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SmoothTrip: Integrated Urban Transit Optimisation

Started January 2025
Lab for Optimising Public Transport

Funder: Independent Research Fund Denmark (DFF) | Partners: Movia, DSB

SmoothTrip: Holistic Methodology for Integrated Urban Transit System Design

๐Ÿ“‰ The Challenge

The main challenge facing the public transport (PT) sector today is the critically low share of PT usage. Across EU28 countries, the mode share has declined from an already low 17.6% in 2013 to 16.7% in 2017.

Improving this share is essential as public transport contributes significantly to various Sustainable Development Goals (SDGs). To provide high-quality services and attract patronage, decision-makers need tools to support the efficient planning and operation of the urban transit system as a whole. However, a holistic methodology to design such an integrated operation plan has been lacking.

๐Ÿ’ก Our Solution

SmoothTrip aims to address this gap by developing an integrated model and corresponding solution method to optimise the urban public transport system holistically.

This project focuses on:

  • Decision Support: Providing a robust tool for efficient planning and operation.
  • Holistic Methodology: Moving beyond isolated planning to an integrated system approach.
  • Operator Cooperation: Facilitating collaboration between different operators, such as Movia and DSB, to streamline decision-making processes.

๐ŸŒ Impact & Benefits

โœจ

Passenger Experience

Optimised coordination leads to reduced waiting times, seamless transfers, and more reliable services.

๐Ÿ™๏ธ

City Livability

Attracting passengers mitigates traffic, reduces CO2 emissions, and creates a more accessible urban environment.

๐Ÿ“š Output

Publications

  • Gaborit, R., van der Hurk, E., Jiang, Y., Zografos, K. G., Kheiri, A., & Nielsen, O. A. (2025). A model for passenger oriented integrated frequency setting, timetabling, and vehicle scheduling. In Conference on Advanced Systems in Public Transport and TransitData 2025.
  • Gaborit, R. P. A., et al. An adaptive large neighbourhood search with MILP and heuristic repair operators for bus timetabling.