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Automated Classification of Different Congestion Types
... We focus on four congestion patterns mentioned by Karl et al. (2019). These are Jam Wave, Stop and Go, Wide Jam, and Mega Jam, whereas they range from a short speed breakdown to more distinctive congestion in time and space. ...
... First, we describe the state of the art of traffic prediction models and the usage of statistical regression models in this field of research. Section 3 presents the data used for this study and explains four congestion patterns proposed in Karl et al. (2019). Thereafter, the prediction model is described in detail. ...
... The congestion classification introduced by Kessler et al. (2020) is then applied to the data set. The algorithm detects individual congestion elements based on the algorithm (Kessler et al., 2018) and assigns them to one of the congestion patterns defined by Karl et al. (2019). The schematic workflow of the methodology is sketched in Fig. 3. ...
... In [11], the authors introduced four congestion types, which were also critically reviewed in [24]- [27]: Jam Wave, Stop and Go, Wide Jam, and Mega Jam. The single congestion wave is a thin stripe within the space-time diagram implying a temporarily low speed. ...
... Depending on the use cases of the data collection, e.g. for traffic control or traffic planning, a differentiation into more than one type is useful. The following approach introduced by [24], [28]- [31] automatically identifies and classifies emerging congestion in a space-time domain. We briefly summarize their methods and results, in order to apply them to various traffic detection technologies in sec. ...
... The presented methodology determines isolated congestion clusters, each having the shape of a convex hull of all affected cells, a certain area, and one of four congestion types. We refer to the original papers for a more detailed description [24], [28]- [32]. The rest of this paper is based upon this methodology. ...
This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from loop detectors, travel time measurements from Bluetooth sensors, and floating car data (FCD) collected from probe vehicles. Each combination of congestion pattern and detection technology is thoroughly examined and evaluated in terms of its capability and suitability for identifying specific traffic congestion patterns. For our experimental site, we selected the freeway A9 in Germany, which spans a length of 157 km . Our findings reveal that Bluetooth sensors, which record travel times between two locations, are barely suited for detecting short traffic incidents such as Jam Waves due to their downstream detection direction, contrasting with the upstream congestion propagation. Segment-based speed calculations prove more effective in identifying significant congestion events. FCD tend to recognize Stop and Go patterns more frequently than loop detectors but often underestimate severe congestion due to their sensitivity to penetration rates and data availability.
... There are certain time windows (rush hours) when one should avoid being on an arterial road, and also certain regions where the probability of congestion is usually high. In our former papers [1] and [2], we analyzed large amounts of traffic speed data and identified four different patterns of congestion that behave differently in space and time, also earlier described in [3]. Our results are based on an algorithm, which automatically identifies a congested area and assigns a congestion pattern [1], as well as a systematical hot spot analysis of occurring congestion locations and times per pattern [2]. ...
... In our former papers [1] and [2], we analyzed large amounts of traffic speed data and identified four different patterns of congestion that behave differently in space and time, also earlier described in [3]. Our results are based on an algorithm, which automatically identifies a congested area and assigns a congestion pattern [1], as well as a systematical hot spot analysis of occurring congestion locations and times per pattern [2]. Several other studies were conducted looking at congestion patterns and congestion hot spots. ...
... The algorithm works in two steps: First, coherent, isolated congestion clusters are identified based on the methods described in [15] and [16]. Second, the methodology from [1], which assigns an appropriate congestion type, is applied to each of the found clusters. The authors of [15] and [16] propose an algorithm to identify congestion clusters. ...
This paper presents an approach that increases the resilience of a freeway network while
differentiating patterns of freeway congestion events and investigating hot spots of each
pattern both spatially and temporally. Based on an automated pattern recognition, an
emerging congestion event can be identified and classified into one of four predefined
congestion patterns. Determining the spatial and temporal extensions of several congestion events, hot spots of each pattern can be localized. Additionally, possible traffic management and control measures are compiled and evaluated by expert statements to mitigate and dissolve the found congestion hot spots. This approach provides a helpful toolbox for freeway operators to classify occurring congestion into predefined categories and to select appropriate countermeasures based on the hot spot analysis to increase the resilience of the overall system. By applying the presented methodology, optimized traffic information is provided to the operator in time-critical situations, which enables an improved decisionmaking process in traffic management. The data base is three large-scale data sets from stationary detectors, vehicle re-identification sensors, and floating car data collected on a German freeway in 2019.
... Section II illustrates the definition of occurring congestion patterns. Four congestion types proposed in [6] are explained and additionally, lanespecific congestion is defined. In section III, the identification of separated congested regimes and the aggregation to clusters is presented. ...
... Helbing et al. [11] extended the prevailing traffic conditions in congestion to five phases. In [6], four congestion types were examined. In section II-B, these four types are explained in detail. ...
... Based on this subdivision, the authors of [6] introduced a methodology to automatically classify occurring congestion events which roughly comprises the following. Basically, virtual trajectories drive through the entire area starting from the spatial beginning of the considered road stretch and continue with the prevailing speed in each discretized cell. ...
... Traffic management involves two operations, namely congestion control and traffic routing. 1 www.statista.com/statistics/664729/total-number-of-vehicles-india/ Congestion can be classified into different types (e.g., Jam Wave, Stop and Go, Wide Jam and Mega Jam) based on the traffic statistics [1]. ...
... Traffic management involves two operations, namely congestion control and traffic routing. 1 www.statista.com/statistics/664729/total-number-of-vehicles-india/ Congestion can be classified into different types (e.g., Jam Wave, Stop and Go, Wide Jam and Mega Jam) based on the traffic statistics [1]. In order to measure the traffic statistics (e.g., traffic volume, traffic density) in road networks, various types of sensing devices (e.g., inductive loops, cameras) and communication devices (e.g., road side unit (RSU)) are deployed [2]. ...
... where the v f ⩾ 50 km/h and v c ⩽ 5 km/h are the actual speed observations under free-flow and jam waves/stop-and-go waves state [62]. ...
Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using lowpenetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12% improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18% improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods.
... where the v f ⩾ 50 km/h and v c ⩽ 5 km/h are the actual speed observations under free-flow and jam waves/stop-and-go waves state [62]. ...
Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using low-penetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12
improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18
improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods.
First, we briefly summarize the state of research in traffic congestion and existing approaches towards jam classification. We describe the assumptions of the adaptive smoothing method used for processing the empirical data. We present the results of our analysis of the spatiotemporal dynamics of traffic flow on the A12 and A13 motorways in Austria in 2016. Next, we discuss the outcomes for the A12 in detail and compare them with those from the A9 motorway in Germany to see if there are specific patterns due to specific conditions on alpine motorways. We postulate that the situation on the A12 is a result of local traffic overlapping with high seasonal tourist traffic waves, the density of bottlenecks and lack of alternative routes. We also describe technical and methodological problems that we have encountered during the work, particularly regarding the A13. Finally, we propose solutions to overcome these issues and give even more precise data about the exact reasons for each of the congestion types. We also outline our further research plans in this area.
https://authors.elsevier.com/a/1VyS-,M0mR5bzd. In this paper, a novel freeway traffic speed estimation method based on probe data is presented. In contrast to other traffic speed estimators, it only requires velocity data from probes and does not depend on any additional data inputs such as density or flow information. In the first step the method determines the three traffic phases free flow, synchronized flow, and Wide Moving Jam (WMJ) described by Kerner et al. in space and time. Subsequently, reported data is processed with respect to the prevailing traffic phase in order to estimate traffic velocities. This two-step approach allows incorporating empirical features of phase fronts into the estimation procedure. For instance, downstream fronts of WMJs always propagate upstream with approximately constant velocity, and downstream fronts of synchronized flow phases usually stick to bottlenecks. The second step assures the validity of measured velocities is limited to the extent of its assigned phase. Effectively, velocity information in space-time can be estimated more distinctively and the result is therefore more accurate even if the input data density is low.
The accuracy of the proposed Phase-Based Smoothing Method (PSM) is evaluated using real floating car data collected during two traffic congestions on the German freeway A99 and compared to the performance of the Generalized Adaptive Smoothing Method (GASM) as well as a naive algorithm. The quantitative and qualitative results show that the PSM reconstructs the congestion pattern more accurately than the other two. A subsequent analysis of the computational efficiency and sensitivity demonstrates its practical suitability.
This paper discusses the reconstruction quality of spatio-temporal congested freeway traffic patterns depending on the information provided by different equipment rates of probe vehicles. In this research Kerner’s three-phase traffic theory is applied, which distinguishes two different phases in congested traffic: synchronized flow and wide moving jam. In the presented approach spatio-temporal congested traffic patterns are reconstructed from intelligent probe vehicle information generated by an on-board traffic state detection, identifying traffic states along a vehicle’s trajectory at any time.
With a data fusion algorithm combining the data of several probe vehicles, a detailed picture of spatio-temporal congested traffic patterns is revealed. Comparing Ground-Truth with the reconstructed traffic pattern shows that a reconstruction quality comparable to that of established traffic flow models is achievable with probe vehicle equipment rates of about 0.5 %. At higher equipment rates of about 1-1.5 % the achievable quality already exceeds the quality established traffic flow models are able to offer based on a dense detector network with average detector distances of 1-2 km.
We present a new method to obtain spatio-temporal information from aggregated data of stationary traffic detectors, the "adaptive smoothing method". In essential, a nonlin-ear spatio-temporal lowpass filter is applied to the input detector data. This filter exploits the fact that, in congested traffic, perturbations travel upstream at a near-constant speed, while in free traffic, information propagates downstream. As a result, one obtains velocity, flow, or other traffic variables as smooth functions of space and time. Applications include traffic-state visualization, reconstruction of traffic situations from incomplete information, fast identifica-tion of traffic breakdowns (e.g., in incident detection), and experimental verification of traffic models, and even a short-term traffic forecast. We apply the adaptive smoothing method to observed congestion patterns on several German freeways. It manages to make sense out of data where conventional visualization techniques fail. By ignoring up to 65 % of the detectors and applying the method to the reduced data set, we show that the results are robust. The method works well if the distances between neighbouring detector cross sections do not exceed 3 km.
Starting from the instability diagram of a traffic flow model, we
derive conditions for the occurrence of congested traffic states, their
appearance, their spreading in space and time, and the related
increase in travel times. We discuss the terminology of traffic phases
and give empirical evidence for the existence of a phase diagram of
traffic states. In contrast to previously presented phase diagrams, it
is shown that “widening synchronized patterns”
are possible, if the maximum flow is located inside of a metastable density
regime. Moreover, for various kinds of traffic models with different
instability diagrams it is discussed, how the related phase diagrams
are expected to approximately look like. Apart from this, it is pointed out that combinations of
on- and off-ramps create different patterns than a
single, isolated on-ramp.
This paper compares speed data measured by induction loops of stationary detectors with reported speeds from floating-car data, which are based on most recent GPS observations of probe vehicles. Detector data are aggregated over a one minute time interval and that means a 30 s delay occurs on average. The time delay issues with respect to floating-car data are quite convoluted with many influences: (i) the update frequencies from vehicles to the backend server, (ii) the fleet size of floating cars, (iii) the current traffic flow, and (iv) the provider treatment. The floating-car dataset has a high spatial resolution with an average segment length of 100 m suited for large-scale traffic observation and management. The spatial dimension of detector data can only be reconstructed ex-post from spotty positions (mean detector positions distance approx. 1.3 km). The paper analyzes which source is more advantageous in terms of detecting traffic jams, high temporal availability of detector data or detailed spatial resolution of floating-car data. An algorithm is presented to compute the jam detection duration, which means we are able to recognize which data source detects the jam earliest. The results demonstrate that there exist regions along certain road stretches where floating-car data clearly outperform stationary data. However, in regions where detectors are densely placed, stationary sensor data recognize a jam situation approx. 2 min earlier than floating-car based speed data. The datasets cover a period of 80 days in 2015 for both driving directions on the German autobahn A9 in the north of Munich.
This in-depth treatment explains the nature of traffic breakdown and the resulting congestion in vehicular traffic on the basis of three-phase traffic theory, in a manner consistent with real measured traffic data. The author also addresses freeway traffic control methods within the framework of the theory. He demonstrates and explains why the earlier theoretical basis of transportation engineering, research and teaching cannot adequately describe traffic breakdown as observed in measured traffic data. Links between three-phase traffic theory and earlier traffic flow theories are discussed. Last but not least, the book provides a new fundament for transportation engineering, in particular highway traffic management, as well as the background needed to research the complex system dynamics in traffic flow and transportation networks. It will appeal to students, engineers, and physicists interested in transportation systems and complex dynamical systems in general.
Three-Phase Traffic Theory.- Definitions of The Three Traffic Phases.- Nature of Traffic Breakdown at Bottleneck.- Infinite Number of Highway Capacities of Free Flow at Bottleneck.- Nature of Moving Jam Emergence.- Origin of Hypotheses and Terms of Three-Phase Traffic Theory.- Spatiotemporal Traffic Congested Patterns.- II Impact of Three-Phase Traffic Theory on.- to Part II:Compendium of Three-Phase Traffic Theory.- Freeway Traffic Control based on Three-Phase Traffic Theory.- Earlier Theoretical Basis of Transportation Engineering: Fundamental Diagram Approach.- Three-Phase Traffic Flow Models.- Linking of Three-Phase Traffic Theory and Fundamental Diagram Approach to Traffic Flow Modeling.- Conclusions and Outlook.
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.
Experimental investigations of a complexity in traffic flow are presented. It is shown that this complexity is linked to space-time transitions between three qualitative different kinds of traffic: ``free'' traffic flow, ``synchronized'' traffic flow, and traffic jams. Peculiarities of ``synchronized'' traffic flow and jams that are responsible for a complex behavior of traffic are found.
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