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In the field of traffic management and control systems, we are witnessing a symbiotic evolution, where intelligent infrastructure is progressively collaborating with smart vehicles to produce benefits for traffic monitoring and security, by rapidly identifying hazardous behaviours. This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. The proposed system employs deep learning algorithms and vision techniques that perform vehicle detection, classification, tracking, speed estimation, and vehicle geolocation. [ABSTRACT FROM AUTHOR]

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management. [ABSTRACT FROM AUTHOR]

Predicting spatiotemporal congestion situations of a traffic network is a prerequisite for urban traffic control. This study proposes a spatiotemporal traffic congestion situation prediction method based on the recurrent gated unit-convolutional neural network (GRU-CNN). Considering the time and space attributes of traffic data, the third-order tensor of the traffic data is extracted from the time domain, and the GRU is used to predict the traffic flow parameters of the traffic network. Then, the third-order tensor of multisource spatiotemporal traffic data is compressed into traffic data images and combined with the spatial structure. The feature extraction technology of a CNN is used to extract and identify the traffic network congestion features. Actual urban traffic network data are selected for model verification. The multistep prediction of the traffic flow parameters effectively ensures prediction accuracy. The proposed model is trained by the actual classification dataset. The prediction results of the test set demonstrate the model's reliability. Based on predicting the traffic parameters of the network, this model can give a highly accurate judgment of the traffic situation for the entire network. Compared with other models, the proposed model further improves the accuracy of road network traffic state discrimination and has better robustness. [ABSTRACT FROM AUTHOR]

The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle. [ABSTRACT FROM AUTHOR]

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure. [ABSTRACT FROM AUTHOR]

In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i -LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships. [ABSTRACT FROM AUTHOR]

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God's perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods. [ABSTRACT FROM AUTHOR]

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle's motion. An accurate and reliable reconstruction of a vehicle's trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval. [ABSTRACT FROM AUTHOR]

Yield control and full signalization are typical traffic control solutions that can be used at large roundabouts. In the face of increasing congestion issues, it is preferred to use yield control during off-peak periods and full signalization during peak periods. To automatically accommodate time-varying vehicular demands, a multi-level traffic control (MTC) is developed to implement hybrid yield control and fully actuated control at large four-leg roundabouts. With new application of traffic control devices and traffic detection system, the right-of-way can be assigned to entering and circulating vehicles in three modes. The 'all entering' mode is equivalent to a yield control. The 'no entering' and 'concurrent entering' modes are equivalent to a fully actuated control. On the basis of time headways and occupancy times that are detected on the entry and circulatory roadways, the mode of right-of-way assignment can be changed in response to actual traffic conditions. For a specific mode of right-of-way assignment, traffic signal operation is managed by some detectable traffic events that are happening. The results of the simulation experiments conducted by VISSIM indicated that: (i) MTC was stabilized at the 'all entering' mode during off-peak periods and at the 'concurrent entering' mode during peak periods; (ii) MTC would typically change the mode of right-of-way assignment according to actual traffic conditions as vehicular demands increased from off-peak to peak or decreased from peak to off-peak; and (iii) statistically speaking, MTC inherited the operational advantages of yield control and fully actuated control, and could be effective in improving the operational performance of large four-leg roundabouts for all hours of the day, regardless of the level of left-turn ratios. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Intersection regions widely exist in road networks as crucial connections, and the congestion effects on traffic in the networks cannot be ignored. In general, a conventional intersection region includes a merging region and more than two ramps, whose congestion brings a serious negative effect on traffic service quality and satisfaction of drivers. Specifically, intersection region congestion is caused by congestion in merging regions. However, current control methods regarding coordination of the variable speed limits and ramp metering in order to solve merging region congestion are insufficient to handle congestion occurring in the intersection region. Therefore, a new method of coordinated control is proposed in this paper based on mainline control using the variable speed limits and coordinated ramp metering. The coordinated control method is formulated considering the degree of crowdedness indexes and implemented based on the queue length, with the goal to relieve congestion in conventional intersection regions caused by merging region congestion. The coordinated control method is further evaluated based on an actual traffic network. The results show that the proposed method is effective in terms of improving traffic service quality and relieving intersection region congestion. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time Automatic Vehicle Location (AVL) and monitoring system for traffic control of pilgrims coming towards the city of Makkah in Saudi Arabia based on Data Distribution Service (DDS) specified by the Object Management Group (OMG). DDS based middleware employs Real-Time Publish/Subscribe (RTPS) protocol that implements many-to-many communication paradigm suitable in massive traffic control applications. Using this middleware approach, we are able to locate and track huge number of mobile vehicles and identify all passengers in real-time who are coming to perform annual Hajj. For validation of our proposed framework, various performance matrices are examined over WLAN using DDS. Results show that DDS based middleware can meet real-time requirements in large-scale AVL environment. [ABSTRACT FROM AUTHOR]

There exist two main drawbacks for traffic images in classic image enhancement methods. First is the performance degradation that occurs under frontlight, backlight, and extremely dark conditions. The second drawback is complicated manual settings, such as transform functions and multiple parameter selection mechanisms. Thus, this paper proposes an effective and adaptive parameter optimization enhancement algorithm based on adaptive brightness baseline drift (ABBD) for color traffic images under different luminance conditions. This method consists of two parts: brightness baseline model acquisition and adaptive color image compensation. The brightness baseline model can be attained by analyzing changes in light along a timeline. The adaptive color image compensation involves color space remapping and adaptive compensation specific color components. Our experiments were tested on various traffic images under frontlight, backlight, and during nighttime. The experimental results show that the proposed method achieved better effects compared with other available methods under different luminance conditions, which also effectively reduced the influence of the weather. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents an integrated model for optimizing lane assignment and signal timing at tandem intersection, which is introduced recently. The pre-signal is utilized in the tandem intersection to reorganize the traffic flow; hence, the vehicles, regardless of whether left-turns or through vehicles, can be discharged in all the lanes. However, the previous work does not consider the extra traffic disruption and the associated delay caused by the additional pre-signal. In the paper, the extra delay aroused by the coordination is incorporated in a lane assignment and signal timing optimization model, and the problem is converted into a mixed-integer non-linear programming. A feasible directions method is hence introduced to solve the mixed-integer non-linear programming. The result of the optimization shows that the performance of the tandem intersection is improved and the average delay is minimized. The comparison between the tandem and the conventional configuration is presented, and the results verify that the former shows better performance than the latter. In addition, the optimal sequence corresponding to the turning proportion and the optimal lane assignment at the upstream approach of the pre-signal are presented. Furthermore, if the number of lanes is equal in all arms, the paper proves that the average delay will be reduced if lane assignment is proportional to the turning proportion and the vehicles with low proportion are discharged in advance. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The concept of Intelligent Transportation Systems (ITSs) has been recently introduced to define modern embedded systems with enhanced digital connectivity, combining people, vehicles, and public infrastructure. The smart transducer concept, on the other hand, has been established by the IEEE 1451 standard to simplify the scalability of networked electronic equipments. The synergy of both concepts will establish a new paradigm in the near future of the ITS area. The purpose of this paper is to analyze the integration of electronic equipments into intelligent road-traffic management systems by using the smart transducer concept. An automated video processing sensor for road-traffic monitoring applications is integrated into an ITS network as a case study. The impact of the IEEE 1451 standard in the development and performance of ITS equipments is analyzed through its application to this video-based system, commercialized under the name VisioWay. [ABSTRACT FROM AUTHOR]

Packet classification plays a crucial role for a number of network services such as policy-based routing, firewalls, and traffic billing, to name a few. However, classification can be a bottleneck in the above-mentioned applications if not implemented properly and efficiently. In this paper, we propose PCIU, a novel classification algorithm, which improves upon previously published work. PCIU provides lower preprocessing time, lower memory consumption, ease of incremental rule update, and reasonable classification time compared to state-of-the-art algorithms. The proposed algorithm was evaluated and compared to RFC and HiCut using several benchmarks. Results obtained indicate that PCIU outperforms these algorithms in terms of speed, memory usage, incremental update capability, and preprocessing time. The algorithm, furthermore, was improved and made more accessible for a variety of applications through implementation in hardware. Two such implementations are detailed and discussed in this paper. The results indicate that a hardware/software codesign approach results in a slower, but easier to optimize and improve within time constraints, PCIU solution. A hardware accelerator based on an ESL approach using Handel-C, on the other hand, resulted in a 31x speed-up over a pure software implementation running on a state of the art Xeon processor [ABSTRACT FROM AUTHOR]

We find a simple, partially altruistic mechanism that can increase global gain for a community of selfish agents. The mechanism is implied in the phenomena found in the minority game. We apply the mechanism to a two-road traffic system to maximise traffic flow. [ABSTRACT FROM AUTHOR]

The stability of road networks has become an increasingly important issue in recent times, since the value of time has increased considerably and unexpected delay can results in substantial loss to road users. Road network reliability has now become an important performance measure for evaluating road networks, especially when considering changes in OD traffic demand and link flow capacity over time. This paper outlines the basic concepts, remaining problems and future directions of road network reliability analysis. There are two common definitions of road network reliability, namely, connectivity reliability and travel time reliability. As well, reliability analysis is generally undertaken in both normal and abnormal situations. In order to analyse the reliability of a road network, the reliability of the links within the network must be first determined. A method for estimating the reliability of links within road networks is also suggested in this paper. [ABSTRACT FROM AUTHOR]

This paper is the second of a pair of papers discussing two main themes concerning dense network modelling. These themes are: (1) the changing nature of traffic management technology and the underlying objectives behind traffic management practice, and (2) the use of measures of network reliability in models, especially as an element of the evaluation of alternative network configurations. This paper develops and applies the second theme, the use of network reliability concepts in the evaluation of traffic networks, through consideration of variations in travel times, distinction between local street and arterial road networks, and the definition and application of a set of reliability indices that may be used to study different trip movements in a network. It indicates how these indices may be used in appraising different traffic management plans for a dense network of local streets and arterial roads, using a case study application. [ABSTRACT FROM AUTHOR]

A capacity related reliability for transportation networks with random link capacity is introduced. It is defined as the probability that the road network can accommodate a certain level of traffic demand, and is built on the concept of network reserve capacity. Network reserve capacity is defined as the largest multiplier applied to an existing origin-destination demand matrix that can be allocated to a transportation network in a user-optimal way without violating the link capacities. Due to large variability associated with link capacities, a probabilistic approach is adopted to model the different physical and operational factors that often degrade the capacity of roadways. A Monte Carlo simulation procedure is developed to estimate the capacity related reliability measure. Numerical results are provided to demonstrate the feasibility of the approach. [ABSTRACT FROM AUTHOR]

This paper investigates the transportation network reliability based on the information provided by detectors installed on some links. A traffic flow simulator (TFS) model is formulated for assessing the network reliability (in terms of travel time reliability), in which the variation of perceived travel time error and the fluctuations of origin-destination (OD) demand are explicitly considered. On the basis of prior OD demand and partial updated detector data, the TFS can estimate the link flows for the whole network together with link/path travel times, and their variance and covariance. The travel time reliability by OD pair can also be assessed and the OD matrix can be updated simultaneously. A Monte Carlo based algorithm is developed to solve the TFS model. The application of the proposed TFS model is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

This paper is the first of a pair of papers discussing two main themes concerning dense network modelling. These themes are: (1) the changing nature of traffic management technology and the underlying objectives behind traffic management practice, and (2) the use of measures of network reliability in models, especially as an element of the evaluation of alternative network configurations. This paper considers the role and function of dense network models, and their relationships with other transport network models on the basis of the hierarchical view of models. It then explores the peculiar characteristics of dense network models and provides examples of typical models. Changing needs for modelling capabilities, in terms of the evolution of traffic management technology and practice are discussed, pointing the way to future model developments. The companion paper develops the second theme through the definition and application of a set of network reliability indices that may be applied to different trip movements. [ABSTRACT FROM AUTHOR]

When we evaluate the performance reliability of a network, it is necessary to describe user's behaviour in a partially degraded network. This paper shows that the Stochastic User Equilibrium (SUE) model assuming user's route choice behaviour under uncertain network conditions can be incorporated in the performance reliability model. The effects of providing information are analyzed using the SUE model with two different groups of route choice; informed drivers and non informed drivers. It is found that providing information generally increases network performance reliability. This depends, however, on the probability distribution of the network states. [ABSTRACT FROM AUTHOR]

Despite the importance of assessing the reliability of transport networks in general there is a paucity of suitable techniques. In part this is due to the fact that network performance depends both on the state of the infrastructure and on the behaviour of network users, where user behaviour is governed by expectations about the state of the network. An approach based on game theory is proposed whereby the performance of the network is estimated for the case where network users are extremely pessimistic about the state of the network. Where the routes are prespecified and route utilities depend only on exogenously given scenarios, the estimation problem may be formulated as a linear program. A reformulation of the problem as a non-linear program allows the impact of the degree of user pessimism on expected network utility to be studied. The problems of implementing the method for large networks with multiple origins and destinations is discussed and an algorithm is proposed. [ABSTRACT FROM AUTHOR]

This paper presents an iterative scheme for a combined signal optimization and assignment problem, using a traffic model from the well-known procedure TRANSYT. The signal settings are optimized by means of a group-based technique, in which the signal timings are specified by the common cycle time, the start time and duration of the period of right of way for each signal group in the network. The optimization problem was formulated as an integer program and solved by a set of heuristics. Given the optimized signal settings determined from the group-based technique, a path-based assignment algorithm is employed to obtain the equilibrium traffic pattern using the sensitivity information for TRANSYT model and a Frank-Wolf method. Based on the equilibrium flow pattern, the group-based optimization algorithm is then used to determine a better set of signal timings. The procedure is repeated until certain convergence criteria are satisfied. A numerical example is employed to demonstrate the benefits obtained from this iterative scheme. Encouraging results are obtained. [ABSTRACT FROM AUTHOR]