1,720,998 research outputs found
Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges
Full text link© 2020 For next-generation smart cities, small UAVs (also known as drones) are vital to incorporate in airspace for advancing the transportation systems. This paper presents a review of recent developments in relation to the application of UAVs in three major domains of transportation, namely; road safety, traffic monitoring and highway infrastructure management. Advances in computer vision algorithms to extract key features from UAV acquired videos and images are discussed along with the discussion on improvements made in traffic flow analysis methods, risk assessment and assistance in accident investigation and damage assessments for bridges and pavements. Additionally, barriers associated with the wide-scale deployment of UAVs technology are identified and countermeasures to overcome these barriers are discussed, along with their implications
V2V and V2I communications for traffic safety and CO\u3csub\u3e2\u3c/sub\u3e emission reduction: A performance evaluation
Full text link© 2019 The Authors. Published by Elsevier B.V. In this paper, we consider a special scenario where connected (V2V and V2I) vehicular technologies are used to alert motorists when they approach a hazardous zone, such as a low visibility area, and recommend proper speeds. We present the principles of the proposed safety driving system and compare the performance of V2V and V2I communications in terms of road safety effectiveness and network communication efficiency. This performance analysis is based on extensive computer simulation experiments by adapting the iTetris platform under various scenarios. We also explore, via simulations, whether CHAA systems, based on V2V and V2I communications can potentially contribute towards eco-driving by reducing Carbon Dioxide (CO2) emissions. Our simulation results showed that our alerting system, based on V2I communication, yields better message reception rate and better safety efficiency compared to a V2V alert system. The results also show that the proposed CHAA system can contribute as well towards reduced CO2 emissions by promoting speed harmonization
Evaluating Active Traffic Management (ATM) Strategies under Non-Recurring Congestion: Simulation-Based with Benefit Cost Analysis Case Study
No full textDynamic hard shoulder running and ramp closure are two active traffic management (ATM) strategies that are used to alleviate highway traffic congestion. This study aims to evaluate the effects of these two strategies on congested freeways under non-recurring congestion. The study's efforts can be considered in two parts. First, we performed a detailed microsimulation analysis to quantify the potential benefits of these two ATM strategies in terms of safety, traffic operation, and environmental impact. Second, we evaluated the implementation feasibility of these two strategies. The simulation results indicated that the implementation of the hard shoulder showed a 50%-57% reduction in delay, a 41%-44% reduction in fuel consumption and emissions, and a 15%-18% increase in bottleneck throughput. By contrast, the implementation of ramp closure showed a 20%-34% decrease in travel time, a 6%-9% increase in bottleneck throughput, and an 18%-32% reduction in fuel consumption and emissions. Eventually, both strategies were found to be economically feasible.Funding:
This research received no external funding.
Acknowledgments:
The authors acknowledge the research support provided by IMOB Hasselt University Belgium, Middle East College-Oman, Directorate General of Traffic, Royal Oman Police (ROP), Supreme Council for Planning, and Muscat Municipality for their support and providing the data that makes this research viable and effective. The authors also acknowledge the research support given by Zayed University, UAE. This work would not have possible without their help
Estimating ambient visibility in the presence of fog: a deep convolutional neural network approach
No full textNext-generation intelligent transportation systems are based on the acquisition of ambient data that influence traffic flow and safety. Among these, is the ambient visibility range whose estimation, in the presence of fog, is extremely useful for next-generation intelligent transportation systems. However, existing camera-based approaches are based on "engineered features" extraction methods that use computer algorithms and procedures from the image processing field. In this contribution, a novel approach to estimate visibility range under foggy weather conditions is proposed which is based on "learned features" instead. More precisely, we use AlexNet deep convolutional neural network (DCNN), trained with raw image data, for feature extraction and a support vector machine (SVM) for visibility range estimation. Our quantitative analysis showed that the proposed approach is very promising in estimating the visibility range with very good accuracy. The proposed solution can pave the way towards intelligent driveway assistance systems to enhance awareness of driving weather conditions and hence mitigate the safety risks emanating from fog-induced low visibility conditions.This research was financially supported by Zayed University Cluster Research Grant No. R17075.Kamoun, F (reprint author), ESPRIT Sch Engn, Tunis, Tunisia.
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Estimating meteorological visibility range under foggy weather conditions: A deep learning approach
Full text link© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog
BTEM : belief based trust evaluation mechanism for wireless sensor
Full text linkWith the emergence of WSNs in the recent times, providing trustworthy and reliable data delivery is challenging task due to unique characteristics and constraints of nodes. Malicious node can easily disrupt the integrity of network through the inclusion of false and malicious data and initiate internal attacks. Detection of malicious nodes using trust-based security is an effective and lightweight countermeasure as compared to key based security schemes which incurs higher overhead costs. The WSNs will play greater role in the next-generation IoT systems and a compromised node can jeopardize the availability and authenticity of sensory layer. In this paper, an efficient Belief based trust evaluation mechanism (BTEM) is proposed which isolates the malicious node from trust-worthy nodes and defend against Bad-mouth, On–Off and Denial of Service (DoS) attacks. Bayesian estimation approach is used in gathering direct and In-direct trust values of the sensor nodes which further considers the correlation of the data collected over the time and then estimate imprecise knowledge in decision making for secure delivery of data thus avoiding the malicious nodes. Compared with existing approaches, the proposed BTEM performs better in the detection of malicious node (MN), with lesser delay and improved network throughput
Examining queue-jumping phenomenon in heterogeneous traffic stream at signalized intersection using UAV-based data
Full text link© 2020, Springer-Verlag London Ltd., part of Springer Nature. This research presents an in-depth microscopic analysis of heterogeneous and undisciplined traffic at the signalized intersection. Traffic data extracted from the video recorded using an unmanned aerial vehicle (UAV) at an approach of a signalized intersection is analyzed to study the within green time dynamics of traffic flow. Various parameters of Wiedemann 74, Wiedemann 99, and lateral behavior models used in microscopic traffic simulation package, Vissim, are calibrated for the local heterogeneous traffic. This research is aimed at exploring the queue-jumping phenomenon of motorbikes at signalized intersections and its impact on the saturation flow rate, travel time, and delay. The study of within green time flow dynamics shows that the flow of traffic within green time is not uniform. Surprisingly, the results indicate that the traffic flow for the first few seconds of the green time is significantly higher than the remaining period of green time, which shows a contradiction to the fact that traffic flow for the first few seconds is lower due to accelerating vehicles. Mode-wise traffic counted per second shows that this anomaly is attributed to the presence of motorbikes in front of the queue. Consequently, the outputs of simulation results obtained from calibrated Vissim show that the simulated travel time for motorbikes is significantly lower than the field-observed travel times even though the average simulated traffic flow matches accurately with the field-observed traffic flow. The findings of this research highlight the need to incorporate the queue-jumping behavior of motorbikes in the microsimulation packages to enhance their capability to model heterogeneous and undisciplined traffic
A Neural network approach to visibility range estimation under foggy weather conditions
Full text link© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. Peer-review under responsibility of the Conference Program Chairs
Toward the improvement of traffic incident management systems using Car2X technologies
Full text link© 2020, Springer-Verlag London Ltd., part of Springer Nature. In addition to their environmental impact, road traffic congestions have been recognized to seriously affect commuters’ safety as well as the performance of transportation systems. To address these issues, various Traffic Incident Management (TIM) systems have been implemented. Recent systems are particularly focusing on the integration of promising emergent technologies such as the Internet of Things. However, thorough studies are still necessary to make sure that these technologies are compatible with existing systems and effective within their context of use. The main goal of this research is to develop a smart TIM system which is based on Car2X communications and which aims at improving both traffic safety, commuters’ mobility, and gas emissions. To assess the effectiveness of our solution, we use the following measures: stops delay, stops all, vehicle delay, travel time, gas emissions, and fuel consumption. This paper also outlines how we use our simulation platform (which was developed based on VISSIM and Python) to quantify the benefits of using Car2X communications. We run our simulations on Muscat Expressway in the Sultanate of Oman. Results are promising and include (1) the travel time decreased by 6%; (2) the average stop delay and vehicle stops were reduced by at least 9% and 27% respectively; and (3) there is a total decrease in fuel consumption and carbon monoxide emissions by approximately 16%
Integrated agent-based microsimulation framework for examining impacts of mobility-oriented policies
Full text linkTravel demand management measures/policies are important to sustain positive changes among individuals’ travel behaviour. An integrated agent-based microsimulation platform provides a rich framework for examining such interventions to assess their impacts using indicators about demand as well as supply side. This paper presents an approach where individual schedules, derived from a lighter version of an activity-based model, are fed into a Multi-Agent Transport Simulation (MATSIM) framework. Simulations are performed for two European cities i.e. Hasselt (Belgium) and Bologna (Italy). After calibrating the modelling framework against aggregate traffic counts for the base case, the impacts of a few traffic management policies (restricting car access, increase in bus frequency) are examined. The results indicate that restricting car access is more effective in terms of reducing traffic from the network and also shifting car drivers/passengers to other modes of travel. The enhancement of bus infrastructure in relation to increase in frequency caused shifting of bicyclist towards public transport, which is an undesirable result of the policy if the objective is to improve sustainability and environment. In future research, the framework will be enhanced to integrate emission and air dispersion models to ascertain effects on air quality as a result of such interventions
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