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SumoConvert

Source code in src/gotrackit/netxfer/SumoConvert.py 
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def __init__(self):
    pass

SumoConvert类静态方法 - get_net_shp

  • 解析net.xml路网获取GeoDataFrame

Parameters:

Name Type Description Default
net_path str

net.xml路网文件路径

 required
core_num int
1
l_threshold float

线型简化阈值(米)

 1.0

Returns:

Type Description
tuple[GeoDataFrame, GeoDataFrame, GeoDataFrame, GeoDataFrame, GeoDataFrame]

车道骨架gdf、路口面域gdf、车道面域gdf、路段中心线gdf、路口连接器gdf
Source code in src/gotrackit/netxfer/SumoConvert.py 
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@function_time_cost
def get_net_shp(self, net_path: str, core_num: int = 1, l_threshold: float = 1.0) -> \
        tuple[gpd.GeoDataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame, gpd.GeoDataFrame]:
    """SumoConvert类静态方法 - get_net_shp

    - 解析net.xml路网获取GeoDataFrame

    Args:
        net_path: net.xml路网文件路径
        core_num:
        l_threshold: 线型简化阈值(米)

    Returns:
        车道骨架gdf、路口面域gdf、车道面域gdf、路段中心线gdf、路口连接器gdf
    """
    core_num = os.cpu_count() if core_num > os.cpu_count() else core_num
    net_tree = ET.parse(net_path)

    net_root = net_tree.getroot()
    location_ele = net_root.findall('location')[0]
    try:
        prj4_str = location_ele.get('projParameter')
    except:
        raise ValueError('There is no projParameter in net.xml file')


    code = prj4_2_crs(prj4_str=prj4_str)
    if code is None:
        crs, no_crs_flag = None, True
    else:
        crs, no_crs_flag = rf'EPSG:{code}', False

    try:
        x_offset, y_offset = list(map(float, location_ele.get('netOffset').split(',')))
    except:
        x_offset, y_offset = 0, 0

    all_edge_ele = list(net_root.findall('edge'))
    all_junction_ele = list(net_root.findall('junction'))
    all_conn_ele = list(net_root.findall('connection'))

    if core_num > 1:
        # 分组
        edge_ele_group = cut_group(obj_list=all_edge_ele, n=core_num)
        junction_ele_group = cut_group(obj_list=all_junction_ele, n=core_num)
        conn_ele_group = cut_group(obj_list=all_conn_ele, n=core_num)

        edge_ele_group_len, junction_ele_group_len, conn_ele_group_len = \
            len(edge_ele_group), len(junction_ele_group), len(conn_ele_group)

        max_len = max([edge_ele_group_len, junction_ele_group_len, conn_ele_group_len])

        junction_ele_group.extend([] * (max_len - junction_ele_group_len))
        edge_ele_group.extend([] * (max_len - edge_ele_group_len))
        conn_ele_group.extend([] * (max_len - conn_ele_group_len))

        del all_edge_ele, all_junction_ele, all_conn_ele

        pool = multiprocessing.Pool(processes=core_num)
        result_list = []
        for i in range(len(edge_ele_group)):
            result = pool.apply_async(self.parse_elements,
                                      args=(edge_ele_group[i], junction_ele_group[i], conn_ele_group[i], x_offset, y_offset, prj4_str, no_crs_flag))
            result_list.append(result)
        pool.close()
        pool.join()

        # 车道线, edge中心线, 交叉口面域
        lane_gdf, avg_edge_gdf, junction_gdf, conn_df = pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()

        for res in result_list:
            _lane_df, _avg_edge_df, _junction_df, _conn_df = res.get()
            lane_gdf = pd.concat([lane_gdf, _lane_df])
            avg_edge_gdf = pd.concat([avg_edge_gdf, _avg_edge_df])
            junction_gdf = pd.concat([junction_gdf, _junction_df])
            conn_df = pd.concat([_conn_df, conn_df])
        lane_gdf.reset_index(inplace=True, drop=True)
        avg_edge_gdf.reset_index(inplace=True, drop=True)
        junction_gdf.reset_index(inplace=True, drop=True)
        conn_df.reset_index(inplace=True)
        del result_list
    else:
        lane_gdf, avg_edge_gdf, junction_gdf, conn_df = self.parse_elements(edge_ele_list=all_edge_ele,
                                                                            junction_ele_list=all_junction_ele,
                                                                            conn_ele_list=all_conn_ele,
                                                                            x_offset=x_offset, y_offset=y_offset,
                                                                            proj=prj4_str, no_crs_flag=no_crs_flag)

    avg_edge_gdf.drop(index=avg_edge_gdf[avg_edge_gdf['function'] == 'internal'].index, inplace=True)

    # crs convert
    if no_crs_flag:
        crs = 'EPSG:3857'
        if not lane_gdf.empty:
            lane_gdf = gpd.GeoDataFrame(lane_gdf, geometry='geometry', crs='EPSG:4326')
        if not avg_edge_gdf.empty:
            avg_edge_gdf = gpd.GeoDataFrame(avg_edge_gdf, geometry='geometry', crs='EPSG:4326')
        if not junction_gdf.empty:
            junction_gdf = gpd.GeoDataFrame(junction_gdf, geometry='geometry', crs='EPSG:4326')
        lane_gdf = lane_gdf.to_crs(crs)
        avg_edge_gdf = avg_edge_gdf.to_crs(crs)
        junction_gdf = junction_gdf.to_crs(crs)

    try:
        lane_gdf[geometry_field] = gpd.GeoSeries(lane_gdf[geometry_field]).remove_repeated_points(l_threshold)
        avg_edge_gdf[geometry_field] = gpd.GeoSeries(avg_edge_gdf[geometry_field]).remove_repeated_points(
            l_threshold)
    except:
        lane_gdf[geometry_field] = gpd.GeoSeries(lane_gdf[geometry_field]).simplify(l_threshold / 5)
        avg_edge_gdf[geometry_field] = gpd.GeoSeries(avg_edge_gdf[geometry_field]).simplify(l_threshold / 5)

    lane_polygon_gdf = lane_gdf[lane_gdf['function'] != 'internal'].copy()
    lane_polygon_gdf[geometry_field] = \
        lane_polygon_gdf.apply(lambda item:
                               get_off_polygon(l=item[geometry_field],
                                               off_line_l=(item[
                                                               LANE_WIDTH_KEY] - 1e-7) / 2),
                               axis=1)
    conn_df = self.process_conn(pre_conn_df=conn_df)

    if not no_crs_flag:
        junction_gdf = gpd.GeoDataFrame(junction_gdf, geometry=geometry_field, crs=crs)
        lane_gdf = gpd.GeoDataFrame(lane_gdf, geometry=geometry_field, crs=crs)
        avg_edge_gdf = gpd.GeoDataFrame(avg_edge_gdf, geometry=geometry_field, crs=crs)
        lane_polygon_gdf = gpd.GeoDataFrame(lane_polygon_gdf, geometry=geometry_field, crs=crs)

    if not conn_df.empty:
        conn_gdf = self.tess_lane(conn_df=conn_df, lane_gdf=lane_gdf)
        conn_gdf = conn_gdf.explode(ignore_index=True)
        avg_conn = conn_gdf.drop_duplicates(subset=['from_edge', 'to_edge'], keep='first')[['from_edge', 'to_edge']]
        avg_edge_geo_map = {edge: geo for edge, geo in zip(avg_edge_gdf['edge_id'], avg_edge_gdf[geometry_field])}
        avg_conn[geometry_field] = avg_conn.apply(
            lambda row: LineString([list(avg_edge_geo_map[row['from_edge']].coords)[-1],
                                    list(avg_edge_geo_map[row['to_edge']].coords)[0]]), axis=1,
            result_type='expand')
        del avg_conn['from_edge'], avg_conn['to_edge']
        avg_conn['function'] = 'conn'
        avg_conn = gpd.GeoDataFrame(avg_conn, geometry=geometry_field, crs=avg_edge_gdf.crs)
        avg_edge_gdf = pd.concat([avg_edge_gdf, avg_conn])
        avg_edge_gdf.reset_index(inplace=True, drop=True)
    else:
        conn_gdf = gpd.GeoDataFrame()

    return lane_gdf, junction_gdf, lane_polygon_gdf, avg_edge_gdf, conn_gdf

SumoConvert类方法 - generate_hd_map

  • 基于宏观路网生产.net.xml高精地图文件

Parameters:

Name Type Description Default
sumo_home_fldr str

sumo安装目录

 required
single_link_gdf GeoDataFrame

路网线层(单向路段表达形式)

 required
node_gdf GeoDataFrame

路网点层

 required
junction_gdf GeoDataFrame

节点面域

 None
use_lane_ele bool

是否启用车道信息自定义

 False
x_offset float

x坐标偏移值

 0.0
y_offset float

y坐标偏移值

 0.0
lane_info_reverse bool

是否反转车道索引

 True
join_dist float

路口合并阈值, 米

 10.0
out_fldr str

.net.xml路网输出目录

 './'
flag_name str

.net.xml路网的名称

 'prj'
plain_crs str

平面投影坐标系

 'EPSG:3857'
use_conn bool

是否手动指定路口连接关系

 True
conn_tree ElementTree

路口连接对象信息

 None

Returns:

Source code in src/gotrackit/netxfer/SumoConvert.py 
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def generate_hd_map(self, sumo_home_fldr: str, single_link_gdf: gpd.GeoDataFrame, node_gdf: gpd.GeoDataFrame,
                    junction_gdf: gpd.GeoDataFrame = None,
                    use_lane_ele: bool = False, lane_info_reverse: bool = True, join_dist: float = 10.0,
                    x_offset: float = 0.0, y_offset: float = 0.0, out_fldr: str = r'./',
                    flag_name: str = 'prj', plain_crs: str = 'EPSG:3857',
                    use_conn: bool = True, conn_tree: ET.ElementTree = None) -> gpd.GeoDataFrame:
    """SumoConvert类方法 - generate_hd_map

    - 基于宏观路网生产.net.xml高精地图文件

    Args:
        sumo_home_fldr: sumo安装目录
        single_link_gdf: 路网线层(单向路段表达形式)
        node_gdf: 路网点层
        junction_gdf: 节点面域
        use_lane_ele: 是否启用车道信息自定义
        x_offset: x坐标偏移值
        y_offset: y坐标偏移值
        lane_info_reverse: 是否反转车道索引
        join_dist: 路口合并阈值, 米
        out_fldr: .net.xml路网输出目录
        flag_name: .net.xml路网的名称
        plain_crs: 平面投影坐标系
        use_conn: 是否手动指定路口连接关系
        conn_tree: 路口连接对象信息

    Returns:

    """

    if SPREAD_TYPE not in single_link_gdf.columns:
        single_link_gdf[SPREAD_TYPE] = 'right'

    bound = node_gdf.bounds

    origin_minx, origin_miny, origin_maxx, origin_max_y = bound['minx'].min(), bound['miny'].min(), bound[
        'maxx'].max(), bound['maxy'].max()
    single_link_gdf = single_link_gdf.to_crs(plain_crs)
    node_gdf = node_gdf.to_crs(plain_crs)
    # if x_offset == 0:
        # x_offset = -node_gdf[geometry_field].x.min()
        # x_offset = 0
    # if y_offset == 0:
        # y_offset = -node_gdf[geometry_field].y.min()
        # y_offset = 0
    single_link_gdf[net_field.LINK_ID_FIELD] = [i for i in range(1, len(single_link_gdf) + 1)]
    edge_tree = self.generate_plain_edge(link_gdf=single_link_gdf, use_lane_ele=use_lane_ele,
                                         lane_info_reverse=lane_info_reverse)
    node_tree = self.generate_plain_node(node_gdf=node_gdf, x_offset=x_offset, y_offset=y_offset,
                                         junction_gdf=junction_gdf, origin_bounds=(origin_minx, origin_miny,
                                                                                   origin_maxx, origin_max_y))
    edge_tree.write(os.path.join(out_fldr, rf'{flag_name}.edg.xml'))
    node_tree.write(os.path.join(out_fldr, rf'{flag_name}.nod.xml'))

    if use_conn:
        conn_tree.write(os.path.join(out_fldr, rf'{flag_name}.con.xml'))
        plain_conn_path = os.path.join(out_fldr, rf'{flag_name}.con.xml')
    else:
        plain_conn_path = None

    self.generate_net_from_plain(sumo_home_fldr=sumo_home_fldr,
                                 plain_edge_path=os.path.join(out_fldr, rf'{flag_name}.edg.xml'),
                                 plain_node_path=os.path.join(out_fldr, rf'{flag_name}.nod.xml'),
                                 plain_conn_path=plain_conn_path,
                                 x_offset=x_offset,
                                 y_offset=y_offset, join_dist=join_dist, out_fldr=out_fldr,
                                 out_file_name=flag_name)

    return single_link_gdf

SumoConvert类函数 - match2rou(v0.3.15提供)

  • 将路径匹配结果表转化为SUMO的车流路径文件

Parameters:

Name Type Description Default
match_res_df DataFrame | GeoDataFrame

匹配结果表

 required
time_format str

时间列字符串模板

 '%Y-%m-%d %H:%M:%S'
time_unit str

时间列单位

 's'
edge_id_field str

edge_id列字段名称

 'edge_id'
out_fldr str

rou文件输出目录

 './'
file_name str

rou文件名称

 'flow'

Returns:

Type Description
ElementTree

DocTree
Source code in src/gotrackit/netxfer/SumoConvert.py 
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@staticmethod
def match2rou(match_res_df: pd.DataFrame | gpd.GeoDataFrame, edge_id_field: str = 'edge_id',
              time_format: str = '%Y-%m-%d %H:%M:%S', time_unit: str = 's',
              out_fldr: str = r'./', file_name: str = 'flow') -> ET.ElementTree:
    """SumoConvert类函数 - match2rou(v0.3.15提供)

    - 将路径匹配结果表转化为SUMO的车流路径文件

    Args:
        match_res_df: 匹配结果表
        time_format: 时间列字符串模板
        time_unit: 时间列单位
        edge_id_field: edge_id列字段名称
        out_fldr: rou文件输出目录
        file_name: rou文件名称

    Returns:
        DocTree
    """

    assert edge_id_field in match_res_df.columns, rf'edge_id_field: {edge_id_field} is not found in columns'

    match_res_df.dropna(subset=[edge_id_field], inplace=True, how='any')
    match_res_df.reset_index(inplace=True, drop=True)
    try:
        match_res_df[edge_id_field] = match_res_df[edge_id_field].astype(int)
    except:
        pass
    match_res_df = del_dup_links(match_res_df=match_res_df, use_time=True, time_unit=time_unit,
                                 time_format=time_format)
    match_res_df['dt'] = (match_res_df[time_field] - match_res_df[time_field].min()).dt.total_seconds()
    routes_root = ET.Element('routes')
    tree = ET.ElementTree(routes_root)
    iter_agents(routes_root, match_res_df, edge_id_field)
    if not os.path.exists(out_fldr):
        os.makedirs(out_fldr)
    tree.write(os.path.join(out_fldr, rf'{file_name}.rou.xml'))
    return tree

SumoConvert类静态方法 - generate_sumocfg(v0.3.15提供)

  • 生成sumo仿真配置文件

Parameters:

Name Type Description Default
net_file_path str

.net.xml路网的绝对路径

 'net.net.xml'
rou_file_path str

.rou.xml车流文件的绝对路径

 'rou.rou.xml'
start_time int

仿真开始时间戳, 秒

 0
end_time int

仿真结束时间戳, 秒

 1200
out_fldr str

sumo仿真配置文件的存储目录

 './'
file_name str

sumo仿真配置文件的名称

 'sim'

Returns:

Type Description

None
Source code in src/gotrackit/netxfer/SumoConvert.py 
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@staticmethod
def generate_sumocfg(net_file_path: str = r'net.net.xml',
                     rou_file_path: str = r'rou.rou.xml', start_time: int = 0,
                     end_time: int = 1200, out_fldr: str = r'./', file_name: str = r'sim'):
    """SumoConvert类静态方法 - generate_sumocfg(v0.3.15提供)

    - 生成sumo仿真配置文件

    Args:
        net_file_path: .net.xml路网的绝对路径
        rou_file_path: .rou.xml车流文件的绝对路径
        start_time: 仿真开始时间戳, 秒
        end_time: 仿真结束时间戳, 秒
        out_fldr: sumo仿真配置文件的存储目录
        file_name: sumo仿真配置文件的名称

    Returns:
        None
    """
    config_root = ET.Element('configuration')
    tree = ET.ElementTree(config_root)
    input_ele = element('input', None, None)
    config_root.append(input_ele)
    net_file_ele, route_file_ele = \
        element('net-file', 'value', net_file_path), \
        element('route-files', 'value', rou_file_path)
    ign_error_ele = element('ignore-route-errors', 'value', "True")
    input_ele.append(net_file_ele)
    input_ele.append(route_file_ele)
    input_ele.append(ign_error_ele)
    sim_time_ele = element('time', None, None)
    begin_time_ele, end_time_ele = \
        element('begin', 'value', rf'{start_time}'), element('end', 'value', rf'{end_time}')
    sim_time_ele.append(begin_time_ele)
    sim_time_ele.append(end_time_ele)
    input_ele.append(sim_time_ele)
    if not os.path.exists(out_fldr):
        os.makedirs(out_fldr)
    tree.write(os.path.join(out_fldr, file_name + '.sumocfg'))

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