---
title: How can new forms of mobility be modelled?
url: "https://www.systra.com/en/expert_insights/how-can-new-forms-of-mobility-be-modelled/"
type: expert_insights
date_published: 2021-03-23
date_modified: 2021-06-24
schema:
  @type: WebPage
language: en-US
word_count: 1398
reading_time: 7 min
canonical: "https://www.systra.com/en/expert_insights/how-can-new-forms-of-mobility-be-modelled/"
featured_image: "https://www.systra.com/wp-content/uploads/2021/03/header_re_nouvelles_mobilites.png"
type-insight:
  - New mobilities
---

# How can new forms of mobility be modelled?

![How can new forms of mobility be modelled?](https://www.systra.com/wp-content/uploads/2021/03/header_re_nouvelles_mobilites.png)

### summary

- The development of new mobility services reflects transformations in mobility
  modes: flexibility, use versus possession, intermodality, service economy.
- Conventional traffic forecasting models have limitations. Multi-agent
  simulation, by taking into consideration a certain number of characteristics
  related to these new mobility services, is a solution to forecast their uses
  more accurately.
- However, the use of these new models, which are complex and require much
  longer calculation times, must be the result of joint consideration with the
  contracting authorities, based on a cost-benefit analysis.

With the possibilities offered by digital technology, the development of new
mobility services for users has accelerated in recent years: self-service
bicycles, electric scooters, car-sharing… Access to these services broadens the
range of solutions available to them for getting around, and thus contributes to
the emergence of what is known as service-based and shared mobility, based on
the use of a mode of transport that is uncorrelated to one’s possession.

This increasingly diversified offer implies profound changes in mobility
practices with:

- Self-service vehicles (cars, motorised two-wheelers, bicycles, scooters – with
  or without terminals)
- Transport-on-demand (TOD) services
- Autonomous transport-on-demand services (AMoD – autonomous shuttles, Personal
  Rapid Transit)
- Vehicle-sharing services
- Trip sharing services (car-sharing)

These services each have their own specificity: for short or long distances, for
‘door-to-door’ or ‘stop-to-stop’ journeys, in urban or sparsely populated areas.
But **what characterises them above all is their flexibility.**

**Mobility is increasingly determined by user demand**. It is less routine, more
flexible and less constrained by a fixed transport offer. So
** how can these new forms of mobility be modelled? **How can their development
be predicted? How can decision-makers be given the keys to organising the
provision of services, often of a private nature, in their region?

### Are conventional modelling tools still relevant?

![ordinateur_mark-1](https://www.systra.com/wp-content/uploads/2021/03/ordinateur_mark-1-1024x953.png)

Conventional traffic forecasting models are generally ‘4-stage models\*’ or
simplified choice models.

These models face several constraints which do not allow them to take into
consideration the new mobility services in their entirety:

- The spatial aggregation of journeys does not allow for the detailed
  consideration of short distance journeys, unless a very fine grid is
  established
- Their temporal aggregation makes it difficult to estimate demand over all
  periods of the day Successive trips from home or secondary trips not linked to
  home are less well described
- Multimodal trips are often neglected
- Only travellers are modelled, without taking into consideration empty vehicles

To date, the use of conventional modelling has been encouraged because of the
aggregated nature of the available data and the limitations of information
processing systems.

However, the continuous improvement of computer tools and computing power now
makes it possible to **consider modelling movements at the individual level.**
Moreover, more and more data are available for the development of these models,
which increases their efficiency.

The new approaches to travel modelling thus make it possible to overcome the
limitations of conventional models. However, the result is, on the one hand,
**increasing model complexity** and, on the other,
**much longer calculation times**.

\*
_The 4-stage model is a general conventional transport study plan enabling transport demand to be forecast by following four stages: the generation of trips (how many trips?), the distribution of trips (for which destination?), the choice of transport mode (with which transport mode?), and which way (using which itinerary?)._

### Multi-agent simulations at the service of new mobilities

Among the new modelling approaches, Multi-Agent Systems (MAS) are attracting a
lot of interest.

In the MAS approach, each object (individual, vehicle, etc.) can be monitored
individually. This approach makes it possible to explicitly model individual
decision-making processes. It also makes it possible to track vehicles in a
disaggregated (spatio-temporal) manner and to turn them into autonomous
entities.
**This makes it possible to model all types of transport services, especially those requiring optimal fleet management.**

The MAS approach also has other advantages: it enables dynamic simulations to be
carried out and offers very broad analysis perspectives, going beyond the
framework of transport systems, by integrating models of interaction with land
use, systems for measuring environmental impacts, or regional economic analysis
tools.

Nevertheless, there is a major prerequisite:
**MAS simulation requires very fine input data** on transport mode use and user
preferences. The dissemination of all the information on different media also
requires a **significant effort in terms of processing the input data**. In
addition, considerable computing power is required to be able to simulate
passenger movements on the scale of a large city, in which several million
people travel.

### How can regions be supported in order to structure the available and future mobility offer?

![Smiling women with protective face masks in ride sharing car](https://www.systra.com/wp-content/uploads/2021/02/gettyimages-1264503560-1-1024x683.jpg)

In order to organise the mobilities offer in a region, today and in the years to
come, it is necessary to evaluate the use of each service, the choice of
transport modes and the expected development of uses.

In practice, the choice of modelling approach will depend on the characteristics
of the new service, what is to be modelled, and the model’s ability to take this
into account.

The table below shows the comparative analysis between the ‘classical model’
approach versus the ‘agent model’ approach, depending on the nature of the
service to be modelled (car-sharing, shared micro-mobility, Vehicle-sharing
services, PRT or autonomous shuttle).

| 󠀁**Mode**󠁿 | 󠀁**Conventional model vs agent model**󠁿 |
| --- | --- |
| 󠀁**Car sharing**󠁿 | Car-sharing can be developed effectively using conventional models. |
| 󠀁**Micro-mobilities**󠁿 | Micro-mobilities are handled better by multi-agent simulations that take into consideration service flexibility. However, the implementation of such a simulation remains complex and data-consuming. |
| 󠀁**Vehicle-sharing services**󠁿 | The choice of vehicle-sharing model depends on the level of detail sought in the results. |
| 󠀁**PRT, Autonomous shuttle, Self-service vehicle**󠁿 | Multi-agent simulations will allow a detailed consideration of PRT or autonomous vehicles whereas conventional models must be made more complex with new modules for an adequate consideration. |

Currently, multi-agent simulations are the dominant approach to modelling new
mobilities in the research world. The simulation of travel on this microscopic
scale (traveller/vehicle etc.) is relevant for the detailed study of the impacts
of new transport offers that are increasingly individualised.

Nevertheless,
**the data currently available are not always in line with the design of such simulations**
(low volumes in surveys leading to large margins of error, commercial and
non-public ridership data, large and complex databases to be analysed, etc.).
The resulting calculation times do not allow these tools to be used in a
pragmatic way, with a view to multiplying tests and scenarios.

### AIRBUS CASE STUDY

While MAS models are very powerful, they require a high investment to ensure
robust output results. This is at least what we observed during a consultancy
mission for Airbus, which was carried out in 2019 with the support of the ETH
University of Zurich, on the modelling of a service offer for flying shared
vehicles (Urban Air Mobility).

SYSTRA modelled the service offer for flying shared vehicles using a price-time
model, based on matrices derived from a 4-stage model in the Greater Paris
region. This approach was compared with the results from MatSim\*.

The comparison of the volumes of all-mode flows between Greater Paris
departments derived from the four-stage model of Greater Paris and from MatSim
show real similarities. The comparison of the shares of each mode of transport
showed the need to improve the calibration of MatSim with the data from the
Household Travel Survey, which is very time-consuming.

Once calibrated, the 4-stage model of the Greater Paris region can be simulated
in several hours while the MatSim simulation takes place over several days. The
price-time model, once configured, is immediate. It is nevertheless very
simplified and only offers a partial approach to the problem.

_\*Open source multi-agent simulation platform._

### to conclude

For a public authority or contractor, the ways to deal with the new services and
the choice of tools must be considered  according to the available data, the
ability to obtain complementary data, and the time required to construct models
adapted to the objectives of the study. SYSTRA accompanies project owners to
assist them in choosing the best solutions, in line with their needs.