Defining model parameters per pedestrian type according to the social force model

You can set parameters for each pedestrian type derived from the original model. In addition, you can set Vissim-specific parameters for each pedestrian type.

• tau (τ) [s] (Defining walking behavior)

Tau represents the relaxation time or inertia that can be related to a response time, as it couples the difference between desired speed and desired direction v_0 with the current speed and direction v for acceleration .

Tip: Your Vissim installation provides example data and a description for testing with these parameters. ..\Examples Training\Pedestrians\Parameter Demonstration\01 - Tau

• lambda_mean (λ_mean) (Defining walking behavior)

Lambda governs the amount of anisotropy of the forces from the fact that events and phenomena in the back of a pedestrian do not influence him (psychologically and socially) as much as if they were in his sight. Based on lambda and the angle φ between the current direction of a pedestrian and the source of a force a, factor w is calculated for all social (e.g. non-physical) forces that suppress the force, if:

and

Based on the above, then and yields

Tip: Your Vissim installation provides example data and a description for testing with these parameters. ..\Examples Training\Pedestrians\Parameter Demonstration\06 - Lambda

• A_soc_isotropic [m/s2] and B_soc_isotropic [m] (Defining walking behavior)

These two parameters and λ govern one of the two forces between pedestrians:

with d as distance between the pedestrians (body surface to body surface) and n as unit vector, pointing from one to the other.

• A_soc_mean [m/s2], B_soc_mean [m] and VD [s] (Defining walking behavior)

These parameters define strength (A) and the typical range (B) of the social force between two pedestrians. The social force between pedestrians is calculated according to the following formula, if the influencing pedestrian is in front of the one being influenced (180°) and exerts his influence from the front (+/- 90°), otherwise it is zero:

Thereby the following applies:

d, in the simplest case of VD = 0, is the distance between two pedestrians (body surface to body surface).

n is the unity vector, pointing from the influencing to the influenced pedestrian.

Note: In addition, the relative velocities of the pedestrians are considered, if parameter VD > 0.

If parameter VD > 0, distance d is generalized and replaced by:

Where

• : current distance between two pedestrians 0 and 1
• : expected distance between two pedestrians on the basis of VD in seconds, if both pedestrians keep their speed:

Apart from the last term below the root, the geometric mean between the current and expected distance is calculated and applied.

d points from the influencing to the influenced pedestrian, wit . The force is calculated for the "influenced pedestrian".

Tip: Your Vissim installation provides example data and a description for testing with these parameters. ..\Examples Training\Pedestrians\Parameter Demonstration\09 - VD

• noise [m/s2](Defining walking behavior)

The greater this parameter value, the stronger the random force that is added to the systematically calculated forces if a pedestrian remains below his desired speed for a certain time.

Checking the noise value effect:

Have a group of pedestrians pass a narrow alleyway of approx. 70 cm width.

With noise = 0, so called pedestrian "arches" will form and remain stable. If the noise value lies within the range [0.8 to 1.4], one of the pedestrians will step back after a while and another one will pass through. Default 1.2

Tip: Your Vissim installation provides example data and a description for testing with these parameters. You can find further information in the following directory: ..\Examples Training\Pedestrians\Parameter Demonstration\10 - Noise

• react_to_n (Defining walking behavior)

During calculation of the total force for a pedestrian, only the influence exerted by the n closest pedestrians is taken into account. Default 8.

Tip: Your Vissim installation provides example data and a description for testing with these parameters. ..\Examples Training\Pedestrians\Parameter Demonstration\03 - React to N

• queue_order: degree of orderliness of a queue and queue_straightness: degree of straightness of a queue (Selecting network settings for pedestrian behavior), (Attributes of areas)

These two parameters specify the shape of queues. Their value range is 0.0 - 1.0. The greater these parameter values, the more straight the queue will look.

• side_preference (Defining walking behavior)

This parameter defines whether opposing pedestrian flows prefer using the right or the left side when passing each other:

-1: for preference of the right side

1: for preference of the left side

Default 0: no preference, behavior as before: pedestrians do not shun each other

Tip: Your Vissim installation provides example data and a description for testing with these parameters. ..\Examples Training\Pedestrians\Parameter Demonstration\ 25 - Side Preference