capetowntraffic's Introduction

Modelling traffic in Capetown

[1] 0.49259

 [1] "geom_num"      "edge_id"       "from_id"       "from_lon"     
 [5] "from_lat"      "to_id"         "to_lon"        "to_lat"       
 [9] "d"             "d_weighted"    "highway"       "lanes"        
[13] "way_id"        "osm_id"        "lanes"         "lit"          
[17] "maxspeed"      "oneway"        "time"          "time_weighted"
[21] "component"     "centrality"   

# A tibble: 6 × 2
  edge_id centrality
  <chr>        <dbl>
1 a343708     714162
2 a343707    1249170
3 a343704     385548
4 a343699         25
5 a343697     601768
6 a343696     601814

Let’s plot the road segments with the top 1% of centrality values:

The dataset with the centrality added is as follows:

geom_num	edge_id	from_id	from_lon	from_lat	to_id	to_lon	to_lat	d	d_weighted	highway	lanes	way_id	osm_id	lanes.1	lit	maxspeed	oneway	time	time_weighted	component	centrality
2596	a343708	4469220754	18.41814	-33.94010	36684068	18.41834	-33.93992	29.18203	58.36406	residential	1	5364234	5364234	1	NA	NA	TRUE	1.750922	3.501843	1	714162
11463	a343707	59964704	18.44622	-33.93685	59964725	18.44371	-33.93658	233.94434	467.88869	residential	2	227878319	227878319	2	NA	60	FALSE	14.036661	28.073321	1	1249170
37783	a343704	2218670211	18.47967	-33.95551	2218670332	18.47913	-33.95683	458.74564	1146.86409	service	NA	211943315	211943315	NA	NA	NA	FALSE	27.524738	68.811846	1	385548
191	a343699	25392638	18.51288	-33.94234	25392222	18.51381	-33.94121	154.05001	308.10002	residential	NA	4245995	4245995	NA	NA	60	FALSE	9.243001	18.486001	1	25
191	a343698	25392222	18.51381	-33.94121	25392638	18.51288	-33.94234	154.05001	308.10002	residential	NA	4245995	4245995	NA	NA	60	FALSE	9.243001	18.486001	1	25
1155	a343697	26685188	18.49099	-33.96974	26685209	18.49165	-33.97025	84.70391	169.40783	residential	NA	4376043	4376043	NA	NA	60	FALSE	5.082235	10.164470	1	601768