Connected Communities Corridor Analysis


This is a web-based viewer for the HTML maps and charts produced by MRCagney NZ for Connected Communities public transport analysis. The study period for this analysis refers to all weekdays in March 2019.

Use the dropdown menus below to select a corridor, direction, time period, indicator, study period and the type of day you wish to view the map and charts for. Click 'GO' to visit that page.


We use the following terms to describe sections of road for analysis.

  • Link: a stretch of road between two bus stops on the same side of the road. The direction of the link is the same as the vehicle direction between those two stops.
  • Segment: a set of adjacent links on the same side of the road (i.e. in the same direction).
  • Corridor: a collection of segments, not necessarily contiguous or with the same direction, which describe a Connected Communities study area.

We use the following terms to describe the indicators in our analysis.

  • Trip: a single run of a bus, with a given route number, direction and start time, e.g. the route 70 bus, Britomart-bound, departing at 08:00. This is also the definition used in the General Transit Feed Specification (GTFS).
  • Travel time: the run time of a trip along a link or segment (computed from realtime data). We measure travel time from the departure of the first stop to the departure of the last stop, including dwell times of any stops in between (segments only).
  • Speed: the average speed of a trip along a link or segment (computed from realtime data and travel distance).
  • Loading: the number of passengers on board a trip along a link (computed from ticketing data), during a specified time period.
  • Freeflow delay: a trip’s travel time along a link or segment minus the fastest travel time for that link or segment from all trips in the study period.
  • Passenger freeflow delay: a trip's freeflow delay multiplied by its loading along a link or segment.
  • Number of boardings: the number of passengers embarking on a bus at a stop.
  • Number of alightings: the number of passengers disembarking on a bus at a stop.
  • Number of trips: the number of bus trips on a link or segment in a time period.
  • Schedule adherence: the mean absolute error of all trips to depart a bus stop, compared to their scheduled departure time. This is the mean number of minutes early or late that buses depart from a bus stop.

In our analysis we compute various measures (statistics) for each indicator:

  • Median: the 50th percentile of the selected indicator across all trips in the study period
  • Total per day: the sum of the selected indicator across all trips in the study period divided by the number of days in the study period

For travel time, we also calculate the additional measure:

  • Variability (called Reliability in the Connected Communities KPIs): the 95th percentile of travel time in the time period minus the 50th percentile of travel time, all divided by the 50th percentile of travel time. The 50th percentile travel time can either be taken from the time period, or from trips throughout the whole day. These two metrics are labelled Period Median and Day Median respectively. The times are taken from all across all the trips in the study period.

In some charts, you may see p5, p50 and p95. These measures correspond to the 5th, 50th and 95th percentile values respectively. The 'total per day' metric sums the values of an measure for all trips along that link or segment in the study period, and divides by the number of study days (e.g. the total freeflow delay experienced by all trips along a link).



Our analysis combines three kinds of public transport data for the study period: scheduling, realtime, and ticketing. We acquired scheduling data in the form of a General Transit Feed Specification (GTFS) feed from OpenMobilityData. We acquired realtime data in the form of General Transit Feed Specification Realtime (GTFSR) feeds from Auckland Transport's API. We received ticketing data from Auckland Transport in the form of daily tables of all passenger trips.


First we cleaned the data by removing school trips from the scheduling data, consolidating scheduled route short names, linking scheduling and ticketing data by matching with vehicle trip routes and departure times, and using boarding and alighting counts to estimate stop dwell times.

We used for our time periods of analysis 07:00--09:00, 09:00--15:00, and 16:00--18:00, which match the time periods used in the Auckland Forecasting Centre's Macro Strategic Model. Our analysis is for weekdays only.

We divided the study area into corridors, segments, and links. The segments we defined were used to maximise the number of buses and bus routes that are counted in our analysis, creating a new segment every time a bus joins or diverges from the corridor. For each segment, we identified all scheduled trips that traversed the entire length of the segment without exiting it at any point. For each such trip, we computed the indicators described above for every link of the segment. The maps and charts generated from these statistics are called 'Link' maps. For each stop of the trip, we also computed its boardings and alightings.

In addition, some corridor teams defined segments that cover areas of interest. We also computed the indicators described above for each of these new segments as a whole. The maps and charts from these statistics are called 'Segment' maps.

We discarded from our sample some trips with impossible speeds (negative or extremely fast), which likely arose from poorly geocoded trip geometries in the scheduling data.

Levels of Service

In these maps and charts, we use Levels of Service (LOS) to visualise the Speed, and Travel Time Reliability indicators. These LOS categorise the indicators as A (best) to F (worst).

The LOS for speed including running and dwell times are:

  • A: 45+ km/h
  • B: 35-45 km/h
  • C: 25-35 km/h
  • D: 20-25 km/h
  • E: 15-20 km/h
  • F: 0-15 km/h

LOS for Time Travel Reliability are determined using the variability metric described above. The LOS for reliability (as decided by the Connected Communities PT advisory team) are:

  • A: 0-20 %
  • B: 20-40 %
  • C: 40-50 %
  • D: 50-70 %
  • E: 70-100 %
  • F: 100+ %

Maps and Charts

Displayed on this site are interactive maps and charts illustrating the statistics we computed and described above for all the links of the study corridors. There are maps for each corridor, time period, direction, indicator, and measure. There is one chart for each link (of each segment) and each indicator.

Regarding the maps, you can isolate individual segments for closer inspection by toggling the segments in the map's layer menu (in the top right of the map). Clicking on a link or bus stop triggers a popup with more detailed link/stop information.

Regarding the charts, you can isolate individual lines by toggling them in the chart's legend. You can also pan within a chart by clicking the pan symbol and moving the chart. Click the zoom tool and click and drag to create zoom to the selected region.

All maps, charts (in HTML format) and data (in CSV form) will be made available on ProjectWise. Maps and charts for the following corridors and indicators for both inbound and outbound directions are available in this web-based viewer.


  • All Corridors
  • Ellerslie-Pakuranga
  • FN32
  • Great North Rd - East
  • Great North Rd - West
  • Great North Rd - Grey Lynn
  • Great South Rd - North
  • Great South Rd - South
  • Manukau Rd
  • Massey
  • Mount Eden Rd
  • New North Rd
  • Parnell
  • Ponsonby
  • Remuera Rd
  • Sandringham Rd


  • Travel Time
  • Travel Time Reliability - Day Median
  • Travel Time Reliability - Period Median
  • Freeflow Delay
  • Loading
  • Passenger Freeflow Delay
  • Speed
  • Schedule Adherence
  • Alightings by stop
  • Boardings by stop
  • Bus trips by link

NOTE: not all corridors have 'segment' maps, as the information has not been provided by the corridor teams. In addition, segment level maps and charts have only been produced for weekdays.


As mentioned above our analysis does not contain all trips in the study period, because we discarded ones with unusable or anomalous data. In total there were 793472 good trips from a total of 801550 trips (99%).

Consequently, the total per day measure is slightly underestimated for all indicators.

This also impacts the accuracy of the other measures slightly, but because they are based on percentiles, we expect the impact to be smaller than for the total per day measure.

Last updated 2020-07-29 10:29.