SAE AutoSim Hub

To assist traffic simulation researchers worldwide.

Bridging the Local Calibration Deficit

Pre-calibrated vehicle fleets for chaotic, aggressive, non-lane-based traffic environments

Standard simulation defaults (Wiedemann 74/99) collapse in mixed aggressive environments like the Egyptian Ring Road.

Download pre-calibrated configuration files for PTV VISSIM, SUMO, and Aimsun Next — plus Python automation scripts.

Supporting researchers in Egypt, the Arab world, and other developing markets.

Select Platform
30%

Adjust the slider to set the proportion of autonomous vehicles in the traffic stream.

Live Traffic Flow Simulation

Visualize how increasing AV penetration smooths chaotic Egyptian Ring Road traffic in real-time.

MPR: 30%
0.86
Weighted Avg tau (s)
2,132
Est. Capacity (veh/hr/lane)
69%
Human %
Capacity vs. Baseline

SAE Level 0 - Conventional Fleet (Egyptian)

The Egyptian conventional fleet dominates todays roads. Microbuses, passenger cars (Mlaiky), and heavy trucks (Naql) exhibit aggressive, non-lane-based driving patterns.

Egyptian Microbus

High acceleration/deceleration, zero lateral headway, aggressive lane changes, frequent roadside stops (mean dwell ~25s, ~0.4 stops/km).

Accel: 3.0 | Decel: 6.0 | tau: 0.7s | Safety: 0.35 | sigma: 0.95

Mlaiky (Passenger Cars)

Reduced safety distances, high cooperative lane-changing thresholds, unpredictable cut-ins.

Accel: 2.4 | Decel: 5.0 | tau: 0.85s | Safety: 0.45 | sigma: 0.85

Naql (Heavy Trucks)

Low power-to-weight, extended braking distances, night-dominant lane usage.

Accel: 0.9 | Decel: 4.5 | tau: 1.3s | Safety: 0.55 | sigma: 0.75 | Night

SAE Level 2 - Partial Automation

ACC with rigid lane-keeping. Reaction ~1.1s, safety 0.75, sigma 0.15.

Accel: 1.8 | Decel: 3.2 | tau: 1.1s | Safety: 0.75 | sigma: 0.15

SAE Level 4 & 5 - Full Automation

V2X-enabled CACC platoons with near-zero safety headways. Defensive mode activates when cut off by Level 0 vehicles.

Accel: 2.2 | Decel: 3.5 | tau: 0.35s | Safety: 0.95 | sigma: 0.01 | CACC: 0.3s

Mathematical Mappings

Wiedemann 99 * SUMO Krauss * IDM - All share the same kinematic base sqrt(a*b)

Wiedemann 99 (VISSIM)

d = CC0 + v*tau*safety + v*(v-vl)/(2*sqrt(a*b))*safety

Krauss (SUMO)

s* = minGap + v*tau - v*(vl-v)/(2*sqrt(a*b))

TTC (Conflict Detection)

TTC = gap / (v_follower - v_leader)

Emergency Braking

a = v^2 / (2*gap)

Parameter Comparison

Vehicle Level Reaction tau (s) Safety Accel Decel Sigma
Egyptian MicrobusL00.700.353.06.00.95
Mlaiky (Car)L00.850.452.45.00.85
Naql (Truck)L01.300.550.94.50.75
AV SAE L2L21.100.751.83.20.15
AV SAE L4-5L4-50.350.952.23.50.01
Current Fleet Weighted -- 0.86 0.43 2.22 4.88 0.85
Aggressive (Short tau, Low Safety) Conservative (CACC, High Safety)

Localized File Download Hub

Download pre-calibrated configuration files mapped to standard directory targets.

Download VISSIM Fleet

/fleets/custom_egypt_fleet.inpx

PTV VISSIM .inpx configuration with Wiedemann 99 parameters for all Egyptian vehicle types.

Download SUMO Fleet

/fleets/egypt_sumo_fleet.rou.xml

SUMO .rou.xml with Krauss car-following and SL2015 lane-changing parameters.

Download Override Script

/scripts/vissim_sae_override.py

Python script using TraCI/COM/Aimsun API for real-time TTC-based conflict resolution.

Software Integration Guides

Step-by-step instructions to deploy our pre-calibrated fleets into your simulation environment.

Deploying Fleet into PTV VISSIM

1

Open Your Base Network

Launch PTV VISSIM and open your existing base network layout file (.inpx) containing your road geometry, signal controllers, and desired traffic demand.

2

Read Additional Network

Navigate to File → Read Additional → Network from the top menu bar. This command allows you to merge vehicle type definitions without overwriting your existing road links, connectors, or signal plans.

3

Select Fleet File

Browse to and select our downloaded /fleets/custom_egypt_fleet.inpx file. Confirm the import dialog — VISSIM will inject the Wiedemann 99 parameters (CC0-CC9, OP0-OP16) for all Egyptian vehicle types into your vehicle type library.

Verify & Run

Open the vehicle type list to confirm the new types appear. Assign them to your desired vehicle inputs. Run the simulation and verify that the aggressive Egyptian driving behavior matches expected calibrations.

Contribute on GitHub

Are you a researcher from India, Brazil, or another developing market? Upload your local calibration set and help us expand globally!

github.com/sae-calibration-hub

Academic References

Scientific foundation for all calibrated parameters:

Ahmed, S. et al. (2023). "Chaotic Traffic Driving Characteristics on the Cairo Ring Road." Journal of Traffic Engineering.

El-Baset, M. et al. (2022). "Calibration of Microsimulation Models for Non-Lane-Based Traffic in Egypt." IEEE Transactions on Intelligent Transportation Systems.

SAE International. (2021). "J3016 - Taxonomy and Definitions for Terms Related to Driving Automation Systems."

PTV Group. (2024). "VISSIM 2024 User Manual - Wiedemann 99 Car-Following Model."

DLR Institute of Transportation Systems. (2024). "SUMO Documentation - Car-Following Models."

Aimsun. (2024). "Aimsun Next 24 User Guide - Microscopic Traffic Simulation."