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Due to rapid population growth, traffic congestion has become one of the major issues in urban areas. The utilization of technology may help to address this issue. This paper proposes a new Multi-head Self-attention Vision Transformer (MSViT) based macroscopic approach, for road traffic congestion classification. To evaluate this approach, we use t...
Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these...
Not keeping an adequate safe distance is one of the elements that are directly related to traffic accidents. The main objective of this research was to identify the aspects that modulate the safe distance-accidents relation. Specifically, the frequency and reasons why drivers do not keep the safe distance, the perception of drivers regarding the pr...
![Fig. 2. Illustration of UPFC control capability via Phasor diagram [5]](profile/Salman-Salman-19/publication/340892983/figure/fig2/AS:885667777224705@1588171061225/llustration-of-UPFC-control-capability-via-Phasor-diagram-5_Q320.jpg)
Citations
... Thus, they provided a framework that handles uncertainty under the different paradigms: deterministic, probabilistic, or possibilistic. Further studies in this field include [16,[29][30][31]. On the basis of the described analyses, it is indicated that fuzzybased applications have a preferable performance. ...
... Following this idea, we developed a method-the two-stage traffic congestion detector-that takes as input the real traffic data for each road section and identifies the relationships between the fundamental variables (fk-v), before estimating the speed values and determining the level of congestion according to the speeds. We focused on the speed variable instead of density in general, because speed can be measured directly, and is directly related to drivers' experiences and to the total time spent on the network, which is a frequently used performance indicator for road traffic [8,[24][25][26]28,30]. In the first step, fuzzy average speed values are computed using both flow and sity inputs. ...
This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.
... According to them, the suggested parameters were among the best inputs for quantifying congestion. Different related work in using the fuzzy logic application in evaluating congestion levels can be found in [5], [13], [14]. ...
... We can consider, after reviewing previous works related to the assessing congestion levels using the fuzzy approach, that the majority of studies are applicable for a certain application, like network analysis [12], urban congestion management [5], [9], [10], [13], [14], [15], congestion detection [18], [11], and transport planning [11], but a few are applicable on quantifying urban street congestion for traffic control on expressways. Besides that, much research [9], [10], [15] used combined indexes as inputs for the assessment of congestion levels. ...
... Similarly, Afrin and Yodo [7] stated that it is difficult to conceive of a single value that will describe all of the travelers' concerns about congestion. More references on evaluation of congestion on expressways can be found in [20][21][22][23][24]. ...
... The authors concluded that the proposed parameters were among the best inputs which quantify the congestion. Other research works on application of fuzzy logic approach for quantification of traffic congestion can be found in [24,27,36]. ...
... In reviewing the previous works in quantification of congestion following fuzzy logic approach, it can be seen that a majority of studies are applicable for specific applications such as urban congestion management [24,25,27,31,32,36], transport planning [34], network analysis [35], and congestion detection [33,34], but little is known about research on quantifying congestion for traffic control on expressways. Apart from that, many of the works [25,31,32] used composite measures as inputs for evaluation of congestion level. ...
This paper presents a fuzzy-based methodology for quantification of congestion level for traffic control on expressways using traffic flow speed and density. Inductive loop detector data on the Interstate 880 obtained through the Freeway Performance Measurement System were used to estimate congestion levels following the fuzzy logic approach. In comparison with the Highway Capacity Manual, the results generally show a good correspondence. However, unlike the Highway Capacity Manual that defines step-wise measurement of levels of service based entirely on density, the proposed fuzzy inference system allows a flexible combination between speed and density to provide a more detailed indication of congestion intensity to describe the gradual transition of traffic state. For comparison, the congestion indices evaluated with both density and speed were compared to those evaluated with either speed or density using the same data set. Results from this comparative study reinforce the statements from previous studies that expressway speed is conservative under free-flow and light traffic conditions, but decreases significantly just before the flow rate approaches the road capacity. The results also show significant differences between the congestion indices evaluated using a single quantity, while the congestion indices using both density and speed tend to neutralize in between and scale up in a stable manner with the levels of service. Considering the abstract nature of congestion terminology, it is necessary to quantity traffic congestion on the expressways using both variables to minimize the potential bias in representing the operation of expressway traffic properly, which is particularly important under heavy congested conditions.
... The use of speed as an indicator of congestion is straightforward and intuitive. Many researchers have used speed to define congestion level such as [8,9]. Density is another primary measure for characterizing operational conditions on expressways. ...
This paper presents a methodology for appraisal of congestion level for traffic control on expressways using fuzzy logic. The congestion level indicates the severity of congestion and is estimated using speed and density, being the basic traffic parameters that describe state of a traffic stream. Formulation of the fuzzy rule base is made based on knowledge on traffic flow theory and engineering judgments. Field data on a segment of the Pan-Island Expressway of Singapore were used to estimate the congestion levels for three scenarios: single input variable (speed or density) and combined input variables (speed and density), represented by congestion level on a [0 1] scale. The results showed that there were big gaps between the congestion levels evaluated based specifically on speed and density alone (single state variable), and the congestion levels estimated from both variables lie in between. Given the uncertainty in traffic data collection and dynamic nature of traffic flow, this indicates that it may be inadequate to evaluate traffic congestion level using a single variable, and the use of both speed and density represent the state of a traffic stream more properly. The study results also show that the fuzzy logic approach provides flexible combination of state variables to obtain the congestion level and to describe gradual transition of traffic state, which is particularly important under the heavy congested conditions.
