@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final InferredEdge other = (InferredEdge) obj;
if (endVertex == null) {
if (other.endVertex != null) {
return false;
}
} else if (!endVertex.equals(other.endVertex)) {
return false;
}
if (startVertex == null) {
if (other.startVertex != null) {
return false;
}
} else if (!startVertex.equals(other.startVertex)) {
return false;
}
return true;
}
@Override
public List<GraphPath> plan(State origin, Vertex target, int nItineraries) {
TraverseOptions options = origin.getOptions();
if (_graphService.getCalendarService() != null)
options.setCalendarService(_graphService.getCalendarService());
options.setTransferTable(_graphService.getGraph().getTransferTable());
options.setServiceDays(origin.getTime(), _graphService.getGraph().getAgencyIds());
if (options.getModes().getTransit()
&& !_graphService.getGraph().transitFeedCovers(new Date(origin.getTime() * 1000))) {
// user wants a path through the transit network,
// but the date provided is outside those covered by the transit feed.
throw new TransitTimesException();
}
// always use the bidirectional heuristic because the others are not precise enough
RemainingWeightHeuristic heuristic =
new BidirectionalRemainingWeightHeuristic(_graphService.getGraph());
// the states that will eventually be turned into paths and returned
List<State> returnStates = new LinkedList<State>();
// Populate any extra edges
final ExtraEdgesStrategy extraEdgesStrategy = options.extraEdgesStrategy;
OverlayGraph extraEdges = new OverlayGraph();
extraEdgesStrategy.addEdgesFor(extraEdges, origin.getVertex());
extraEdgesStrategy.addEdgesFor(extraEdges, target);
BinHeap<State> pq = new BinHeap<State>();
// List<State> boundingStates = new ArrayList<State>();
// initialize heuristic outside loop so table can be reused
heuristic.computeInitialWeight(origin, target);
// increase maxWalk repeatedly in case hard limiting is in use
WALK:
for (double maxWalk = options.getMaxWalkDistance();
maxWalk < 100000 && returnStates.isEmpty();
maxWalk *= 2) {
LOG.debug("try search with max walk {}", maxWalk);
// increase maxWalk if settings make trip impossible
if (maxWalk
< Math.min(
origin.getVertex().distance(target),
origin.getVertex().getDistanceToNearestTransitStop()
+ target.getDistanceToNearestTransitStop())) continue WALK;
options.setMaxWalkDistance(maxWalk);
// reinitialize states for each retry
HashMap<Vertex, List<State>> states = new HashMap<Vertex, List<State>>();
pq.reset();
pq.insert(origin, 0);
long startTime = System.currentTimeMillis();
long endTime = startTime + (int) (_timeouts[0] * 1000);
LOG.debug("starttime {} endtime {}", startTime, endTime);
QUEUE:
while (!pq.empty()) {
if (System.currentTimeMillis() > endTime) {
LOG.debug("timeout at {} msec", System.currentTimeMillis() - startTime);
if (returnStates.isEmpty()) continue WALK;
else {
storeMemory();
break WALK;
}
}
State su = pq.extract_min();
// for (State bs : boundingStates) {
// if (eDominates(bs, su)) {
// continue QUEUE;
// }
// }
Vertex u = su.getVertex();
if (traverseVisitor != null) {
traverseVisitor.visitVertex(su);
}
if (u.equals(target)) {
// boundingStates.add(su);
returnStates.add(su);
if (!options.getModes().getTransit()) break QUEUE;
// options should contain max itineraries
if (returnStates.size() >= _maxPaths) break QUEUE;
if (returnStates.size() < _timeouts.length) {
endTime = startTime + (int) (_timeouts[returnStates.size()] * 1000);
LOG.debug(
"{} path, set timeout to {}",
returnStates.size(),
_timeouts[returnStates.size()] * 1000);
}
continue QUEUE;
}
for (Edge e : u.getEdges(extraEdges, null, options.isArriveBy())) {
STATE:
for (State new_sv = e.traverse(su); new_sv != null; new_sv = new_sv.getNextResult()) {
if (traverseVisitor != null) {
traverseVisitor.visitEdge(e, new_sv);
}
double h = heuristic.computeForwardWeight(new_sv, target);
// for (State bs : boundingStates) {
// if (eDominates(bs, new_sv)) {
// continue STATE;
// }
// }
Vertex v = new_sv.getVertex();
List<State> old_states = states.get(v);
if (old_states == null) {
old_states = new LinkedList<State>();
states.put(v, old_states);
} else {
for (State old_sv : old_states) {
if (eDominates(old_sv, new_sv)) {
continue STATE;
}
}
Iterator<State> iter = old_states.iterator();
while (iter.hasNext()) {
State old_sv = iter.next();
if (eDominates(new_sv, old_sv)) {
iter.remove();
}
}
}
if (traverseVisitor != null) traverseVisitor.visitEnqueue(new_sv);
old_states.add(new_sv);
pq.insert(new_sv, new_sv.getWeight() + h);
}
}
}
}
storeMemory();
// Make the states into paths and return them
List<GraphPath> paths = new LinkedList<GraphPath>();
for (State s : returnStates) {
LOG.debug(s.toStringVerbose());
paths.add(new GraphPath(s, true));
}
// sort by arrival time, though paths are already in order of increasing difficulty
// Collections.sort(paths, new PathComparator(origin.getOptions().isArriveBy()));
return paths;
}
@Override
public List<GraphPath> getPaths(RoutingRequest options) {
if (options.rctx == null) {
options.setRoutingContext(graphService.getGraph(options.getRouterId()));
// move into setRoutingContext ?
options.rctx.pathParsers =
new PathParser[] {new BasicPathParser(), new NoThruTrafficPathParser()};
}
RemainingWeightHeuristic heuristic;
if (options.getModes().isTransit()) {
LOG.debug("Transit itinerary requested.");
// always use the bidirectional heuristic because the others are not precise enough
heuristic = new BidirectionalRemainingWeightHeuristic(options.rctx.graph);
} else {
LOG.debug("Non-transit itinerary requested.");
heuristic = new DefaultRemainingWeightHeuristic();
}
// the states that will eventually be turned into paths and returned
List<State> returnStates = new LinkedList<State>();
BinHeap<State> pq = new BinHeap<State>();
// List<State> boundingStates = new ArrayList<State>();
Vertex originVertex = options.rctx.origin;
Vertex targetVertex = options.rctx.target;
// increase maxWalk repeatedly in case hard limiting is in use
WALK:
for (double maxWalk = options.getMaxWalkDistance(); returnStates.isEmpty(); maxWalk *= 2) {
if (maxWalk != Double.MAX_VALUE && maxWalk > MAX_WALK) {
break;
}
LOG.debug("try search with max walk {}", maxWalk);
// increase maxWalk if settings make trip impossible
if (maxWalk
< Math.min(
distanceLibrary.distance(originVertex.getCoordinate(), targetVertex.getCoordinate()),
originVertex.getDistanceToNearestTransitStop()
+ targetVertex.getDistanceToNearestTransitStop())) continue WALK;
options.setMaxWalkDistance(maxWalk);
// cap search / heuristic weight
final double AVG_TRANSIT_SPEED = 25; // m/sec
double cutoff =
(distanceLibrary.distance(originVertex.getCoordinate(), targetVertex.getCoordinate())
* 1.5)
/ AVG_TRANSIT_SPEED; // wait time is irrelevant in the heuristic
cutoff += options.getMaxWalkDistance() * options.walkReluctance;
options.maxWeight = cutoff;
State origin = new State(options);
// (used to) initialize heuristic outside loop so table can be reused
heuristic.computeInitialWeight(origin, targetVertex);
options.maxWeight = cutoff + 30 * 60 * options.waitReluctance;
// reinitialize states for each retry
HashMap<Vertex, List<State>> states = new HashMap<Vertex, List<State>>();
pq.reset();
pq.insert(origin, 0);
long startTime = System.currentTimeMillis();
long endTime = startTime + (int) (_timeouts[0] * 1000);
LOG.debug("starttime {} endtime {}", startTime, endTime);
QUEUE:
while (!pq.empty()) {
if (System.currentTimeMillis() > endTime) {
LOG.debug("timeout at {} msec", System.currentTimeMillis() - startTime);
if (returnStates.isEmpty()) break WALK; // disable walk distance increases
else {
storeMemory();
break WALK;
}
}
// if (pq.peek_min_key() > options.maxWeight) {
// LOG.debug("max weight {} exceeded", options.maxWeight);
// break QUEUE;
// }
State su = pq.extract_min();
// for (State bs : boundingStates) {
// if (eDominates(bs, su)) {
// continue QUEUE;
// }
// }
Vertex u = su.getVertex();
if (traverseVisitor != null) {
traverseVisitor.visitVertex(su);
}
if (u.equals(targetVertex)) {
// boundingStates.add(su);
returnStates.add(su);
if (!options.getModes().isTransit()) break QUEUE;
// options should contain max itineraries
if (returnStates.size() >= _maxPaths) break QUEUE;
if (returnStates.size() < _timeouts.length) {
endTime = startTime + (int) (_timeouts[returnStates.size()] * 1000);
LOG.debug(
"{} path, set timeout to {}",
returnStates.size(),
_timeouts[returnStates.size()] * 1000);
}
continue QUEUE;
}
for (Edge e : options.isArriveBy() ? u.getIncoming() : u.getOutgoing()) {
STATE:
for (State new_sv = e.traverse(su); new_sv != null; new_sv = new_sv.getNextResult()) {
if (traverseVisitor != null) {
traverseVisitor.visitEdge(e, new_sv);
}
double h = heuristic.computeForwardWeight(new_sv, targetVertex);
if (h == Double.MAX_VALUE) continue;
// for (State bs : boundingStates) {
// if (eDominates(bs, new_sv)) {
// continue STATE;
// }
// }
Vertex v = new_sv.getVertex();
List<State> old_states = states.get(v);
if (old_states == null) {
old_states = new LinkedList<State>();
states.put(v, old_states);
} else {
for (State old_sv : old_states) {
if (eDominates(old_sv, new_sv)) {
continue STATE;
}
}
Iterator<State> iter = old_states.iterator();
while (iter.hasNext()) {
State old_sv = iter.next();
if (eDominates(new_sv, old_sv)) {
iter.remove();
}
}
}
if (traverseVisitor != null) traverseVisitor.visitEnqueue(new_sv);
old_states.add(new_sv);
pq.insert(new_sv, new_sv.getWeight() + h);
}
}
}
}
storeMemory();
// Make the states into paths and return them
List<GraphPath> paths = new LinkedList<GraphPath>();
for (State s : returnStates) {
LOG.debug(s.toStringVerbose());
paths.add(new GraphPath(s, true));
}
// sort by arrival time, though paths are already in order of increasing difficulty
// Collections.sort(paths, new PathComparator(origin.getOptions().isArriveBy()));
return paths;
}