Final Customer

Prime Contractor



Bristol Airport


October 2017 – September 2020

Evaluation of an autonomous, on-demand POD shuttle service at Bristol Airport




For this project the modeling team developed a model of Bristol Airport to demonstrate the benefits and prove the business case for an autonomous pod on demand (POD) shuttle service, over and above the existing shuttle bus service available on the site.

Project Location

The study required a model of Bristol Airport to be constructed from scratch as no previous base model or model import was available. The model area covered 16 hectares of the site including the “premier” northern car park area, containing a total of 5,280 parking spaces. Journey times by foot from the car park to the terminal entrance ranged from 3-10 minutes, depending on the parking spot.

Aimsun Solution

The Bristol Airport study was conducted as part of the Capri Project.


Capri was a collaborative research and development project, set up to support the early market for Connected and Autonomous Vehicles (CAVs). The aim of Capri was to build passenger, regulatory and market trust in autonomous pods as a practical, safe and affordable way to travel. Capri was a pilot project that includes the design, development and testing of connected and autonomous pods.


Aimsun’s role in Capri was to use simulation as a pre deployment tool for assessing the feasibility of a pod service. Aimsun has developed a methodology for testing deployments at any scale, and by varying parameters can offer optimized delivery of a system.


Congitial’s ConOPTIUM™ Fleet Management System (FMS) is an AI decision-making platform that was integrated with the Aimsun Ride Mobility-as-a-Service solution to identify optimized fleet deployments under a broad range of scenarios. The technology tested optimized services by minimizing the number of pods required and their operating costs, while maximizing the benefit to passengers in terms of reduced and more reliable travel times.


The Bristol Airport study had three objectives: firstly, to integrate Conigital ConOPTIUM™ FMS AI decision-making platform with Aimsun Ride; secondly, to optimize the fleet, and thirdly. to investigate how the results from the optimized solution could support a business case for a POD service at other locations.

Benefit to Customer

Aimsun Ride integrated with Conigital’s FMS optimization software to provide evidence for the running operation cost of the proposed autonomous POD service.


The evaluation proved that a streamlined service would reduce initial costs by using the minimum number of PODs necessary to maintain an optimal customer experience. The use of the pre-deployment tools provided accurate data to support a robust business case and justify investment.


Simulations also provided a clear visualization of how the system might work for any non-technical decision-makers who did not want to review all the data.

Reason for Success

The team carried out an assessment using a fleet of five vehicles, without any service optimization of the service. The results indicated that there was scope to reduce the fleet size, particularly for periods of low demand. By optimizing the service, using Aimsun Ride and its integration of Conigital’s FMS, the fleet sizes required for the low- and medium-demand cases could be reduced while maintaining the same level of service or slightly better, with faster average journey times.

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Cite Aimsun Next

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].


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 = {},

Aimsun Next 24

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: