Introduction
Bus lanes have the potential to improve the reliability of bus operations by limiting the negative impacts of traffic congestion, reducing the variability of travel times, and increasing the average commercial speed - which can ultimately be used to increase service frequency.
However, introducing them on road infrastructure affects other modes, such as private vehicles, which may experience a decrease in the level of service due to reduced allocated space, potentially jeopardizing public acceptance.
Common criteria for implementing bus lanes include:
- Frequency of buses (and trams) per hour and direction, at a peak hour;
- Number of lanes in the same direction;
- Existing traffic conditions;
- Existing bus lanes in the area (from a network continuity perspective).
GTFShift provides methods to analyse these dimensions, namely:
GTFShift::get_way_frequency_hourly(), to obtain the frequency of services per hour for each road segment with transit service and its associated characteristics (such as number of lanes).GTFShift::osm_bus_lanes(), to identify existing bus lanes in the road network.GTFShift::rt_collect_json()orGTFShift::rt_collect_protobuf(), to collect GTFS-RT data, which can be later used withGTFShift::rt_extend_prioritization()to include real-time operational metrics in the prioritization analysis.
This document explores how to use these methods in a combined way to assist public transport planners in prioritizing bus lane implementations. For details on the several encapsulated features and method variations, refer to the numbered articles in the menu, that explore in detail each of the specific approaches followed.
Prioritize lanes
Generate base indicators
GTFShift::prioritize_lanes() is a simple method that
generates indicators for most of the criteria mentioned above using GTFS
and OpenStreetMaps data (service frequency and lane characteristics).
With a single call, it returns a data.frame with the relevant metrics
for each road segment with transit service.
# Get GTFS from library GTFS database for Portugal
data = read.csv(system.file("extdata", "gtfs_sources_pt.csv", package = "GTFShift"))
gtfs_id = "lisboa"
gtfs = GTFShift::load_feed(data$URL[data$ID == gtfs_id], create_transfers=FALSE)
osm_q = opq(bbox=sf::st_bbox(tidytransit::shapes_as_sf(gtfs$shapes))) |>
add_osm_feature(key = "route", value = c("bus", "tram")) |>
add_osm_feature(key = "network", value = "Carris", key_exact = TRUE)
lanes = prioritize_lanes(gtfs, osm_q)
summary(lanes)
#> way_osm_id hour frequency is_bus_lane
#> Length:132056 Min. : 0.00 Min. : 1.000 Mode :logical
#> Class :character 1st Qu.: 8.00 1st Qu.: 3.000 FALSE:120040
#> Mode :character Median :13.00 Median : 6.000 TRUE :12016
#> Mean :12.56 Mean : 9.673
#> 3rd Qu.:18.00 3rd Qu.:13.000
#> Max. :23.00 Max. :99.000
#> n_lanes n_directions n_lanes_direction routes
#> Min. :1.000 Min. :1.000 Min. :1.000 Length:132056
#> 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 Class :character
#> Median :2.000 Median :1.000 Median :1.000 Mode :character
#> Mean :2.149 Mean :1.364 Mean :1.703
#> 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000
#> Max. :7.000 Max. :2.000 Max. :6.000
#> geom
#> LINESTRING :132056
#> epsg:4326 : 0
#> +proj=long...: 0
#>
#>
#> Extend with GTFS-RT data
If GTFS-RT data is available, it can be used to extend the prioritization analysis with real-time operational metrics, such as average speed. This can help identify road segments where buses are experiencing significant delays due to traffic congestion, which may benefit from bus lane implementation.
rt_collection = read.csv("rt_collect_file.csv") |>
sf::st_as_sf(coords = c("vehicle.position.longitude", "vehicle.position.latitude"), crs = 4326)
lanes = GTFShift::rt_extend_prioritization(
lane_prioritization = lanes,
rt_collection = rt_collection
)Refer to the GTFS Real Time article for details on how to collect GTFS-RT data and extend the prioritization analysis.
Visualize results
The aggregated data can then be manipulated according to the prioritization criteria defined by the user. For instance, the following code highlights (in red) the road segments as high priority for bus lane implementation if they have more than 1 lane per direction and a frequency above the median number of buses per hour registered at 8:00.
lanes_0800 = lanes |> filter(hour==8)
summary(lanes_0800)
#> way_osm_id hour frequency is_bus_lane n_lanes
#> Length:6665 Min. :8 Min. : 1.00 Mode :logical Min. :1.000
#> Class :character 1st Qu.:8 1st Qu.: 5.00 FALSE:6114 1st Qu.:1.000
#> Mode :character Median :8 Median :10.00 TRUE :551 Median :2.000
#> Mean :8 Mean :13.32 Mean :2.121
#> 3rd Qu.:8 3rd Qu.:18.00 3rd Qu.:3.000
#> Max. :8 Max. :99.00 Max. :7.000
#>
#> n_directions n_lanes_direction routes speed_avg
#> Min. :1.000 Min. :1.000 Length:6665 Min. : 0.01819
#> 1st Qu.:1.000 1st Qu.:1.000 Class :character 1st Qu.: 8.42633
#> Median :1.000 Median :1.000 Mode :character Median : 9.76760
#> Mean :1.364 Mean :1.679 Mean :10.13130
#> 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:11.27158
#> Max. :2.000 Max. :6.000 Max. :43.63347
#> NA's :186
#> speed_median speed_p25 speed_p75 speed_count
#> Min. : 0.007136 Min. : 0.000 Min. : 0.01992 Min. : 1
#> 1st Qu.: 7.862506 1st Qu.: 5.979 1st Qu.:10.10566 1st Qu.: 41
#> Median : 9.150032 Median : 7.122 Median :11.65674 Median : 104
#> Mean : 9.400438 Mean : 7.198 Mean :12.26544 Mean : 181
#> 3rd Qu.:10.546549 3rd Qu.: 8.276 3rd Qu.:13.58913 3rd Qu.: 223
#> Max. :47.008923 Max. :42.811 Max. :61.81473 Max. :5760
#> NA's :186 NA's :186 NA's :186 NA's :186
#> route_names geom
#> Length:6665 LINESTRING :6665
#> Class :character epsg:4326 : 0
#> Mode :character +proj=long...: 0
#>
#>
#>
#>
p50_frequency = quantile(lanes_0800$frequency, 0.5, na.rm=TRUE)
p50_speed = quantile(lanes_0800$speed_avg, 0.5, na.rm=TRUE)
mapview::mapview(
lanes_0800 |> filter(is_bus_lane & (frequency<p50_frequency | (is.na(n_lanes) | n_lanes_direction<=1) | speed_avg<=p50_speed)),
layer.name=sprintf("Bus lane with -%d bus/h OR -2 lane/dir OR %.2f km/h or - avg. speed", p50_frequency, p50_speed),
color="#DAD887",
homebutton=FALSE,
lwd=3
) + mapview::mapview(
lanes_0800 |> filter(is_bus_lane & frequency>=p50_frequency & !is.na(n_lanes) & n_lanes_direction>1 & speed_avg>p50_speed),
layer.name=sprintf("Bus lane with +%d bus/h AND +1 lane/dir AND +%.2f km/h avg.speed", p50_frequency-1, p50_speed),
color="#3BC1A8",
homebutton=FALSE,
lwd=3
) + mapview::mapview(
lanes_0800 |> filter(!is_bus_lane & frequency>=p50_frequency & !is.na(n_lanes) & n_lanes_direction>1 & speed_avg<=p50_speed),
layer.name=sprintf("NO bus lane with +%d bus/h AND +1 lane/dir AND %.2f km/h or - avg.speed", p50_frequency-1, p50_speed),
color="#F63049",
homebutton=FALSE,
lwd=3
)This visual representation allows to easily identify not only the high-priority segments for bus lane implementation, but also their spatial distribution across the existent network. A process that extends the results by incorporating the network continuity perspective, enabling the identification and eventual prioritization of critical segments that connect bus lanes but have a bad performance.