European Journal of Intelligent Transportation Systems

Dear Colleagues,

RS Global publisher invites you to publish your research in the European Journal of Intelligent Transportation Systems (EJITS) or recommend the journal among your colleagues to raise awareness of impactful studies in areas such as Autonomous and Connected Vehicles, Traffic Management, Public Transport Innovation, Smart Infrastructure, Wireless Communications, and Sustainable Urban Mobility.

The journal is currently accepting manuscripts for the current issue (Vol. 5, 2025). Embracing the Continuous Article Publishing (CAP) model, EJITS publishes articles promptly after the peer-review process, ensuring timely dissemination of research to the global community.

The European Journal of Intelligent Transportation Systems (EJITS) is a peer-reviewed, international scientific Open Access journal dedicated to the field of Intelligent Transportation Systems (ITS). Published annually using a Continuous Article Publishing (CAP) model, EJITS ensures that accepted articles are made available online promptly, providing timely access to innovative research findings.

Established in 2018 and published in English in Warsaw, Poland, the journal serves as a vital platform for researchers, practitioners, and policymakers seeking to improve the efficiency, safety, sustainability, and user experience of global transportation systems through cutting-edge technologies and data-driven methodologies.

Aims and Scope

EJITS covers a wide spectrum of topics in the ITS domain, embracing both foundational theories and practical applications. It welcomes Original Articles and Review Articles that present significant advances in research and development across intelligent transportation technologies, systems integration, and mobility innovation.

The scope of the journal includes the following key sections:

1. Road Transport Systems and Technologies

This section explores innovations aimed at optimizing the safety, efficiency, and flow of road-based transportation networks.

Adaptive Traffic Signal Control – Real-time adjustment of traffic lights using sensor and camera data.
Intelligent Speed Assistance – Speed regulation based on traffic, weather, and zone data.
Lane Departure and Collision Avoidance – Vehicle systems to detect lane drifts or obstacles.
Real-Time Route Optimization – Navigation systems that minimize travel time based on current conditions.
Smart Highway Infrastructure – Deployment of embedded sensors and AI-based controls.
Vehicle Tracking and Telematics – Monitoring fleet behavior using GPS and cloud platforms.
AI-Driven Incident Detection – Automated systems for identifying crashes or hazards.
Road Surface Condition Monitoring – Use of IoT sensors to detect potholes or icy conditions.
Driver Behavior Monitoring – Analysis of fatigue, distraction, or aggressive driving.
Dynamic Road Pricing – Pricing strategies that adjust tolls based on demand and congestion.

2. Public Transportation and Mobility-as-a-Service (MaaS)

Focused on digital platforms and data-driven approaches to improve accessibility and efficiency of public transit systems.

Integrated Mobility Platforms – Apps that merge buses, metro, rideshare, and biking services.
Dynamic Scheduling – AI tools to adjust public transport schedules in real time.
Smart Ticketing – Contactless and mobile-based fare collection.
Passenger Flow Prediction – Forecasting crowd levels using machine learning.
Accessibility Optimization – Solutions for people with reduced mobility.
Real-Time Passenger Information – Digital signage and mobile alerts.
Demand-Responsive Transit – Routing services based on real-time demand.
Transit Performance Monitoring – Analytics dashboards for transit KPIs.
Sustainable Transit Planning – Integration of electric/hybrid buses.
Open Data for Public Transport – Using shared datasets for research and innovation.

3. Railway and Multimodal Transport Systems

Enhancing rail operations and multimodal integration to streamline mobility and logistics.

Automatic Train Operation (ATO) – AI systems for driverless train operation.
Predictive Maintenance – Condition monitoring of rolling stock and tracks.
Train Scheduling Algorithms – Optimization models for minimizing delays.
Rail Signal Systems – Digital interlocking and train detection technologies.
Station Design for Intermodality – Transfer hubs combining multiple transport modes.
Freight Rail Logistics – Use of ITS to enhance rail freight efficiency.
Crowd Management at Stations – AI tools for passenger routing and safety.
Digital Twin Models – Simulation of rail infrastructure for planning.
Intermodal Freight Tracking – Real-time container location and handoffs.
Integration with Urban Mobility – Seamless planning between rail and city transport.

4. Aviation and Unmanned Aerial Systems (UAS)

Addressing the transformation of aviation through automation, AI, and drone technology.

Intelligent Air Traffic Management – AI-based conflict detection and rerouting.
Drone-Based Delivery – Autonomous UAVs for parcel and medical transport.
UAS Traffic Management (UTM) – Systems to integrate drones in shared airspace.
Flight Scheduling Optimization – Tools for dynamic gate and runway allocation.
Digital Airport Operations – Automation of baggage handling and passenger flows.
Noise and Emission Monitoring – Environmental impact control in urban airports.
Surveillance via UAVs – Drones for disaster response and public safety.
Airspace Modeling – Simulation platforms for drone flight testing.
Regulatory Frameworks for UAS – Policy development for safe drone integration.
Autonomous Taxi Drones – Research into human-passenger carrying UAVs.

5. Maritime and Port Logistics

Exploring the digitization and automation of seaports and maritime transport systems.

Port Community Systems (PCS) – Digital platforms for coordination among port users.
Smart Berth Allocation – AI-based vessel scheduling and docking.
Autonomous Maritime Navigation – Self-steering ships and ferries.
Vessel Traffic Management Systems (VTMS) – Real-time maritime monitoring.
Cargo Handling Robotics – Automation of cranes and container transfers.
Blockchain in Maritime Logistics – Secure documentation and tracking.
Environmental Monitoring at Ports – Tracking emissions and water quality.
Predictive ETA Algorithms – Anticipating vessel arrival times using AI.
Cybersecurity for Smart Ports – Protecting port infrastructure and data.
Intermodal Sea-Rail Integration – Optimizing cargo transfer across modes.

6. Micromobility and Active Transportation

Highlighting infrastructure and systems that support small-scale, low-emission transportation.

Bike-Sharing Optimization – Placement and maintenance of stations via data analytics.
E-Scooter Fleet Management – Real-time tracking and service balancing.
Pedestrian Safety Systems – ITS for crosswalk signals and blind-spot alerts.
Smart Sidewalks – Embedded sensors and adaptive lighting.
Shared Mobility Planning – Policies and tools for integrating micromobility.
Active Travel Route Planning – Algorithms for safe walking/cycling routes.
Urban Design for Micromobility – Streetscapes and zones supporting small vehicles.
Charging Infrastructure for Micromobility – Solutions for lightweight EVs.
Citizen Engagement Platforms – Crowdsourcing feedback on walking/biking paths.
Regulation of Shared Vehicles – Governance models and safety enforcement.

7. Electric and Autonomous Vehicles

This section highlights the integration of electric and autonomous vehicles into modern transportation systems, focusing on infrastructure, policy, and technical innovation.

EV Charging Infrastructure Planning – Placement optimization, smart charging, and grid integration.
Battery Management Systems – Monitoring, diagnostics, and optimization for electric vehicle batteries.
Vehicle-to-Grid (V2G) Technology – Enabling EVs to feed electricity back into the power grid.
Autonomous Driving Algorithms – AI models for perception, prediction, and motion planning.
Sensor Fusion for AVs – Integration of radar, LiDAR, cameras, and GPS for navigation.
Autonomous Vehicle Testing Frameworks – Simulation and real-world test environments.
Human-Machine Interfaces (HMI) – Communication interfaces between AVs and passengers/pedestrians.
Legal and Ethical Challenges in AV Deployment – Liability, safety, and societal readiness.
EV Adoption Incentives and Policy – Government initiatives for promoting electric mobility.
Urban Planning for AVs and EVs – Infrastructure adaptation for new vehicle types.

8. Traffic Flow Modeling and Simulation

This section focuses on quantitative methods to understand and forecast traffic dynamics at both microscopic and macroscopic levels.

Macroscopic Traffic Flow Models – Fluid-dynamic models for large-scale traffic patterns.
Microscopic Simulation Models – Driver behavior and vehicle interaction simulations.
Multi-Agent Traffic Simulations – Modeling vehicles and infrastructure as intelligent agents.
Cellular Automata for Traffic – Discrete simulation of lane changes and congestion.
Data-Driven Calibration Techniques – Using real-world data to tune traffic models.
Traffic Shockwave Analysis – Identifying and mitigating stop-and-go waves.
Vehicle Platoon Behavior Modeling – Dynamics of AV convoys in urban and highway contexts.
Simulation of Emergency Scenarios – Modeling evacuation or disaster traffic response.
Crowd and Pedestrian Flow Models – Simulating large-scale human movement.
Traffic Simulation Platforms – Software tools like SUMO, Aimsun, or VISSIM for experimentation.

9. Traffic Management Strategies

This section explores intelligent approaches to control traffic congestion and enhance network performance.

Adaptive Signal Control Systems – Traffic lights that adjust based on real-time flow.
Congestion Pricing Mechanisms – Economic incentives to manage peak traffic demand.
Intelligent Ramp Metering – Managing freeway access points using sensor data.
Priority Management for Transit and Emergency Vehicles – Dynamic lane allocation systems.
Integrated Corridor Management – Coordinated operation of multiple transport modes.
Real-Time Incident Management – Systems for rapid detection and response to road events.
Urban Traffic Control Centers – Centralized platforms for traffic oversight and intervention.
AI-Based Traffic Prediction – Forecasting future conditions for proactive management.
Traffic Signal Optimization Algorithms – Coordination of intersections for smooth flow.
Environmental Traffic Management – Adjusting strategies to minimize pollution hotspots.

10. Sustainable Urban Transportation Solutions

This section promotes low-emission, inclusive, and multimodal transport systems that support climate goals and urban livability.

Integrated Multimodal Planning – Coordinating transport options for smooth transitions.
Public Transit Electrification – Buses and trams powered by clean energy.
Shared Mobility Ecosystems – Carpooling, vanpooling, and microtransit models.
Low-Emission Urban Zones – Restricted areas for polluting vehicles.
Active Transport Incentives – Programs to encourage walking and cycling.
Transit-Oriented Development (TOD) – Land-use planning centered around transport hubs.
Green Infrastructure for Transport – Tree-lined corridors and permeable surfaces.
Climate Resilient Mobility Planning – Adapting systems for floods, heatwaves, or snowstorms.
Sustainable Freight Logistics – Urban consolidation centers and EV fleets.
Monitoring and Reporting Tools – Metrics and dashboards for tracking sustainability performance.

11. Intelligent Transportation Technologies and Systems

This section is dedicated to systems integration, automation, and intelligence in mobility services and infrastructure.

AI-Based Traffic Control Systems – Predictive, self-optimizing management tools.
Real-Time Transport Data Integration – Fusion of GPS, CCTV, IoT, and social media inputs.
Smart Parking Systems – Space availability detection and dynamic pricing.
Multimodal Transport Integration Platforms – Unified apps and infrastructure for diverse modes.
Cyber-Physical Transportation Systems – Interaction of software, hardware, and physical processes.
Smart Tolling Solutions – Contactless, demand-adjusted fare collection.
Data Analytics for Infrastructure Maintenance – Predictive models for asset lifecycle management.
Decision Support Systems for Planners – Tools for urban and regional mobility planning.
AI in Fleet Management – Optimization of route planning, fuel use, and maintenance.
Augmented Reality (AR) for Traffic Guidance – Head-up displays and wearable interfaces.

12. Wireless Communications in Transportation

This section focuses on the infrastructure and protocols that enable real-time, reliable communication within intelligent transportation networks.

Vehicle-to-Vehicle (V2V) Communication – Data exchange for cooperative driving.
Vehicle-to-Infrastructure (V2I) Communication – Signals between vehicles and traffic systems.
Dedicated Short-Range Communication (DSRC) – Low-latency radio for high-speed messaging.
Cellular V2X (C-V2X) Technologies – 4G/5G networks for connected transport systems.
Edge Computing for ITS – Local data processing to reduce latency and load.
Wireless Sensor Networks – Real-time environmental and traffic sensing.
Communication Protocols for AVs – Standards ensuring safety and interoperability.
Latency and Bandwidth Optimization – Ensuring stability in high-mobility environments.
Security in Vehicular Networks – Protection against cyber-attacks and data breaches.
Hybrid Communication Models – Blending Wi-Fi, satellite, and mobile data in transportation.

13. Internet of Things (IoT) in ITS

This section investigates the use of interconnected sensors and devices in optimizing and automating transportation systems.

IoT-Based Traffic Monitoring Systems – Sensor networks for real-time congestion analysis.
Smart Street Infrastructure – Poles, lights, and signage that respond to conditions.
Vehicle Health Monitoring via IoT – Real-time diagnostics for safety and efficiency.
Infrastructure Condition Monitoring – Bridge, tunnel, and road performance sensors.
IoT for Parking Management – Smart meters and occupancy sensors.
Fleet Telematics and Analytics – Managing commercial vehicle fleets remotely.
Urban Air Quality Monitoring through IoT – Pollution detection and response systems.
IoT Device Interoperability – Ensuring seamless communication among varied devices.
Data Privacy and Cybersecurity – Safeguards for public IoT networks.
Cloud and Edge Integration – Hybrid systems for scalable processing of IoT data.

14. Autonomous and Connected Vehicles

This section covers the design, deployment, and regulation of intelligent vehicle technologies and their role in transforming transportation.

Autonomous Vehicle Perception Systems – Computer vision and sensor fusion.
High-Definition Mapping for AVs – Real-time, precise spatial awareness tools.
Connected Vehicle Ecosystems – Infrastructure and policy for data exchange.
Ethical Decision-Making Algorithms – Navigating dilemmas in automated driving.
AV Safety and Compliance Testing – Standards for simulation and real-world trials.
Human Factors in AVs – Interaction design, trust, and acceptance.
Platooning and Cooperative Driving – Convoy behavior for energy and space efficiency.
Policy and Liability Frameworks – Legal considerations for deployment.
Connected Vehicle Cybersecurity – Safeguarding vehicle systems from intrusion.
AV Integration in Urban Planning – Designing streets for autonomous flow.

15. Traffic Estimation and Prediction Systems

This section highlights the use of advanced analytics to forecast traffic conditions and proactively manage mobility.

Short-Term Traffic Prediction Models – Forecasts minutes ahead using real-time data.
Long-Term Demand Forecasting – Projections based on land use, economy, and policy.
Machine Learning in Traffic Forecasting – Neural networks and time series models.
Crowdsourced Data Utilization – Using smartphones and apps for real-time insights.
Spatiotemporal Data Fusion – Integrating time, location, and mobility data sources.
Traffic Anomaly Detection – Identifying unusual flow patterns or disruptions.
Predictive Congestion Management – Preemptive strategies for peak demand periods.
Weather-Aware Prediction Systems – Integrating meteorological variables.
Multimodal Flow Forecasting – Predicting interactions among transit, road, and bikes.
Visualization Dashboards – Tools for presenting predictions to operators and public.

16. Transportation Network Analysis

This section emphasizes optimization, robustness, and structural understanding of complex transportation systems.

Network Topology Analysis – Identifying critical nodes and links.
Multimodal Network Modeling – Representing interactions between different transport types.
Accessibility and Connectivity Metrics – Tools to assess spatial transport equity.
Resilience Assessment – Evaluating response to disruptions and failures.
Capacity Analysis of Corridors and Intersections – Bottleneck identification and planning.
Graph-Based Route Optimization – Algorithms for shortest or safest paths.
Infrastructure Investment Evaluation – Modeling the impact of upgrades.
Simulation of Disruption Scenarios – Natural disasters or large-scale events.
Data-Driven Transport Policy Analysis – Using evidence to guide infrastructure decisions.
Land Use and Transport Integration – Coupling urban planning with mobility networks.

Bibliographic information

e-ISSN: 2657-4225
DOI: 10.31435/rsglobal_ejits
OCLC Number: 1150485226
Publisher: RS Global Sp. z O.O., Poland
Format: e-version
Frequency: Annual (Continuous Article Publishing (CAP) model)
Content type: Academic/Scholarly
Language: English

Article Types Published

Original Articles: Presenting original research findings that contribute significantly to the field.
Review Articles: Providing comprehensive and critical assessments of current knowledge and developments in specific areas.