Examples with AStar org.opentripplanner.routing.algorithm.AStar used on opensource projects

Example 1 with AStar

use of org.opentripplanner.routing.algorithm.AStar in project OpenTripPlanner by opentripplanner.

the class SampleGridRenderer method getSampleGrid.

/**
 * @param spgRequest
 * @param sptRequest
 * @return
 */
public ZSampleGrid<WTWD> getSampleGrid(SampleGridRequest spgRequest, RoutingRequest sptRequest) {
    final double offRoadDistanceMeters = spgRequest.offRoadDistanceMeters;
    // m/s, off-road walk speed
    final double offRoadWalkSpeedMps = 1.00;
    // 1. Compute the Shortest Path Tree.
    long t0 = System.currentTimeMillis();
    long tOvershot = (long) (2 * offRoadDistanceMeters / offRoadWalkSpeedMps);
    sptRequest.worstTime = (sptRequest.dateTime + (sptRequest.arriveBy ? -spgRequest.maxTimeSec - tOvershot : spgRequest.maxTimeSec + tOvershot));
    sptRequest.batch = (true);
    sptRequest.setRoutingContext(graph);
    // TODO swap in different state dominance logic (earliest arrival, pareto, etc.)
    final ShortestPathTree spt = new AStar().getShortestPathTree(sptRequest);
    // 3. Create a sample grid based on the SPT.
    long t1 = System.currentTimeMillis();
    Coordinate coordinateOrigin = spgRequest.coordinateOrigin;
    if (coordinateOrigin == null)
        coordinateOrigin = sptRequest.from.getCoordinate();
    final double gridSizeMeters = spgRequest.precisionMeters;
    final double cosLat = FastMath.cos(toRadians(coordinateOrigin.y));
    double dY = Math.toDegrees(gridSizeMeters / SphericalDistanceLibrary.RADIUS_OF_EARTH_IN_M);
    double dX = dY / cosLat;
    SparseMatrixZSampleGrid<WTWD> sampleGrid = new SparseMatrixZSampleGrid<WTWD>(16, spt.getVertexCount(), dX, dY, coordinateOrigin);
    sampleSPT(spt, sampleGrid, gridSizeMeters, offRoadDistanceMeters, offRoadWalkSpeedMps, sptRequest.getMaxWalkDistance(), spgRequest.maxTimeSec, cosLat);
    sptRequest.cleanup();
    long t2 = System.currentTimeMillis();
    LOG.info("Computed SPT in {}msec, {}msec for sampling ({} msec total)", (int) (t1 - t0), (int) (t2 - t1), (int) (t2 - t0));
    return sampleGrid;
}

Example 2 with AStar

use of org.opentripplanner.routing.algorithm.AStar in project OpenTripPlanner by opentripplanner.

the class GraphPathFinder method getPaths.

/**
 * Repeatedly build shortest path trees, retaining the best path to the destination after each try.
 * For search N, all trips used in itineraries retained from trips 0..(N-1) are "banned" to create variety.
 * The goal direction heuristic is reused between tries, which means the later tries have more information to
 * work with (in the case of the more sophisticated bidirectional heuristic, which improves over time).
 */
public List<GraphPath> getPaths(RoutingRequest options) {
    RoutingRequest originalReq = options.clone();
    if (options == null) {
        LOG.error("PathService was passed a null routing request.");
        return null;
    }
    // Reuse one instance of AStar for all N requests, which are carried out sequentially
    AStar aStar = new AStar();
    if (options.rctx == null) {
        options.setRoutingContext(router.graph);
    // The special long-distance heuristic should be sufficient to constrain the search to the right area.
    }
    // If this Router has a GraphVisualizer attached to it, set it as a callback for the AStar search
    if (router.graphVisualizer != null) {
        aStar.setTraverseVisitor(router.graphVisualizer.traverseVisitor);
    // options.disableRemainingWeightHeuristic = true; // DEBUG
    }
    // Without transit, we'd just just return multiple copies of the same on-street itinerary.
    if (!options.modes.isTransit()) {
        options.numItineraries = 1;
    }
    // FORCING the dominance function to weight only
    options.dominanceFunction = new DominanceFunction.MinimumWeight();
    LOG.debug("rreq={}", options);
    // Choose an appropriate heuristic for goal direction.
    RemainingWeightHeuristic heuristic;
    RemainingWeightHeuristic reversedSearchHeuristic;
    if (options.disableRemainingWeightHeuristic) {
        heuristic = new TrivialRemainingWeightHeuristic();
        reversedSearchHeuristic = new TrivialRemainingWeightHeuristic();
    } else if (options.modes.isTransit()) {
        // Only use the BiDi heuristic for transit. It is not very useful for on-street modes.
        // heuristic = new InterleavedBidirectionalHeuristic(options.rctx.graph);
        // Use a simplistic heuristic until BiDi heuristic is improved, see #2153
        heuristic = new InterleavedBidirectionalHeuristic();
        reversedSearchHeuristic = new InterleavedBidirectionalHeuristic();
    } else {
        heuristic = new EuclideanRemainingWeightHeuristic();
        reversedSearchHeuristic = new EuclideanRemainingWeightHeuristic();
    }
    options.rctx.remainingWeightHeuristic = heuristic;
    /* In RoutingRequest, maxTransfers defaults to 2. Over long distances, we may see
         * itineraries with far more transfers. We do not expect transfer limiting to improve
         * search times on the LongDistancePathService, so we set it to the maximum we ever expect
         * to see. Because people may use either the traditional path services or the 
         * LongDistancePathService, we do not change the global default but override it here. */
    options.maxTransfers = 4;
    // Now we always use what used to be called longDistance mode. Non-longDistance mode is no longer supported.
    options.longDistance = true;
    /* In long distance mode, maxWalk has a different meaning than it used to.
         * It's the radius around the origin or destination within which you can walk on the streets.
         * If no value is provided, max walk defaults to the largest double-precision float.
         * This would cause long distance mode to do unbounded street searches and consider the whole graph walkable. */
    if (options.maxWalkDistance == Double.MAX_VALUE)
        options.maxWalkDistance = DEFAULT_MAX_WALK;
    if (options.maxWalkDistance > CLAMP_MAX_WALK)
        options.maxWalkDistance = CLAMP_MAX_WALK;
    long searchBeginTime = System.currentTimeMillis();
    LOG.debug("BEGIN SEARCH");
    List<GraphPath> paths = Lists.newArrayList();
    while (paths.size() < options.numItineraries) {
        // TODO pull all this timeout logic into a function near org.opentripplanner.util.DateUtils.absoluteTimeout()
        int timeoutIndex = paths.size();
        if (timeoutIndex >= router.timeouts.length) {
            timeoutIndex = router.timeouts.length - 1;
        }
        double timeout = searchBeginTime + (router.timeouts[timeoutIndex] * 1000);
        // Convert from absolute to relative time
        timeout -= System.currentTimeMillis();
        // Convert milliseconds to seconds
        timeout /= 1000;
        if (timeout <= 0) {
            // Catch the case where advancing to the next (lower) timeout value means the search is timed out
            // before it even begins. Passing a negative relative timeout in the SPT call would mean "no timeout".
            options.rctx.aborted = true;
            break;
        }
        // Don't dig through the SPT object, just ask the A star algorithm for the states that reached the target.
        aStar.getShortestPathTree(options, timeout);
        if (options.rctx.aborted) {
            // Search timed out or was gracefully aborted for some other reason.
            break;
        }
        List<GraphPath> newPaths = aStar.getPathsToTarget();
        if (newPaths.isEmpty()) {
            break;
        }
        // Do a full reversed search to compact the legs
        if (options.compactLegsByReversedSearch) {
            newPaths = compactLegsByReversedSearch(aStar, originalReq, options, newPaths, timeout, reversedSearchHeuristic);
        }
        // Find all trips used in this path and ban them for the remaining searches
        for (GraphPath path : newPaths) {
            // path.dump();
            List<AgencyAndId> tripIds = path.getTrips();
            for (AgencyAndId tripId : tripIds) {
                options.banTrip(tripId);
            }
            if (tripIds.isEmpty()) {
                // This path does not use transit (is entirely on-street). Do not repeatedly find the same one.
                options.onlyTransitTrips = true;
            }
        }
        paths.addAll(newPaths.stream().filter(path -> {
            double duration = options.useRequestedDateTimeInMaxHours ? options.arriveBy ? options.dateTime - path.getStartTime() : path.getEndTime() - options.dateTime : path.getDuration();
            return duration < options.maxHours * 60 * 60;
        }).collect(Collectors.toList()));
        LOG.debug("we have {} paths", paths.size());
    }
    LOG.debug("END SEARCH ({} msec)", System.currentTimeMillis() - searchBeginTime);
    Collections.sort(paths, new PathComparator(options.arriveBy));
    return paths;
}

Example 3 with AStar

use of org.opentripplanner.routing.algorithm.AStar in project OpenTripPlanner by opentripplanner.

the class SIsochrone method getIsochrone.

/**
 * Calculates walksheds for a given location, based on time given to walk and the walk speed.
 *
 * Depending on the value for the "output" parameter (i.e. "POINTS", "SHED" or "EDGES"), a
 * different type of GeoJSON geometry is returned. If a SHED is requested, then a ConcaveHull
 * of the EDGES/roads is returned. If that fails, a ConvexHull will be returned.
 * <p>
 * The ConcaveHull parameter is set to 0.005 degrees. The offroad walkspeed is assumed to be
 * 0.83333 m/sec (= 3km/h) until a road is hit.
 * <p>
 * Note that the set of EDGES/roads returned as well as POINTS returned may contain duplicates.
 * If POINTS are requested, then not the end-points are returned at which the max time is
 * reached, but instead all the graph nodes/crossings that are within the time limits.
 * <p>
 * In case there is no road near by within the given time, then a circle for the walktime limit
 * is created and returned for the SHED parameter. Otherwise the edge with the direction
 * towards the closest road. Note that the circle is calculated in Euclidian 2D coordinates,
 * and distortions towards an ellipse will appear if it is transformed/projected to the user location.
 * <p>
 * An example request may look like this:
 * localhost:8080/otp-rest-servlet/ws/iso?layers=traveltime&styles=mask&batch=true&fromPlace=51.040193121307176
 * %2C-114.04471635818481&toPlace
 * =51.09098935%2C-113.95179705&time=2012-06-06T08%3A00%3A00&mode=WALK&maxWalkDistance=10000&walkSpeed=1.38&walkTime=10.7&output=EDGES
 * Though the first parameters (i) layer, (ii) styles and (iii) batch could be discarded.
 *
 * @param walkmins Maximum number of minutes to walk.
 * @param output Can be set to "POINTS", "SHED" or "EDGES" to return different types of GeoJSON
 *        geometry. SHED returns a ConcaveHull or ConvexHull of the edges/roads. POINTS returns
 *        all graph nodes that are within the time limit.
 * @return a JSON document containing geometries (either points, lineStrings or a polygon).
 * @throws Exception
 * @author sstein---geo.uzh.ch
 */
@GET
@Produces({ MediaType.APPLICATION_JSON })
public String getIsochrone(@QueryParam("walkTime") @DefaultValue("15") double walkmins, @QueryParam("output") @DefaultValue("POINTS") String output) throws Exception {
    this.debugGeoms = new ArrayList();
    this.tooFastTraversedEdgeGeoms = new ArrayList();
    RoutingRequest sptRequestA = buildRequest();
    String from = sptRequestA.from.toString();
    int pos = 1;
    float lat = 0;
    float lon = 0;
    for (String s : from.split(",")) {
        if (s.isEmpty()) {
            // no location
            Response.status(Status.BAD_REQUEST).entity("no position").build();
            return null;
        }
        try {
            float num = Float.parseFloat(s);
            if (pos == 1) {
                lat = num;
            }
            if (pos == 2) {
                lon = num;
            }
        } catch (Exception e) {
            throw new WebApplicationException(Response.status(Status.BAD_REQUEST).entity("Could not parse position string to number. Require numerical lat & long coords.").build());
        }
        pos++;
    }
    GeometryFactory gf = new GeometryFactory();
    Coordinate dropPoint = new Coordinate(lon, lat);
    int walkInMin = (int) Math.floor(walkmins);
    double walkInSec = walkmins * 60;
    LOG.debug("given travel time: " + walkInMin + " mins + " + (walkInSec - (60 * walkInMin)) + " sec");
    // graph dynamically by 1.3 * min -> this should save processing time
    if (walkInMin < 30) {
        sptRequestA.worstTime = sptRequestA.dateTime + (30 * 60);
    } else {
        sptRequestA.worstTime = sptRequestA.dateTime + Math.round(walkInMin * 1.3 * 60);
    }
    // set the switch-time for shed/area calculation, i.e. to decide if the hull is calculated based on points or on edges
    TraverseModeSet modes = sptRequestA.modes;
    LOG.debug("mode(s): " + modes);
    if (modes.contains(TraverseMode.TRANSIT)) {
        // 20min (use 20min for transit, since buses may not come all the time)
        shedCalcMethodSwitchTimeInSec = 60 * 20;
    } else if (modes.contains(TraverseMode.CAR)) {
        // 10min
        shedCalcMethodSwitchTimeInSec = 60 * 10;
    } else if (modes.contains(TraverseMode.BICYCLE)) {
        // 10min
        shedCalcMethodSwitchTimeInSec = 60 * 10;
    } else {
        // 20min
        shedCalcMethodSwitchTimeInSec = 60 * 20;
    }
    // set the maxUserSpeed, which is used later to check for u-type streets/crescents when calculating sub-edges;
    // Note, that the car speed depends on the edge itself, so this value may be replaced later
    this.usesCar = false;
    int numberOfModes = modes.getModes().size();
    if (numberOfModes == 1) {
        if (modes.getWalk()) {
            this.maxUserSpeed = sptRequestA.walkSpeed;
        } else if (modes.getBicycle()) {
            this.maxUserSpeed = sptRequestA.bikeSpeed;
        } else if (modes.getCar()) {
            this.maxUserSpeed = sptRequestA.carSpeed;
            this.usesCar = true;
        }
    } else {
        // for all other cases (multiple-modes)
        // sstein: I thought I may set it to 36.111 m/sec = 130 km/h,
        // but maybe it is better to assume walk speed for transit, i.e. treat it like if the
        // person gets off the bus on the last crossing and walks the "last mile".
        this.maxUserSpeed = sptRequestA.walkSpeed;
    }
    if (doSpeedTest) {
        LOG.debug("performing angle and speed based test to detect u-shapes");
    } else {
        LOG.debug("performing only angle based test to detect u-shapes");
    }
    // TODO: OTP prefers to snap to car-roads/ways, which is not so nice, when walking,
    // and a footpath is closer by. So far there is no option to switch that off
    Router router = otpServer.getRouter(routerId);
    // create the ShortestPathTree
    try {
        sptRequestA.setRoutingContext(router.graph);
    } catch (Exception e) {
        // if we get an exception here, and in particular a VertexNotFoundException,
        // then it is likely that we chose a (transit) mode without having that (transit) modes data
        LOG.debug("cannot set RoutingContext: " + e.toString());
        LOG.debug("cannot set RoutingContext: setting mode=WALK");
        // fall back to walk mode
        sptRequestA.setMode(TraverseMode.WALK);
        sptRequestA.setRoutingContext(router.graph);
    }
    ShortestPathTree sptA = new AStar().getShortestPathTree(sptRequestA);
    StreetLocation origin = (StreetLocation) sptRequestA.rctx.fromVertex;
    // remove inserted points
    sptRequestA.cleanup();
    // create a LineString for display
    Coordinate[] pathToStreetCoords = new Coordinate[2];
    pathToStreetCoords[0] = dropPoint;
    pathToStreetCoords[1] = origin.getCoordinate();
    LineString pathToStreet = gf.createLineString(pathToStreetCoords);
    // get distance between origin and drop point for time correction
    double distanceToRoad = SphericalDistanceLibrary.distance(origin.getY(), origin.getX(), dropPoint.y, dropPoint.x);
    long offRoadTimeCorrection = (long) (distanceToRoad / this.offRoadWalkspeed);
    // 
    // --- filter the states ---
    // 
    Set<Coordinate> visitedCoords = new HashSet<Coordinate>();
    ArrayList<Edge> allConnectingEdges = new ArrayList<Edge>();
    Coordinate[] coords = null;
    long maxTime = (long) walkInSec - offRoadTimeCorrection;
    // if the initial walk is already to long, there is no need to parse...
    if (maxTime <= 0) {
        noRoadNearBy = true;
        long timeToWalk = (long) walkInSec;
        long timeBetweenStates = offRoadTimeCorrection;
        long timeMissing = timeToWalk;
        double fraction = (double) timeMissing / (double) timeBetweenStates;
        pathToStreet = getSubLineString(pathToStreet, fraction);
        LOG.debug("no street found within giving travel time (for off-road walkspeed: {} m/sec)", this.offRoadWalkspeed);
    } else {
        noRoadNearBy = false;
        Map<ReversibleLineStringWrapper, Edge> connectingEdgesMap = Maps.newHashMap();
        for (State state : sptA.getAllStates()) {
            long et = state.getElapsedTimeSeconds();
            if (et <= maxTime) {
                // 250 points away (while 145 were finally displayed)
                if (visitedCoords.contains(state.getVertex().getCoordinate())) {
                    continue;
                } else {
                    visitedCoords.add(state.getVertex().getCoordinate());
                }
                // -- get all Edges needed later for the edge representation
                // and to calculate an edge-based walkshed
                // Note, it can happen that we get a null geometry here, e.g. for hop-edges!
                Collection<Edge> vertexEdgesIn = state.getVertex().getIncoming();
                for (Iterator<Edge> iterator = vertexEdgesIn.iterator(); iterator.hasNext(); ) {
                    Edge edge = (Edge) iterator.next();
                    Geometry edgeGeom = edge.getGeometry();
                    if (edgeGeom != null) {
                        // make sure we get only real edges
                        if (edgeGeom instanceof LineString) {
                            // allConnectingEdges.add(edge); // instead of this, use a map now, so we don't have similar edge many times
                            connectingEdgesMap.put(new ReversibleLineStringWrapper((LineString) edgeGeom), edge);
                        }
                    }
                }
                Collection<Edge> vertexEdgesOut = state.getVertex().getOutgoing();
                for (Iterator<Edge> iterator = vertexEdgesOut.iterator(); iterator.hasNext(); ) {
                    Edge edge = (Edge) iterator.next();
                    Geometry edgeGeom = edge.getGeometry();
                    if (edgeGeom != null) {
                        if (edgeGeom instanceof LineString) {
                            // allConnectingEdges.add(edge); // instead of this, use a map now, so we don't similar edge many times
                            connectingEdgesMap.put(new ReversibleLineStringWrapper((LineString) edgeGeom), edge);
                        }
                    }
                }
            }
        // end : if(et < maxTime)
        }
        // --
        // points from list to array, for later
        coords = new Coordinate[visitedCoords.size()];
        int i = 0;
        for (Coordinate c : visitedCoords) coords[i++] = c;
        // connection edges from Map to List
        allConnectingEdges.clear();
        for (Edge tedge : connectingEdgesMap.values()) allConnectingEdges.add(tedge);
    }
    StringWriter sw = new StringWriter();
    GeometryJSON geometryJSON = new GeometryJSON();
    // 
    try {
        if (output.equals(SIsochrone.RESULT_TYPE_POINTS)) {
            // and return those points
            if (noRoadNearBy) {
                Geometry circleShape = createCirle(dropPoint, pathToStreet);
                coords = circleShape.getCoordinates();
            }
            // -- the states/nodes with time elapsed <= X min.
            LOG.debug("write multipoint geom with {} points", coords.length);
            geometryJSON.write(gf.createMultiPoint(coords), sw);
            LOG.debug("done");
        } else if (output.equals(SIsochrone.RESULT_TYPE_SHED)) {
            Geometry[] geomsArray = null;
            // in case there was no road we create a circle
            if (noRoadNearBy) {
                Geometry circleShape = createCirle(dropPoint, pathToStreet);
                geometryJSON.write(circleShape, sw);
            } else {
                if (maxTime > shedCalcMethodSwitchTimeInSec) {
                    // eg., walkshed > 20 min
                    // -- create a point-based walkshed
                    // less exact and should be used for large walksheds with many edges
                    LOG.debug("create point-based shed (not from edges)");
                    geomsArray = new Geometry[coords.length];
                    for (int j = 0; j < geomsArray.length; j++) {
                        geomsArray[j] = gf.createPoint(coords[j]);
                    }
                } else {
                    // -- create an edge-based walkshed
                    // it is more exact and should be used for short walks
                    LOG.debug("create edge-based shed (not from points)");
                    Map<ReversibleLineStringWrapper, LineString> walkShedEdges = Maps.newHashMap();
                    // add the walk from the pushpin to closest street point
                    walkShedEdges.put(new ReversibleLineStringWrapper(pathToStreet), pathToStreet);
                    // get the edges and edge parts within time limits
                    ArrayList<LineString> withinTimeEdges = this.getLinesAndSubEdgesWithinMaxTime(maxTime, allConnectingEdges, sptA, angleLimitForUShapeDetection, distanceToleranceForUShapeDetection, maxUserSpeed, usesCar, doSpeedTest);
                    for (LineString ls : withinTimeEdges) {
                        walkShedEdges.put(new ReversibleLineStringWrapper(ls), ls);
                    }
                    geomsArray = new Geometry[walkShedEdges.size()];
                    int k = 0;
                    for (LineString ls : walkShedEdges.values()) geomsArray[k++] = ls;
                }
                // end if-else: maxTime condition
                GeometryCollection gc = gf.createGeometryCollection(geomsArray);
                // create the concave hull, but in case it fails we just return the convex hull
                Geometry outputHull = null;
                LOG.debug("create concave hull from {} geoms with edge length limit of about {} m (distance on meridian)", geomsArray.length, concaveHullAlpha * 111132);
                // (see wikipedia: http://en.wikipedia.org/wiki/Latitude#The_length_of_a_degree_of_latitude)
                try {
                    ConcaveHull hull = new ConcaveHull(gc, concaveHullAlpha);
                    outputHull = hull.getConcaveHull();
                } catch (Exception e) {
                    outputHull = gc.convexHull();
                    LOG.debug("Could not generate ConcaveHull for WalkShed, using ConvexHull instead.");
                }
                LOG.debug("write shed geom");
                geometryJSON.write(outputHull, sw);
                LOG.debug("done");
            }
        } else if (output.equals(SIsochrone.RESULT_TYPE_EDGES)) {
            // in case there was no road we return only the suggested path to the street
            if (noRoadNearBy) {
                geometryJSON.write(pathToStreet, sw);
            } else {
                // -- if we would use only the edges from the paths to the origin we will miss
                // some edges that will be never on the shortest path (e.g. loops/crescents).
                // However, we can retrieve all edges by checking the times for each
                // edge end-point
                Map<ReversibleLineStringWrapper, LineString> walkShedEdges = Maps.newHashMap();
                // add the walk from the pushpin to closest street point
                walkShedEdges.put(new ReversibleLineStringWrapper(pathToStreet), pathToStreet);
                // get the edges and edge parts within time limits
                ArrayList<LineString> withinTimeEdges = this.getLinesAndSubEdgesWithinMaxTime(maxTime, allConnectingEdges, sptA, angleLimitForUShapeDetection, distanceToleranceForUShapeDetection, maxUserSpeed, usesCar, doSpeedTest);
                for (LineString ls : withinTimeEdges) {
                    walkShedEdges.put(new ReversibleLineStringWrapper(ls), ls);
                }
                Geometry mls = null;
                LineString[] edges = new LineString[walkShedEdges.size()];
                int k = 0;
                for (LineString ls : walkShedEdges.values()) edges[k++] = ls;
                LOG.debug("create multilinestring from {} geoms", edges.length);
                mls = gf.createMultiLineString(edges);
                LOG.debug("write geom");
                geometryJSON.write(mls, sw);
                LOG.debug("done");
            }
        } else if (output.equals("DEBUGEDGES")) {
            // -- for debugging, i.e. display of detected u-shapes/crescents
            ArrayList<LineString> withinTimeEdges = this.getLinesAndSubEdgesWithinMaxTime(maxTime, allConnectingEdges, sptA, angleLimitForUShapeDetection, distanceToleranceForUShapeDetection, maxUserSpeed, usesCar, doSpeedTest);
            if (this.showTooFastEdgesAsDebugGeomsANDnotUShapes) {
                LOG.debug("displaying edges that are traversed too fast");
                this.debugGeoms = this.tooFastTraversedEdgeGeoms;
            } else {
                LOG.debug("displaying detected u-shaped roads/crescents");
            }
            LineString[] edges = new LineString[this.debugGeoms.size()];
            int k = 0;
            for (Iterator iterator = debugGeoms.iterator(); iterator.hasNext(); ) {
                LineString ls = (LineString) iterator.next();
                edges[k] = ls;
                k++;
            }
            Geometry mls = gf.createMultiLineString(edges);
            LOG.debug("write debug geom");
            geometryJSON.write(mls, sw);
            LOG.debug("done");
        }
    } catch (Exception e) {
        LOG.error("Exception creating isochrone", e);
    }
    return sw.toString();
}

Example 4 with AStar

use of org.opentripplanner.routing.algorithm.AStar in project OpenTripPlanner by opentripplanner.

the class RepeatedRaptorProfileRouter method findInitialStops.

/**
 * Find all transit stops accessible by streets around the origin, leaving behind a shortest path tree of the
 * reachable area in the field preTransitSpt.
 *
 * @param data the raptor data table to use. If this is null (i.e. there is no transit) range is extended,
 *             and we don't care if we actually find any stops, we just want the tree of on-street distances.
 */
@VisibleForTesting
public TIntIntMap findInitialStops(boolean dest, RaptorWorkerData data) {
    LOG.info("Finding initial stops");
    double lat = dest ? request.toLat : request.fromLat;
    double lon = dest ? request.toLon : request.fromLon;
    QualifiedModeSet modes = dest ? request.egressModes : request.accessModes;
    RoutingRequest rr = new RoutingRequest(modes);
    rr.batch = true;
    rr.from = new GenericLocation(lat, lon);
    // rr.walkSpeed = request.walkSpeed;
    rr.to = rr.from;
    rr.setRoutingContext(graph);
    rr.dateTime = request.date.toDateMidnight(DateTimeZone.forTimeZone(graph.getTimeZone())).getMillis() / 1000 + request.fromTime;
    rr.walkSpeed = request.walkSpeed;
    rr.bikeSpeed = request.bikeSpeed;
    if (data == null) {
        // Non-transit mode. Search out to the full 120 minutes.
        // Should really use directModes.
        rr.worstTime = rr.dateTime + RaptorWorker.MAX_DURATION;
        rr.dominanceFunction = new DominanceFunction.EarliestArrival();
    } else {
        // Transit mode, limit pre-transit travel.
        if (rr.modes.contains(TraverseMode.BICYCLE)) {
            rr.dominanceFunction = new DominanceFunction.EarliestArrival();
            rr.worstTime = rr.dateTime + request.maxBikeTime * 60;
        } else {
            // We use walk-distance limiting and a least-walk dominance function in order to be consistent with egress walking
            // which is implemented this way because walk times can change when walk speed changes. Also, walk times are floating
            // point and can change slightly when streets are split. Street lengths are internally fixed-point ints, which do not
            // suffer from roundoff. Great care is taken when splitting to preserve sums.
            // When cycling, this is not an issue; we already have an explicitly asymmetrical search (cycling at the origin, walking at the destination),
            // so we need not preserve symmetry.
            // We use the max walk time for the search at the origin, but we clamp it to MAX_WALK_METERS so that we don;t
            // have every transit stop in the state as an initial transit stop if someone sets max walk time to four days,
            // and so that symmetry is preserved.
            // FIXME kind of arbitrary
            rr.maxWalkDistance = Math.min(request.maxWalkTime * 60 * request.walkSpeed, GraphIndex.MAX_WALK_METERS);
            rr.softWalkLimiting = false;
            rr.dominanceFunction = new DominanceFunction.LeastWalk();
        }
    }
    rr.numItineraries = 1;
    rr.longDistance = true;
    AStar aStar = new AStar();
    preTransitSpt = aStar.getShortestPathTree(rr, 5);
    // otherwise return null and leave preTransitSpt around for later use.
    if (data != null) {
        TIntIntMap accessTimes = data.findStopsNear(preTransitSpt, graph, rr.modes.contains(TraverseMode.BICYCLE), request.walkSpeed);
        LOG.info("Found {} transit stops", accessTimes.size());
        return accessTimes;
    } else {
        return null;
    }
}

Example 5 with AStar

use of org.opentripplanner.routing.algorithm.AStar in project OpenTripPlanner by opentripplanner.

the class RoundBasedProfileRouter method makeSurfaces.

/**
 * analyst mode: propagate to street network
 */
private void makeSurfaces() {
    LOG.info("Propagating from transit stops to the street network...");
    List<State> lower = Lists.newArrayList();
    List<State> upper = Lists.newArrayList();
    List<State> avg = Lists.newArrayList();
    RoutingRequest rr = new RoutingRequest(TraverseMode.WALK);
    rr.batch = (true);
    rr.from = new GenericLocation(request.fromLat, request.fromLon);
    rr.setRoutingContext(graph);
    rr.longDistance = true;
    rr.dominanceFunction = new DominanceFunction.EarliestArrival();
    rr.setNumItineraries(1);
    rr.worstTime = rr.dateTime + CUTOFF_SECONDS;
    long startTime = rr.dateTime;
    State origin = new State(rr);
    // Multi-origin Dijkstra search; preinitialize the queue with states at each transit stop
    for (Collection<ProfileState> pss : retainedStates.asMap().values()) {
        TransitStop tstop = null;
        int lowerBound = Integer.MAX_VALUE;
        int upperBound = Integer.MAX_VALUE;
        for (ProfileState ps : pss) {
            if (tstop == null)
                tstop = ps.stop;
            if (ps.lowerBound < lowerBound)
                lowerBound = ps.lowerBound;
            if (ps.upperBound < upperBound)
                upperBound = ps.upperBound;
        }
        if (lowerBound == Integer.MAX_VALUE || upperBound == Integer.MAX_VALUE)
            throw new IllegalStateException("Invalid bound!");
        lower.add(new State(tstop, null, lowerBound + startTime, startTime, rr));
        upper.add(new State(tstop, null, upperBound + startTime, startTime, rr));
        // TODO extremely incorrect hack!
        avg.add(new State(tstop, null, (upperBound + lowerBound) / 2 + startTime, startTime, rr));
    }
    // get direct trips as well
    lower.add(origin);
    upper.add(origin);
    avg.add(origin);
    // create timesurfaces
    timeSurfaceRangeSet = new TimeSurface.RangeSet();
    AStar astar = new AStar();
    timeSurfaceRangeSet.min = new TimeSurface(astar.getShortestPathTree(rr, 20, null, lower), false);
    astar = new AStar();
    timeSurfaceRangeSet.max = new TimeSurface(astar.getShortestPathTree(rr, 20, null, upper), false);
    astar = new AStar();
    timeSurfaceRangeSet.avg = new TimeSurface(astar.getShortestPathTree(rr, 20, null, avg), false);
    rr.cleanup();
    LOG.info("Done with propagation.");
/* Store the results in a field in the router object. */
}