Bus lanes

Motivation

Limit impacts of congestion
(Cesme et al. 2018)

Reducing travel time variability
(Surprenant-Legault and El-Geneidy 2011)

Increase commercial speed
(Basso et al. 2011)

Other modes can loose
quality of service
(Arasan and Vedagiri 2010)

Potential to jeopardize
public acceptance
(Batty et al. 2014)

It is crucial to underlie these interventions on data and evidence to ensure they are effective and accepted by the public

Criteria

Bus frequency 🚍

Number of lanes 🛣️

Traffic conditions 🚗

Network continuity 🌐

What if we could analyse these dimensions altogether in an open and reproducible way?

Data sources

Bus frequency 🚍
General Transit Feed Specification (GTFS)

Traffic conditions 🚗
GTFS Real Time (GTFS-RT)

Number of lanes 🛣 / Network continuity 🌐
OpenStreetMap (OSM)

Data sources | GTFS

Bus frequency 🚍
General Transit Feed Specification (GTFS)

Data sources | GTFS

Bus frequency 🚍
General Transit Feed Specification (GTFS)

GTFS

Feeds travel planning apps

Data sources | GTFS-RT

Traffic conditions 🚗
GTFS Real Time (GTFS-RT) 

{
    "agency_id": "41",
    "bikes_allowed": true,
    "capacity_seated": 29,
    "capacity_standing": 28,
    "capacity_total": 57,
    "contactless": false,
    "id": "41|300",
    "license_plate": "AC-45-FH",
    "make": "CaetanoBus",
    "model": "e.City Gold",
    "owner": "SCOTTURB",
    "propulsion": "electricity",
    "registration_date": "20141110",
    "wheelchair_accessible": true,
    "bearing": 167,
    "block_id": "1_1325-11",
    "current_status": "INCOMING_AT",
    "event_id": "6KLCD-41|300-1612_0_2_0800_0829_0_1",
    "lat": 38.76739501953125,
    "line_id": "1612",
    "lon": -9.2985200881958,
    "pattern_id": "1612_0_2",
    "route_id": "1612_0",
    "schedule_relationship": "SCHEDULED",
    "shift_id": "TP 1+1325",
    "speed": 3.611111111111111,
    "stop_id": "172486",
    "timestamp": 1778574545,
    "trip_id": "[6KLCD]1612_0_2_0800_0829_0_1",
    "door_status": "CLOSED"
}

GTFS-RT

Follow vehicles in real-time

GTFS-RT

Follow vehicles in real-time

Data sources | GTFS

Number of lanes 🛣 / Network continuity 🌐
OpenStreetMap (OSM)

Methodology | Bus frequency 🚍 with GTFS

Initial approach: Overline route frequencies to aggregate overlapping values

GTFShift::get_route_frequency_hourly

GTFShift::get_route_frequency_hourly(overline=TRUE)

Methodology | Bus frequency 🚍 with GTFS

Initial approach: Overline route frequencies to aggregate overlapping values

1st challenge: Shapes have distinct geometries, leading to inconsistent results

GTFShift::get_route_frequency_hourly(overline=TRUE)

Methodology | Bus frequency 🚍 with GTFS

Initial approach: Overline route frequencies to aggregate overlapping values

1st challenge: Shapes have distinct geometries, leading to inconsistent results

2nd challenge: Shapes don’t match OSM geometries (and are not required to do so)

Methodology | Bus frequency 🚍 with GTFS

An example from

GTFS shapes geometries
GTFS geometry edges
OSM highway network
OSM h. network edges

Methodology | Bus frequency 🚍 with GTFS

An example from

GTFS shapes geometries
GTFS geometry edges
OSM highway network
OSM h. network edges

Methodology | Bus frequency 🚍 with GTFS and OSM

2nd approach: Use OSM routes geometry to aggregate routes frequency

But how?

Methodology | Bus frequency 🚍 with GTFS and OSM

2nd approach: Use OSM routes geometry to aggregate routes frequency

GTFShift::get_route_frequency_hourly

GTFShift::osm_shapes_match_routes

GTFShift::get_way_frequency_hourly

Methodology | Nr of lanes 🛣 / Network continuity 🌐 with OSM

Approach: Get road network topology from OSM

osmdata::opq

GTFShift::osm_bus_lanes

Methodology | Traffic conditions 🚗 with GTFS-RT and OSM

Approach: Compute speed per OSM road segment

GTFShift::rt_collect

sf::st_buffer

GTFShift::rt_extend_prioritization

Methodology | Wrap up

GTFShift::get_way_frequency_hourly

Bus frequency 🚍

Number of lanes 🛣

osmdata::opq

GTFShift::osm_bus_lanes

Network continuity 🌐

Traffic conditions 🚗

GTFShift::rt_extend_prioritization

What if we could analyse these dimensions altogether in an ✅ open and reproducible way?

What if we could analyse these dimensions altogether in an ✅ open and reproducible way?

Reproducibility

📦 Open source R package

📊 Comprehensive framework for bus lane prioritization

🔎 Provides methods for the analysis of each individual dimension

🚀 Enables aggregated analysis with one simple method

Reproducibility

🚀 Enables aggregated analysis with one simple method 

library(GTFShift)

gtfs = GTFShift::load_feed("https://gateway.carris.pt/gateway/gtfs/api/v2.8/GTFS")

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)

#  Bus frequency 🚍,  Number of lanes 🛣️,  Network continuity 🌐 
lanes = GTFShift::prioritize_lanes(gtfs, osm_q)

rt_collection = read.csv("rt_collect_file.csv") |> 
  sf::st_as_sf(coords = c("vehicle.position.longitude", "vehicle.position.latitude"), crs = 4326)

#  Traffic conditions 🚗 
lanes = GTFShift::rt_extend_prioritization(
  lane_prioritization = lanes,
  rt_collection = rt_collection
)

What if we could analyse these dimensions altogether in an ✅ open and ✅ reproducible way?

What if we could analyse these dimensions altogether in an ✅ open and ✅ reproducible way?

What if we could make it accessible to everyone, without requiring coding skills?

Future work

Keep contributing to OSM

Extend methods to evaluate operational performance

Apply methodology to international case studies

Write a paper