Smart Traffic Control Based on Sensor Technology
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 1874
Special Issue Editors
Interests: intelligent transportation systems; adaptive traffic control; artificial intelligence; deep reinforcement learning; multi-agent systems; microscopic traffic simulation; connected and autonomous vehicle
Special Issues, Collections and Topics in MDPI journals
Interests: optimization; estimation and regression; computer simulations; traffic assignment; neural networks; sensors and signal conditioning; data and signal processing
Interests: intelligent transport systems; adaptive traffic control; artificial intelligence; reinforcement learning; traffic simulations and traffic modelling
Special Issue Information
Dear Colleagues,
It is a very common fact that large urban areas are prone to recurring daily congestion, which requires innovative solutions to address it. Today, new challenges also arise, with the increased negative impact from rapid climate change. This development compromises the quality of life through congested urban motorways, long queues at intersections, increased travel time, delayed public transportation, temporary unavailability of existing infrastructure, slower response times for emergency services, and lower air quality. Classic solutions to address these problems, such as building new transportation infrastructure, mode shift, and urban traffic control, cannot adequately cope with the increasing traffic demand, more stringent environmental demands, and the optimal usage of transportation infrastructure in the event of accidents or extreme weather conditions in crucial transport network areas. Such situations can easily negatively affect the possibility of using the transport network to alleviate the congestion that has occurred or to enable an adequate response of emergency services. The domain of Intelligent Transportation Systems provides a structured framework for creating effective and integrated solutions for city-wide traffic management, taking into account all the aforementioned aspects. New, heterogeneous data-rich approaches to traffic control could help create innovative solutions for addressing urban mobility challenges and monitoring the availability of transportation infrastructure during various incidents in crucial transport network areas. Additionally, the increasing levels of road vehicle automation and their connectivity create a new channel for gathering measurements about the traffic state and for changing it. Thus, the dawn of Connected and Autonomous Vehicles marks a new research direction in traffic management, where the vehicle itself becomes an integral part of the traffic control loop. It becomes a moving sensor and actuator, adding additional monitoring and feedback possibilities to the classic roadside-mounted sensors and signalization devices. However, this possibility comes with its own challenges. The large amount of microscopic data available in the form of data streams requires real-time processing and fusion with data from traditional sensors. Therefore, new approaches to sensor fusion are necessary among both moving and stationary sensors. Additionally, except for stationary traffic signalization, control output must also be generated for moving actuators, as Connected and Autonomous Vehicles can receive information for better routes and for variable speed limit control. Thus, the vehicles in the traffic flow can be optimally assigned to adequate travel routes that change rapidly during incident situations that affect large, important areas of the transportation network. The fused measurements from stationary and moving sensors create a virtual sensor with a constantly changing configuration, also creating useful information for optimizing new shared mobility services. Thus, Connected and Autonomous Vehicles being part of Mobility as a Service frameworks can be better deployed with their routes being created adequately to the current state of the transportation network and passenger needs. Additionally, such gathered data creates a new dataset for analyzing travel patterns using methods from data science. Thus, the identified hidden user habits and new user behaviors during city-wide incident situations can be applied to improve urban mobility, especially when the needed amount of vehicles can be reduced using the Mobility as a Service framework. This consequently positively affects congestion by optimally assigning vehicles to free areas of the transportation network, improving monitoring of the transportation network state, and increasing air quality by reducing traffic-related pollution when applied in new concepts of managing urban mobility.
This Special Issue will focus on exploring potential new solutions for enhancing urban mobility by leveraging Intelligent Transportation Systems, Connected and Autonomous Vehicles, Mobility as a Service, and the application of data science. It will provide the most recent advances in the application of data science for processing large amounts of real-time traffic measurements, the integration of CAVs into the traffic control loop, creating better user and mobility provider behavior patterns for Mobility as a Service-based approaches, and the more effective assignment of vehicles to rapidly changing available travel routes during city-wide incident situations.
Dr. Edouard Ivanjko
Dr. Nikica Hlupić
Guest Editors
Dr. Daniela Koltovska Nechoska
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- intelligent transportation systems
- connected autonomous vehicles as mobile sensors and actuators
- shared mobility
- demand-responsive transport (DRT)
- spatial-temporal travel pattern modeling
- dynamic traffic assignment
- Mobility as a Service (MaaS)
- data science in urban mobility
- sustainable traffic management
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