Now imagine if cities could predict demand before it happens—ensuring the right number of bikes are always in the right place at the right time.
That’s exactly what the EU-funded research initiative SPINE is working toward. The City of Valladolid (Spain), local operator AUVASA, and Aimsun are collaborating to make this vision a reality. They’re using Aimsun’s Mobility Digital Twins—a suite of AI-powered, simulation-based tools that enhance the planning and operational efficiency of shared mobility systems.
Among these tools:
Could innovations like these finally put an end to the “great bike hunt”?
The SPINE project aims to accelerate progress toward climate neutrality by supporting public transport systems and new mobility services in 11 European cities serving as “living labs.” One of these cities, Valladolid, is working to enhance the capabilities of BIKI, its bike-sharing system, which currently operates across 98 stations citywide.
The challenge for Aimsun is to help deliver a bike-sharing service that is both efficient and reliable—where BIKI users can consistently find a bike to pick up or a free slot to park when they finish their ride.
In this use case, the SPINE project applies a Digital Twin framework composed of two core components—Aimsun Predict and Aimsun Ride (see Image 2). Together, these tools support data-driven decision-making for identifying optimal bike rebalancing strategies.
The current focus is on implementing and deploying Aimsun Predict, the first component of the Digital Twin. This solution enables the anonymisation and analysis of demand data—specifically, bike trips between stations—collected since BIKI launched in 2023. From this data, the team has identified distinct travel patterns that virtually replicate how users behave when riding BIKI.
Once these patterns are established, Aimsun Predict forecasts station-level bike demand across various time horizons (e.g., 15 to 60 minutes). The next step involves Aimsun Ride, which will simulate and evaluate different fleet rebalancing strategies based on these forecasts.
Together, these tools form Aimsun’s Mobility Digital Twin—a powerful framework for proactive and optimised bike fleet management. The result: higher user satisfaction, reduced operational costs, and increased revenue for the bike-share operator.
As a sample of current results, the plot below highlights the average daily bike demand across all BIKI stations between 10–12 September 2024. The red line represents actual demand, while the blue line shows the predicted demand generated by Aimsun Predict. As shown, the tool successfully identifies recurring demand patterns and forecasts the evolution of incoming and outgoing trips over the next few hours and on any day of the year.
In summary, the two main impacts that Aimsun’s Digital Twin will bring to Valladolid and AUVASA, within SPINE, are:
If you’re a city planner, mobility operator, or innovator looking to transform how your city moves—get in touch.
Let’s explore how Aimsun’s Digital Twin technology can help you deliver smarter, more sustainable shared mobility services.
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Aimsun Next 24
Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].
Available: https://docs.aimsun.com/next/24.0.0/
Aimsun Next 24
@manual {AimsunManual,
title = {Aimsun Next 24 User’s Manual},
author = {Aimsun},
edition = {Aimsun Next 24.0.0},
address = {Barcelona, Spain},
year = {2024. [Online]},
month = {Accessed on: Month, Day, Year},
url = {https://docs.aimsun.com/next/24.0.0},
}
Aimsun Next 24
TY – COMP
T1 – Aimsun Next 24 User’s Manual
A1 – Aimsun
ET – Aimsun Next Version 24.0.0
Y1 – 2024
Y2 – Accessed on: Month, Day, Year
CY – Barcelona, Spain
PB – Aimsun
UR – [In software]. Available:
https://docs.aimsun.com/next/24.0.0/
