1. Characteristics of Transitions in Freeway Traffic By Manasa Rayabhari Soyoung Ahn

2. Outline: Dynamic Transition  Introduction  Objectives  Literature Review  Sites and data  Database development  Preliminary analysis  Summary of findings  Future work and time-frame

3. Introduction Significance and Priorities  U.S. DOT’s mobility strategic plan - Congestion and bottlenecks have negative impact on the quality of life in terms of air quality, energy consumption and our economy.  Wasted time and fuel resulting from congestion are equivalent to $68 billion a year.  This research will provide a valuable insight on how congested traffic behaves under various transitions that frequently occur on urban freeways.  Understanding of the transition properties will expand the current knowledge on traffic congestion and serve as a building block for future traffic modeling and management practice as well as other outputs such as delay & travel time.

4. Objectives  Understanding different types of transitions that freeway traffic undergoes at different queue locations. The Tail of a Queue :  Studying the relationship between the characteristics of transition and traffic variables such as initial flow (or speed) and changes in flow upon a regime change. The Head of a Queue :  Properties of regime transition at as vehicles discharge from an active bottleneck.  Quantifying characteristics such as the length of transition, discharge rate, free-flow speed, etc from congested to freely flowing regimes. Inhomogeneous Section :  Analyzing the transitions near freeway ramps, lane-reduction and/or grade change.  Quantifying features such as the length and their relationship with traffic variables (e.g. change in flow, congestion level, etc.) on an individual lane basis.

5. Literature Review 1.Kinematic wave model by Lighthill-Whitham (1955) and Richards (1956) (LWR) model and its simplified version by Newell (1993). »Key traffic evolutions at a macroscopic level 2. Cassidy (1998) »Identification of periods of stationary traffic »congested flow-occupancy relationship using the aggregated data over the stationary periods. 3. Muñoz and Daganzo (2003) »“Transition zones” emerge when a queue forms at a bottleneck. »Propagates as a “shock” upstream and then dissipates with decreasing demand

6. Proposed Research  Dynamic Transition  What we have done so far  Transition near the tail-end of a queue while expanding and receding  Static Transition  Transition near the head of a queue (i.e. near an active bottleneck)  Transition near inhomogeneous section »Ramps »Lane reduction or expansion

7. Sites and Data

8. Site 1: Queen Elizabeth Way (QEW)  Canadian freeway, “Queen Elizabeth Way” Schematic of QEW

9. Site 2: M4  British expressway M4 Schematic of M4 Travel Direction

10. Site Characteristics QEWM4 No of lanes33 and then 2 No of loop stations1613 Length10.05 KM6.8 KM Bottleneck typeMergeLane-drop Congestion Time6:00 – 10:00 AM6:00 – 9:00 AM No of ramps5 on-ramps 3 off-ramps None

11. Data QEWM4 Data type20-sec loop dataEvent data aggregated to 20 seconds Dates09/13/1999 – 09/24/1999 (weekdays) 11/1/1998 – 12/05/1998 (weekdays) AM and/or PM peakAM Peak

12. Speed Contour: QEW QUEUE FORMATION 6:12:00 AM – 6:35:00 AM QUEUE DISSIPATION 9:49:00 AM – 9:56:40 AM

13. Speed Contour: M4

14. Database Development

15. Variables Included  Dependent variable: Transition duration  Potential explanatory variables  Speed or flow change  Speed or flow before transition  Wave speed  Presence of on-ramp or off-ramp (QEW only)  Lane number  Distance from the bottleneck  Weather

16. Measurement of Variables  Transition duration: speed curves Transition Start Time (t 1 ) Transition End Time (t 2 ) Transition from Free Flow State to Congested State Transition Duration (t 2 – t 1 )

17. Measurement of Variables  Speed : S b and S a are the average speeds before and after transitions respectively  Change in Speed : S b ~ S a Onset Regime Clear Regime S b = Average (s i ) i jS a = Average (s j )i S b = Average (s i ) S a = Average (s j ) j

18. Measurement of Variables  Flow and change in flow: Oblique cumulative count curves Q b = Average (q i ) Q a = Average (q j ) Change in flow = Q b ~ Q a

19. Measurement of Variables  Wave speed  The wave speed is obtained using the following formula:  Wave speed = Distance traveled by the queue --- (1) Time Duration = Distance between detector stations --- (2) Time Duration Distance from BN Time Duration 1 1 2 3 4 5 (1) (2)

20. Measurement of Variables  Presence of on-ramp or off-ramp Presence of Off -ramp Presence of On-ramp

21. Measurement of Variables  Distance from bottleneck  For M4 : The bottleneck is assumed to be at the merge i.e., at the 2 nd station.  For QEW: There are 2 bottlenecks during queue formation at this site. - One bottleneck is situated in between stations 47 and 48. - The bottleneck is between the stations 51 and 52.  Distance from bottleneck is thus obtained for each station.

22. Measurement of Variables  Weather  Weather data for the study days was obtained from the following website. http://www.wunderground.com  It was found that the weather was quite consistent in all the study days.  There was no precipitation on the analyzed days and temperatures were above freezing.

23. Example Database

24. Analysis

25. Analysis Process 1.Transitions Duration (for each site)  Duration vs. Distance from Bottleneck  Duration vs. Distance from Bottleneck (average, standard error for each location)  Duration Vs Ramp type (QEW)  Durations Vs Average Wave-speed of queues 2. Lane – Specific Behavior of transition (for each site)  Arrival times of queues in each lane  Duration of transition in each lane

26. 1. Transition Duration - Distance from BN  Queue Formation : As the queue propagates backward from the bottle-neck, the transition duration increases.  Queue Dissipation : As the queue propagates forward towards the bottle-neck, the transition duration increases.  Presence of On-ramp/ Off ramp affects the transition duration.

27. Transition Duration: QEW (Onset, BN : 47) - sudden reduction in transition duration - quicker transition from free flow - congestion

28. Transition Duration: QEW (Onset, BN : 51) - sudden reduction in transition duration - quicker transition from free flow - congestion

29. Transition Duration: QEW (Clear) - sudden reduction in transition duration - quicker transition from congestion – free flow

30. Transition Duration: M4 (Onset)

31. Transition Duration: M4 (Clear)

32. 2. Transition Duration Vs Dist BN (QEW) - Average and Std. Error Values

33. Transition Duration Vs Dist BN (M4) - Average and Std. Error Values

34. 3. Transition Duration – Ramp Condition Queue Formation: Backward Propagation  On-ramp adds more traffic to the queue thus accelerating the transition from free flow to congested flow. This decreases the transition duration.  Off-ramp reduces the traffic on the freeway thus decelerating the transition from free flow to congested flow. This increases the transition duration. Queue Dissipation: Forward Propagation  On-ramp adds more traffic to the queue thus decelerating the transition from congested flow to free flow. This increases the transition duration.  Off-ramp reduces the traffic on the freeway thus accelerating the transition from congested flow to free flow. This decreases the transition duration.

35. Transition Duration vs. Ramp condition On- RampOff - Ramp OnsetDecreasesIncreases ClearIncreasesDecreases Change in Transition Duration with On Ramp and Off Ramp

36. 4. Transition Duration vs. Wave speed for each queue  The Average Wave speed of each queue is calculated using the total queue formation/queue dissipation time. Avg. Wave Speed : Total Distance traveled from St 8 to St 1 Total time for queue formation/dissipation  Wave Speed was found to be inversely related to Transition Duration

37. Transition Duration vs. Wave speed : M4

38. Transition Duration vs. Wave speed : QEW  Noise in the data due to the presence of On and Off Ramps

39. 5. Lane-wise Arrival Time (M4 onset)  On most of the study days, queuing started in Lane 2 first, then Lane 1 and finally Lane 3.

40. Lane-wise Arrival Time (M4 clear)  On most of the study days, clearing started in Lane 2 first, then Lane 1 and finally Lane 3.

41. Lane-wise Arrival Time (QEW onset, BN at 47)  On most of the study days, queuing started in Lane 2 first, then Lane 1 and finally Lane 3.

42. Lane-wise Arrival Time (QEW onset, BN at 51)  On most of the study days, queuing started in Lane 2 first, then Lane 1 and finally Lane 3.

43. Problems/Issues with the Database  Noisy QEW Data : QEW database was found to be very noisy and the transitions were not clear.  Difficulty in identifying clearing queue: QEW database has 20 second loop data starting from 6 AM to 10 AM. But, on few days, the final clearing occurred after 10 AM.  Data Precision: 20 second data was not precise enough for transition identification. Using 1 second data will increase the accuracy.  Correlations: Most of the variables were found to be highly correlated making Statistical Modeling difficult.  For developing linear models for transition duration, larger database is required.

44. Summary of Findings  Change in Duration with respect to the following variables VariableOnsetClear Distance from BN IncreasesDecreases On-RampDecreasesIncreases Off-RampIncreasesDecreases Wave SpeedDecreases

45. On-going Analysis  Head of the queue:  The head of a queue will be analyzed using the trajectory data available for U.S. Highway 101 in Los Angeles, CA.  Microscopic level: Evolution of speed-spacing relations for individual vehicles in the vicinity of the active bottleneck.  Macroscopic level : Examining the flow-density relations in an attempt to bridge the micro- and macro-level features.  Inhomogeneous sections:  Trajectory data from I-80E near San Francisco and U.S. Highway 101 in Los Angeles, CA will be included in the analyses.  Distance over which a transition occurs due to a merge, a diverge or a lane-reduction is analyzed.  Freeway stretch near an inhomogeneous point will be divided into multiple contiguous segments, and a flow-density relation will be estimated for each segment.