SAE AutoSim Hub: Pre-Calibrated Vehicle Fleets for Chaotic and Mixed-Autonomy Traffic Environments

SAE AutoSim Hub

SAE AutoSim Hub

Traffic simulation researchers worldwide

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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

AV Market Penetration Rate (MPR) 30%

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

دراسة الحالة: الطريق الدائري — قطاع السلام ⇄ القليوب

مسار مساحي 20 كم • 7 تقاطعات • حصر باسوس • محاكاة SAE حية • النقط السوداء التفاعلية

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

Cairo Ring Road — Egypt Case Study

32 km two-way corridor from Qalyub Interchange through the Dar Al Salam bottleneck to the Adly Mansour Intermodal Hub, with live Google traffic telemetry and incline-grade micro-simulation

Simulation Mode

Export & Tools

Qalyub to Adly Mansour Hub

Two-Way Dynamic Flow

Live Traffic Categorization: -- Weather: -- Volume: -- HV: --

Environmental & Air Quality Tracker

AQI

CO2

NOx

Advanced Traffic Physics

Active Grade--

Avg PCE--

Heavy-Vehicle fHV--

PCE Capacity--

Entry Ramp Exit Lane Grade (Miyool) AV Lane (Scenario B) Passenger Car Equivalent (PCE) Microbus Mlaiky Tuk-Tuk

Weather Impact Modeling

Real-time speed reduction coefficients applied to corridor simulation based on ambient weather conditions (NCHRP Report 586)

☀️

Clear

No speed reduction

🌧️

Rain

-20%

Reduced friction + visibility

🌫️

Fog

-35%

Severe visibility reduction

❄️

Ice/Snow

-50%

Critical surface condition

Current Conditions: Loading...

Temperature: --

Humidity: --

Visibility: --

Reference: NCHRP Report 586 — Vehicle Speeds on Wet Pavements

مصفوفة الانبعاثات الموثقة — تقليدي مقابل ذاتي (SAE 0–5)

المستوى	CO₂ g/mile	CO	NOx	PM2.5	الأساس العلمي
Traditional	350–450	2.1–3.4	0.11–0.20	0.008–0.012	EPA 2024
SAE L1–L2	320–380	1.8–2.5	0.09–0.15	0.006–0.010	IEA 2024 — ACC ~10%
SAE L3	280–310	1.2–1.8	0.07–0.11	0.004–0.007	ICCT 2024 — إخماد الموجات
SAE L4–L5 ⚡	10–25*	<0.05	<0.005	<0.0008	IEEE 2024 — Lifecycle*

* انبعاثات دورة حياة من الشبكة الكهربائية — العادم المباشر صفر.

Conflict Hotspot Analysis & SSAM

Surrogate Safety Assessment Model — Real-time TTC/PET conflict detection with dynamic hotspot visualization on corridor

Avg TTC (s)

0.0

Range: 0.6s – 1.8s

Avg PET (s)

0.0

Range: 1.2s – 4.5s

Rear-End Conflicts

per hour

Lane-Change Conflicts

per hour

Conflict Hotspot Map Overlay

Show Hotspots Show Labels

TTC @ Incline (+3.5%)0.0s

PET @ Weaving Zone0.0s

Conflict ResolutionUnresolved

البيانات الميدانية الحقيقية

قطاع السلام ⇄ القليوب • مساحياً 20.01 كم (45+708 → 65+722) • حصر باسوس • محاكاة SUMO 1,962 رحلة • نقط سوداء موثقة

زمن الرحلة والتوفير (3.05 كم)

المستوى	رحلات	زمن	km/h	توفير

توفير لكل رحلة (أفضل مستوى)

لوب 100 كم

140–180د ← 60–75د

المصدر: pure_python_results.csv — تقليدي 503ث مقابل L4 283ث (-43.8%)

النقط السوداء (اضغط للتحديد على الخريطة)

الحصر المروري — محطة باسوس

المركبة	PCU	عدلي ↗	×PCU	6أoct↘

مستكشف تقنيات القيادة الذاتية — 40 ميزة موثقة

#	الميزة والوصف	SAE	التقنية

إسقاط الأسطول (CAPMAS): نمو سيارات خاصة 5.5% سنوياً من قاعدة 5.7M

2026

6.01M

2030

7.44M

2035

9.71M

MPR 50% عند 2035

≈4.85M ذاتية

Corridor Side-by-Side Comparison

Compare two corridors under the same AV penetration rate — maps, capacity and speed limits update live with the MPR slider

Corridor A

Speed Limit

Capacity

AV Share

Corridor B

Speed Limit

Capacity

AV Share

Carbon Credit & Economic Analysis

Monetize CO2 reduction from increased AV penetration — EU ETS carbon market pricing

CO2 Reduced

tons/year

Carbon Credit Value

EU ETS @ €85/ton

Time Saved

hrs/day corridor

Crashes Prevented

per year

Benefit-Cost Summary

Crash Cost Avoided$0

Fuel Savings$0

Emission Penalty-$0

Net Annual Benefit$0

Reference: USDOT Benefit-Cost Analysis Guidance (2022), EU ETS Carbon Market

📌 التوفير السنوي الموثق: $450–950 /مركبة/سنة (Cairo Ring Road) • $1.8B–$2.4B على مستوى المدينة عند L3 (Riyadh arterials) — العملى.xlsx / التوفير السنوى المتوقع

Platooning & V2X Communication

Cooperative Adaptive Cruise Control (CACC) formation display and Vehicle-to-Everything communication simulation

Platoon Formation

CACC Headway

0.6s

Platoon Size

Fuel Savings

15%

V2X Communication Status

V2V (Vehicle-to-Vehicle)Active

V2I (Vehicle-to-Infrastructure)Active

V2P (Vehicle-to-Pedestrian)Limited

V2N (Vehicle-to-Network)5G Connected

── DSRC/C-V2X Protocol Stack ──

Latency: 20ms | Range: 300m

Messages/sec: 10 | Bandwidth: 10 Mbps

Reference: IEEE 802.11p (DSRC), 3GPP C-V2X, SAE J2735 Message Set

AV Penetration Forecasting

Bass Diffusion Model for autonomous vehicle adoption forecasting — S-curve adoption lifecycle

2026 Adoption

2030 Forecast

25%

2035 Forecast

60%

2040 Forecast

90%

Reference: Bass (1969) Diffusion of Innovations, ITS America Adoption Forecasts

Crash Prediction Model

Empirical crash frequency estimation using Safety Performance Functions (SPF) — Highway Safety Manual methodology

Estimated Crashes/Year

Based on HSM Safety Performance Function

Crash Reduction Factor

From AV penetration increase

Crash Cost Avoided

USDOT crash cost values (2022)

Safety Performance Function

N_spf = AADT × L × 365 × 10^-6 × e^{(-0.4865 + 0.0925×ln(AADT) - 0.0001×AADT)}

Reference: Highway Safety Manual (HSM), AASHTO (2010), USDOT Crash Cost Guidelines

Export Simulation Report

Generate a comprehensive academic paper-ready PDF report with all current simulation parameters

Night-Time & Road Surface Conditions

Headlight visibility modeling, lane marking detection, and pavement friction coefficients (AASHTO Green Book)

Night-Time Visibility

Headlight Range80m

Lane Detection Rate92%

Night Speed Reduction-15%

Crash Risk Multiplier2.1x

Road Surface Condition

Friction Coefficient0.70

Braking Distance Factor1.0x

Surface ConditionDry

Tire Grip LevelOptimal

Reference: AASHTO Green Book (2018), ASTM E1911 (Pendulum Test), FMVSS 108 (Lighting)

Intersection & Work Zone Analysis

Signalized vs unsignalized conflict analysis, work zone queue modeling, and heavy vehicle climbing lane assessment

Signalized Intersection

Cycle Length90s

Green Ratio0.45

Capacity1,800 veh/h

Avg Delay35s

Work Zone Management

Lane Closure1 of 3

Queue Length0.8 km

Work Zone Speed50 km/h

TTC (Work Zone)3.2s

Climbing Lane Analysis

Grade Segment+4.5%

HV Speed on Grade35 km/h

Overtaking Frequency12/hr

Climbing Lane NeededYes

Reference: HCM Chapter 19 (Intersections), MUTCD Chapter 6 (Work Zones), AASHTO Green Book (Climbing Lanes)

Simulation Replay

Record and replay simulation scenarios for analysis and comparison

Ready to record

SAE Level 0 — Standard Conventional Fleet

The heterogeneous baseline fleet dominating Egyptian roads. Each vehicle type exhibits unique kinematic signatures critical for valid micro-simulation calibration.

Mlaiky (Passenger Car)

25%4.5×1.8m

Local small/mid-size sedans. Reduced safety distances, high cooperative lane-changing thresholds.

a:2.4 d:5.0 t:0.85 s:0.45

Production Models:

Hyundai Elantra Kia Cerato Chevrolet Optra Toyota Corolla Nissan Sentra

Egyptian Microbus

20%5.3×1.9m

High accel/decel, zero lateral headway, aggressive lane changes, frequent roadside stops (~25s dwell).

a:3.0 d:6.0 t:0.7 s:0.35

Production Models:

Toyota HiAce Hyundai H-1 Starex Nissan Urvan Chevrolet Express

Noss-Naql (Half-Truck)

6.5×2.2m8%

Medium commercial distribution vans, moderate braking distances, moderate lane discipline.

a:1.8 d:5.2 t:0.9 s:0.40

Production Models:

Isuzu N-Series Hyundai Porter Toyota Dyna

Rob-Naql (Dababa)

7%5.1×1.8m

Quarter-truck pickups (Chevrolet T-Series). High-frequency local logistics, frequent stops.

a:2.0 d:5.5 t:0.8 s:0.38

Production Models:

Chevrolet T-Series Toyota Hilux Ford Ranger Nissan Navara

Naql-Taqeel (Semitrailer)

5%16.5×2.6m

Heavy freight trucks, massive braking distances, very low power-to-weight, night-dominant.

a:0.7 d:4.0 t:1.4 s:0.60

Production Models:

Mercedes Actros MAN TGX Scania R-Series Volvo FH

Motorcycle (Darrah)

5%2.0×0.8m

Ultra-narrow lateral footprint, high filtering through queues, aggressive gap acceptance.

a:3.5 d:6.5 t:0.6 s:0.25

Production Models:

Honda CD 110 Bajaj RE 150 Yamaha SZ-RR

Bicycle (Ajala)

2%1.7×0.6m

Low speed, operates on extreme right boundaries, minimal lateral footprint.

a:0.8 d:1.5 t:0.5 s:0.20

SAE Level 0 — Chaotic / Informal Vehicles

Critical for local calibration. These informal vehicles exhibit zero lane discipline, erratic overtaking, and sudden cross-lane maneuvers that standard simulation defaults fail to capture.

Trooscoor (Tricycle)

1.5%3.2×1.3m

Three-wheeled open freight, highly erratic overtaking, unpredictable stop patterns, non-lane-based weaving.

a:1.5 d:4.5 t:0.65 s:0.22 sigma:0.95

Production Models:

TVS King Bajaj RE Cargo Piaggio Ape

Tuk-Tuk (Auto-Rickshaw)

1.5%2.6×1.3m

Small footprint, sudden cross-lane maneuvers, zero lane discipline, high agility in gaps.

a:1.8 d:5.0 t:0.55 s:0.18 sigma:0.98

Production Models:

Bajaj RE 177 TVS King 200

SAE Levels 1–5 — Autonomous Vehicles

Each isolated SAE automation level with kinematic profiles, core capabilities, and real-world production fleet catalogs.

Driver Assistance

4.6m × 1.8m

Single automated system (steering OR acceleration/braking). Human driver remains fully responsible.

t:1.0 s:0.70 sigma:0.25 headway:2.2m

Real-World Models:

Partial Automation

4.7m × 1.9m

Simultaneous lane centering + Adaptive Cruise Control. Human monitors environment.

t:0.9 s:0.80 sigma:0.12 headway:2.5m

Real-World Models:

Conditional Automation

4.6m × 1.8m

Vehicle drives itself in geofenced ODD (e.g., highway jams up to 130 km/h). Human must intervene upon request. Certified redundant LiDAR environmental tracking.

t:0.5 s:0.88 sigma:0.06 headway:2.0m

Real-World Models:

Manufacturer Catalog:

Mercedes-Benz DRIVE PILOT Manual / BMW Personal Pilot L3 — Certified LiDAR tracking up to 130 km/h

Official Demo Video

Mercedes-Benz DRIVE PILOT — Level 3 Automated Driving up to 95 km/h (2026)

Sensor Hardware Blueprint

Mercedes DRIVE PILOT LiDAR, camera, and moisture sensor schematic layout

LiDAR placement, redundant steering cameras, and moisture sensor positions

High Automation

4.9m × 2.0m

Fully driverless within predefined areas. No steering wheel option required for purpose-built deployments. Driverless geofenced sensor arrays with Connected Cooperative ACC (CACC).

t:0.3 s:0.92 sigma:0.03 CACC:0.4s

Real-World Models:

Manufacturer Catalog:

Waymo 5th-Gen Driver / Jaguar I-PACE Sensor Array Platform — Driverless geofenced deployment

Official Demo Video

Inside Waymo's Self-Driving Car — Full Autonomous Ride Demonstration

Sensor Hardware Blueprint

Waymo 5th-Gen Driver Jaguar I-PACE sensor schematic layout

Waymo 5th-Gen Driver: 29 cameras, 5 LiDAR, 6 radar units on I-PACE platform

Full Automation

4.7m × 2.0m

Complete autonomous driving under any environment worldwide. Zero operational domain boundaries. Pure vision/AI end-to-end automation without domain limits.

t:0.2 s:0.98 sigma:0.01 CACC:0.3s

Real-World / Target:

Manufacturer Catalog:

Tesla Cybercab Engineering Concept — Pure Vision/AI End-to-End Full Automation. EPA Registry reference sheets.

Official Design Reveal

Tesla Cybercab — Official Design, Capabilities & Core Specs

Architecture Diagram

Tesla Cybercab Technical Architecture and AI End-to-End System Diagram

Tesla FSD Architecture: vision-only, no LiDAR, end-to-end neural net

Autonomous Fleet Comparison Engine

Select a SAE automation tier, choose vehicles, and perform side-by-side analytical benchmarking with live performance charts

SAE Level

Vehicle A

Vehicle B

Sensor Density vs. Cost

Safety Intervention Curves

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)

Methodology & Analytical Derivations

Transparent documentation of how physical vehicle dimensions and kinematic constants are converted client-side into Weighted Average Reaction Time (τ_avg) and Estimated Roadway Capacity (C)

Effective Space per Vehicle in Queue

Each vehicle occupies a physical space in the queue equal to its body length plus its safe standstill gap (CC0). This is the minimum bumper-to-bumper distance at zero speed:

Effective_Space_i = L_i + CC0_i | where L = vehicle length (m), CC0 = minGap (m)

Example: Mlaiky → 4.5m + 1.8m = 6.3m effective space. Naql-Taqeel → 16.5m + 3.5m = 20.0m effective space.

Dynamic Space Headway at Speed

At operational speed v, the dynamic space headway is a direct function of the vehicle's reaction time (τ) and safety factor. The browser computes:

Dynamic_Headway_i = L_i + CC0_i + v · τ_i · safety_i + v² / (2√(a·b)) · safety_i

This is the Wiedemann 99 safe distance formula, computed per-vehicle for the Cairo Ring Road free-flow speed (v = 80 km/h = 22.22 m/s).

Weighted Fleet Averages (τ_avg)

When the MPR slider is adjusted, the client-side engine computes a weighted average across the heterogeneous fleet. For each parameter p:

p_avg = Σ_i=1..9 (MPR_L0,i · p_i) + Σ_j=1..5 (MPR_AV,j · p_j)

The L0 human-driven share is distributed proportionally across 9 vehicle types (Mlaiky 30%, Microbus 20%, etc.). The AV share is split equally across 5 SAE levels.

Estimated Roadway Capacity (C)

The browser computes the capacity metric (veh/hr/lane) using a weighted macro-mapping derived from individual micro-parameters. The formula explicitly incorporates physical vehicle length and gap:

C = 3600 / ( Σ_i=1..n(MPR_i · τ_i) + (&overline;L + &overline;Gap) / v )

Where:

&overline;L = dynamic weighted fleet average length (m), computed from FLEET[i].len × MPR[i]
&overline;Gap = weighted safe buffer distance (m) = Σ(MPR_i · CC0_i)
v = operational free-flow speed = 80 km/h (22.22 m/s) for Cairo Ring Road
τ_i = per-vehicle reaction time (s), from FLEET[i].cf.reaction

At MPR = 0% (all human): C ≈ 1800 veh/hr/lane. At MPR = 100% (all AV): C ≈ 3200 veh/hr/lane. The linear interpolation between these bounds is the capacity bar shown in the dashboard.

Parameter Comparison Matrix

All 14 vehicle profiles with kinematic coefficients for micro-simulation calibration

Vehicle	SAE	Dimensions (L×W)	tau (s)	Safety	Accel	Decel	Sigma	Gap (m)	Manufacturer Catalog	Production Models
Mlaiky (Car) Elantra / Cerato / Corolla / Sentra	L0	4.5×1.8	0.85	0.45	2.4	5.0	0.85	1.8	Hyundai Elantra / Toyota Corolla Specs	Hyundai Elantra, Kia Cerato, Toyota Corolla, Nissan Sentra
Microbus HiAce / H-1 / Urvan / Express	L0	5.3×1.9	0.70	0.35	3.0	6.0	0.95	1.2	Toyota Hiace (2.5L Diesel) Catalog	Toyota HiAce, Hyundai H-1, Nissan Urvan, Chevrolet Express
Noss-Naql Isuzu N / Porter / Dyna	L0	6.5×2.2	0.90	0.40	1.8	5.2	0.80	2.2	Isuzu NPR Commercial Truck Series	Isuzu N-Series, Hyundai Porter, Toyota Dyna
Rob-Naql (Dababa) Hilux / Ranger / Navara / T-Series	L0	5.1×1.8	0.80	0.38	2.0	5.5	0.88	1.8	Chevrolet T-Series Dababa Catalog	Chevrolet T-Series, Toyota Hilux, Ford Ranger, Nissan Navara
Naql-Taqeel Actros / TGX / Scania R / Volvo FH	L0	16.5×2.6	1.40	0.60	0.7	4.0	0.70	3.5	Mercedes-Benz Actros 1845 Freight Catalog	Mercedes Actros, MAN TGX, Scania R, Volvo FH
Motorcycle Honda CD / Bajaj RE / Yamaha SZ	L0	2.0×0.8	0.60	0.25	3.5	6.5	0.95	0.5	Honda CG125 Local Spec Sheet	Honda CD 110, Bajaj RE 150, Yamaha SZ-RR
Bicycle Generic / Delivery Trike	L0	1.7×0.6	0.50	0.20	0.8	1.5	0.90	0.3	Trinx Mountain Bike Baseline Specs	Generic City Bicycle, Delivery Tricycle
Trooscoor TVS King / Bajaj Cargo / Piaggio Ape	L0	3.2×1.3	0.65	0.22	1.5	4.5	0.95	0.8	Local 200cc Cargo Tricycle Specs	TVS King, Bajaj RE Cargo, Piaggio Ape
Tuk-Tuk Bajaj RE 177 / TVS King 200	L0	2.6×1.3	0.55	0.18	1.8	5.0	0.98	0.6	Bajaj RE Optima 4-Stroke Catalog	Bajaj RE 177, TVS King 200
SAE L1	L1	4.6×1.8	1.00	0.70	2.0	3.5	0.25	2.2	Toyota TSS 2.0 — Single LKA	Toyota Corolla TSS, Ford F-150 Co-Pilot360
SAE L2	L2	4.7×1.9	0.90	0.80	1.8	3.2	0.12	2.5	Tesla Autopilot / Cadillac Super Cruise	Tesla Model 3, Cadillac Lyriq, BMW 7
SAE L3	L3	5.2×1.9	0.50	0.88	2.0	3.5	0.06	2.0	Mercedes DRIVE PILOT / BMW L3 ≤130 km/h	Mercedes S-Class, EQS, BMW 7 Series
SAE L4	L4	4.9×2.0	0.30	0.92	2.2	3.5	0.03	1.2	Waymo I-PACE / Zoox / CACC	Waymo One, Zoox, Baidu Apollo, Cruise Origin
SAE L5	L5	4.7×2.0	0.20	0.98	2.5	4.0	0.01	0.8	Tesla Cybercab — Pure Vision/AI	Tesla Cybercab, Nuro, Motional, WeRide
Current Fleet Weighted	--	--	0.86	0.43	2.22	4.88	0.85	1.72	--	--

L0 Standard L0 Chaotic L1 L2 L3 L4 L5

Multi-Country Regional Calibration Hub

Select a country to load localized fleet composition, road parameters, and calibration weights

Fleet Composition

Road Parameters

Calibration Summary

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

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.

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.

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.

Deploying Fleet into Aimsun Next

Open Your Aimsun Model

Launch Aimsun Next and open your existing micro-simulation model (.ang or .aprx). Ensure your network geometry and demand are properly defined.

Register API Extension

Navigate to Project → Properties → API Extension. In the Python/ANSDK script field, browse to and register our /scripts/vissim_sae_override.py script. This will execute dynamic vehicle kinematic overrides on each simulation time step.

Configure Vehicle Types

Open the Vehicle Types editor within Aimsun. Create or import vehicle types matching the profiles in our script (Egyptian Microbus, Mlaiky, Naql, AV L2, AV L4-5). Link each type to the corresponding IDM car-following parameters defined in the override script.

Run & Inspect

Start the micro-simulation. The registered Python script will dynamically apply TTC-based conflict resolution, override vehicle kinematics during cut-in events, and log calibration data to the output. Check the Aimsun output console for parameter application confirmations.

Proprietary Research Platform

Powered by Fimtosoft. All empirical data and traffic physics parameters are protected pending official journal publication.

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. (2026). "VISSIM 2026 User Manual - Wiedemann 99 Car-Following Model."

DLR Institute of Transportation Systems. (2026). "SUMO Documentation - Car-Following Models."

Aimsun. (2026). "Aimsun Next 26 User Guide - Microscopic Traffic Simulation."

Bridging the Local Calibration Deficit

دراسة الحالة: الطريق الدائري — قطاع السلام ⇄ القليوب

Live Traffic Flow Simulation

Cairo Ring Road — Egypt Case Study

Environmental & Air Quality Tracker

Advanced Traffic Physics

Weather Impact Modeling

Clear

Rain

Fog

Ice/Snow

مصفوفة الانبعاثات الموثقة — تقليدي مقابل ذاتي (SAE 0–5)

Conflict Hotspot Analysis & SSAM

Avg TTC (s)

Avg PET (s)

Rear-End Conflicts

Lane-Change Conflicts

Conflict Hotspot Map Overlay

البيانات الميدانية الحقيقية

زمن الرحلة والتوفير (3.05 كم)

النقط السوداء (اضغط للتحديد على الخريطة)

الحصر المروري — محطة باسوس

مستكشف تقنيات القيادة الذاتية — 40 ميزة موثقة

إسقاط الأسطول (CAPMAS): نمو سيارات خاصة 5.5% سنوياً من قاعدة 5.7M

Corridor Side-by-Side Comparison

Carbon Credit & Economic Analysis

Benefit-Cost Summary

Platooning & V2X Communication

Platoon Formation

V2X Communication Status

AV Penetration Forecasting

Crash Prediction Model

Estimated Crashes/Year

Crash Reduction Factor

Crash Cost Avoided

Safety Performance Function

Export Simulation Report

Night-Time & Road Surface Conditions

Night-Time Visibility

Road Surface Condition

Intersection & Work Zone Analysis

Signalized Intersection

Work Zone Management

Climbing Lane Analysis

Simulation Replay

SAE Level 0 — Standard Conventional Fleet

Mlaiky (Passenger Car)

Egyptian Microbus

Noss-Naql (Half-Truck)

Rob-Naql (Dababa)

Naql-Taqeel (Semitrailer)

Motorcycle (Darrah)

Bicycle (Ajala)

SAE Level 0 — Chaotic / Informal Vehicles

Trooscoor (Tricycle)

Tuk-Tuk (Auto-Rickshaw)

SAE Levels 1–5 — Autonomous Vehicles

Driver Assistance

Partial Automation

Conditional Automation

High Automation

Full Automation

Autonomous Fleet Comparison Engine

Sensor Density vs. Cost

Safety Intervention Curves

Mathematical Mappings

Wiedemann 99 (VISSIM)

Krauss (SUMO)

TTC (Conflict Detection)

Emergency Braking

Methodology & Analytical Derivations

Effective Space per Vehicle in Queue

Dynamic Space Headway at Speed

Weighted Fleet Averages (τavg)

Estimated Roadway Capacity (C)

Parameter Comparison Matrix

Multi-Country Regional Calibration Hub

Fleet Composition

Road Parameters

Calibration Summary

Weighted Fleet Averages (τ_avg)