使用pgrouting和geotools实现最短路径,服务区分析

1本文主要讲解服务区分析的实现(最优路径已经有很多文章了)

设施服务范围指在一定限制条件下(如时间、费用或路程等)设施所能提供服务的最大空间领域, 在道路网络环境中,它通常由一系列结点及边组成。例如, 某救助站在接到求救电话后10 min 所能到达的区域;某物流公司在配送货物时500元花费所能到达的区域等。

(1)根据拓扑关系,计算地理网络的最大邻接结点数;

(2)构造邻接结点矩阵和初始判断矩阵描述地理网络结构;

(3)应用广度优先搜索算法确定地理网络中心服务范围。

本算法是对Dijkstra最短路径算法的改进(简称“最短路径算法”)。首先, 将网络中所有结点初始化为未标记结点。然后从起点(第一次搜索的起点为网络中心)开始搜索与其有路径连通的未标记结点, 计算阻值, 并将起点标记为已标记结点, 重复上述过程, 直到某结点的阻值超过网络中心的阻值。最后, 基于结点及边的阻值搜索并存储所有在中心阻值范围内的边, 这些边和结点的集合为网络中心的服务范围。

(但实际情况中可能需要内插一些点,直到找到阻值等于网络中心的阻值为止)

2实现过程:<1>数据读取:直接读取shp

//1读取shp文件,得到pgDatastore
	public static void conShp(String path){
	try {
	File file=new File(path);
	Map<String, Object> map = new HashMap<String, Object>();
	map.put("url", file.toURI().toURL());
	System.out.println(map);
	pgDatastore = DataStoreFinder.getDataStore(map);
	} catch (Exception e) {
	  e.printStackTrace();
	}
	}

  从postgis中读取

首先读取postgis数据库得到DataStore对象,然后用getfeaturesource(LayerName)得到SimpleFeatureSource即可(注意:这里的LayerName即为表名)

	 //2读取postgis,得到pgDatastore
	  	 //链接postgis
		public static void conPostGis(String dbtype, String host, String port, 
	            String database, String userName, String password) { 
	        Map<String, Object> params = new HashMap<String, Object>(); 
	        params.put(PostgisNGDataStoreFactory.DBTYPE.key, dbtype); 
	        params.put(PostgisNGDataStoreFactory.HOST.key, host); 
	        params.put(PostgisNGDataStoreFactory.PORT.key, new Integer(port)); 
	        params.put(PostgisNGDataStoreFactory.DATABASE.key, database); 
	        params.put(PostgisNGDataStoreFactory.SCHEMA.key, "public"); 
	        params.put(PostgisNGDataStoreFactory.USER.key, userName); 
	        params.put(PostgisNGDataStoreFactory.PASSWD.key, password); 
	        try { 
	        	  pgDatastore = DataStoreFinder.getDataStore(params); 
	            if (pgDatastore != null) { 
	                System.out.println("系统连接到位于:" + host + "的空间数据库" + database 
	                        + "成功!"); 
	            } else { 
	                System.out.println("系统连接到位于:" + host + "的空间数据库" + database 
	                        + "失败!请检查相关参数"); 
	            } 
	        } catch (IOException e) { 
	            e.printStackTrace(); 
	            System.out.println("系统连接到位于:" + host + "的空间数据库" + database 
	                    + "失败!请检查相关参数"); 
	        } 
	    }
	//3利用pgDatastore,得到featuresource(表)
	public static SimpleFeatureSource getFeatureSource(String LayerName) throws IOException{
		if(pgDatastore==null){
			System.out.println("还未导入数据源,请导入pgDatastore");
			return null;
		}
		featureSource = pgDatastore.getFeatureSource(LayerName);
		System.out.println(featureSource.getCount(Query.ALL));
	    return featureSource;
	}

 注意事项:

读取postgis时,数据库里面的geom字段不能为二进制

读取文件时,文件中最好不要有中文

<2>进行拓扑将数据处理为Graph

(1)得到SimpleFeatureCollection

(2)创建一个FeatureGraphGenerator利用它添加SimpleFeature元素并调用其getGraph方法创建Graph

(3)创建出来的Graph中保存着V(节点)和E(边),这样就可以进行网络分析了

        //创建graph
	public static Graph getGraph(SimpleFeatureSource source) throws IOException{
		   if(source==null)
		  {   
			System.out.println("资源不存在,请先得到featureSource");
			return null;
			}
		  SimpleFeatureCollection fCollection = source.getFeatures();
	       //create a linear graph generate
		  //构图时也可以创建一个DirectedLineStringGraphGenerator构建有向图
	       LineStringGraphGenerator lineStringGen = new LineStringGraphGenerator();
	       //wrap it in a feature graph generator
	       FeatureGraphGenerator featureGen = new FeatureGraphGenerator( lineStringGen );
	       //throw all the features into the graph generator
	       FeatureIterator<SimpleFeature> iter = fCollection.features();
	       try {
	         while(iter.hasNext()){
	            Feature feature = iter.next();
	            featureGen.add(feature);
	         }
	       } finally {
	         iter.close();
	       }
	       graph = featureGen.getGraph();
	       return graph;
	}

<3>最短路径

(1)最短路径:

使用Astar算法:

1.首先利用AstarFunctions设定权值(即通过此边的消耗)

2.然后设定start点(起点)和target点(终点)

3.调用AstarShortestFinder()来进行处理

具体代码如下:

设定权(成本):

		public static double discost(Edge e ){
			  SimpleFeature feature = (SimpleFeature) e.getObject();
		      Geometry geom = (Geometry) feature.getDefaultGeometry();
		      //geom.convexHull()将其构成一个图形
		      if(Barriers!=null){
			for(int i=0;i<Barriers.size();i++){
				Geometry g=Barriers.get(i);
				if(geom.intersects(g)){
					return Double.POSITIVE_INFINITY;
					}
			}
		      }
		      return geom.getLength();
		}
		
		public static double discost(AStarNode n1, AStarNode n2){
		   Node nd1=n1.getNode();
		    Node nd2=n2.getNode();
			Edge e=nd1.getEdge(nd2);
			if(e!=null){
				SimpleFeature feature=(SimpleFeature)e.getObject();
				Geometry geom=(Geometry) feature.getDefaultGeometry();
				if(Barriers!=null){
					for(int i=0;i<Barriers.size();i++){
						Geometry g=Barriers.get(i);
						if(geom.intersects(g)){
							return Double.POSITIVE_INFINITY;
						}
					}
				}
			return ((Point) n1.getNode().getObject())
					.distance((Point)n2.getNode().getObject());

			}else{
			return ((Point) n1.getNode().getObject())
					.distance((Point)n2.getNode().getObject());
			}
		}
               //Astar方法的最短路径计算
		public static Path searchRouteByAstar(Node source,Node destination) throws Exception{
			if(graph==null){
				System.out.println("graph不存在,请构建graph");
				return null;
			}
			if(source.equals(destination)){
				System.out.println("起点和终点相同,请重新选点");
				return null;
			}
			Path path=null;
			AStarFunctions afuncs=new AStarFunctions(destination) {
				@Override
				public double h(Node n) {
				//结合Astar的算法可以知道这里的h指的是一个预估的距离destination的消耗值
					//disPoint指的是预估的终点
					Point disPoint=(Point)this.getDest().getObject();
					return ((Point)n.getObject()).distance(disPoint);		
				}
				
				@Override
				public double cost(AStarNode n1, AStarNode n2) {
					//注意矢量性和有向性
					return discost(n1, n2);
				}
			};
			
			AStarShortestPathFinder finder=new AStarShortestPathFinder(graph, source, destination, afuncs);
			finder.calculate();
			path=finder.getPath();
			return path;
		}

这里是可以看到传入的变量是node节点,但是我们实际中是要在地图上点击一个起点终点求出最优路径,因此还需要将鼠标点击的任意一点归算的graph的节点里去,这里最好使用数据库空间查询来算,本文只是用了最简单的遍历,算法如下:

	//搜寻graph上最近节点的方法
	//暂时先采用遍历的方法
	//这里如果点隔的太远会直接把pointy输出,调用最短路径算法会抛出空指针异常
	public static Node getNearestGraphNode(Point pointy){
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return null;
		}
		double dist=0;
		Node nearestNode=null;
		for(Object o:graph.getNodes()){
			Node n=(Node)o;
			Point gPoint=(Point)n.getObject();
			double distance=gPoint.distance(pointy);	
			if(nearestNode==null||distance<dist){
				dist=distance;
				nearestNode=n;
			}
		}
		return nearestNode;
	}

归算到节点之后就可以改造下Astar算法了:

public static Path searchRouteByAstar(Point startPoint,Point endPoint) throws Exception{
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return null;
		}
		Node source=getNearestGraphNode(startPoint);
		Node destination=getNearestGraphNode(endPoint);
		if(source.equals(destination)){
			System.out.println("起点和终点相同,请重新选点");
			return null;
		}
		Path path=null;
		AStarFunctions afuncs=new AStarFunctions(destination) {
			@Override
			public double h(Node n) {
			//结合Astar的算法可以知道这里的h指的是一个预估的距离destination的消耗值
				//disPoint指的是预估的终点
				Point disPoint=(Point)this.getDest().getObject();
				return ((Point)n.getObject()).distance(disPoint);		
			}
			
			@Override
			public double cost(AStarNode n1, AStarNode n2) {
				//注意矢量性和有向性
				return discost(n1, n2);
			}
		};
		
		AStarShortestPathFinder finder=new AStarShortestPathFinder(graph, source, destination, afuncs);
		finder.calculate();
		path=finder.getPath();
		return path;
	}

这样看起来就挺完美了,但是如果要加入障碍点怎么办那?

其实我们在成本计算中已经考虑障碍物了,如果是个障碍范围就与当前的graph求交集,交集处的权设置成无穷就好了,这样就解决了障碍点的问题。

如果是停靠点那?

那就每段都计算一次最优路径加起来就行了。

使用Dijkstra算法:

1.首先利用Edgeweighter设定权值(即通过此边的消耗)

2.然后设定start点(起点)和target点(终点)

3.调用DijkstraShortestPathFinder()来进行处理

dijkstra算法大概差不多,直接贴代码:

	//dijkstra方法
	public static Path searchRouteByDijkstra(Node source,Node destination) throws Exception{
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return null;
		}
		Path path=null;
		 EdgeWeighter weighter = new EdgeWeighter() {
				@Override
				public double getWeight(Edge e) {
					return discost(e);
				}
			};
			// Create GraphWalker - in this case DijkstraShortestPathFinder
		   DijkstraShortestPathFinder pf = new DijkstraShortestPathFinder( graph, source, weighter );
		   pf.calculate();
		   path= pf.getPath(destination);		   
	       return path;
	}
	
	public static Path searchRouteByDijkstra(Point startPoint,Point endPoint) throws Exception{
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return null;
		}
		Node source=getNearestGraphNode(startPoint);
		Node destination=getNearestGraphNode(endPoint);
		Path path=null;
		 EdgeWeighter weighter = new EdgeWeighter() {
				@Override
				public double getWeight(Edge e) {
					return discost(e);
				}
			};
			// Create GraphWalker - in this case DijkstraShortestPathFinder
		   DijkstraShortestPathFinder pf = new DijkstraShortestPathFinder( graph, source, weighter );
		   pf.calculate();
		   path= pf.getPath(destination);		   
	       return path;
	}

<4>服务区分析

改造DijkstraShortestPathFinder方法:

1.首先通过Edgeweighter设定权值(即通过此边的消耗)

2.然后设定start点(起点)

3.最后通过设置一个判定(该判定可能是根据距离也可能是根据时间)来终止该方法的搜索,然后得到该方法返回的所有边和节点。

	public static List<Point> getAdjancyPoint(Node node){
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return null;
		}
		List<Point> points=new ArrayList<Point>(); 
		Point pt=(Point)node.getObject();
		System.out.println("传入的节点:"+pt);
		List<Edge> edges=node.getEdges();
		for(Edge e:edges){
			Node nodeA=e.getNodeA();
			Point pa=(Point)nodeA.getObject();
			Node nodeB=e.getNodeB();
			Point pb=(Point)nodeB.getObject();
			if(!pt.equals(pa)){
				points.add(pa);
			}else if(!pt.equals(pb)){
				points.add(pb);
			}
		}
		List<Point>points1=(List<Point>) CollectionUtils.subtract(points,serviceAreaPoints);
		System.out.println("加入的临近点:"+points1);
		return points1;
	}
	
	//服务区范围,目前我只是把节点加入进去
	public static void ServiceArea(Point startPoint, double cost) throws Exception{
		if(graph==null){
			System.out.println("graph不存在,请构建graph");
			return;
		}
		Node source=getNearestGraphNode(startPoint);
		Point pt=(Point)source.getObject();
		serviceAreaPoints.add(pt);
		//其实递归应该从这里开始,前面的不用递归
		List<Point> pts=getAdjancyPoint(source);
		for(Iterator<?>itr=pts.iterator();itr.hasNext();){
			Point p=(Point)itr.next();	
			if(p!=null){
				Geometry geo=iterRoute(searchRouteByAstar(serviceAreaPoints.get(0), p)).getRoutePath();
				double len=geo.getLength();
				if(len<=cost){
					ServiceArea(p, cost);
					System.out.println("点"+p+"加人serviceArea");
				}
				else{
					System.out.println("点"+p+"不加人serviceArea");
				}
			}
		}
	}
	
	//获得服务区点集合
	public static Set<Point> getServiceAreaPoints() {
	    serviceAreaPoints1.clear();
		serviceAreaPoints1.addAll(serviceAreaPoints);
		return serviceAreaPoints1;
	}

这样就完成了服务范围分析。

有什么问题欢迎大家评论与交流。

转载自:https://blog.csdn.net/zhgu1992/article/details/78883993

You may also like...