Examples with TObjectIntIterator gnu.trove.iterator.TObjectIntIterator used on opensource projects

Example 1 with TObjectIntIterator

use of gnu.trove.iterator.TObjectIntIterator in project OpenTripPlanner by opentripplanner.

the class ConvertToFrequency method apply.

public void apply(List<FrequencyEntry> frequencyEntries, List<TripTimes> scheduledTrips, Graph graph, BitSet servicesRunning, RaptorWorkerTimetable.BoardingAssumption assumption) {
    // preserve existing frequency entries
    this.frequencyEntries.addAll(frequencyEntries);
    Set<String> 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<String, Collection<TripTimes>> e : tripsToConvert.asMap().entrySet()) {
        // get just the running services
        List<TripTimes> 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<TripPattern> 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<TripPattern> 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<Stop> 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));
                // 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<TripTimes> 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);
    }
}