The German Autobahn: An ITS Test Bed for Examining Dynamic Traffic Flow Phenomena

Transcription

1 The German Autobahn: An ITS Test Bed for Examining Dynamic Traffic Flow Phenomena Robert L. Bertini, Roger V. Lindgren, Dirk Helbing and, Martin Schönhof Abstract Traffic conditions were examined along a 3- km section of northbound Autobahn 5 near Frankfurt, Germany using archived inductive loop detector data. Fifteen bottleneck activations were identified and their reproducible features were described. Bottlenecks became active in the vicinity of on-ramps and off-ramps and once a bottleneck became active, its queue discharge flow was reproducible across multiple activations and across multiple days. The analysis tools used in this study were transformed curves of cumulative vehicle count and cumulative timemean velocity. These cumulative curves provided the resolution necessary to reveal the spatial and temporal aspects of dynamic freeway traffic flow phenomena. With increasing availability of reliable freeway sensor data, it is important to continue the systematic empirical analysis of freeways in different countries with varying geometric configurations. The results of this research will assist with all aspects of traffic flow modeling, operations and control. T I. INTRODUCTION HE spatio-temporal evolution of traffic between freely flowing and congested conditions was studied on a 3-km section of a German Autobahn. Several bottlenecks were identified by examining the excess vehicle accumulation and excess travel time that arose between measurement locations. Bottlenecks became active in the vicinity of on-ramps and off-ramps on this section of freeway. It is also shown that once a bottleneck was identified, its measured outflow was reproducible across multiple activations on multiple days. The analysis tools used in this study were transformed curves of cumulative vehicle count and cumulative timemean velocity constructed from archived inductive loop detector data. These curves provided the resolution necessary to reveal spatial and temporal aspects of traffic flow phenomena. With the availability of reliable freeway sensor data, it is important to continue the systematic empirical analysis of freeways at sites in different Manuscript received Feb. 28, 25. Oregon s Engineering Technology Industry Council, Portland State University Department of Civil & Environmental Engineering and Oregon Institute of Technology Department of Civil Engineering & Geomatics supported this project. R.L. Bertini is with the Department of Civil & Environmental Engineering, Portland State University, Portland, OR USA (phone: ; fax: ; bertini@ pdx.edu). R.V. Lindgren is with the Department of Civil Engineering & Geomatics, Oregon Institute of Technology, Klamath Falls, OR 9761 ( lindgrer@oit.edu). D. Helbing and M. Schönhof are with the Institute for Economics and Traffic, Dresden University of Technology, D-162 Dresden, Germany ( helbing@trafficforum.de; martin@vwisb7.vkw.tu-dresden.de) countries and with varying geometric configurations. The results of this research will assist with all aspects of traffic flow modeling, operations and control. II. BACKGROUND In earlier empirical studies, congested traffic conditions have been analyzed upstream and downstream of freeway bottlenecks near on-ramps [3,4,15,16,17] and off-ramps [1,1]. In this study, a bottleneck was defined as a restriction that separates upstream queued traffic from downstream unrestricted traffic [5]. Bottlenecks can be static (e.g., tunnel entrance, lane drop, diverge area) or dynamic (e.g., incident or slow moving vehicle). A bottleneck is considered active when it meets the definition presented above and is deactivated when there is either an upstream decrease in flow (decrease in demand or upstream flow restriction) or when a queue spills back from a bottleneck further downstream [14]. Other researchers [7,8,9] have reported on congested traffic on German Autobahns. Using other analysis techniques, some research has identified variations in bottleneck discharge flow and has postulated the evolution of congested traffic without bottlenecks. The objective of this paper is to diagnose the activation and deactivation of several bottlenecks using a site on a German Autobahn that has been analyzed by others in the past, and to present some of its notable features that were revealed. III. DATA The study site (Fig. 1), a 3 km section of northbound A5 near Frankfurt, Germany, is equipped with inductive loop detectors (labeled as D1 through D3) in each lane and on most ramps. The detectors record separate counts and velocities for autos and trucks at one minute intervals. The analysis described here uses data from Wednesday, September 19, 21 from the northbound direction plus data from five additional days in 21 to demonstrate reproducibility. Fig. 1 shows the spacing between detector stations (in meters) and the vertical geometry (elevations in meters). Fig. 1 also shows that there are two locations (between D7 D8 and D2 D21) where local access off- and on-ramps are present, but no through traffic leaves or enters the system at these locations. A comparison of counts from upstream and downstream locations indicates that vehicle conservation is

2 D3 D29 D28 D27 D26 D25 D24 D23 D22 D21 D2 D19 D18 D17 D16 D15 D14 D13 D12 D11 D1 D9 D8 D7 D6 5 D5 D4 1 D D2 D1 [m] [Sta] A455 Friedberg A661 Bad Homburger Kreuz A66 Nordwest Kreuz Frankfurt am Main A648 West Kreuz Frankfurt am Main 14:21 G1 14:35 14:35 G2 14:51 14:58 14:47 14:47 14:58 14:55 15:3 G12 G7 G14 G3 15:3 15:34 G15 15:49 15:48 15:59 15:4 15:47 G4 15:4 15:47 16: G8 G1 G13 16:7 16:13 16:32 16:29 G9 16:46 16:42 G5 G :5 17:7 17:12 G11 19:2 Elevation [m] Fig. 1. Autobahn A5 Northbound Speed Diagram. maintained. IV. METHODOLOGY AND OBSERVATIONS Fig. 1 shows speeds averaged across all lanes for each 1-min interval, with time as the x-axis, distance as the y- axis, and speed variation in color. Speed disturbances began shortly after 14: and continued until 2:. 1 This study adds to previous work by diagnosing the activation of fifteen bottlenecks along A5 on September 19, 21 (day G). These activations (labeled G1 through G15) are mapped in time and space on Fig. 1. We also examined activations on five more days and reports on several features that were reproducible across all study days. To identify time-dependent features, we used cumulative curves of vehicle count and time-mean velocity using data measured at neighboring freeway detectors [2]. Transformations of these curves provided the resolution necessary to observe transitions between unqueued and queued conditions and to identify notable, time-dependent features. Fig. 2 shows transformed oblique curves of cumulative vehicle count (N(x,t)) for detectors D15 D24 from 14:4 15:1. Using one-minute counts measured across all lanes, piece-wise linear 1 September is in Middle European Summer Time; therefore one hour should be added to the recorded times to obtain the actual clock times. approximations of the cumulative counts were constructed (the slope of the unaltered N(x,t) would be the flow past location x at any time t). All curves began (N=) so that they describe the same collection of vehicles. For vehicle conservation, the D15 curve includes on-ramp counts. The horizontal and vertical separations between unaltered N(x,t) would have been the travel times and vehicle accumulations between measurement locations, respectively [13,12]. However, in Fig. 2 the curves were altered by shifting each upstream curve to the right by the free-flow travel time from its location to detector D24. Any resulting displacements are now the excess accumulation and travel time (delay) between detector pairs, respectively [13,12]. An oblique scaling rate, q, was applied to the N(x,t), more clearly revealing the times at which notable flow changes occurred [2]. The same q was applied to all curves; the oblique N(x,t) are shown using an amplified vertical scale. This method, described in more detail in several references [2,1], takes advantage of the loop detector data in their most raw form and also takes into account the spatial aspects of queue formation, which is a limitation of some other bottleneck identification methods. All ten curves in Fig. 2 remained superimposed until

3 17 Fig. 2. Oblique N(x,t) for D15-D24 14:5, indicating that traffic flowed freely between all stations. Just before 14:5, excess accumulation arose between stations D17 D18 as indicated by the divergence of the N(x,t). The divergence of curve D17 from curve D16 (and flow reduction at D17) marked the passage of the backward-moving queue at D17. This activation was labeled as G7. All N(x,t) downstream of D17 remained superimposed until 14:55, when excess accumulation arose between D2 D21. There was a deviation of curve D2 from curve D19 (and a visible flow reduction measured at D2), mapping the queue passage time at D2. G12 was briefly activated between D2 D21 until 15:3 when G12 was deactivated. After the G12 activation, excess vehicle accumulation was visible between D22 D23 (Fig. 2). For the remainder of the period shown in the figure, curves for D23 D24 were superimposed, indicating freely-flowing traffic in this section. At 14:58, upstream accumulations (and accompanying flow reductions at D22 and D21) are visible, marking G14 between D22 D23 (also see Fig. 1). To identify trends in measured velocity and to identify times at which notable velocity changes occurred, oblique V(x,t) were constructed for each detector. V(x,t) was the cumulative speed measured at detector x by time t, where the slope of the V(x,t) was a speed rate for that location. As shown in Fig. 3, an oblique scaling rate of v was applied using an amplified vertical scale, and periods of nearly constant average speed and times marking changes in average speed were labeled on the figure. Fig. 3 shows three oblique V(x,t) for D22 D24 (values of v are labeled on the y-axes). The V(x,t) in the first column correspond to the time that G14 became active. While speeds at D23 and D24 remained high, minor speed reductions associated with the activation of G14 are shown in the first column of V(x,t) curves and confirm the 14:58 activation. The speed reduction occurred at progressively later times at D23 and D24 which is consistent with a forward-moving wave traveling downstream from the bottleneck location. To examine the flow features of G14 through its active period, Fig. 4 contains N(x,t) from detectors D15 D24 from 14:4 17:1. Fig. 4 shows that G14 was deactivated at 15:3 when excess accumulation between D22 D23 was no longer present. This deactivation time can be verified by the oblique V(x,t) shown in the second column of Fig. 3. A speed increase was visible at 15:3 at D22, indicating the reestablishment of freely flowing conditions between D Prior to this time, speeds were recorded between km/h, noticeably slower than the speeds measured at D23 and D24. The freely flowing traffic between D22 D23 after 15:3 was short-lived and a bottleneck was again activated between D22 D23 at 15:34 (G15). As shown in Fig. 4, the N(x,t) for D23 and D24 remained superimposed indicating freely flowing traffic whereas there was excess accumulation between the N(x,t) at D22 and D23 beginning at about 15:34. This indicated that G15 was activated at 15:34 and remained active until 15:49 when excess upstream accumulation dissipated. The G15 activation and deactivation times can also be confirmed by examination of the second and third V(x,t) columns of Fig. 3. The D22 V(x,t) clearly shows a speed decrease at 15:34 and a speed increase at 15:49. Fig. 4 also shows that by 15:, the queues that propagated upstream from G12 and G14 merged and propagated further upstream; the queue s progress can be Fig. 3. Oblique V(x,t) for D22, D23, and D24.

4 N(x,t) - q (t-t ), q =489 vph 14:5 D17 14:58 D22 14:58 D17 G7 G12 D2 D21 14:55 D2 15:3 D2 D17 D18 15:5 D19 15:8 D18 15:14 D17 G14 15:22 D16 15:3 D2 15:28 D21 15:3 D22 Figure 4: Oblique N(x,t) for D15-D24. 15:34 D19 15:37 D18 15:4 D17 Time, t at D15 mapped on Fig. 4 as it passed stations D19, D18, D17 and D16. Fig. 4 also diagnoses all eight bottleneck activations during this period. As shown by the excess accumulation arising between D17 D18 at about 15:48, a bottleneck between D17 D18 was activated as G8 until 15:59 (these and other activations and deactivations were verified by V(x,t) not shown here). Upon excess accumulation being visible between D2 D21 at 16:, G13 was activated between D2 D21 and remained active until the reduced flow from upstream G9 deactivated G13 at 16:32. G9 became active between D17 D18 at 16:29, marking the third activation at this location on this day. G9 remained active until the reduced flow from upstream G6 deactivated G9 at 16:46. G6 was activated at 16:42 between D15 D16 near where traffic from Motorway A661 entered the A5 at an on-ramp downstream of D15. G6 remained active between 16:42 17:7. Fig. 1 shows that G11 was activated between 17: 2: and six additional bottleneck activations occurred in the segment between D6 D14. The details of these six activations were confirmed using N(x,t) for detectors D6 D14 between 14: 17:1. Consistent with the above, Fig. 4 shows the propagation of a shock of lower flow that emanated from between D21 D22 at 15:28. The shock s passage can be traced upstream (see circular markers on Fig. 4) to D6 more than 45 minutes later. The backward recovery wave was also traced in Fig. 4 (square markers), reaching D6 by 16:21. The velocities of the shock and recovery wave appeared to be stable and were reproducible from day to day. Possible origins of this phenomenon will be 15:34 D22 15:45 D15 15:42 D16 G15 15:49 D22 15:48 D17 G8 17 D17 D18 16: D2 15:59 D17 G13 D2 D21 16:32 D2 16:29 D G9 D17 D18 16:46 D17 16:42 D15 G6 D15 D16 17:7 D15 discussed below. Fig. 1 showed the speed dynamics on this 3 km freeway section and mapped fifteen bottleneck activations diagnosed for one day. Figs 2 4 have verified the bottleneck locations, the times at which they became active, and the times that they were deactivated. The discharge characteristics of bottleneck activations G14 and G15 are examined in detail in Fig. 5. The figure contains oblique N(x,t) and V(x,t) measured across all lanes at detector D23, just downstream of the bottleneck, between 14:3 16:. Periods of nearly constant flow and speed (in vph and km/h, respectively) were delineated using dashed lines. The curves do not display any abrupt reductions in the N(x,t) accompanied by abrupt reductions in speed during the periods when the bottleneck is active; thus it is apparent that there was no disruption of active bottleneck discharge caused by a queue from anywhere further downstream. Prior to the G14 activation (at 14:58), a flow of 567 vph was measured across all three lanes. Upon queue discharge, a mean discharge flow of 532 vph prevailed, a 6% flow reduction. Upon G15 queue discharge, a flow of 525 vph was measured. The flow drop upon queue discharge is consistent with some previous research, as is the stability of the discharge flow. V. BOTTLENECK TRIGGERS A study of speed, flow, traffic composition and lane positioning has revealed possible signals of bottleneck activation. G14, located between D22 D23, m upstream of an off-ramp near the crest of a vertical curve with an incoming grade of 3.2%. Flow characteristics of the off-ramp near D25 were analyzed, indicating a 66% surge in off-ramp flow, which was measured as 136 vph for the 3 minutes following activation. The activation also coincided with a period of high flow at D22 that was marked by high truck flows. Analyses of vehicle speeds and counts at D22 revealed that the surge noted above in the right lane corresponded with high flows also measured in the middle and left

5 N(d23,t) - q (t-t ), q =49 vph 14:49 D :58 D23 Figure 5: Oblique N(x,t) and V(x,t) for D23. G14 Time, t at D23 lanes. In the two-minute period surrounding the 14:58 G14 activation, the left, middle and right lane flows were measured as 2,76, 2,4 and 1,18 vph, respectively. Using separate truck count and speed data, Fig. 6 shows oblique N(x,t) and V(x,t) (lower) for each lane at station D22. The N(x,t) are annotated with linear approximations, which are labeled to the nearest 1 veh/hr. These curves reveal that trucks indeed remained in the right lane during periods of uncongested flow prior to 14:58. Notice that several minutes prior to activation G14, the truck flow in the left lane surged to 15 trucks per hour. Then, coinciding with activation, a 94% truck flow increase was observed in the right lane, followed closely by movement of trucks to the middle lane where an increase to 19 trucks per hours was observed. Fig. 6 contains V(x,t) for each lane at D22. Notable drops in truck velocity were seen in all three lanes around the time of activation of G14 at 14:58. The right lane trucks speeds dropped from 79 to 68 km/h, the middle Oblique N(22,t) Left/Middle Oblique V(22,t) Left/Middle Trucks Only 5 Trucks Only km/hour 3 61 trucks/hour 9 15 (5 trucks/2 min) (+94%) Time, t : 14:1 14:2 14:3 14:4 14:5 15: 15:1 15:2 15:3 15:4 15:5 16: 19 Time, t Figure 6: Oblique N(x,t) and V(x,t) for Trucks at D Right Lane q =4 vph Middle Lane q =8 vph Left Lane q =3 vph Left Lane v =561 km/h 2 Middle Lane v =58 km/h 2 Right Lane v =436 km/h 2 Oblique N(22,t) Right Lane Oblique V(22,t) Right Lane 15:3 D :34 D G15 15:49 D23 N(d23,t) 91 V(D23,t) V(D23,t) -v (t-t ), v =46 km/h per hour lane from 93 to 82 km/h, and the left lane from 18 to 9 km/h. This may indicate that the bottleneck and associated shock were triggered by a surge in truck flows in the middle and left lanes creating a blockage for car drivers who would have preferred to travel at higher speeds. VI. REPRODUCIBILITY OF OBSERVATIONS The analyses described above were repeated using data from five additional days on this same section of this Autobahn. A bottleneck arose between stations D22 D24 a total of 19 times over the six days. Table 1 shows key characteristics of the activations. The discharge flows across all lanes and in each lane were measured for each activation. The mean discharge flow measured across all lanes was 483 vph with a standard deviation of 297 vph. The mean discharge flow (SD) measured in the left, middle and right lanes were 195 (13), 167 (14) and 121 (11) veh/hr, respectively. The discharge duration ranged between 13 and 223 minutes. Queue discharge flows appear reproducible from day to day across all lanes and in the individual lanes. Eleven activations (including G14) were preceded by freely flowing conditions, not influenced by other traffic disturbances. These activations are referred to as isolated activations and their values for the durations and magnitudes of pre-queue flows are shown in Table 1 in italics. These pre-queue periods ranged between 2 14 minutes and the pre-queue flows averaged 516 vph with a standard deviation of 266 vph. Since the mean discharge flows for these 11 isolated activations averaged 482 vph, there was, on average, a 6.2% flow reduction that accompanied these 11 bottleneck activations. This is consistent with past research e.g., [4] that have documented flow reductions accompanying queue formation on North American freeway sections. Research is ongoing to investigate the reproducibility of bottleneck activation at other locations on the A5 on other days. The potential triggers for the G14 activation can be summarized by a) surge in off-ramp flow b) high prequeue flow in all lanes, and c) high, truck dominated flow in the right lane. Analyses of all 11 isolated bottleneck activations found these three triggers to be highly reproducible. There was a surge in off-ramp flow observed prior to 7 of the 11 activations. In all 11 activations, high flows in all lanes with high truck flows

6 in the right lanes were observed at the station upstream of the bottleneck. Research is continuing to examine other potential bottleneck activation triggers at additional locations on the A5, as well as on additional days. VII. CONCLUDING REMARKS This study has analyzed traffic conditions along a 3- km freeway segment over a six-hour period on one day. Oblique curves of cumulative vehicle count and time mean speed versus time provided the measurement resolution necessary to diagnose fifteen distinct bottleneck activations and deactivations, where queued traffic prevailed upstream and unqueued traffic was present downstream. After diagnosing each bottleneck s location and the times it remained active, we examined discharge flow characteristics and found that discharge flows appeared to be reproducible from day to day, on the six days examined. Further, we identified a set of bottleneck activations that were considered to be isolated, and documented measurable flow reductions upon queue formation. This work has verified similar findings from past studies using data from North American freeways. Toward identifying the potential causes of bottleneck activation near an off-ramp, the study focused on the traffic patterns that preceded and followed bottleneck activation by vehicle type and in each travel lane. Surges in off-ramp flow, high pre-queue flow in all lanes, and high, truck dominated flow in the right lane preceded 11 activations on this freeway near the off-ramp. Research is continuing at this site on other days and in other locations (e.g., near the on-ramp near station D8) to identify additional potential bottleneck activation triggers and explore their reproducibility. This freeway site provides a valuable opportunity to document the systematic empirical observation of traffic flow phenomena in a way that can be replicated by other researchers in the future. This research is only a first step toward understanding bottleneck behavior in relation to geometric features of the roadway. Further analyses are being conducted at this site and other sites in Europe and the U.S. ACKNOWLEDGMENT The authors gratefully acknowledge the support of the Hessische Date Date Location Landesamt für Strassen-und Verkehrswesen for generously providing the archived data used in this study. REFERENCES [1] M. Cassidy, S. Anani and J. Haigwood. Study of freeway traffic near an off-ramp. Transpn Res., 36A, , 22. [2] M. Cassidy and J. Windover. Methodology for assessing dynamics of freeway traffic flow. Transpn Res. Rec., 1484, 73-79, [3] M. Cassidy and R. Bertini. Some traffic features at freeway bottlenecks. Transpn Res., 33B, 25-42, [4] M. Cassidy and R.Bertini. 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TABLE I REPRODUCIBILITY OF DISCHARGE AND PRE-QUEUE FLOW IN D22-D24 REGION Pre Queue Flow Pre-Queue Duration (min) Duration (min) Discharge Isolated Discharge Flow (vph) Flow (vph) Left (vph) Mid (vph) Right (vph) Sep. 2 E1 D22/D Sep. 2 E3 D22/D Sep. 14 C2 D22/D Sept. 19 G14 D22/D Aug. 17 B6 D22/D Dec. 4 F12 D22/D May. 18 A6 D22/D Dec. 4 F9 D22/D Dec. 4 F2 D22/D Sept. 19 G15 D22/D May. 18 A2 D23/D Aug. 17 B8 D23/D Aug. 17 B3 D23/D Sep. 14 C8 D23/D Aug. 17 B7 D23/D May. 18 A1 D23/D Aug. 17 B2 D23/D Sep. 14 C1 D22/D Dec. 4 F5 D23/D Mean SD