Evaluation of the Effectiveness of Potential ATMIS Strategies Using Microscopic Simulation

1. Evaluation of the Effectiveness of Potential ATMIS Strategies Using Microscopic Simulation Lianyu Chu,Henry X. Liu, Will Recker PATH ATMS Center @ UC Irvine Steve Hague Traffic operations, Caltrans

2. Presentation overview • Background • Calibration • ATMIS strategies • Evaluation studies • Conclusions

3. Background • Caltrans TMS master plan • ATMIS Strategies • Incident management • Adaptive ramp metering • Adaptive signal control • Traveler information system • Combination / integrated control

4. I-405 Study network

5. Scenario description • northbound of freeway I-405 is highly congested from 7:30 to 8:30 AM • The merge area of SR-133 and I-405 (on the northbound I-405) is the location where incidents happen most frequently • Shoulder incident: causes the speed of passing vehicles to be 10 mph for the first ten minutes and 15 mph thereafter • purpose: evaluate under incident scenario

6. Calibration: data preparation • Arterial volume data / cordon traffic counts • Freeway loop detector data • Travel time data • Reference OD matrix (from OCTAM model) • Vehicle performance and characteristics data • Vehicle mix by type

7. Calibration procedure • Assumptions • Driver behaviors distribution (awareness and aggressiveness): normal distribution • Traffic assignment method: stochastic assignment • Adjustment of route choice pattern • OD estimation • Adjustment of the total OD matrix • Reconstruction of time-dependent OD demands • Parameter fine-tuning

8. Adjustmentof route choice pattern • Route choices: • determined by stochastic assignment, which calculates shortest path based on speed limits • not affected by traffic signals and ramp metering (PARAMICS) • How to adjust: • Adding tolls to entrance ramps • Decreasing the speed limit of arterial links

9. OD estimation • an under-defined problem, finding an optimal point in a huge parameter space using limited measurement data • Our method: two-stage approach • estimation of total OD matrix • profile-based time-dependent OD demands

10. Total OD matrix (I) • Reference OD matrix from OCTAM • OCTAM: social-economic data and OD matrix of OC • sub-extracted OD matrix based on four-step model • limited to the nearest decennial census year • Adjustment of the total OD matrix: • traffic counts at all cordon points (i.e. total inbound and outbound traffic counts ) • balancing the OD table: FURNESS technique

11. Total OD matrix (II) • Objective function: • Minimize the difference of estimated traffic flow with observation • Measurement points: freeway loop stations at on-ramps, off-ramps and along the mainline freeway, and several important arterial links • Iterative process: simulation->modify OD->simulation • overall quality of the calibration: GEH < 5

12. Time-dependent OD demand (I) • Most theoretical methods: only apply to simple network • Our method: profile-based method • Profile: representation of the variation of OD flow within the whole study time period, which include multiple sample points(16 points) • Cordon flow (traffic counts): 15-minute interval • how many vehicles generated from a zone within each interval: profile of the zone

13. Time-dependent OD demand (II) • General case: • For any origin i, profile(i, j) = profile(i) , j =1 to N • Special cases: • If profile can be roughly determined by loop data • If the corresponding OD flow has strong effects on the traffic condition • Special OD profiles: • freeway to freeway, • arterial to freeway, • freeway to arterial

14. Time-dependent OD demand (III)

15. Time-dependent OD demand (IV)

16. Time-dependent OD demand (V) • Optimization objectives: • Min (difference between the traffic counts of simulation and observation over all points and periods) • 85% of the GEH value smaller than 5(during congestion period: 7:30-8:30AM) • Iteration is required • Pros: reduction in number of parameter to be estimated: • 30x30x16 -> 30x16 • Totally, 30 profiles in the calibrated model

17. Parameter fine-tuning • Link specific parameters • Parameters for the car-following and lane-changing models • Objective: • Minimize (observed travel time, simulated travel time) • Minimize the difference between the traffic counts of simulation and observation over all points and periods

18. Calibration results (I)

19. Calibration results (II) Comparison of observed and simulated travel time of northbound I-405

20. Calibration results (III) • The measure of goodness of fit is the mean abstract percentage error (MAPE): • MAPE error of traffic counts at selected measurement locations range from 5.8% to 8.7%. • The comparison of observed and simulated point-to-point travel time for the northbound and the southbound I-405, which have the MAPE errors of 8.5% and 3.1%, respectively.

21. ATMIS strategies • Strategy 1: Incident management • decreasing the response time and clearance time caused by incidents • For Caltrans: • no incident management: 33 minutes • existing incident management: 26 minutes • improved incident management: 22 minutes

22. ATMIS strategies • Strategy 2: Ramp metering • an effective freeway management strategy to avoid or ameliorate freeway traffic congestion by limiting vehicles access to the freeway from on-ramps. • Current implemented ramp metering: fixed-time • Potential improvement: adaptive ramp metering • local adaptive ramp metering • coordinated ramp metering

23. ATMIS strategies: ramp metering • ALINEA: a local feedback ramp metering policy • maximize the mainline throughput by maintaining a desired occupancy on the downstream mainline freeway.

24. ATMIS strategies:ramp metering • BOTTLENECK, coordinated ramp metering • applied in Seattle, Washington State • Two components: • a local algorithm computing local-level metering rates based on local conditions, • a coordination algorithm computing system-level metering rates based on system capacity constraints. • the more restrictive rate will obey further adjustment • within the range of the pre-specified minimum and maximum metering rates • queuing control

25. ATMIS strategies • Strategy 3: travel information • all kinds of traveler information systems, including VMS routing, highway radios, in-vehicle equipment, etc. • pure traveler information system: no traffic control supports • how to model in PARAMICS: using dynamic feedback assignment • assumptions: instantaneous traffic information is used for the calculation of the resulting route choice

26. ATMIS strategies • Strategy 4: advanced signal control • adaptive signal control, and • signal coordination • Actuated signal coordination: • baseline situation: 11 signal intersections in the study network are coordinated • Adaptive signal control: • use SYNCHRO to optimize signal timing of those signals along major diversion routes during the incident period based on estimated traffic flow

27. Evaluation: Modeling ATMIS strategies

28. Evaluation: MOEs (I) • MOE #1 system efficiency measure: average system travel time (weighted mean OD travel time over the whole period) • MOE #2 system reliability measure: weighted std of mean OD travel time over the whole period

29. Evaluation: MOEs (II) • MOE #3 freeway efficiency measure: average mainline travel speed during the whole period and during the congestion period(7:30-9:30) • MOE #4 on-ramp efficiency measure • total on-ramp delay • average time percentage of the on-ramp queue spillback to the local streets • MOE #5 arterial efficiency measure • average travel time from the upstream end to the downstream end of an arterial and its std

30. Evaluation: number of runs

31. Evaluation results (I): overall performance

32. Evaluation results (II):Freeway performance

33. Evaluation results (III):Arterial performance

34. Evaluation results (IV): IM • Incident management • fast incident response is of particular importance to freeway traffic management and control • To achieve this, comprehensive freeway surveillance system and automatic incident detection are both required

35. Evaluation results (V): ramp metering • performance improvement introduced by adaptive ramp metering is minor under the incident scenarios • If the congestion becomes severe, the target LOS could not be maintained by using ramp metering and the effectiveness of ramp control is marginal • adaptive ramp metering performs worse than the improved incident management scenario • BOTTLENECK performs a little bit better than ALINEA in term of overall performance, but, BOTTLENECK causes higher on-ramp delay and spillback.

36. Evaluation results (VI): TI related scenarios • traveler information • network topology -- one major freeway segment (I405) with two parallel arterial streets • traveler information systems can greatly improve overall system performance • Adaptive signal control: • shorter travel time along diversion route (westbound ALTON parkway) • Combination scenarios: perform the best • integration of traffic control & traveler information

37. Conclusions • Evaluate the effectiveness of potential ATMIS strategies in our API-enhanced PARAMICS environment. • Findings: • All ATMIS strategies have positive effects on the improvement of network performance. • Adaptive ramp metering cannot improve the system performance effectively under incident scenario. • Real-time traveler information systems have the strong positive effects to the traffic systems if deployed properly • Proper combination of ATMIS strategies yields greater benefits.