway[i - 1] = arrivalTimeArray[i] - arrivalTimeArray[i - 1];
}
Arrays.sort(headway);
// the headway that we will use
int aggregateHeadway;
if (assumption == RaptorWorkerTimetable.BoardingAssumption.WORST_CASE)
// simple: worst case analysis should use the worst case headway
aggregateHeadway = Ints.max(headway);
else {
// we want the average headway, but we we want the average of the headways weighted
// by themselves as if there is a two minute headway then a twenty-minute headway,
// customers are ten times as likely to experience the twenty minute headway
// (we want the average from the user's perspective, not the vehicle's perspective)
// This is a weighted average where the weight is the same as the headway so it simplifies
// to sum (headway^2) / sum(headway)
aggregateHeadway =
IntStream.of(headway).map(h -> h * h).sum() /
IntStream.of(headway).sum();
}
LOG.info("Headway for route {} ({}) in direction {}: {}min", tripPattern.route.getShortName(), tripPattern.route.getId().getId(), tripPattern.directionId, aggregateHeadway / 60);
// figure out running/dwell times based on the trips on this pattern
final TripPattern chosenTp = tripPattern;
List candidates = group.stream()
.filter(tt -> graph.index.patternForTrip.get(tt.trip) == chosenTp)
.collect(Collectors.toList());
// transposed from what you'd expect: stops on the rows
int[][] hopTimes = new int[tripPattern.getStops().size() - 1][candidates.size()];
int[][] dwellTimes = new int[tripPattern.getStops().size()][candidates.size()];
int tripIndex = 0;
for (TripTimes tt : candidates) {
for (int stopIndex = 0; stopIndex < tripPattern.getStops().size(); stopIndex++) {
dwellTimes[stopIndex][tripIndex] = tt.getDwellTime(stopIndex);
if (stopIndex > 0)
hopTimes[stopIndex - 1][tripIndex] = tt.getArrivalTime(stopIndex) - tt.getDepartureTime(stopIndex - 1);
}
tripIndex++;
}
// collapse it down
int[] meanHopTimes = new int[tripPattern.getStops().size() - 1];
int hopIndex = 0;
for (int[] hop : hopTimes) {
meanHopTimes[hopIndex++] = IntStream.of(hop).sum() / hop.length;
}
int[] meanDwellTimes = new int[tripPattern.getStops().size()];
int dwellIndex = 0;
for (int[] dwell : dwellTimes) {
meanDwellTimes[dwellIndex++] = IntStream.of(dwell).sum() / dwell.length;
}
// phew! now let's make a frequency entry
TripTimes tt = new TripTimes(candidates.get(0));
int cumulative = 0;
for (int i = 0; i < tt.getNumStops(); i++) {
tt.updateArrivalTime(i, cumulative);
cumulative += meanDwellTimes[i];
tt.updateDepartureTime(i, cumulative);
if (i + 1 < tt.getNumStops())
cumulative += meanHopTimes[i];
}
FrequencyEntry fe = new FrequencyEntry(windowStart - 60 * 60 * 3, windowEnd + 60 * 60 * 3, aggregateHeadway, false, tt);
this.frequencyEntries.add(fe);
}
}
public static enum ConversionGroup {
ROUTE_DIRECTION, ROUTE, PATTERN;
}
}
