Search Results (220)

The advancement of Artificial Intelligence (AI) and the Internet of Things (IoT) has accelerated the development of Intelligent Transportation Systems (ITS) in smart cities, playing a crucial role in optimizing traffic flow, enhancing road safety, and improving the driving experience. With urban traffic becoming increasingly complex, timely detection and response to congestion and accidents are critical to ensuring safety and situational awareness. This paper presents Passable, an intelligent and adaptive traffic light control system that monitors traffic conditions in real time using deep learning and computer vision. By analyzing images captured from cameras at traffic lights, Passable detects road incidents and dynamically adjusts signal timings based on current vehicle density. It also employs wireless communication to alert drivers and update a centralized dashboard accessible to traffic management authorities. A working prototype integrating both hardware and software components was developed and evaluated. Results demonstrate the feasibility and effectiveness of designing an adaptive traffic signal control system that integrates incident detection, instantaneous communication, and immediate reporting to the relevant authorities. Such a design can enhance traffic efficiency and contribute to road safety. Future work will involve testing the system with real-world vehicular communication technologies on multiple coordinated intersections while integrating pedestrian and emergency vehicle detection. Full article

This study evaluates the safety impacts of upgrading traditional STOP signs to light-emitting diode (LED)-illuminated backlit STOP signs at urban intersections, aiming to address visibility and conspicuity concerns that affect driver behavior and intersection safety. STOP signs are critical for regulating traffic flow and minimizing conflicts, yet their effectiveness can diminish under low-visibility conditions. To assess the effectiveness of LED-enhanced signage, a before–after study was conducted using surrogate safety measures. Key performance indicators included vehicle speeds, driver compliance rates, and vehicle-to-vehicle interactions, recorded both prior to and following LED implementation. A multinomial logistic regression model was used to analyze driver behaviors, and a calibrated microscopic simulation model, optimized using a genetic algorithm (GA), was applied to estimate traffic conflict frequencies. Video data were processed to extract driver trajectories and reactions under varying signage conditions. Results showed LED STOP signs improved compliance rates from 60% to 85%, reduced average vehicle speeds by 25%, and increased post-encroachment times. Conflict analysis revealed significant reductions in vehicle-to-vehicle and pedestrian conflicts, particularly at night. These findings highlight the effectiveness of LED signage in enhancing intersection safety and offer important implications for urban traffic management and the adoption of advanced traffic control technologies. Full article

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments. Full article

In this study, the effects of speed and red-light cameras (SRLCs) and traffic signal countdown timers (TSCTs) on the operation of pre-timed signalized intersections were studied through startup lost times (SLTs) and saturation time headways (STHs). The study used the beanplots package version 1.3.1 in R statistical software to graph and find the first STH that occurred in a queue. Then, one-way analysis of variance was used twice to explore the effects of the separate and joint use of SRLCs and TSCTs on the operation of pre-timed signalized intersections. The results show that SRLC use does not have a significant direct impact on the operation of pre-timed signalized intersections, but SRLC interacts negatively with TSCT use. In addition, TSCT use was shown to improve the operation of pre-timed signalized intersections by decreasing the SLT and STH. For SLT, the effect size of TSCT use depends on the presence or absence of SRLC use, and its reduction ranges from 0.5 to 1.25 s per queue. As for STH, the effect size of TSCT use does not depend on the presence or absence of SRLC use, and its reduction ranges from 0.08 to 0.12 s per vehicle, corresponding to 0.8–1.2 s per queue, given that there are 10 vehicles in the queue. Full article

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters. Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

Traffic in large cities is increasing due to continuous urbanization, the construction of new housing complexes and the accompanying new street network. The growth of cities creates prerequisites for increasing the intensity of transport, pedestrian, and bicycle flows, especially during peak periods. To improve the conditions in which traffic flows, it is necessary to introduce an effective method for reducing delays that arise at intersections, especially those regulated by traffic light systems. One of the possible approaches to this is to coordinate the operation of traffic light systems. The main thing in this is to determine relatively accurate times for the movement of individual flows, for which adequate traffic models are needed. This article presents a model of the movement of transport flows when starting from the first intersection in a coordinated mode of operation of traffic light systems. This is of particular importance when determining the times of individual signals and, above all, has an impact on the moment for switching on the permitting signal at the next intersection. The presented model aims to provide an opportunity to determine accurate times of passage of vehicles through consecutive intersections that operate in a coordinated mode of traffic light systems. Full article

In recent years, the number of vehicles in cities has visibly increased, leading to continuous modifications in general mobility. Pollution levels and congestion cases are reaching higher numbers as well, pointing to a need for better optimization solutions. Several existing control systems still rely on fixed timings for traffic lights, lacking an adaptive approach that can adjust the timers depending on real-time conditions. This study aims to provide a design for such a tool, by implementing two different approaches: Fuzzy Logic Optimization and an Adaptive Traffic Management strategy. The first controller involves Fuzzy Logic based on rule-based that adjust green and red-light timings depending on the number of vehicles at an intersection. The second model provides traffic adjustments based on external equipment such as road sensors and cameras, offering dynamic solutions tailored to current traffic conditions. Both methods are tested in a simulated environment using SUMO (Simulation of Urban Mobility). They were evaluated according to key efficiency indicators, namely average waiting time, lost time per cycle, number of stops per intersection, and overall traffic fluidity. Results demonstrate that Q-learning maintains consistent waiting times between 2.57 and 3.71 s across all traffic densities while achieving Traffic Flow Index values above 85%, significantly outperforming Fuzzy Logic, which shows greater variability and lower efficiency under high-density conditions. Full article

Pulse and Glide (PnG), as a fuel-saving technique, has primarily been applied to manual transmission vehicles. So, its effectiveness when integrated with a novel vehicle type like connected and automated vehicles (CAVs) remains largely unexplored. On the other hand, CAVs have evidently received less attention regarding energy conservation, and their prominent perception capabilities clearly exhibit individual variations. In light of this, this study investigates the impacts of PnG combined with CAVs on energy conservation and safety within the emerging mixed traffic flow composed of CAVs with varying sensing accuracies. The results indicate the following: (i) compared to the traditional driving modes, the PnG can achieve a maximum fuel-saving rate of 39.53% at Fuel Consumption with Idle (FCI), reducing conflicts by approximately 30% on average; (ii) CAVs, equipped with sensors boasting a greater detection range, markedly enhance safety during vehicle operation and contribute to a more uniform distribution of individual fuel consumption; (iii) PnG modes with moderate acceleration, such as 1–2 m/s², can achieve excellent fuel consumption while ensuring safety and may even slightly enhance the operational efficiency of the intersection. The findings could provide a theoretical reference for the transition of transportation systems toward sustainability. Full article

Many research studies have designed intelligent traffic light scheduling algorithms. Some researchers rely on specialized sensors and hardware to gather real-time traffic data at signalized road intersections. Others benefit from artificial intelligence techniques and/or cloud computing technologies. The technology of vehicular networks has been widely used to gather the traffic characteristics of competing traffic flows at signalized road intersections. Intelligent traffic light controlling systems aim to fairly liberate competing traffic at signalized road intersections and eliminate traffic crises. These algorithms have been initially developed without focusing on the consequences of security threats or attacks. However, the accuracy of gathered traffic data at each road intersection affects its performance. Fake and corrupted packets highly affect the accuracy of the gathered traffic data. Thus, in this work, we aim to investigate the aspects of security and confidentiality of intelligent traffic light systems. The possible attacks on the confidentiality of intelligent traffic light systems are examined. Then, a confidential traffic light control system that protects the privacy of traveling vehicles and drivers is presented. The proposed algorithm mainly prevents unauthorized traceability and linkability attacks that threaten people’s lives and violate their privacy. Finally, the proposed algorithm is evaluated through extensive experiments to verify its correctness and benefits compared to traditional insecure intelligent traffic light systems. Full article

To enable autonomous driving in real-world environments that involve a diverse range of geographic variations and complex traffic regulations, it is essential to investigate Deep Reinforcement Learning (DRL) algorithms capable of policy learning in high-dimensional environments characterized by intricate state–action interactions. In particular, closed-loop experiments, which involve continuous interaction between an agent and their driving environment, serve as a critical framework for improving the practical applicability of DRL algorithms in autonomous driving systems. This study empirically analyzes the capabilities of several representative DRL algorithms—namely DDPG, SAC, TD3, PPO, TQC, and CrossQ—in handling various urban driving scenarios using the CARLA simulator within a closed-loop framework. To evaluate the adaptability of each algorithm to geographical variability and complex traffic laws, scenario-specific reward and penalty functions were carefully designed and incorporated. For a comprehensive performance assessment of the DRL algorithms, we defined several driving performance metrics, including Route Completion, Centerline Deviation Mean, Episode Reward Mean, and Success Rate, which collectively reflect the quality of the driving in terms of its completeness, stability, efficiency, and comfort. Experimental results demonstrate that TQC and SAC, both of which adopt off-policy learning and stochastic policies, achieve superior sample efficiency and learning performances. Notably, the presence of geographically variant features—such as traffic lights, intersections, and roundabouts—and their associated traffic rules within a given town pose significant challenges to driving performance, particularly in terms of Route Completion, Success Rate, and lane-keeping stability. In these challenging scenarios, the TQC algorithm achieved a Route Completion rate of 0.91, substantially outperforming the 0.23 rate observed with DDPG. This performance gap highlights the advantage of approaches like TQC and SAC, which address Q-value overestimation through statistical methods, in improving the robustness and effectiveness of autonomous driving in diverse urban environments. Full article

Accurate traffic light status detection and the appropriate response to changes in that status are crucial for autonomous driving systems (ADSs) starting from SAE Level 3 automation. The dilemma zone problem occurs during the amber phase of traffic lights, when the ADS must decide whether to stop or proceed through the intersection. This paper proposes a methodology for developing a runtime monitor that addresses the dilemma zone problem and monitors the autonomous vehicle’s behavior at traffic lights, ensuring that the ADS’s decisions align with the system’s safety requirements. This methodology yields a set of safety requirements formulated in controlled natural language, their formal specification in linear temporal logic (LTL), and the implementation of a corresponding runtime monitor. The monitor is integrated within a safety-oriented software architecture through a modular autonomous driving system pipeline, enabling real-time supervision of the ADS’s decision-making at intersections. The results show that the monitor maintained stable and fast reaction times between 40 ms and 65 ms across varying speeds (up to 13 m/s), remaining well below the 100 ms threshold required for safe autonomous operation. At speeds of 30, 50, and 70 km/h, the system ensured correct behavior with no violations of traffic light regulations. Furthermore, the monitor achieved 100% detection accuracy of the relevant traffic lights within 76 m, with high spatial precision (±0.4 m deviation). While the system performed reliably under typical conditions, it showed limitations in disambiguating adjacent, irrelevant signals at distances below 25 m, indicating opportunities for improvement in dense urban environments. Full article

In existing traffic simulation software or studies related to traffic flow at signalized intersections, the treatment of yellow lights is often simplified or overlooked. However, driving behavior during the yellow phase is characterized by significant uncertainty, which can lead to discrepancies between simulation results and real-world conditions. To address this issue, this paper develops a driving behavior model based on risk perception and fuzzy decision-making and integrates it into a simulation framework to replicate continuous driving behaviors at isolated signalized intersections. The performance of intersections under varying yellow light durations is analyzed, yielding some key findings. For instance, when vehicles strictly adhere to the designed speed, increasing the yellow light duration from 3 s to 5 s results in higher traffic volumes under high traffic density. Furthermore, real-time traffic speed fluctuations stabilize, and the occurrence of unsafe driving behaviors decreases. The concept of risk perception is employed to explain the underlying mechanisms behind these phenomena. This paper provides both a theoretical foundation and a simulation framework for more detailed representations of driving behaviors and for explaining the fundamental principles governing intersection performance. Full article

Computer-aided transport modelling is essential for testing different control strategies for traffic lights. One approach to modelling traffic control is by heuristically defining fuzzy rules for the control of traffic light systems and applying them to a network of hierarchically dependent crossroads. In this paper, such a network is investigated through modelling the geometry of the network in the simulation environment Aimsun. This environment is based on real-world traffic data and is used in this paper with the MATLAB R2019a-Fuzzy toolbox. It focuses on the development of a network of intersections, as well as four fuzzy models and the behaviour of these models on the investigated intersections. The transport network consists of four intersections. The novelty of the proposed approach is in the application of heuristic fuzzy rules to the modelling and control of traffic flow through these intersections. The motivation behind the use of this approach is to address inherent uncertainties using a fuzzy method and analyse its main findings in relation to a classical deterministic approach. Full article

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO₂ emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life. Full article