Transportation & Logistics
D-transport

Public transport route search system

About The

Project

Digital service providing live and offline data on public transport routes and navigation in the cities that includes a mobile application for iOS and Android, a website, and an administration panel for the entire system, powered with AI/ML algorithms. In the app or the website, the user can track the vehicle and see the actual time of arrival of the vehicle on the map, as well as set up AI-predicted routes for transitions. The admin panel allows you to create, edit and disable routes, and create information messages for users to receive as push notifications.

.NET

MongoDB

RabbitMQ

Docker

Identity Server

React Native

React

HTML5

Redis Cache

About The

Client

D-transport is a team of specialists with the creation and implementation of automation processes of the modern city. Their products create a convenient interface between government and residents of transparent and automatic control of construction works. The company's portfolio includes lots of solutions for automating processes in cities, some of them: "United dispatch service", Smart School complex, Safe City complex, and others.

Location:
Ukraine
Partnership period:
2019-2022
Industry:
Transportation & Logistics
OUR

Business Challenge

Our team had a goal to create a smart AI-managed algorithm for creating custom routes and built-in A-to-B route search and let users track their movements employing GPS technologies, while allowing for admin users to create optimal routes.

OUR

Project Goals

1

Provide information on local public transport in real-time

2

Show local public transport fare

3

Calculate the cheapest or shortest route of passenger location

4

Send news and route changes to local citizens via Push notifications

5

Predictions of transport arrivals down to a minute

6

Predict traffic conditions and create/schedule routes

Results

Lionwood.software Implemented 2 mobile applications for Android and iOS and 2 Web apps for passengers and administrators.
Features that are available to the end-users in Mobile App and Client Web App:

  • see a map of the city;
  • track each type of public transport (metro, tram, trolleybus, and bus) in real-time;
  • track selected vehicles on a route (vehicles are displayed as markers on the map and the end-user is able to see how each vehicle is moving around the city);
  • check AI-endorsed transport arrival predictions in real-time;
  • track vehicle arrival time on the selected route;
  • create a list of favorite stops and list of preferred routes;
  • find more suitable routes from point A to point B and display them with details.

Implications

  • Machine Learning (ML) can easily be used for route creation in transport management (especially easily in urban conditions)
  • announcing route alterations to passengers and stakeholders can be automated for higher efficiency
  • AI use for transport database management is a realistic scenario for 2024-2026
  • user adoption for platforms able to predict transport arrival with AI is likely to rise as soon as corresponding solutions hit the market
  • once expanded to non-urban settings, AI systems can facilitate logistics at large, ultimately contributing to lower pricing on delivery, as well as higher standards of service, leveling the emerging companies somewhat with Amazon-level competitors
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