The mission of the Aimsun Ride Research Program (ARRP) is to collaboratively develop and utilize decision-support tools that harness the power of Aimsun simulation solutions to design sustainable, efficient, and smart multimodal transport systems.
Together, we want to:
The program is aimed at the research and academic community, focusing on the analysis and assessment of urban and peri-urban multimodal and shared mobility as well as last-mile logistics systems.
From policy and regulation scheme assessment to optimal design and within-day service and fleet operations management, the program enables a broad analysis portfolio. The offering comprises the Aimsun Next Academic Edition together with Aimsun Ride, our latest plugin development for simulating mobility services.
“Working with the Aimsun Ride research program has been a smooth and rewarding process.
The team was always available and helpful, their support allowed us to overcome multiple obstacles and achieve our aims. Using Aimsun Ride has allowed us to design innovative solutions for the city of Tallinn, capital of Estonia, while obtaining relevant research insights about the impacts of automation in on-demand shared mobility services.
Overall, we couldn’t have asked for more!”
“It’s been a pleasure working with the brilliant Aimsun Ride Research team.
You guys have offered me ample guidance about how to build the model from the scratch and how to code operators to communicate with Ride.
Whenever there is a bug or problem, I know I can count on you to provide me with enough support.
Thank you for your help!”
Ze
“As a research student, working with Aimsun Ride gave me the tools to design complex simulations, while fully adapting to the models that I wanted to develop.
The flexibility of the tool opened up a whole world of possibilities for me, which in other cases would have required a significant investment in setting up the systems associated with the simulation environment.”
Gabriel
On top of background traffic modelled in Aimsun Next, with Aimsun Ride you can provide agent-based demand as input, code different service operators managing multi-class fleets and simulate the daily system and service dynamics.
Your code can be connected through a TCP/IP interface with the (mesoscopic) simulator. This allows your operator to send fleet vehicle commands to the simulator and multimodal service offerings to potential users. Your operator will receive information about potential trip requests and agents’ responses to service offers, the status of the simulated services and the performance of the transport network, such as congestion levels.
The platform simulates the execution of operator instructions, updates the fleet vehicles’ status when interacting with the background traffic and computes chosen fleet, service and network performance indicators.
Do you want to assess optimal fleet and service management strategies for on-demand and shared services (e.g., ride-sharing, e-hailing, bike-sharing, carsharing) under different service and infrastructure design scenarios?
Aimsun Ride enables you to design your own service operator in Python, emulating the operations of any service type with a multi-class fleet using your own algorithms. The Ride API enables time-based and event-based communication between Ride and the operators so you can have full control of the fleet’s actions and status during the simulation. It produces both video recordings and service-specific event and performance data that allow you derive KPIs necessary for service/fleet performance and user satisfaction assessment.
Are you interested in evaluating service designs and pricing strategies in multi-operator ecosystems where different services and different operators are competing? Or do you want to design services as first- and last-mile feeders to Public Transportation, assess whether new services would compete with it and quantify its impact?
With Aimsun Ride, you have the possibility to code and concurrently simulate different service operators offering the same type of service and different ones. Its API allows you to query multimodal shortest paths from A to B based on user-defined mode sequences and costs and simulate multimodal transit-based journeys.
Would you like to assess where to optimally locate charging infrastructure or build fleet depots? Or would you be interested to assess how different electric fleet penetration rates would impact your daily fleet operations?
You can test what-if scenarios for different depot and charging station locations by simply loading or creating this infrastructure on the network with Aimsun Next. The Ride API allows you to generate vehicle commands for any type of operation, including charging.
Would investing in expanding your fleet with new electric vans or more cargo-bikes result to reduced operating costs, better service performance and higher demand satisfaction? Or would you like to investigate how efficient a routing strategy is for next-day deliveries?
Via Aimsun Ride you can simulate fleet vehicle schedules regardless of whether they are computed at run-time or not. We offer an interface that allows you to translate external optimization’s outputs to fleet schedule input compatible with Ride. You can thus simulate your fleet’s picks-ups and deliveries and assess your service’s efficiency and costs.
Narayanan, S., Salanova Grau, J.M., Frederix, R., Tympakianaki, A., Masegosa, A.D. and Antoniou, C., 2023.
Zhou, Z., Agriesti, S., Roncoli, C., Yfantis, L., Nahmias-Biran, B.H. and Casas, J., 2024.
Roy, S., Dadashev, G., Yfantis, L., Nahmias-Biran, B.H. and Hasan, S., 2024.
Yfantis, L., Stebbins, S., Gerostathopoulos, I., Djukic, T., Casas, J., Garcia, D., Kamargianni, M. and Chaniotakis, M., 2021,
SHARE
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/
