Authors:
Bereket K. Basa Multidisciplinary Doctoral School of Engineering Sciences, Faculty of Architecture, Civil and Transport Engineering, Széchenyi István University, Győr, Hungary

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Dániel Miletics Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil and Transport Engineering, Széchenyi István University, Győr, Hungary

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Abstract

Analyzing the capacity of a signalized circular intersection is an essential aspect of traffic flow management. With the increased number of vehicles at the intersection, it is preferable to examine ways to increase capacity without altering the existing geometric features. A signalized circular intersection on the national highway in Győr, Hungary, between 47° 40′ 43.7988″ N and 17° 39′ 37.6668″ E is chosen and analyzed for capacity enhancement. The survey is conducted using 360-degree cameras. The PTV Vissim software is then used to construct a model based on the current and projected vehicle counts, as well as the current and proposed options. The result shows that it is possible to increase the capacity of signalized circular intersections without altering the geometric features.

Abstract

Analyzing the capacity of a signalized circular intersection is an essential aspect of traffic flow management. With the increased number of vehicles at the intersection, it is preferable to examine ways to increase capacity without altering the existing geometric features. A signalized circular intersection on the national highway in Győr, Hungary, between 47° 40′ 43.7988″ N and 17° 39′ 37.6668″ E is chosen and analyzed for capacity enhancement. The survey is conducted using 360-degree cameras. The PTV Vissim software is then used to construct a model based on the current and projected vehicle counts, as well as the current and proposed options. The result shows that it is possible to increase the capacity of signalized circular intersections without altering the geometric features.

1 Introduction

Studying traffic flow is becoming a significant issue for nations as the volume of traffic increases and quality-of-service declines [1, 2]. Assuming that vehicles can arrive at the roundabout separately from the major stream and the minor stream, a new capacity model for multilane roundabouts was developed based on capacity evaluation [3]. In terms of safety, signalized circular intersections (often called signalized roundabouts, but according to Hungarian guidelines and throughout this research paper, they are called signalized circular intersections or used interchangeably) have the advantages of being able to handle large numbers of commuters and requiring vehicles entering the intersection to slow down for a yield sign [4]. According to Barna et al. [5], the Hungarian signal control system consists of three components: “leg-by-leg” control, two-phase control, and an all-phase control system. The “leg-by-leg” program controls the green time individually.

In the end, capacity models have their limits when it comes to figuring out the capacity of multilane signalized circular intersections in cities with heavy traffic and traffic congestion. Instead of using these models to figure out how many vehicles can go through the proposed intersection, it would be better to use a more advanced simulation method to test different options. This will aid in determining the delays and entry flow. However, the goal of this research is to analyze how to improve the capacity by providing different options without changing the existing geometric features and by using simulation methods.

2 Data and methods

In Hungary, there are about 5 signalized circular intersections, and among them, the research focuses on the one with multilane entry, which is located in Győr, between 47° 40′ 43.554″ N and 17° 39′ 37.3284″ E. The intersection has 3 entry lanes in Fehervari Street named South-East (SE); in Fehervari Street named North-West (NW); and 4 entry lanes in Ipar Street named North-East (NE); and 4 lanes in Szigethy Attila Street named South-West (SW) at entry lanes. In addition to this, the geometric feature of the intersection is shown in Table 1.

Table 1.

Geometry parameter of a signalized circular intersection

Approach'sSWSENWNE
Entry width (m)18.0018.0015.0015.00
Approach width (m)8.008.004.008.00
Number of lanes at the approach2212
Give way length (m)18.0020.0015.0014.00
Circulation road width (m)9.0013.0013.5013.50
Radius of the inscribed circle (m)38.5038.5038.5038.50

The “leg-by-leg” principle governs the six different signal groups for incoming flows at the signalized circular intersection, which turn green one after another. For this study, the signal groups are named P1, P2, P3, P4, P5, and P6, and happen once every 24 h. The signal groups have different cycle lengths and signal coordination. Then, among the signal groups, P1, with a cycle length of 64 s, is chosen to analyze the capacity since it has congestion and an unbalanced traffic flow compared to others in the morning session between 7:30 and 8:30. The survey was conducted in 2022 on April 14, 16, 19, and 22 on weekdays and May 6, 7, 13, and 14 on weekends, with the help of the video-capture method in addition to personal counting to make data-gathering procedures, operations, and accuracy easier.

The researchers counted the number of vehicles on the road every 15 min by first classifying them by size and then assigning each size to a set of categories [6]. Then the Average Daily Traffic (ADT) is calculated for entering vehicles into circulation in terms of Passenger Car Unit (PCU) using the Peak Hour Factor (PHF) for each lane. For current conditions, the ADT is about 4287 PCU/h in the morning session, and it is forecasted for the goal of this research for the next ten years and was calculated to be 5,573 PCU/h [7]. It is observed that the Origin-Destination (OD) matrix for the current peak hour situation is shown in Table 2, and the posted limit speed was 50 km h−1, but vehicles were observed entering the roundabout at speeds between 55 km h−1 and 65 km h−1. It was found that the critical gap is 3 s, and the follow-up time is 2 s.

Table 2.

Origin destination matrix for surveyed traffic volume

ApproachesTurn to
ULeftThroughRight
FromSW0.000.410.470.12
SE0.000.570.330.10
NW0.000.060.510.43
NE0.000.170.650.18

Then PTV Vissim software is used to simulate and create a new model. In the PTV Vissim software, the survey hourly vehicle volume and the future hourly vehicle volume on each entry approach based on their respective time intervals will be used for comparison purposes. To comply with the existing system, PTV Vissim software is calibrated as follows:

In general, when the cycle length increases, the capacity of an intersection can increase. But this is the new approach to testing that, with a reduction in cycle length and an increase in the capacity of intersection. For the projected vehicles, the vehicle speed is set at 60 km h−1 with a reduced speed of 30 km h−1, the lost time per cycle (L) is 8 s, and the sum of the critical flow for all critical phases (Y) is 0.725 s. Then the new cycle time (C) is determined based on the given capacity and the Webster method [8],
C=1.5L+51Y.

Then the new cycle length is 62 s with a 2 s reduction in cycle time to be on the safe side of capacity determination; because of this, a readjustment is made to the signal time for each approach. The width of the road lane is 4.0 m, and the length of the passenger cars ranges from 3.75 to 15 m on average. The width of the average car ranges from 1.7 to 1.9 m.

Each stage has a different inter-green period because it is the time between the end of one green light on one of the legs and the start of another green light on another leg. The Longest waiting length (LW) between the stop line and the giveaway line is 15 m on the SE approach. Based on the rule of thumb, the safest trailing distance (δ) is 3 m, and the queue between two vehicles is kept to a minimum (ΔN) of 3 s for any speed. The simulation runs for 1 h, dividing it into 15-min intervals, like 0–15 min; 15–30 min; 30–45 min; and 45–60 min. This study used Δ (the maximum headway) of 3.0 m as a gap between vehicles since one vehicle may be singled out, whereas vehicles with headways above Δ are made up of more than one vehicle, and then modeled the intersection with PTV Vissim software.

Since the degree of variability of the PTV Vissim code is not known, it may cause some errors in future model determinations because running the code can have an infinite sample size. Therefore, it is crucial to determine the sample size (N) to run the simulation for an infinite population using the formula Cochran [9, 10] discovered in order to avoid this error and have a good simulation result. Based on previous research and observation through a survey, the standard deviation (σ) is assumed to be 0.25, and again, by assuming the Marginal Error (ME) of 15% and a confidence level of 95%, the significance level α is calculated as
α=1levelofconfidence=100%95%=5%.
In other words, α of 0.05 indicates a 95% confidence level, which is used to reject the null hypothesis. Using α value of 5%, the critical standard score (Z) value is determined as
Z=1α2=10.052=0.975or97.5%.
According to the Z score in table of [11], the chance of getting a result with a value of 97.5% is 1.96, and the sample size (N) needed to run a simulation is calculated as follows:
N=Z2·σ2ME2=1.9620.2520.15211.

For this study to come to a conclusion, the simulation needs to be run about 11 times.

3 Result and discussion

Using the survey traffic volume with the current options and the proposed options, a comparison is made. On the other hand, the comparison was also made using the projected traffic volume for the next ten years with the current options and with the proposed options. For the proposed plan, the options provided are:

  • Option 1: Use the minimum speed of 30 km h−1 as the reduced speed and the maximum speed of 60 km h−1 as the desired speed;

  • Option 2: The cycle length (C) for P1 is reduced from 64 to 62 s;

  • Option 3: A signal's timing is rearranged with a corresponding sequence and new vehicle composition.

From the current signal time for P1, it is found that the maximum green entry time for SE is 18 s, while the maximum red entry time for NW and NE is 47 s. The green times for pedestrians at the NW, NE, SE, and SW approaches are 27, 37, 29, and 32 s, respectively. The inter-green time between the NW and NE entries is 2 s; 1 s between the NE and SE entries; 0 s between the SE entry and the SW entry; and 4 s between the SW entry and the NW.

On the other hand, the proposed options can change all signal timing parameters at the entry and exit points and be related to the number of future hourly volumes. The red times for the NW and NE have decreased from 47 to 43 and 41 s, respectively. In contrast, the green time for the SW is reduced from 15 to 11 s, and the green time for the SE is reduced from 18 to 15 s. The inter-green time between NW and NE entries has decreased by 2 s; the inter-green time between NE and SE entries has remained unchanged; and the inter-green time between SW and NW entries has decreased by 3 s; however, the inter-green time for SE and SW entries has increased by 3 s due to the change in signal timings for entry approaches. The new vehicle composition increases the number of public transports like a single bus and an articulated bus in the given system.

Figures 1 and 2 show how the delay is simulated for P1 using data from a survey of traffic volume during the morning peak hour before and after using the proposed options for each approach. This gave the average vehicle delay and the average stopped delay for each approach and its turns. Since a “stop delay” is a delay per vehicle in seconds absent public transit stops (which is a pursuit intervention technique maneuver) and in parking lots, it is a pursuit intervention technique maneuver. A vehicle delay is the difference between the theoretical (ideal) travel time and the actual travel time. The vehicle delay and stop delay are different based on time intervals.

Fig. 1.
Fig. 1.

The delay caused by P1's traffic volume survey compared to the current options

Citation: Pollack Periodica 18, 3; 10.1556/606.2023.00766

Fig. 2.
Fig. 2.

The delay caused by P1's traffic volume survey compared to the proposed options

Citation: Pollack Periodica 18, 3; 10.1556/606.2023.00766

The result shows, for example, that the maximum vehicle and stop delay is between 15 and 30 min at the entry of the SE approach, with delay values of 10.91 and 2.87 s. When comparing each approach, the delay spontaneously increases from SW to the other approaches. Specifically, at the SE approach, the delays are higher than other consecutive approaches. The lengthened intervals between green lights and the lengthy red light duration are to blame for this delay.

On the other hand, the survey traffic volume with the proposed options has a vehicle and stop delay at a time interval between 15 and 30 min on SE of 11.10 and 2.74 s, respectively. As it is shown in Figs 1 and 2, based on the proposed options, the average vehicle and stop delay can decrease or increase at the bounds because the newly adjusted inter-green time with signal time adjustment between the bounds influences delay at all bounds. Comparing the two results, the delay with the proposed one is higher for the same traffic volume and entry speed, but the adjusted signal timing with reduced cycle length in the model shows that there is an increase in delay in all approaches, which may lead to a long queue. But in the SW approach, the delays are less comparatively, so it may happen that the length of entry for this approach is longer, and it has an exclusive lane for right turning vehicles, which helps them approach faster than others.

In addition to the above results, Table 3 shows that the simulation for P1 with the survey traffic volume and proposed option yields the same number of vehicles. This means that the signalized circular intersection can handle the total number of vehicles that are expected to arrive, which is about 4,287 PCU/h, no matter, which of the proposed options is used without changing the shape of the intersection.

Table 3.

Traffic volume of a survey period with the current and proposed options

Time intervalSWSENWNETotal
7:30–7:453674812191471,214
7:45–8:003644422211901,217
8:00–8:152683162291881,001
8:15–8:30211289205150855
Total1,2101,5288746754,287

So, the capacity of the roundabout can be the same as either the current survey volume or the proposed options. This could mean that the capacity of the roundabout can be slowly increased by adding new options.

Then, based on the above results for improvement scenarios, the shorter cycle length and higher number of vehicles for each approach of the improved plan are put into a PTV Vissim simulation program. But the projected traffic volume led to a new OD matrix, as it is shown in Table 4. Figure 3 shows what happens when improvements are made to vehicles in P1 in their original conditions. Figure 4 shows what happens when improvements are made to vehicles in P1 with a desired speed of 60 km h−1 and a reduced speed of 30 km h−1 as proposed options for their delays.

Table 4.

Origin destination matrix for proposed traffic volume

ApproachesTurn to
ULeftThroughRight
FromSW0.000.510.330.16
SE0.000.390.460.15
NW0.000.130.470.41
NE0.000.180.710.11
Fig. 3.
Fig. 3.

Delay caused by a projected traffic volume in comparison to the current options

Citation: Pollack Periodica 18, 3; 10.1556/606.2023.00766

Fig. 4.
Fig. 4.

Delay caused by a projected traffic volume in comparison to the proposed

Citation: Pollack Periodica 18, 3; 10.1556/606.2023.00766

The results demonstrate, for instance, that the maximum vehicle and stop delay at the SE approach entry is between 30 and 45 min, with delay values ranging from 11.00 to 2.77 s. When comparing SW to other methods, the delay grows on its own without any intervention. In particular, delays are longer at the SE approach compared to other subsequent approaches. This holdup is due to the increased length of red lights and decreased frequency of green ones.

However, at 15–30 min on SE, the predicted traffic flow with the proposed choices results in a vehicle and stop delay of 11.27 and 3.21 s, respectively. As can be seen in Figs 3 and 4, the delay across all bounds is affected by the adjusted inter-green time and the signal time adjustment across the bounds. This indicates that the proposed options may lower or increase the typical wait time for automobiles and pedestrians at the crosswalk. At the same volume and rate of entry, the model shows that adjusting the signal timing to a lower cycle length causes an increase in delay at all approaches, which could lead to a long queue. Right-turning vehicles, however, have their own dedicated lane on the SW approach, allowing them to get there significantly faster than those coming in from any other direction.

When the survey traffic volume was compared to the projected traffic volume using the current options, the new option changed the average time it took for a vehicle to move and the time it took for a vehicle to stop. The speed and type of vehicles, as well as the timing of the lights, have a significant impact on the delays in both scenarios. Based on this, it is better to check whether the projected number of vehicles is still present or not in the given roundabout. Tables 5 and 6 illustrate the number of vehicles entering the roundabout after using the projected traffic flow with the current and proposed options for P1 using PTV Vissim code. Based on the current options, the result shows that there are approximately 5,573 PCU/h entering the roundabout. Hence, the given signalized circular intersection can accommodate the projected traffic volume with an enhanced balanced flow pattern, and there are no residue vehicles at this time without changing the geometric features but by using the proposed options.

Table 5.

Vehicle measurement for a projected traffic volume with current options

Time intervalSWSENWNETotal
7:30–7:454575412942531,545
7:45–8:004565022992951,552
8:00–8:153603563032921,311
8:15–8:303033292782551,165
Total1,5761,7281,1741,0955,573
Table 6.

Vehicle measurement for a projected traffic volume with a proposed option

Time intervalSWSENWNETotal
7:30–7:453804302912651,366
7:45–8:003824272892701,368
8:00–8:153744252902721,361
8:15–8:303744242882711,357
Total1,5101,7061,1581,0785,452

Using the current options, comparing the survey traffic volume to the projected traffic volume shows that there could be a 23% increase in total traffic flow. On the other hand, if both the survey traffic volume and the projected traffic volume are used, both with the proposed options, about 21% more vehicles can use the signalized circular intersection, no matter how long their delays are.

Again, the difference between the projected traffic flow, with the current, and the proposed options is about 2.17%. Due to the proposed options and projected traffic volumes, certain vehicles will be unable to enter a signalized circular intersection's circulation. This may occur if some vehicles are stopped inside the circle for an indicated green time before being released to exit the circle immediately, or if some vehicles are making the right turn due to the assigned desired speed, priority rules, or reduced speed for all flows; however, the proposed plans have a larger number of vehicles accessing the roundabout than during the survey period, with adjustments to cycle time and signal timings.

Based on the results of the simulation, the proposed options make the roundabout more useful by letting more vehicles enter from each approach. This means that the current roundabout can handle more vehicles and have a better flow pattern without changing its shape. This can be done by changing the cycle time and the timing of the traffic lights. This means that over the next ten years, this roundabout will be able to fit an average of 23% more vehicles on all approaches if a projected traffic volume is used with the current situation, and 21% more vehicles on all approaches if a projected traffic volume is used with the proposed options. The number of cars that can enter the multi-lane signalized circular intersection every hour can be increased if the proposed options are used over the next ten years at the best time for P1 (the morning peak hour).

4 Conclusion

In this research, it is said that studying how traffic flows at signalized circular intersections is an important part of designing traffic flow in the transportation field. In this research, the ways to improve the capacity of the intersection without changing the existing geometric features of the intersection are studied. For this aim, one of the signalized circular intersections in Győr, Hungary, is selected and analyzed. The flow of traffic is studied by collecting data with the help of a 360° camera and defining the geometric parameters. Based on this, options are proposed, and traffic volumes are forecasted for the next ten years, like altering the cycle length by modifying the entry speed and turning speed to 60 km h−1 and 30 km h−1. Then PTV Vissim software is used after calibrating at the intersection to analyze the capacity.

The result shows that using the current options, the difference between surveyed and projected traffic volume is about 23%, and using the proposed options, the difference between traffic volume is about 21%, no matter how long their delays are. Since the goal of this research is to increase capacity, it has been shown that if the new model with the projected traffic volume is used along with the proposed option, the current infrastructure can handle more vehicles without changing the shape of the roundabout. This can be an input for decision-makers to see options on how to increase capacity. In the future, the capacity can be set by changing the speed limits in cities based on how safe they are for pedestrians and cyclists. Also, in the future, the capacity of a signalized circular intersection can be studied by changing the geometry.

Acknowledgements

The authors would like to thank PTV Group for the thesis license of PTV Vissim microsimulation software and Dr. Balázs Horváth for making it easy to use PTV Vissim at Széchenyi Istvan University.

References

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    W. Cheng, X. Zhu, and X. Song, “Research on capacity model for large circular signalized intersections,” Proced. Eng., vol. 137, pp. 352361, 2016.

    • Search Google Scholar
    • Export Citation
  • [2]

    Roundabouts: an informational guide, 2000. [Online]. Available: https://www.fhwa.dot.gov/publications/research/safety/00067/00067.pdf. Accessed: Dec. 14, 2022.

    • Search Google Scholar
    • Export Citation
  • [3]

    A. Šarić and I. Lovrić, “Multi-lane roundabout capacity evaluation,” Front. Built Environ., vol. 3, pp. 112, 2017.

  • [4]

    Z. Magyari and C. Koren, “Visibility studies at roundabouts entries,” Pollack Period., vol. 14, no. 3, pp. 6374, 2019.

  • [5]

    S. Barna and G. Schuchmann, “Traffic performance of signalized circular intersections,” Pollack Period., vol. 12, no. 2, pp. 6778, 2017.

    • Search Google Scholar
    • Export Citation
  • [6]

    EU, Vehicle definitions, Transport policy. [Online]. Available: https://www.transportpolicy.net/standard/eu-vehicle-definitions/. Accessed: Dec. 14, 2022.

    • Search Google Scholar
    • Export Citation
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    Traffic Data Computation Method, Pocket Guide, Report No. FHWA-PL-18-027, 2018.

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    F. V. Webster, “Traffic Signal Settings,” Road Research Technique, Road Research Laboratory, London, Paper no. 39, 1958.

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Editor(s)-in-Chief: Iványi, Amália

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  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
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  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
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  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
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  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
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  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
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  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
Publication Model Hybrid
Submission Fee none
Article Processing Charge 900 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
Print + online subscription: 405 EUR / 492 USD
Subscription Information Online subscribers are entitled access to all back issues published by Akadémiai Kiadó for each title for the duration of the subscription, as well as Online First content for the subscribed content.
Purchase per Title Individual articles are sold on the displayed price.

 

Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jan 2024 0 175 11
Feb 2024 0 159 6
Mar 2024 0 261 7
Apr 2024 0 46 5
May 2024 0 59 8
Jun 2024 0 90 4
Jul 2024 0 0 0