/**
* Implements a multi-objective goal-directed search algorithm like the one in Sec. 4.2 of: Perny
* and Spanjaard. Near Admissible Algorithms for Multiobjective Search.
*
* <p>The ideas being tested here are: Pruning search based on paths already found Near-admissible
* search / relaxed dominance Allow resource constraints on transfers and walking
*
* <p>This approach seems to need a very accurate heuristic to achieve reasonable run times, so for
* now it is hard-coded to use the Bidirectional heuristic.
*
* <p>It will return a list of paths in order of increasing weight, starting at a weight very close
* to that of the optimum path. These paths can vary quite a bit in terms of transfers, walk
* distance, and trips taken.
*
* <p>Because the number of boardings and walk distance are considered incomparable to other weight
* components, including aggregate weight itself, paths can be pruned due to excessive walking
* distance or excessive number of transfers without compromising other paths.
*
* <p>This path service cannot be used with edges that return multiple (chained) states.
*
* @author andrewbyrd
*/
@Component
public class MultiObjectivePathServiceImpl extends GenericPathService {
private static final Logger LOG = LoggerFactory.getLogger(MultiObjectivePathServiceImpl.class);
private static final MonitoringStore store = MonitoringStoreFactory.getStore();
private double[] _timeouts = new double[] {4, 2, 0.6, 0.4}; // seconds
private double _maxPaths = 4;
private TraverseVisitor traverseVisitor;
/** Give up on searching for itineraries after this many seconds have elapsed. */
public void setTimeouts(List<Double> timeouts) {
_timeouts = new double[timeouts.size()];
int i = 0;
for (Double d : timeouts) _timeouts[i++] = d;
}
public void setMaxPaths(double numPaths) {
_maxPaths = numPaths;
}
public void setTraverseVisitor(TraverseVisitor traverseVisitor) {
this.traverseVisitor = traverseVisitor;
}
@Override
public List<GraphPath> plan(
NamedPlace fromPlace,
NamedPlace toPlace,
Date targetTime,
TraverseOptions options,
int nItineraries) {
ArrayList<String> notFound = new ArrayList<String>();
Vertex fromVertex = getVertexForPlace(fromPlace, options);
if (fromVertex == null) {
notFound.add("from");
}
Vertex toVertex = getVertexForPlace(toPlace, options);
if (toVertex == null) {
notFound.add("to");
}
if (notFound.size() > 0) {
throw new VertexNotFoundException(notFound);
}
Vertex origin = null;
Vertex target = null;
if (options.isArriveBy()) {
origin = toVertex;
target = fromVertex;
} else {
origin = fromVertex;
target = toVertex;
}
State state = new State((int) (targetTime.getTime() / 1000), origin, options);
return plan(state, target, nItineraries);
}
@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;
}
private void storeMemory() {
if (store.isMonitoring("memoryUsed")) {
System.gc();
long memoryUsed = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
store.setLongMax("memoryUsed", memoryUsed);
}
}
// private boolean eDominates(State s0, State s1) {
// final double EPSILON = 0.05;
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON) &&
// s0.getTime() <= s1.getTime() * (1 + EPSILON) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// }
private boolean eDominates(State s0, State s1) {
final double EPSILON = 0.05;
if (s0.similarTripSeq(s1)) {
return s0.getWeight() <= s1.getWeight() * (1 + EPSILON)
&& s0.getTime() <= s1.getTime() * (1 + EPSILON)
&& s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON)
&& s0.getNumBoardings() <= s1.getNumBoardings();
} else {
return false;
}
}
// private boolean eDominates(State s0, State s1) {
// final double EPSILON1 = 0.1;
// if (s0.similarTripSeq(s1)) {
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON1) &&
// s0.getElapsedTime() <= s1.getElapsedTime() * (1 + EPSILON1) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON1) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// } else if (s0.getTripId() != null && s0.getTripId() == s1.getTripId()) {
// return s0.getNumBoardings() <= s1.getNumBoardings() &&
// s0.getWeight() <= s1.getWeight() * (1 + EPSILON2) &&
// s0.getElapsedTime() <= s1.getElapsedTime() * (1 + EPSILON2) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON2);
// } else {
// return false;
// }
// }
// private boolean eDominates(State s0, State s1) {
// if (s0.similarTripSeq(s1)) {
// return s0.getWeight() <= s1.getWeight();
// } else if (s0.getTrip() == s1.getTrip()) {
// if (s0.getNumBoardings() < s1.getNumBoardings())
// return true;
// return s0.getWeight() <= s1.getWeight();
// } else {
// return false;
// }
// }
// private boolean eDominates(State s0, State s1) {
// final double EPSILON = 0.1;
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON) &&
// s0.getTime() <= s1.getTime() * (1 + EPSILON) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// }
@Override
public List<GraphPath> plan(
NamedPlace fromPlace,
NamedPlace toPlace,
List<NamedPlace> intermediates,
boolean ordered,
Date targetTime,
TraverseOptions options) {
return null;
}
}
// @Component
public class MultiObjectivePathServiceImpl implements PathService {
@Autowired public GraphService graphService;
private static final Logger LOG = LoggerFactory.getLogger(MultiObjectivePathServiceImpl.class);
private static final MonitoringStore store = MonitoringStoreFactory.getStore();
private static final double MAX_WALK = 100000;
private double[] _timeouts = new double[] {4, 2, 0.6, 0.4}; // seconds
private double _maxPaths = 4;
private TraverseVisitor traverseVisitor;
private DistanceLibrary distanceLibrary = SphericalDistanceLibrary.getInstance();
/** Give up on searching for itineraries after this many seconds have elapsed. */
public void setTimeouts(List<Double> timeouts) {
_timeouts = new double[timeouts.size()];
int i = 0;
for (Double d : timeouts) _timeouts[i++] = d;
}
public void setMaxPaths(double numPaths) {
_maxPaths = numPaths;
}
public void setTraverseVisitor(TraverseVisitor traverseVisitor) {
this.traverseVisitor = traverseVisitor;
}
@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;
}
private void storeMemory() {
if (store.isMonitoring("memoryUsed")) {
System.gc();
long memoryUsed = Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();
store.setLongMax("memoryUsed", memoryUsed);
}
}
// private boolean eDominates(State s0, State s1) {
// final double EPSILON = 0.05;
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON) &&
// s0.getTime() <= s1.getTime() * (1 + EPSILON) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// }
// TODO: move into an epsilon-dominance shortest path tree
private boolean eDominates(State s0, State s1) {
final double EPSILON = 0.05;
if (s0.similarRouteSequence(s1)) {
return s0.getWeight() <= s1.getWeight() * (1 + EPSILON)
&& s0.getElapsedTime() <= s1.getElapsedTime() * (1 + EPSILON)
&& s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON)
&& s0.getNumBoardings() <= s1.getNumBoardings()
&& (s0.getWeight() < s1.getWeight()
|| s0.getElapsedTime() < s1.getElapsedTime()
|| s0.getWalkDistance() < s1.getWalkDistance()
|| s0.getNumBoardings() < s1.getNumBoardings());
} else {
return false;
}
}
// private boolean eDominates(State s0, State s1) {
// final double EPSILON1 = 0.1;
// if (s0.similarTripSeq(s1)) {
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON1) &&
// s0.getElapsedTime() <= s1.getElapsedTime() * (1 + EPSILON1) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON1) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// } else if (s0.getTripId() != null && s0.getTripId() == s1.getTripId()) {
// return s0.getNumBoardings() <= s1.getNumBoardings() &&
// s0.getWeight() <= s1.getWeight() * (1 + EPSILON2) &&
// s0.getElapsedTime() <= s1.getElapsedTime() * (1 + EPSILON2) &&
// s0.getWalkDistance() <= s1.getWalkDistance() * (1 + EPSILON2);
// } else {
// return false;
// }
// }
// private boolean eDominates(State s0, State s1) {
// if (s0.similarTripSeq(s1)) {
// return s0.getWeight() <= s1.getWeight();
// } else if (s0.getTrip() == s1.getTrip()) {
// if (s0.getNumBoardings() < s1.getNumBoardings())
// return true;
// return s0.getWeight() <= s1.getWeight();
// } else {
// return false;
// }
// }
// private boolean eDominates(State s0, State s1) {
// final double EPSILON = 0.1;
// return s0.getWeight() <= s1.getWeight() * (1 + EPSILON) &&
// s0.getTime() <= s1.getTime() * (1 + EPSILON) &&
// s0.getNumBoardings() <= s1.getNumBoardings();
// }
}
