Smart City & Mobility

Urbanization, demographic and social change, as well as the ongoing mobility and energy transition, are placing cities and municipalities under increasing digital transformation pressure. At the same time, the sustainable improvement of residents’ quality of life remains at the center of attention. The digital pioneers among cities and municipalities are already on their way to becoming Smart Cities—leveraging data intelligently and digitizing infrastructure to develop and test new smart applications and services for the benefit of their citizens.

OmegaLambdaTec supports cities, municipalities, utilities, and public enterprises in meeting these challenges. Through our data- and simulation-driven analyses and the development of innovative digital services in mobility, utilities, infrastructure, and smart public data, we provide tailored solutions to empower the public sector.

OUR SERVICES AT A GLANCE

Simulation-Based Business Case Analyses for New IoT Applications
Development of Digital Twins for Simulation-Based Optimization
IoT-Driven Route Optimization for Supply and Disposal Operations
Analysis of Parking and Mobility Data
AI-Based Transport Mode Detection Using Anonymized Mobile Data
Data-Driven Asset and Fleet Optimization to Improve Investment Decisions
Predictive Maintenance Solutions for Municipal Fleets and Utility Infrastructure
Analysis and Optimization of Sharing Services
Data-Driven Forecasting (e.g., Traffic Congestion)
Anomaly Detection in Infrastructure Networks (Electricity, Water, Gas, District Heating)
GIS Data Analytics
Automated Generation of 3D Models from Technical Drawings
Smart City Applications Using Satellite Data
Development of Smart Public Data Concepts and Applications

REAL-WORLD USE CASES WE’VE SUCCESSFULLY DELIVERED

IoT-Based Route Optimization and Business-Case Simulation

Key Challenges and Goals

A core public-service offering for municipalities is the routine filling and emptying of containers throughout the city — from trash bins and clothing/glass recycling containers to bulky-waste pickups and grit-sand refills. Traditionally, these tasks are performed on fixed routes at regular intervals, regardless of the actual day-to-day demand at each location. New, low-cost sensor technologies with mobile real-time connectivity (for example, via LoRaWAN) enable targeted, intelligent logistics improvements. How much time and cost can be saved by shifting from scheduled, interval-based servicing of distributed containers to an event-driven, sensor-enabled strategy?

Fig.: OLT Business Case Simulator.
Our IoT-based business case simulation for 50 distributed containers demonstrates the clear advantages of sensor-driven logistics. Compared to fixed-route operations, optimized event-based routing reduces working hours, travel distance, and operating costs — delivering savings of up to 30%.

OLT-Solution

Development of a digital twin–based simulation framework with integrated route optimization
Automated, optimized route recommendations based on real-time fill-level measurements
Business-case simulations to quantify savings for specific use cases
Fleet operations optimization across multiple KPIs (e.g., time, total cost, CO_₂ footprint)

Benefit

Quantifiable efficiency gains simulated before hardware investments are made
Reduced costs and time savings through optimized operations
Minimized disruptions and service failures thanks to continuous fill-level monitoring
More efficient use of staff and fleet resources
Improved customer satisfaction through demand-driven services
Lower CO_₂ emissions and reduced environmental impact

Simulation- and Data-Driven Parking Management

Key Challenges and Goals

Traffic and environmental pressures are steadily increasing in many German city centers. A major contributor to urban congestion is parking search traffic, which in many areas can account for up to one-third of total traffic. Data-driven approaches and applications are therefore of high interest to cities and municipal mobility providers. How can parking search traffic be reduced, the parking experience simplified for drivers, and which new digital services offer the greatest potential?

*Fig.: Visualizing local parking activity — heat map based on data analysis (left) and 3D view of parking patterns (right).*

OLT-Solution

Analysis and visualization of parking service and mobility data
Development of a digital twin of urban street infrastructure to model parking search traffic
Scenario simulations for different urban parking management strategies
Potential analysis of various approaches, considering economic, ecological, and traffic reduction impacts
Data- and simulation-driven recommendations for efficiently reducing parking search traffic

Benefit

Identify and compare optimization potential for different concepts
Data- and simulation-based evaluation of implementation options for urban parking management
Actionable recommendations for developing new services for drivers searching for parking
Predictive models for local parking availability
Reduced parking search traffic, contributing to lower congestion and environmental impact in city centers

Data Discovery for Optimizing Airport Operations

Key Challenges and Goals

All airport operations must adapt to handle the continuous increase in flights efficiently and cost-effectively. How can airport processes be optimized under these challenges? Are there capacity bottlenecks in the airport vehicle fleet? Which flights cause operational delays? What would be the business impact of potential changes to the billing model?

*Fig.:* *Digital Twin Operations Simulation of Vehicle Movements at the Airport.*

OLT-Solution

Visualization of daily operations using a digital twin simulation environment
Detailed quantitative data exploration and analysis to reveal the current status and temporal development of operations
Evaluation of predictive potential using AI based on available data
Scenario simulations of different billing models using operational data
Identification of optimization opportunities in airport operations
Targeted root-cause analysis to enable efficient responses to process issues
Development of a digital twin for a specific airport sub-process

Benefit

Solid data-driven foundation for future business planning and coordination with airport partners
Optimization of fleet size and composition
Recommendations for AI-driven operational optimization
Use of the digital twin framework in scenario simulations to support investment planning and future process improvements