目录一、数据处理二、原理分析三、效率优化四、数据bug处理五、后续规划
对于GIS业务来说，路径规划是非常基础的一个业务，一般公司如果处理，都会直接选择调用已经成熟的第三方的接口，比如高德、百度等。当然其实路径规划的算法非常多，像比较著名的Dijkstra、A*算法等。当然本篇文章不是介绍算法的，本文作者会根据pgrouting已经集成的Dijkstra算法来，结合postgresql数据库来处理最短路径。
一、数据处理
路径规划的核心是数据，数据是一般的路网数据，但是我们拿到路网数据之后，需要对数据进行处理，由于算法的思想是基于有向图的原理，因此首先需要对数据做topo处理，通过topo我们其实就建立了路网中各条道路的顶点关系，下面是主要命令：
–开启执行路网topo的插件create extension postgis;create extension postgis_topology;–数据创建拓扑ALTER TABLE test_road ADD COLUMN source integer;ALTER TABLE test_road ADD COLUMN target integer;SELECT pgr_createTopology(\’test_road\’,0.00001, \’geom\’, \’gid\’);
其中test_road是将路网数据导入到postgresql中的表名。
处理完topo之后，基本就够用了，我们就可以借助pgrouting自带的函数，其实有很多，我们选择pgr_dijkstra
CREATE OR REPLACE FUNCTION public.pgr_dijkstra( IN edges_sql text, IN start_vid bigint, IN end_vid bigint, IN directed boolean, OUT seq integer, OUT path_seq integer, OUT node bigint, OUT edge bigint, OUT cost double precision, OUT agg_cost double precision) RETURNS SETOF record AS$BODY$DECLAREBEGIN RETURN query SELECT * FROM _pgr_dijkstra(_pgr_get_statement($1), start_vid, end_vid, directed, false); END$BODY$ LANGUAGE plpgsql VOLATILE COST 100 ROWS 1000;ALTER FUNCTION public.pgr_dijkstra(text, bigint, bigint, boolean) OWNER TO postgres;
从函数输入参数可以看到，我们需要一个查询sql,一个起始点、一个结束点、以及是否考虑方向，好了了解到调用函数输入参数，我们就来写这个函数。
二、原理分析
一般路径规划，基本都是输入一个起点位置、一个终点位置然后直接规划，那么对于我们来说，要想套用上面的函数，必须找出起点位置target ，以及终点位置的source，然后规划根据找出的这两个topo点，调用上面的函数，来返回自己所需要的结果。
如何根据起始点找到对应的target呢，其实就是找离起点最近线的target，同理终点的source，其实就是找离终点最近线的source,当然将这两个点规划规划好之后，基本就可以了，但是最后还需要将起点到起点最近先的target连接起来，终点到终点最近线的source连接起来，这样整个路径规划就算完成了。
下面我们来看具体的实现存储过程：
CREATE OR REPLACE FUNCTION public.pgr_shortest_road(IN startx double precision,IN starty double precision,IN endx double precision,IN endy double precision,OUT road_name character varying,OUT v_shpath character varying,OUT cost double precision)RETURNS SETOF record AS$BODY$ declare v_startLine geometry;–离起点最近的线 v_endLine geometry;–离终点最近的线 v_startTarget integer;–距离起点最近线的终点 v_endSource integer;–距离终点最近线的起点 v_statpoint geometry;–在v_startLine上距离起点最近的点 v_endpoint geometry;–在v_endLine上距离终点最近的点 v_res geometry;–最短路径分析结果 v_perStart float;–v_statpoint在v_res上的百分比 v_perEnd float;–v_endpoint在v_res上的百分比 v_rec record; first_name varchar;end_name varchar;first_cost double precision;end_cost double precision;begin –查询离起点最近的线 execute \’select geom,target,name from china_road where ST_DWithin(geom,ST_Geometryfromtext(\’\’point(\’|| startx ||\’ \’ || starty||\’)\’\’),0.01) order by ST_Distance(geom,ST_GeometryFromText(\’\’point(\’|| startx ||\’ \’|| starty ||\’)\’\’)) limit 1\’ into v_startLine ,v_startTarget,first_name; –查询离终点最近的线 execute \’select geom,source,name from china_roadwhere ST_DWithin(geom,ST_Geometryfromtext(\’\’point(\’|| endx || \’ \’ || endy ||\’)\’\’),0.01) order by ST_Distance(geom,ST_GeometryFromText(\’\’point(\’|| endx ||\’ \’ || endy ||\’)\’\’)) limit 1\’ into v_endLine,v_endSource,end_name; –如果没找到最近的线，就返回null if (v_startLine is null) or (v_endLine is null) then return; end if ; select ST_ClosestPoint(v_startLine, ST_Geometryfromtext(\’point(\’|| startx ||\’ \’ || starty ||\’)\’)) into v_statpoint; select ST_ClosestPoint(v_endLine, ST_GeometryFromText(\’point(\’|| endx ||\’ \’ || endy ||\’)\’)) into v_endpoint; –计算距离起点最近线上的点在该线中的位置select ST_Line_Locate_Point(st_linemerge(v_startLine), v_statpoint) into v_perStart;select ST_Line_Locate_Point(st_linemerge(v_endLine), v_endpoint) into v_perEnd;select ST_Distance_Sphere(v_statpoint,ST_PointN(ST_GeometryN(v_startLine,1), ST_NumPoints(ST_GeometryN(v_startLine,1)))) into first_cost;select ST_Distance_Sphere(ST_PointN(ST_GeometryN(v_endLine,1),1),v_endpoint) into end_cost; if (ST_Intersects(st_geomfromtext(\’point(\’|| startx ||\’ \’|| starty ||\’) \’), v_startLine) and ST_Intersects(st_geomfromtext(\’point(\’|| endx ||\’ \’|| endy ||\’) \’), v_startLine)) then select ST_Distance_Sphere(v_statpoint, v_endpoint) into first_cost;select ST_Line_Locate_Point(st_linemerge(v_startLine), v_endpoint) into v_perEnd;for v_rec in select ST_Line_SubString(st_linemerge(v_startLine), v_perStart,v_perEnd) as point,COALESCE(end_name,\’无名路\’) as name,end_cost as cost loopv_shPath:= ST_AsGeoJSON(v_rec.point);cost:= v_rec.cost;road_name:= v_rec.name;return next;end loop;return;end if;–最短路径 for v_rec in (select ST_Line_SubString(st_linemerge(v_startLine),v_perStart,1) as point,COALESCE(first_name,\’无名路\’) as name,first_cost as costunion allSELECT st_linemerge(b.geom) as point,COALESCE(b.name,\’无名路\’) as name,b.length as costFROM pgr_dijkstra(\’SELECT gid as id, source, target, length as cost FROM china_roadwhere st_intersects(geom,st_buffer(st_linefromtext(\’\’linestring(\’||startx||\’ \’ || starty ||\’,\’|| endx ||\’ \’ || endy ||\’)\’\’),0.05))\’, v_startTarget, v_endSource , false ) a, china_road b WHERE a.edge = b.gidunion allselect ST_Line_SubString(st_linemerge(v_endLine),0,v_perEnd) as point,COALESCE(end_name,\’无名路\’) as name,end_cost as cost)loopv_shPath:= ST_AsGeoJSON(v_rec.point);cost:= v_rec.cost;road_name:= v_rec.name;return next;end loop; end; $BODY$LANGUAGE plpgsql VOLATILE STRICT;
上面这种实现，是将所有查询道路返回一个集合，然后客户端来将各个线路进行合并，这种方式对最终效率影响比较大，所以一般会在函数中将道路何合并为一条道路，我们可以使用postgis的st_union函数来处理，小编经过长时间的试验，在保证效率和准确性的情况下，对上面的存储过程做了很多优化，最终得出了如下：
CREATE OR REPLACE FUNCTION public.pgr_shortest_road( startx double precision, starty double precision, endx double precision, endy double precision) RETURNS geometry AS$BODY$ declare v_startLine geometry;–离起点最近的线 v_endLine geometry;–离终点最近的线 v_perStart float;–v_statpoint在v_res上的百分比 v_perEnd float;–v_endpoint在v_res上的百分比 v_shpath geometry; distance double precision; bufferInstance double precision; bufferArray double precision[]; begin execute \’select geom, case china_road.direction when \’\’3\’\’ then source else target end from china_road where ST_DWithin(geom,ST_Geometryfromtext(\’\’point(\’|| startx ||\’ \’ || starty||\’)\’\’,4326),0.05) AND width::double precision >= \’||roadWidth||\’ order by ST_Distance(geom,ST_GeometryFromText(\’\’point(\’|| startx ||\’ \’|| starty ||\’)\’\’,4326)) limit 1\’ into v_startLine; execute \’select geom, case china_road.direction when \’\’3\’\’ then target else source end from china_road where ST_DWithin(geom,ST_Geometryfromtext(\’\’point(\’|| endx || \’ \’ || endy ||\’)\’\’,4326),0.05) AND width::double precision >= \’||roadWidth||\’ order by ST_Distance(geom,ST_GeometryFromText(\’\’point(\’|| endx ||\’ \’ || endy ||\’)\’\’,4326)) limit 1\’ into v_endLine; if (v_startLine is null) or (v_endLine is null) then return null; end if; if (ST_equals(v_startLine,v_endLine)) then select ST_LineLocatePoint(st_linemerge(v_startLine), ST_Geometryfromtext(\’point(\’|| startx ||\’ \’ || starty ||\’)\’,4326)) into v_perStart; select ST_LineLocatePoint(st_linemerge(v_endLine), ST_Geometryfromtext(\’point(\’|| endx ||\’ \’ || endy ||\’)\’,4326)) into v_perEnd; select ST_LineSubstring(st_linemerge(v_startLine),v_perStart,v_perEnd) into v_shPath; return v_shPath; end if; select ST_DistanceSphere(st_geomfromtext(\’point(\’|| startx ||\’ \’ || starty ||\’)\’,4326),st_geomfromtext(\’point(\’|| endx ||\’ \’ || endy ||\’)\’,4326)) into distance; if ((distance / 1000) > 50) then bufferArray := ARRAY[0.1,0.2,0.3,0.5,0.8]; else bufferArray := ARRAY[0.02,0.05,0.08,0.1]; end if; forEACH bufferInstance IN ARRAY bufferArray LOOP select _pgr_shortest_road(startx,starty,endx,endy,bufferInstance) into v_shPath; if (v_shPath is not null) then return v_shPath; end if; end loop; end; $BODY$ LANGUAGE plpgsql VOLATILE STRICT COST 100;ALTER FUNCTION public.pgr_shortest_road(double precision, double precision, double precision, double precision ) OWNER TO postgres;DROP FUNCTION public._pgr_shortest_road(double precision, double precision, double precision, double precision, double precision);
上面的函数，其实对于大部分情况下的操作，基本可以满足了。
三、效率优化
其实在数据查询方面，我们使用的是起点和终点之间的线性缓冲来提高效率，如下：
SELECT gid as id, source, target, cost,rev_cost as reverse_cost FROM china_road where geom && st_buffer(st_linefromtext(\’\’linestring(\’||startx||\’ \’ || starty ||\’,\’|| endx ||\’ \’ || endy ||\’)\’\’,4326),\’||bufferDistance||\’)
当然这在大部分情况下，依旧是不错的，然后在有些情况下，并不能起到很好的作用，因为如果起点和终点之间道路偏移较大（比如直线上的山脉较多的时候，路就会比较绕），这个时候，可能会增大缓冲距离，而增加缓冲距离就会导致，部分区域的查询量增大，继而影响效率，因此其实我们可以考虑使用mapid这个参数，这个参数从哪来呢，一般我们拿到的路网数据都会这个字段，我们只需要生成一个区域表，而这个区域表就俩个字段，一个是mapid，一个是这个mapid的polygon范围，这样子，上面的查询条件，就可以换成如下：
SELECT gid as id, source, target, cost,rev_cost as reverse_cost FROM china_road where mapid in (select mapid from maps where geom && st_buffer(st_linefromtext(\’\’linestring(\’||startx||\’ \’ || starty ||\’,\’|| endx ||\’ \’ || endy ||\’)\’\’),\’||bufferDistance||\’))
这样就可以在很大程度上提高效率。
四、数据bug处理
其实有时候我们拿到的路网数据，并不是非常的准确，或者说是录入的有瑕疵，我自己遇到的就是生成的topo数据，本来一条路的target应该和它相邻路的source的点重合，然后实际却是不一样，这就导致最终规划处的有问题，因此，简单写了一个处理这种问题的函数
CREATE OR REPLACE FUNCTION public.modity_road_data()RETURNS void AS$BODY$ declare n integer;begin for n IN (select distinct(source) from china_road ) loop update china_road set geom = st_multi(st_addpoint(ST_geometryN(geom,1), (select st_pointn(ST_geometryN(geom,1),1) from china_road where source = n limit 1), st_numpoints(ST_geometryN(geom,1)))) where target = n; end loop; end; $BODY$ LANGUAGE plpgsql VOLATILE STRICT COST 100;ALTER FUNCTION public.modity_road_data()OWNER TO postgres;
五、后续规划
上面的函数已在百万数据中做过验证，后续还会验证千万级别的路网数据，当然这种级别，肯定要在策略上做一些调整了，比如最近测试的全国路网中，先规划起点至起点最近的高速入口，在规划终点至终点最近的高速出口，然后再高速路网上规划高速入口到高速出口的路径，这样发现效率提升不少，当然，这里面还有很多逻辑和业务，等所有东西都验证完毕，会再出一版，千万级别路径规划的文章。
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