- Email: [email protected]
Insights into the congestion patterns on alpine motorways based on separate traffic lane analysis
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Transportation Research Procedia 37 (2019) 441–448 www.elsevier.com/locate/procedia st 21 EURO Group onon Transportation Meeting, EWGT 2018, 17th 17-19 – 19th September 21st EUROWorking Working Group Transportation Meeting, EWGT 2018, September2018, 2018, 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17-19 September 2018, Braunschweig, Germany Braunschweig, Germany Braunschweig, Germany
Insights into the congestion patterns on alpine motorways based on Insights into the congestion patterns on alpine motorways based on separate traffic lane analysis separate traffic lane analysis Bartosz Bursaaa*, Nemanja Gajicaa, Markus Maileraa Bartosz Bursa *, Nemanja Gajic , Markus Mailer 0
0
Unit for Intelligent Transport Systems, University of Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria a Unit for Intelligent Transport Systems, University of Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria a
Abstract Abstract First, we briefly outline the state of research in traffic congestion and existing approaches towards the classification of congestion First, we We briefly outline the of research trafficofcongestion andofexisting approaches dynamics towards the congestion patterns. summarize the state methodology andinresults our analysis the spatiotemporal of classification traffic flow onofthe A12 and patterns. We summarize thein methodology results of our analysis of the spatiotemporal dynamics of traffic flow on theapproach A12 and A13 motorways in Austria 2016 based and on cross-sectional count data. Built on that research, we introduce a different A13 motorways in Austria 2016 based cross-sectional count data.lane-specific Built on thatresults research, a different approach consisting in the analysis ofin separate trafficon lanes. We compare the new withwe theintroduce previous aggregated ones and consisting the analysisWe of separate lanes. compareapproach the new lane-specific results with into the previous aggregated ones and discuss theindifferences. postulatetraffic that the newWe suggested provides better insights the congestions patterns discuss the differences. We postulate that the new suggested approach provides better insights into the congestions patterns and their causes on alpine motorways and might be used to evaluate traffic management measures more accurately and reliably than their causes on alpine motorways and might be used to evaluate traffic management measures more accurately and reliably than the standard technique. the standard technique. © 2019 The Authors. Published by Elsevier Ltd. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by B.V. committee of the 21st EURO Working Peer-review responsibility of Elsevier the scientific Group on Transportation Meeting. Selection andunder peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting, Peer-review the scientific committee of the 21st EURO Working Group on Transportation Meeting. EWGT 2018,under 17th –responsibility 19th Septemberof2018, Braunschweig, Germany. Keywords: traffic congestion; classification of traffic jams; separate drive lanes; traffic patterns; alpine region; tourist traffic Keywords: traffic congestion; classification of traffic jams; separate drive lanes; traffic patterns; alpine region; tourist traffic
1. Introduction 1. Introduction 1.1. Background and motivation 1.1. Background and motivation Traffic flow on alpine motorways is shaped by the extensive seasonal variability of tourism traffic and leisure Traffic flow onwith alpine motorways shaped the extensive variability of tourism traffic and leisure traffic coinciding daily traffic andishigh truckbytransit volumes. seasonal At the same time, alpine transport systems do not traffic with daily traffic andthe high truck transit At theare same do not providecoinciding many alternative routes and decision pointsvolumes. for re-routing fartime, awayalpine from transport potential systems bottlenecks or provide alternative routes the decision pointstraffic for re-routing are far awayperiods from potential bottlenecks or incident many locations. This results in and frequent and extensive jams in some specific of the year. incident locations. This results in frequent and extensive traffic jams in some specific periods of the year.
* Corresponding author. Tel.: +43 512 507-62405; Fax: +43 512 507-62498. * Corresponding Tel.: +43 512 507-62405; Fax: +43 512 507-62498. Email address: author. [email protected] Email address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. 2352-1465 2018responsibility The Authors. of Published by Elsevier B.V.of the 21st EURO Working Group on Transportation Meeting. Peer-review©under the scientific committee Peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting. 2352-1465 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17th – 19th September 2018, Braunschweig, Germany. 10.1016/j.trpro.2018.12.220
Bartosz Bursa et al. / Transportation Research Procedia 37 (2019) 441–448 Bartosz Bursa et al. / Transportation Research Procedia 00 (2018) 000–000
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During our previous work (Bursa et al., 2018), we encountered difficulties with the proper classification of congestion incidents on the A13. Inasmuch as the inclination grade reaches high values on some sections of the A13, very low velocities are common on the rightmost lane, occupied predominantly by HGVs and caravans. This forced the algorithm to incorrectly detect congestion even though there might still be free flow on the offside lane. As a result, the number of congestion incidents was overestimated. This discovery has inspired us to attempt to categorize the congested traffic states on single drive lanes of the A12 and A13, which is the goal of the current study. We argue that it gives a better overview of the traffic situation in the problematic segments and refines the classification of congested states. To achieve this, we decided to modify the standard method of processing cross-sectional traffic count data. This paper is another step in our ultimate intention to describe the congested traffic states on alpine motorways and to examine whether the methods commonly used are adequate for the analysis of traffic in uncommon alpine conditions (Bursa et al., 2018). 1.2. Organization of this paper This paper starts with an abstract and short introduction into the research subject. In section 2, we describe the measurement site and the characteristics of the dataset. Furthermore, we briefly introduce the theoretical framework applied in our analysis and embedded in the software used. We then propose the modified approach for classification that accounts for differences between traffic lanes. Section 3 of this paper presents the findings of our research. An overview of the general traffic situation on separate traffic lanes on the A12 and A13 motorways is provided along with a detailed evaluation of congestion patterns on the nearside and offside lane of the A12. This section also includes an analysis of untypical issues encountered during the research. Finally, in section 4, we summarize our findings and make conclusions, as well as discuss necessary future work and possible further research steps. 2. Methodology 2.1. Measurement site and data processing The analysis was performed for the Tyrolean sections of the A12 and A13 motorways. Our measurement area extends over approximately 85 km of the A12 from Innsbruck (Völs Kranebitten) to Kufstein (Kufstein Nord). On the A13, it encompasses about 34 km between Innsbruck (Innsbruck Süd) and the Brenner Pass (Brennersee). The dataset for the study was provided by the Austrian motorway administration ASFiNAG and comes from 53 detector stations on the A12 and 29 stations on the A13 motorways. The average distance between detectors is approximately 1.6 km and 1.2 km respectively. Table 1 describes detailed characteristics of the analyzed motorways. Table 1 Characteristics of the measurement site Feature
A12
A13
Section
Völs Kranebitten - Kufstein Nord
Innsbruck Süd - Brennersee
Length
85 km
34 km
No of lanes
2+2 (99%) / 3+3 (1%)
2+2 (47%) / 3+3 (53%)
No of exits
17
7
No of detector stations
53
29
Speed limits
100 km/h (IG-L) (other limits according to dynamic traffic signs)
130/100 km/h (other limits according to dynamic traffic signs)
AADT (in 2016)
48,164 (Ampass)
77,252 (Gärberbach)
Others
Max. inclination 6.1%
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The dataset contains the following information on traffic: Traffic volume [veh/min] Velocity [km/h] Standard deviation [km/h] Net time gap [s] Occupancy rate [%] As far as the analysis of the absolute number of congestion incidents is concerned, this was conducted for the whole of 2016 for both motorways A12 and A13, for nearside and offside lanes in both directions. However, the classification of congested states on separate lanes has so far only been performed for the A12 for 20 days due to the necessity of graphic differentiation between stop-and-go waves and wide jams every time. On the motorway sections with three traffic lanes in one direction, the center lane has been omitted as these sections comprise less than 1% of the whole length (Fig. 1).
Fig. 1 Two-lane (a) and three-lane (b) motorway cross-sections
2.2. Classification of the congested states based on spatiotemporal traffic dynamics In our previous study, we investigated specific flow patterns, traffic flow disturbances and their reasons based on the lane-averaged data for the A12 and A13 motorways in Austria. Among few recognized classification methodologies of differentiation between congested traffic states proposed by e.g. Kerner and Rehborn (1996) or Treiber et al. (1999), we decided to adhere to the approach developed by the consultancy Transver for BMW (Transver GmbH, 2010). The number of congestion types distinguished in this approach is limited to four: Short-term speed breakdown Stop-and-go wave Wide jam Mega jam A spatiotemporal graphic illustration of each of the above congestions states as well as descriptive criteria for the visual analysis are presented in Fig. 2. The reason for this new classification was to combine congestion with the impact it has on energy management in electric vehicles. Therefore, the classification algorithm operates using two jam characteristics: duration and number of stops. Knowledge about these properties allows effective short-term congestion forecasting, which is critical for efficient battery management in electric cars. A detailed description of the classification algorithm can be found in Bursa et al. (2018). We have had the opportunity to cooperate with the company and their approach has already been implemented in the analysis software Consyst, which is facilitative in processing large datasets like ours. During the data processing stage, the data was aggregated across all drive lanes and entered into the software, which applies the adaptive smoothing method (ASM) to the raw detector data and produces space-time-velocity diagrams. In our study, we have chosen default parameters for the exponential filter τ = 1.2 min and σ = 0.6 km. Another feature of the software is the automatic detection of congestion incidents (Fig. 6 - white rectangles around the low-speed area) and calculation of corresponding time losses (difference between the real travel time and the free-flow travel time). For a broad review of classification approaches and details on smoothing methods, we refer the reader to the works of Celikoglu (2013), and Treiber and Kesting (2013).
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Fig. 2 Congestion types and corresponding definition criteria (Transver GmbH, 2010)
2.3. Accounting for differences between traffic lanes As stated before, the standard technique exploits the cross-sectional traffic data aggregated over all drive lanes. The results obtained are thereby aggregated too, making them less reliable for the motorway sections, where the traffic volume, vehicle composition, and velocities differ significantly between traffic lanes. Since these conditions are becoming more frequent with the increasing congestion on motorways in alpine regions (like the A12 and A13), their effect on traffic flow is not negligible. A proper differentiation between the offside and nearside drive lanes gives unique insights into the reasons for and formation of specific congested traffic states. The proposed approach consists in employing the adaptive smoothing method (ASM) (Helbing and Treiber, 2002) to the disaggregated data for each of the traffic lanes separately, which is a step forward compared to the previous analyzes of spatiotemporal characteristics of traffic on alpine motorways. It means that the congestion patterns can be spatially analyzed not only along the motorway but also perpendicularly, across all drive lanes, which enables the lateral interactions between vehicle flows and the influence of congestion incidents on adjacent lanes to be taken into account. Differently from the standard approach, a separate velocity matrix is built and a separate velocity contour plot is produced for each of the lanes (Fig. 6). The approach also allows different parameter values (Treiber and Helbing, 2003) to be used for the exponential filter in the adaptive smoothing method (ASM) for different lanes making it possible to adjust the smoothing intensity according to the nature of traffic on a particular drive lane. In the current study, we have chosen default parameters for the exponential filter τ = 1.2 min and σ = 0.6 km. 3. Results 3.1. Influence on the number of detected congestion incidents The results of the study show differences between the congested states on the nearside and offside lanes on the A12 and A13 motorways in terms of location, length, severity, and duration of congestion. The most significant discrepancies were observed expectedly on the A13 (in the summer months from June to September). Within the space of one year, the number of jams on the nearside lane on the A13 from Innsbruck to Brenner and opposite was approximately 11 and 19 times higher respectively than on the offside lane. At the same time, the A12 proves more stable and the difference between drive lanes comes to about 35-40%. Fig. 3 illustrates the overall outcomes for the A12 and A13 motorways.
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No of detected jams
The observations are plausible considering the annual average daily traffic (AADT, Monday to Sunday) on the A13 between interchanges Zenzenhof and Igls-Patsch reaching over 48,000 vehicles in both directions and the average HGV share estimated at 15%. Surprisingly, the difference between the number of jams on the nearside and offside lane is higher in the direction from Brenner to Innsbruck than in the direction from Innsbruck to Brenner. This contradicts our initial assumption that the upward direction to the Brenner pass will be more exposed to severe congestion on the nearside lane, occupied predominantly with HGVs and characterized by low velocities, especially on the most frequented sections, sections with high inclination grades, and close to the toll collection station Schönberg im Stubaital. A detailed spatial and temporal analysis is currently being conducted to explain this finding and determine the exact locations and time periods of the highest congestion disparity between the lanes on the A13. 3000 2500 2000 1500 1000 500 0
2682 1828 1200
813
462 346 355
300 215 237
173
143
Innsbruck ‐> Kufstein
Kufstein ‐> Innsbruck
Innsbruck ‐> Brenner
Brenner ‐> Innsbruck
A12
A13
Nearside lane
Offside lane
Both lanes
Fig. 3 Number of detected congestion incidents on the A12 and A13 motorways depending on the analyzed traffic lane
3.2. Influence on the types of congestion incidents detected
No of detected jams
The lane-specific classification of congested states was carried out for the A12 motorway over 20 days between 01.01.2016 and 20.01.2016. Although the A13 appears more interesting for the research, its investigation has been temporarily suspended as first the parameters of the classification algorithm and the adaptive smoothing method must be reconsidered. The typical values do not guarantee proper results on this motorway, which is characterized by high inclination grades (up to 6.1%) and very low velocities on the rightmost lane. 2323
25 20 15 10 5 0
20 17
16 13 76
8 3
2
5
19
17
14
1
3
7 0
3
5
7 12
16 6
3
4
2
5
10
8 2
23
3
Nearside Offside lane Both lanes Nearside Offside lane Both lanes Nearside Offside lane Both lanes lane lane lane A12 Short term speed breakdown
A12 Innsbruck ‐> Kufstein Stop‐and‐go wave
A12 Kufstein ‐> Innsbruck Wide jam
Mega jam
Fig. 4 Frequencies of congestion types detected on the A12 over 20 days (1-20.01.2016) depending on the analyzed traffic lane
Fig. 4 illustrates the frequencies of congestion types on the A12 motorway broken down into driving directions and traffic lanes. It is noticeable that, irrespective of the analyzed lane, the mega jam type dominates in the direction
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towards the border with Germany (from Innsbruck to Kufstein), while in the Innsbruck direction the wide jams prevail. In contrast with the previous study, the mega jams triggered by state border controls have not been filtered out from the dataset, which was motivated by the intention to capture the effects of border controls on the traffic situation in single lanes. Furthermore, the order of the congestion types (from the least to the most frequent) remains the same for nearside, offside and both lanes in the direction towards Kufstein, whereas in the direction towards Innsbruck no clear pattern can be distinguished. Fig. 5 presents changes in the frequency of specific congestion classes compared to the results from the whole cross-section. A substantial increase in the severe jam classes (wide jam, mega jam) on the nearside lane in both directions and a corresponding decrease in the offside lane can be observed.
Change in the no of detected jams
8 6 4 2
+35% +133%
+60%
+15%
+200%
+60%
+12% +50% 0% 0%
+20%
0 ‐2
‐33%
‐4 ‐6
+50%
‐100% ‐24% ‐20%
Nearside lane
Offside lane
Nearside lane
A12
0%
0% ‐33% ‐20%
‐29% ‐18%
Offside lane
Nearside lane
A12 Innsbruck ‐> Kufstein
Short term speed breakdown
+67%
+33%
Stop‐and‐go wave
Offside lane
A12 Kufstein ‐> Innsbruck Wide jam
Mega jam
Fig. 5 Changes (in absolute values and in %) in congestion types detected on the A12 over 20 days (1-20.01.2016) compared to the whole crosssectional analysis
To exemplify the effects of the lane-specific evaluation of congested states on the final results, one day, namely 7th January 2016, has been chosen. Fig. 6 presents the velocity contour plots in a dense color scale for the whole cross-section as well as single lanes. The white rectangles outline the congestion clusters detected. Analysis based on the whole cross-sectional data (both lanes) resulted in detecting one congestion incident of the stop-and-go wave type. However, having investigated the drive lanes separately, we have obtained two different congested states on the nearside lane and no congestion at all on the offside lane. The two incidents detected on the nearside lane are a mega jam and a wide jam, both having more adverse effects on traffic flow (in terms of length and duration) than the stop-and-go wave identified for both lanes. The major characteristics of the congestion clusters identified has been summarized in Table 2. Table 2 Characteristics of the congestion cluster (07.01.2016) on the A12 from Kufstein to Innsbruck depending on the analyzed traffic lane Analyzed lane
Start time
End time
Max. duration
Max. length
Congestion type
Both lanes
05:11
06:41
00:08
5,227 m
Stop-and-go wave
Nearside lane
05:10
07:18
00:49
12,813 m
Mega jam
Nearside lane
05:43
07:40
00:29
3,846 m
Wide jam
Offside lanes
-
-
-
-
-
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a)
b)
c)
Fig. 6 Velocity contour plots (07.01.2016) on the A12 from Kufstein to Innsbruck for: both lanes (a), nearside lane (b), and offside lane (c)
3.3. Impact of traffic management measures The separate lane analysis of congestion patterns has revealed that, undoubtedly, the transit truck traffic contributes most to the congestion problems on the A12 as more jams occur on the nearside lane, which is occupied by trucks. More importantly, the biggest increase on the rightmost lane was observed for the types mega jam and wide jam, which have the largest negative implications for the general traffic conditions on the motorway. Our study could provide scientific evidence for traffic management measures like ramp metering or control of the trucks inflow launched recently on the A12 in Kufstein. Moreover, it would help to assess their effectiveness if a comparative study were conducted again with the new data from the period after the introduction of metering on the border with Germany. The proposed methods could also serve to evaluate the precise impact of the controls introduced by Germany in fall 2015 owing to the political situation on its state border with Austria in Kufstein. 4. Conclusions This paper gives an overview of the traffic situation on the A12 and A13 motorways, and provides a detailed insight into congestion patterns on the separate lanes of the A12 between Innsbruck and the state border. Our analysis builds on previous work (Bursa et al., 2018) and is a further contribution to the understanding of traffic congestion patterns on motorways located in specific alpine regions. It is the first instance that assumptions and hypotheses about traffic jams in the Tyrol have undergone a thorough scientific assessment and have been verified by numbers. Our findings prove the superiority of the improved approach over the standard procedure. We argue that the results are more detailed while the mathematics used and computation time remain at the same level. Not only has a dissimilar
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overall number of jams been detected for the separate drive lanes than in the analysis of the whole cross-section. The difference is also in types of congested states. Some traffic patterns (mega jams and wide jams) appear more often on the nearside than on the offside lane. Until now, the lane-specific classification had been carried out only for the A12. However, based on the analysis of the number of congestion incidents, the A13 motorway provides even more potential for this research. Considering its role in the European Corridor E45, it is thus critical to conduct the classification of congested states for the A13 in both directions to account for the interdependencies between traffic lanes and clarify the findings from the general analysis of the number of congestion incidents (Section 3.1). Nevertheless, these macroscopic results should be validated using the microscopic Floating Car Data (FCD) on critical sections of the A12 and A13 motorways, which is expected to confirm our outcomes and provide conclusive evidence on the rightness and suitability of the chosen methods. Van Lint and Hoogendoorn (2010), and Treiber et al. (2011) have extended the generalized adaptive smoothing method (GASM) (Kesting and Treiber, 2008) to any traffic data, and provided a methodology for data fusion from multiple sources. This framework has recently been exploited by Rempe et al. (2016), who successfully applied the extended ASM to FCD on the German motorway A99. The same approach is going to be tested in our case. Eventually, the proposed methodology could be adopted as a standard technique in congestion studies performed by motorway operators and integrated within the commercial software tools used for automated traffic data analysis. It would significantly benefit the accuracy and unambiguousness of the results at relatively small expense. We are planning further activities within this academic field, as there is still an underexplored research potential, particularly within specific motorway networks in mountain regions. Acknowledgements The authors would like to thank ASFiNAG and its employees for providing the necessary traffic data, Prof. Bernhard Friedrich from the consultancy TRANSVER, as well as Stefan Gürtler for his assistance, valuable remarks, and the effort he has put into data processing. References Bursa, B., Gajic, N., Mailer, M., 2018. Classification of traffic jams on alpine motorways, in: Proceedings of 7th Transport Research Arena TRA 2018. 7th Transport Research Arena TRA 2018, Vienna, Austria. April 16-19, 2018. Celikoglu, H.B., 2013. An Approach to Dynamic Classification of Traffic Flow Patterns. Computer-aided Civil Eng 28, 273–288. 10.1111/j.1467-8667.2012.00792.x. Helbing, D., Treiber, M., 2002. Reconstructing the Spatio-Temporal Traffic Dynamics from Stationary Detector Data. Cooperative Transportation Dynamics 1, 3.1-3.24. Kerner, B.S., Rehborn, H., 1996. Experimental properties of complexity in traffic flow. Phys. Rev. E 53, R4275-R4278. Kesting, A., Treiber, M., 2008. Calculating travel times from reconstructed spatiotemporal traffic data, in: Networks for Mobility. Proceedings of the 4th International Symposium. Universität Stuttgart, p. 41. Rempe, F., Franeck, P., Fastenrath, U., Bogenberger, K. (Eds.), 2016. Online Freeway Traffic Estimation with Real Floating Car Data. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1838-1843. Transver GmbH, 2010. Stauklassifizierung und Untersuchung des Zusammenhangs Verkehrsstärke und Verkehrszusammenbruch, Munich. Treiber, M., Helbing, D., 2003. An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information, in: Emmerich, H., Nestler, B., Schreckenberg, M. (Eds.), Interface and transport dynamics. Computational modelling. Springer, Berlin, Heidelberg, pp. 343–360. Treiber, M., Hennecke, A., Helbing, D., 1999. Derivation, properties, and simulation of a gas-kinetic-based, nonlocal traffic model. Phys. Rev. E 59, 239–253. 10.1103/PhysRevE.59.239. Treiber, M., Kesting, A., 2013. Traffic Flow Dynamics: Data, Models and Simulation. Springer, Berlin, Heidelberg, 54 pp. Treiber, M., Kesting, A., Wilson, R.E., 2011. Reconstructing the Traffic State by Fusion of Heterogeneous Data. Computer-aided Civil Eng 26, 408–419. Van Lint, J.W.C., Hoogendoorn, S.P., 2010. A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways. Computer-aided Civil Eng 25, 596–612.
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Transportation Research Procedia 37 (2019) 441–448 www.elsevier.com/locate/procedia st 21 EURO Group onon Transportation Meeting, EWGT 2018, 17th 17-19 – 19th September 21st EUROWorking Working Group Transportation Meeting, EWGT 2018, September2018, 2018, 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17-19 September 2018, Braunschweig, Germany Braunschweig, Germany Braunschweig, Germany
Insights into the congestion patterns on alpine motorways based on Insights into the congestion patterns on alpine motorways based on separate traffic lane analysis separate traffic lane analysis Bartosz Bursaaa*, Nemanja Gajicaa, Markus Maileraa Bartosz Bursa *, Nemanja Gajic , Markus Mailer 0
0
Unit for Intelligent Transport Systems, University of Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria a Unit for Intelligent Transport Systems, University of Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria a
Abstract Abstract First, we briefly outline the state of research in traffic congestion and existing approaches towards the classification of congestion First, we We briefly outline the of research trafficofcongestion andofexisting approaches dynamics towards the congestion patterns. summarize the state methodology andinresults our analysis the spatiotemporal of classification traffic flow onofthe A12 and patterns. We summarize thein methodology results of our analysis of the spatiotemporal dynamics of traffic flow on theapproach A12 and A13 motorways in Austria 2016 based and on cross-sectional count data. Built on that research, we introduce a different A13 motorways in Austria 2016 based cross-sectional count data.lane-specific Built on thatresults research, a different approach consisting in the analysis ofin separate trafficon lanes. We compare the new withwe theintroduce previous aggregated ones and consisting the analysisWe of separate lanes. compareapproach the new lane-specific results with into the previous aggregated ones and discuss theindifferences. postulatetraffic that the newWe suggested provides better insights the congestions patterns discuss the differences. We postulate that the new suggested approach provides better insights into the congestions patterns and their causes on alpine motorways and might be used to evaluate traffic management measures more accurately and reliably than their causes on alpine motorways and might be used to evaluate traffic management measures more accurately and reliably than the standard technique. the standard technique. © 2019 The Authors. Published by Elsevier Ltd. © 2018 The Authors. by Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by B.V. committee of the 21st EURO Working Peer-review responsibility of Elsevier the scientific Group on Transportation Meeting. Selection andunder peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting, Peer-review the scientific committee of the 21st EURO Working Group on Transportation Meeting. EWGT 2018,under 17th –responsibility 19th Septemberof2018, Braunschweig, Germany. Keywords: traffic congestion; classification of traffic jams; separate drive lanes; traffic patterns; alpine region; tourist traffic Keywords: traffic congestion; classification of traffic jams; separate drive lanes; traffic patterns; alpine region; tourist traffic
1. Introduction 1. Introduction 1.1. Background and motivation 1.1. Background and motivation Traffic flow on alpine motorways is shaped by the extensive seasonal variability of tourism traffic and leisure Traffic flow onwith alpine motorways shaped the extensive variability of tourism traffic and leisure traffic coinciding daily traffic andishigh truckbytransit volumes. seasonal At the same time, alpine transport systems do not traffic with daily traffic andthe high truck transit At theare same do not providecoinciding many alternative routes and decision pointsvolumes. for re-routing fartime, awayalpine from transport potential systems bottlenecks or provide alternative routes the decision pointstraffic for re-routing are far awayperiods from potential bottlenecks or incident many locations. This results in and frequent and extensive jams in some specific of the year. incident locations. This results in frequent and extensive traffic jams in some specific periods of the year.
* Corresponding author. Tel.: +43 512 507-62405; Fax: +43 512 507-62498. * Corresponding Tel.: +43 512 507-62405; Fax: +43 512 507-62498. Email address: author. [email protected] Email address: [email protected] 2352-1465 © 2018 The Authors. Published by Elsevier B.V. 2352-1465 2018responsibility The Authors. of Published by Elsevier B.V.of the 21st EURO Working Group on Transportation Meeting. Peer-review©under the scientific committee Peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting. 2352-1465 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 21st EURO Working Group on Transportation Meeting, EWGT 2018, 17th – 19th September 2018, Braunschweig, Germany. 10.1016/j.trpro.2018.12.220
Bartosz Bursa et al. / Transportation Research Procedia 37 (2019) 441–448 Bartosz Bursa et al. / Transportation Research Procedia 00 (2018) 000–000
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During our previous work (Bursa et al., 2018), we encountered difficulties with the proper classification of congestion incidents on the A13. Inasmuch as the inclination grade reaches high values on some sections of the A13, very low velocities are common on the rightmost lane, occupied predominantly by HGVs and caravans. This forced the algorithm to incorrectly detect congestion even though there might still be free flow on the offside lane. As a result, the number of congestion incidents was overestimated. This discovery has inspired us to attempt to categorize the congested traffic states on single drive lanes of the A12 and A13, which is the goal of the current study. We argue that it gives a better overview of the traffic situation in the problematic segments and refines the classification of congested states. To achieve this, we decided to modify the standard method of processing cross-sectional traffic count data. This paper is another step in our ultimate intention to describe the congested traffic states on alpine motorways and to examine whether the methods commonly used are adequate for the analysis of traffic in uncommon alpine conditions (Bursa et al., 2018). 1.2. Organization of this paper This paper starts with an abstract and short introduction into the research subject. In section 2, we describe the measurement site and the characteristics of the dataset. Furthermore, we briefly introduce the theoretical framework applied in our analysis and embedded in the software used. We then propose the modified approach for classification that accounts for differences between traffic lanes. Section 3 of this paper presents the findings of our research. An overview of the general traffic situation on separate traffic lanes on the A12 and A13 motorways is provided along with a detailed evaluation of congestion patterns on the nearside and offside lane of the A12. This section also includes an analysis of untypical issues encountered during the research. Finally, in section 4, we summarize our findings and make conclusions, as well as discuss necessary future work and possible further research steps. 2. Methodology 2.1. Measurement site and data processing The analysis was performed for the Tyrolean sections of the A12 and A13 motorways. Our measurement area extends over approximately 85 km of the A12 from Innsbruck (Völs Kranebitten) to Kufstein (Kufstein Nord). On the A13, it encompasses about 34 km between Innsbruck (Innsbruck Süd) and the Brenner Pass (Brennersee). The dataset for the study was provided by the Austrian motorway administration ASFiNAG and comes from 53 detector stations on the A12 and 29 stations on the A13 motorways. The average distance between detectors is approximately 1.6 km and 1.2 km respectively. Table 1 describes detailed characteristics of the analyzed motorways. Table 1 Characteristics of the measurement site Feature
A12
A13
Section
Völs Kranebitten - Kufstein Nord
Innsbruck Süd - Brennersee
Length
85 km
34 km
No of lanes
2+2 (99%) / 3+3 (1%)
2+2 (47%) / 3+3 (53%)
No of exits
17
7
No of detector stations
53
29
Speed limits
100 km/h (IG-L) (other limits according to dynamic traffic signs)
130/100 km/h (other limits according to dynamic traffic signs)
AADT (in 2016)
48,164 (Ampass)
77,252 (Gärberbach)
Others
Max. inclination 6.1%
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The dataset contains the following information on traffic: Traffic volume [veh/min] Velocity [km/h] Standard deviation [km/h] Net time gap [s] Occupancy rate [%] As far as the analysis of the absolute number of congestion incidents is concerned, this was conducted for the whole of 2016 for both motorways A12 and A13, for nearside and offside lanes in both directions. However, the classification of congested states on separate lanes has so far only been performed for the A12 for 20 days due to the necessity of graphic differentiation between stop-and-go waves and wide jams every time. On the motorway sections with three traffic lanes in one direction, the center lane has been omitted as these sections comprise less than 1% of the whole length (Fig. 1).
Fig. 1 Two-lane (a) and three-lane (b) motorway cross-sections
2.2. Classification of the congested states based on spatiotemporal traffic dynamics In our previous study, we investigated specific flow patterns, traffic flow disturbances and their reasons based on the lane-averaged data for the A12 and A13 motorways in Austria. Among few recognized classification methodologies of differentiation between congested traffic states proposed by e.g. Kerner and Rehborn (1996) or Treiber et al. (1999), we decided to adhere to the approach developed by the consultancy Transver for BMW (Transver GmbH, 2010). The number of congestion types distinguished in this approach is limited to four: Short-term speed breakdown Stop-and-go wave Wide jam Mega jam A spatiotemporal graphic illustration of each of the above congestions states as well as descriptive criteria for the visual analysis are presented in Fig. 2. The reason for this new classification was to combine congestion with the impact it has on energy management in electric vehicles. Therefore, the classification algorithm operates using two jam characteristics: duration and number of stops. Knowledge about these properties allows effective short-term congestion forecasting, which is critical for efficient battery management in electric cars. A detailed description of the classification algorithm can be found in Bursa et al. (2018). We have had the opportunity to cooperate with the company and their approach has already been implemented in the analysis software Consyst, which is facilitative in processing large datasets like ours. During the data processing stage, the data was aggregated across all drive lanes and entered into the software, which applies the adaptive smoothing method (ASM) to the raw detector data and produces space-time-velocity diagrams. In our study, we have chosen default parameters for the exponential filter τ = 1.2 min and σ = 0.6 km. Another feature of the software is the automatic detection of congestion incidents (Fig. 6 - white rectangles around the low-speed area) and calculation of corresponding time losses (difference between the real travel time and the free-flow travel time). For a broad review of classification approaches and details on smoothing methods, we refer the reader to the works of Celikoglu (2013), and Treiber and Kesting (2013).
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Fig. 2 Congestion types and corresponding definition criteria (Transver GmbH, 2010)
2.3. Accounting for differences between traffic lanes As stated before, the standard technique exploits the cross-sectional traffic data aggregated over all drive lanes. The results obtained are thereby aggregated too, making them less reliable for the motorway sections, where the traffic volume, vehicle composition, and velocities differ significantly between traffic lanes. Since these conditions are becoming more frequent with the increasing congestion on motorways in alpine regions (like the A12 and A13), their effect on traffic flow is not negligible. A proper differentiation between the offside and nearside drive lanes gives unique insights into the reasons for and formation of specific congested traffic states. The proposed approach consists in employing the adaptive smoothing method (ASM) (Helbing and Treiber, 2002) to the disaggregated data for each of the traffic lanes separately, which is a step forward compared to the previous analyzes of spatiotemporal characteristics of traffic on alpine motorways. It means that the congestion patterns can be spatially analyzed not only along the motorway but also perpendicularly, across all drive lanes, which enables the lateral interactions between vehicle flows and the influence of congestion incidents on adjacent lanes to be taken into account. Differently from the standard approach, a separate velocity matrix is built and a separate velocity contour plot is produced for each of the lanes (Fig. 6). The approach also allows different parameter values (Treiber and Helbing, 2003) to be used for the exponential filter in the adaptive smoothing method (ASM) for different lanes making it possible to adjust the smoothing intensity according to the nature of traffic on a particular drive lane. In the current study, we have chosen default parameters for the exponential filter τ = 1.2 min and σ = 0.6 km. 3. Results 3.1. Influence on the number of detected congestion incidents The results of the study show differences between the congested states on the nearside and offside lanes on the A12 and A13 motorways in terms of location, length, severity, and duration of congestion. The most significant discrepancies were observed expectedly on the A13 (in the summer months from June to September). Within the space of one year, the number of jams on the nearside lane on the A13 from Innsbruck to Brenner and opposite was approximately 11 and 19 times higher respectively than on the offside lane. At the same time, the A12 proves more stable and the difference between drive lanes comes to about 35-40%. Fig. 3 illustrates the overall outcomes for the A12 and A13 motorways.
Bartosz Bursa et al. / Transportation Research Procedia 37 (2019) 441–448 Bartosz Bursa et al. / Transportation Research Procedia 00 (2018) 000–000
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No of detected jams
The observations are plausible considering the annual average daily traffic (AADT, Monday to Sunday) on the A13 between interchanges Zenzenhof and Igls-Patsch reaching over 48,000 vehicles in both directions and the average HGV share estimated at 15%. Surprisingly, the difference between the number of jams on the nearside and offside lane is higher in the direction from Brenner to Innsbruck than in the direction from Innsbruck to Brenner. This contradicts our initial assumption that the upward direction to the Brenner pass will be more exposed to severe congestion on the nearside lane, occupied predominantly with HGVs and characterized by low velocities, especially on the most frequented sections, sections with high inclination grades, and close to the toll collection station Schönberg im Stubaital. A detailed spatial and temporal analysis is currently being conducted to explain this finding and determine the exact locations and time periods of the highest congestion disparity between the lanes on the A13. 3000 2500 2000 1500 1000 500 0
2682 1828 1200
813
462 346 355
300 215 237
173
143
Innsbruck ‐> Kufstein
Kufstein ‐> Innsbruck
Innsbruck ‐> Brenner
Brenner ‐> Innsbruck
A12
A13
Nearside lane
Offside lane
Both lanes
Fig. 3 Number of detected congestion incidents on the A12 and A13 motorways depending on the analyzed traffic lane
3.2. Influence on the types of congestion incidents detected
No of detected jams
The lane-specific classification of congested states was carried out for the A12 motorway over 20 days between 01.01.2016 and 20.01.2016. Although the A13 appears more interesting for the research, its investigation has been temporarily suspended as first the parameters of the classification algorithm and the adaptive smoothing method must be reconsidered. The typical values do not guarantee proper results on this motorway, which is characterized by high inclination grades (up to 6.1%) and very low velocities on the rightmost lane. 2323
25 20 15 10 5 0
20 17
16 13 76
8 3
2
5
19
17
14
1
3
7 0
3
5
7 12
16 6
3
4
2
5
10
8 2
23
3
Nearside Offside lane Both lanes Nearside Offside lane Both lanes Nearside Offside lane Both lanes lane lane lane A12 Short term speed breakdown
A12 Innsbruck ‐> Kufstein Stop‐and‐go wave
A12 Kufstein ‐> Innsbruck Wide jam
Mega jam
Fig. 4 Frequencies of congestion types detected on the A12 over 20 days (1-20.01.2016) depending on the analyzed traffic lane
Fig. 4 illustrates the frequencies of congestion types on the A12 motorway broken down into driving directions and traffic lanes. It is noticeable that, irrespective of the analyzed lane, the mega jam type dominates in the direction
Bartosz Bursa et al. / Transportation Research Procedia 37 (2019) 441–448 Bartosz Bursa et al. / Transportation Research Procedia 00 (2018) 000–000
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towards the border with Germany (from Innsbruck to Kufstein), while in the Innsbruck direction the wide jams prevail. In contrast with the previous study, the mega jams triggered by state border controls have not been filtered out from the dataset, which was motivated by the intention to capture the effects of border controls on the traffic situation in single lanes. Furthermore, the order of the congestion types (from the least to the most frequent) remains the same for nearside, offside and both lanes in the direction towards Kufstein, whereas in the direction towards Innsbruck no clear pattern can be distinguished. Fig. 5 presents changes in the frequency of specific congestion classes compared to the results from the whole cross-section. A substantial increase in the severe jam classes (wide jam, mega jam) on the nearside lane in both directions and a corresponding decrease in the offside lane can be observed.
Change in the no of detected jams
8 6 4 2
+35% +133%
+60%
+15%
+200%
+60%
+12% +50% 0% 0%
+20%
0 ‐2
‐33%
‐4 ‐6
+50%
‐100% ‐24% ‐20%
Nearside lane
Offside lane
Nearside lane
A12
0%
0% ‐33% ‐20%
‐29% ‐18%
Offside lane
Nearside lane
A12 Innsbruck ‐> Kufstein
Short term speed breakdown
+67%
+33%
Stop‐and‐go wave
Offside lane
A12 Kufstein ‐> Innsbruck Wide jam
Mega jam
Fig. 5 Changes (in absolute values and in %) in congestion types detected on the A12 over 20 days (1-20.01.2016) compared to the whole crosssectional analysis
To exemplify the effects of the lane-specific evaluation of congested states on the final results, one day, namely 7th January 2016, has been chosen. Fig. 6 presents the velocity contour plots in a dense color scale for the whole cross-section as well as single lanes. The white rectangles outline the congestion clusters detected. Analysis based on the whole cross-sectional data (both lanes) resulted in detecting one congestion incident of the stop-and-go wave type. However, having investigated the drive lanes separately, we have obtained two different congested states on the nearside lane and no congestion at all on the offside lane. The two incidents detected on the nearside lane are a mega jam and a wide jam, both having more adverse effects on traffic flow (in terms of length and duration) than the stop-and-go wave identified for both lanes. The major characteristics of the congestion clusters identified has been summarized in Table 2. Table 2 Characteristics of the congestion cluster (07.01.2016) on the A12 from Kufstein to Innsbruck depending on the analyzed traffic lane Analyzed lane
Start time
End time
Max. duration
Max. length
Congestion type
Both lanes
05:11
06:41
00:08
5,227 m
Stop-and-go wave
Nearside lane
05:10
07:18
00:49
12,813 m
Mega jam
Nearside lane
05:43
07:40
00:29
3,846 m
Wide jam
Offside lanes
-
-
-
-
-
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a)
b)
c)
Fig. 6 Velocity contour plots (07.01.2016) on the A12 from Kufstein to Innsbruck for: both lanes (a), nearside lane (b), and offside lane (c)
3.3. Impact of traffic management measures The separate lane analysis of congestion patterns has revealed that, undoubtedly, the transit truck traffic contributes most to the congestion problems on the A12 as more jams occur on the nearside lane, which is occupied by trucks. More importantly, the biggest increase on the rightmost lane was observed for the types mega jam and wide jam, which have the largest negative implications for the general traffic conditions on the motorway. Our study could provide scientific evidence for traffic management measures like ramp metering or control of the trucks inflow launched recently on the A12 in Kufstein. Moreover, it would help to assess their effectiveness if a comparative study were conducted again with the new data from the period after the introduction of metering on the border with Germany. The proposed methods could also serve to evaluate the precise impact of the controls introduced by Germany in fall 2015 owing to the political situation on its state border with Austria in Kufstein. 4. Conclusions This paper gives an overview of the traffic situation on the A12 and A13 motorways, and provides a detailed insight into congestion patterns on the separate lanes of the A12 between Innsbruck and the state border. Our analysis builds on previous work (Bursa et al., 2018) and is a further contribution to the understanding of traffic congestion patterns on motorways located in specific alpine regions. It is the first instance that assumptions and hypotheses about traffic jams in the Tyrol have undergone a thorough scientific assessment and have been verified by numbers. Our findings prove the superiority of the improved approach over the standard procedure. We argue that the results are more detailed while the mathematics used and computation time remain at the same level. Not only has a dissimilar
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overall number of jams been detected for the separate drive lanes than in the analysis of the whole cross-section. The difference is also in types of congested states. Some traffic patterns (mega jams and wide jams) appear more often on the nearside than on the offside lane. Until now, the lane-specific classification had been carried out only for the A12. However, based on the analysis of the number of congestion incidents, the A13 motorway provides even more potential for this research. Considering its role in the European Corridor E45, it is thus critical to conduct the classification of congested states for the A13 in both directions to account for the interdependencies between traffic lanes and clarify the findings from the general analysis of the number of congestion incidents (Section 3.1). Nevertheless, these macroscopic results should be validated using the microscopic Floating Car Data (FCD) on critical sections of the A12 and A13 motorways, which is expected to confirm our outcomes and provide conclusive evidence on the rightness and suitability of the chosen methods. Van Lint and Hoogendoorn (2010), and Treiber et al. (2011) have extended the generalized adaptive smoothing method (GASM) (Kesting and Treiber, 2008) to any traffic data, and provided a methodology for data fusion from multiple sources. This framework has recently been exploited by Rempe et al. (2016), who successfully applied the extended ASM to FCD on the German motorway A99. The same approach is going to be tested in our case. Eventually, the proposed methodology could be adopted as a standard technique in congestion studies performed by motorway operators and integrated within the commercial software tools used for automated traffic data analysis. It would significantly benefit the accuracy and unambiguousness of the results at relatively small expense. We are planning further activities within this academic field, as there is still an underexplored research potential, particularly within specific motorway networks in mountain regions. Acknowledgements The authors would like to thank ASFiNAG and its employees for providing the necessary traffic data, Prof. Bernhard Friedrich from the consultancy TRANSVER, as well as Stefan Gürtler for his assistance, valuable remarks, and the effort he has put into data processing. References Bursa, B., Gajic, N., Mailer, M., 2018. Classification of traffic jams on alpine motorways, in: Proceedings of 7th Transport Research Arena TRA 2018. 7th Transport Research Arena TRA 2018, Vienna, Austria. April 16-19, 2018. Celikoglu, H.B., 2013. An Approach to Dynamic Classification of Traffic Flow Patterns. Computer-aided Civil Eng 28, 273–288. 10.1111/j.1467-8667.2012.00792.x. Helbing, D., Treiber, M., 2002. Reconstructing the Spatio-Temporal Traffic Dynamics from Stationary Detector Data. Cooperative Transportation Dynamics 1, 3.1-3.24. Kerner, B.S., Rehborn, H., 1996. Experimental properties of complexity in traffic flow. Phys. Rev. E 53, R4275-R4278. Kesting, A., Treiber, M., 2008. Calculating travel times from reconstructed spatiotemporal traffic data, in: Networks for Mobility. Proceedings of the 4th International Symposium. Universität Stuttgart, p. 41. Rempe, F., Franeck, P., Fastenrath, U., Bogenberger, K. (Eds.), 2016. Online Freeway Traffic Estimation with Real Floating Car Data. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1838-1843. Transver GmbH, 2010. Stauklassifizierung und Untersuchung des Zusammenhangs Verkehrsstärke und Verkehrszusammenbruch, Munich. Treiber, M., Helbing, D., 2003. An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information, in: Emmerich, H., Nestler, B., Schreckenberg, M. (Eds.), Interface and transport dynamics. Computational modelling. Springer, Berlin, Heidelberg, pp. 343–360. Treiber, M., Hennecke, A., Helbing, D., 1999. Derivation, properties, and simulation of a gas-kinetic-based, nonlocal traffic model. Phys. Rev. E 59, 239–253. 10.1103/PhysRevE.59.239. Treiber, M., Kesting, A., 2013. Traffic Flow Dynamics: Data, Models and Simulation. Springer, Berlin, Heidelberg, 54 pp. Treiber, M., Kesting, A., Wilson, R.E., 2011. Reconstructing the Traffic State by Fusion of Heterogeneous Data. Computer-aided Civil Eng 26, 408–419. Van Lint, J.W.C., Hoogendoorn, S.P., 2010. A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways. Computer-aided Civil Eng 25, 596–612.