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Team Name:

Pied Piper

Team Members:

Evidence of Work

Redirect

Project Info

Pied Piper thumbnail

Team Name

Pied Piper

Team Members

Callistus and 3 other members with unpublished profiles.

Project Description

Project

Sydney is an amazing city, extremely diverse, business focused and driven. But as with most big cities, congestion of traffic (public and drivers) is a large problem and often a complaint of many locals. Enter Redirect.

Redirect aims to be a platform to model, simulate and plan better cities by modelling road traffic.

While we are not road modelling experts, we enable the people with the right domain knowledge on our platform to model the effects of adding / removing features within a city’s transport network such as reducing the amount of traffic through bottleneck roads and diverting it elsewhere, adding parking spaces that are cheaper near less congested roads as an incentive.

In the future we can incorporate other features such as road speed limits and tolls with the ability to accept a "citizen" AI model as a parameter for analyzing traffic diversion behaviour. We can also leverage public transport APIs to estimate the impact of a new transit station / more vehicles in a particular area.

We do this by querying many APIS. We elaborate on this more in the data story section.

Technologies used

  • HTML/CSS, javascript
  • React.js, node.js
  • Google Maps, Places, Directions, Traffic APIs
  • NSW transport APIs
  • Python

Data Story

We built a frontend in react.js and a backend in node.js.
The basemap for our frontend is the Google Maps Traffic layer.
Typing in an address queries the Google Places API.
With the start and destination places we query the Google Directions API to get the suggested route. We also query near by locations using Google Places Nearby queries and drive routes from the start to the destination via these nearby places to check the route is less congested.

We use open data parking geojson[1] and nsw geojson[2] to map out features. 2019 congestion audit data as part of our investigation and heuristics [3, 4]. The NSW transport road count API for recent data.

[1] https://opendata.transport.nsw.gov.au/dataset/street-parking
[2] https://data.gov.au/dataset/ds-dga-91e70237-d9d1-4719-a82f-e71b811154c6/details
[3] https://data.gov.au/dataset/ds-dga-d42b84fb-9f07-400e-af7e-133389ddeccb/details
[4] https://data.gov.au/dataset/ds-dga-bcdaedcc-c208-480b-97ca-805a62c65316/details
[5] https://opendata.transport.nsw.gov.au/node/2171/exploreapi#!/default/get_spatial

Evidence of Work

Video

Homepage

Project Image

Team DataSets

Offstreet Parking

Description of Use parking spaces geo json + locations

Data Set

OpenData Transport NSW Road traffic data

Description of Use Traffic volume counts on roads

Data Set

NSW Suburb/Locality Boundaries - PSMA Administrative Boundaries

Description of Use segment nsw on map

Data Set

(Audit 2019) Congestion on all roads during 2016 AM peak

Description of Use Analysis & making heuristics

Data Set

(Audit 2019) Congestion on all roads during 2031 AM peak

Description of Use Analysis & making heuristics

Data Set

Challenge Entries

What are the key levers that can be affected to ease congestion in NSW?

Using open data and other data sources, what can you infer that can be changed by Transport for NSW to help ease congestion? This can be congestion from people, cars, train passengers, on a platform or queuing for a bus or just generally on a road. What has happened in the past? What information can we provide customers, bus drivers or employers to assist in easing congestion? Note: this is not just road congestion. It can be viewed holistically or at a microlevel – for say an intersection.

Eligibility: Use of open data or other data. Please reference what datasets you have used (or would have to use) for your solution.

Go to Challenge | 7 teams have entered this challenge.

Reducing CBD Traffic Congestion

How to reduce traffic congestion or parking problems in CBD?

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 39 teams have entered this challenge.

Ultimo: what are the building blocks of an innovative precinct?

What makes an innovative precinct? With huge infrastructural developments in the Ultimo community changing the way people live, work and play in and around the Ultimo area, how might we more accurately predict what the precinct will need in the future to ensure it is a hub for innovation, creativity and entrepreneurship?

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 7 teams have entered this challenge.

Public Transport for the Future

How might we combine data with modern technologies - such as AI/ML, IoT, Analytics or Natural Language interfaces - to better our public transport services. Outcomes could take the form of new commuter experiences, reduced environmental impact, or helping plan for the future.

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 45 teams have entered this challenge.

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