Skip to content

Latest commit

 

History

History
46 lines (36 loc) · 2.31 KB

mobility-corridors-analysis.md

File metadata and controls

46 lines (36 loc) · 2.31 KB

Mobility Corridors Analysis

This report is a small description of Mobility Corridors analysis project.

Table of Contents

  1. Overview
  2. Data description
  3. Downloading data
  4. Corridors Analysis

Overview

Skyss is the public authority that plans, purchases, and markets the public transport services governed by the county authority in Hordaland, Norway. Skyss offers trips by light rail (Bybanen), bus, local boat, and ferry throughout Hordaland. As part of this analysis, we looked at the data to understand areas and corridors for the public transport routes and passengers count, in order to optimize public transport and suggest the routes in order to reduce the cost and increase the ridership.

Data description

Various datasets are available through the Skyss Public API. Descriptions of the variables and fields are available on the API website. Following datasets are currently available:

  1. Calender
  2. Lines
  3. Routes
  4. Stop Points
  5. Trips information (raw & aggregated)

Data downloading

  • Period : 2 Months (Oct & Nov 2019) - chosen as Normal Traffic Period.
  • Trips Data is very large and granular, so created day wise csv/parquet files.
  • Stop Points and Lines data.

Corridors Analysis

  • Defined geometry and visualized geodata, for example, Bybanen corridors for a line and stops within a certain radius with average onboard passengers at each segment.

Bybanen corridors geometry

  • Read and wrote geodata on Postgres.
  • Defined shapefiles and read shapefiles in Kepler map to get a map view of geometry data for line's corridors, for example:

Map view of corridors geometry

  • Analysed and visualized average and total passengers counts for each segment of the Bybanen line.
  • Defined period by dividing a day into different parts and calculated average onboard for the line in a different part of the day and plotted period data.
  • Filtered data for weekdays and again calculated average passengers counts of weekdays data for the line and plotted this data.