1. L04 Deployment Discussion

2. Questions from Yi-Chang
a. for Sarath to give a brief overview of the history of L04 as the chair of the L04 TEG, recommendations of the L04 TEG, 
>> We also want to know the results from L35: Local Methods for Modeling, Economic Evaluation, Justification and Use of the Value of Travel Time Reliability in Transportation Decision Making: L35A- Yi-Chang Chiu, UAZ; L35B- Thomas Jacobs; UMD
b. Xuesong to discuss how to decouple the scenario manager from DYNASMART hard-wired 18 parameters that are affected by weather conditions. Most of which are traffic flow model and intersection capacity parameters. These setup are hard-wired only for DYNASMART. What is the implication when linking scenario manager with other simulation models that utilize completely different traffic flow models?
>> We will read the data from the DYNASMART scenario files to make them available through open AMS data hub with easy-to-access format
c. The incident random generation is based on spatial Poisson distribution which is distant from reality. How do we incorporate real crash data if available, such as in MAG region?
>> Scenario data representation is the key, but we might need 400 days of simulation to obtain stable results (result from BAA safety planning in DTA project)
d. How to separate model randomness and scenario (demand and supply) randomness from reporting? 
>> Research question, more simulation runs lead to stable results
e. How to address the computational issues?
>> Existing computer performance of Dynus-T? Use parallel computing hardware?
>> Corridor level study for better data quality control (but extracted data from DTA simulation results)

3. Question a: Many SHRP reliability projects
L02 (NCSU and KAI):
L04 (NW and PB):
L35 (UofA):
L08 (KAI):
L13A (UofU)
We should try to incorporate different existing tools

5. Result from L04

6. L35: Travel time reliability has been considered in Dynus-T (available at public on-line version?)

11. Crash Event Modeling Approach for Dynamic Traffic Assignment
Jay Przybyla
Jeffrey Taylor
Dr. Xuesong Zhou
Dr. Richard Porter
4th Transportation Research Board Conference on Innovations in Travel Modeling (ITM)
Tampa, Florida
April 30th, 2012
FHWA Planning BAA Project : Open-source DTA Tools for Assessing the Effects of Pricing and Crash Reduction Strategies

12. Presentation Outline Why model crashes? How to model crashes?
Crash prediction
Simulation tools
Safety improvement strategies
Working example with “Road Diet”
Modeling complications, limitations

13. Why Model Crashes?
How to capture effects of traffic incidents in traffic assignment? (DTA)
How to enable system-wide or network-wide safety planning?
Started with operations application first since audience will likely be more interested in this application (due to conference topic) than in safety planning.

14. Safety Planning Applications
Incorporating safety in transportation planning
Transportation Improvement Plans
Implemented by state DOTs & MPOs
Highway Safety Manual
Static crash predictions
Safety Surrogates
Microsimulation
Conflicts, speed, etc.
Hot Spot Analysis
Incident rates predicted based on AADT
Source: WFRC (MPO in Salt Lake City, UT)

15. How to Model Crashes? Crash Prediction Simulation Tools
Safety Improvements

16. Safety Performance Function for Single Vehicle Crashes on Segments
Crash Prediction
Predict crash frequency (using AADT, V/C, etc.)
Highway Safety Manual methodology
Safety Performance Function for Single Vehicle Crashes on Segments

17. Analytical / Simulation Methods
Option 1: Average Capacity Reduction
Option 2: Probabilistic Capacity Reduction
Option 3: Incident Calendar
Option 4: Hybrid Approach

18. Option 1: Avg. Capacity Reduction
Crashes have same average capacity reduction
Pros: Pre-set capacity reduction for each iteration
Cons: Simplistic traffic results, cannot capture day-to-day traffic variations

19. Option 2: Prob. Capacity Reduction
Analytical point-queue model
Can’t capture queue spillback

20. How to Correctly Model Travel Time Impacts?
Approach 1: Probabilistic analytical model
Avg TT = Crash prob. * Crash TT + (1-prob.)* Link TT
22min = 20% * 30 min + 80%* 20 min
Approach 2: Simulation to capture queue spillback
A (incident)

21. Option 3: Crash Calendar
Pros: captures impacts of different event types over multiple days
Cons: Numerically intensive, sampling errors
Incident No.
Starting Time
Incident Duration (min)
Capacity Reduction Ratio
Additional Delay (min) 1
Day 8AM 30
0.3
10.0 2
Day
0.23
6.9 3
Day 7AM
0.13
3.81 4
Day 9AM 15
0.12
1.84 5
Day 8AM
0.07
1.12

22. Option 4: Hybrid Analytical/Simulation
Long-term traffic equilibrium
With Incident
Evaluate the probabilistic impact of traffic incidents based on queueing model
Without Incident
Use light-weight DTA to simulate recurring traffic congestion

23. Selecting Simulation Option
Trade-offs between event modeling approaches:
Different resolutions lead to different degrees of modeling accuracy
Requires balance between data availability, output uncertainty/accuracy and computational effort

24. Safety Improvements Safety Improvement Strategy Evaluation
Improve geometric design (crash prob.)
Incident management/response (capacity)
Real-time incident information
Road Diet Example

25. Road Diet Example Application
Taking an even closer look, we will focus on this small East-West corridor with Walker Road (highlighted in white) and Jenkins Road (highlighted in yellow). In this example, we will implement a road diet treatment on Jenkins Road, and use the safety modeling tool to estimate the impacts to nearby streets. These roads both have somewhat similar traffic volumes (though Jenkins has slightly lower volumes), but Walker Road appears to be more suburban while Jenkins Road along this segment appears to be more rural.
These characteristics make this corridor an interesting example for comparison with the safety modeling tool. As you can see, there are two alternative routes on major arterials nearby – Sunset Highway to the north, and Tualatin Valley Highway to the South. Other minor arterials are also located nearby for immediate diversion. The limited route choices and similar traffic volumes should help us to observe the impacts of using DTA for this type of analysis.

26. Step 1: Traffic Volume Calibration
Crash prediction from AADT – calibrate first
The first step in our analysis methodology is to calibrate the AADTs from our network. Since the crash prediction is based on the AADT, we need to verify that our network can reproduce AADT measurements for our network. You can see the green squares on the map – these are virtual sensors with our AADT estimates. For our calibration efforts, we used data from ODOT for freeways and Washington County for local arterials. To estimate the demand, we used the OD Matrix Estimation feature in DTALite.

27. Step 1: Traffic Volume Calibration
Using NEXTA’s calibration feature, we can show the simulation results alongside the observed volumes visually. The calibration feature offers a simple graphics overlay to allow the user to quickly compare observed and estimated volumes, with the purple overlay representing the calibration data (best example shown in top right of image on Sunset Highway).
As you can see, our estimated and observed AADTs are relatively similar after calibration. The OD Estimation results had roughly 15% error, an absolute average difference of 89 vehicles for each measurement, and an R2 of 0.91 for the entire network.

28. Treatment Results Comparison (Expected # Crashes/Year)
Street
From To
Est.
AADT
Length (mi)
Base
Case
Treatment
158th Ave
Jenkins
Walker
23,240
1.35
20.1
22.2
Murray Ave.
25,520
1.05
18.5
18.6
Jenkins Rd.
Murray
15,980
1.49
22.6
Walker Rd.
19,680
1.94
14.2
19.0
Jay St.
13,650
1.14
5.0
6.7
Total
80.4
85.1
Taking a look at the summary statistics, we can see that choosing Jenkins Road for the road diet may not have been the best decision. The simulation results show that we may expect a significant increase in crashes on Walker Road, and a pretty large reduction in crashes on Jenkins Road. However, over the entire small area analyzed in this example, the overall number of crashes has increased. Why did we see an overall increase in crashes over the small area?
Why did crashes increase?

29. Estimated AADT Comparison
Street
From To
Base
Case
Treatment
Difference
158th Ave
Jenkins
Walker
21,560
23,240
1680
Murray Ave.
25,300
25,520
220
Jenkins Rd.
Murray
17,080
15,980
-1100
Walker Rd.
19,020
19,680
660
Jay St.
11,890
13,650
1760
Our assignment results seem to point out the cause for the change. As we can see, the volume reduction on Jenkins Road does not account for the larger AADT change on the surrounding segments. This indicates that the capacity reduction on this segment (caused by removing lanes through the road diet) has affected the assignment for longer paths through the network, exhibiting a larger influence on the entire network as a whole. This might be due to the relatively low number of alternative routes in this portion of the network.
Additionally, Jenkins Road had the lowest volume of the 4 major arterials (158th, Murray, Walker, and Jenkins), and thus we were bound to move more vehicles to routes with higher volumes. The Highway Safety Manual predictive method models typically estimate a greater change in the number of expected crashes with increasing AADT, which may be an additional reason why the overall number of crashes in the small area increased.
Walker Rd.
Jay St.
158th Ave.
Murray Ave.
Jenkins Rd.

30. Output Visualization: Crash Heat Map

31. Modeling Complications, Limitations

32. Levels of Detail: Planning Safety
A small portion of this 4-lane undivided segment has a median barrier.
Planning: One-way link, one-way volumes
Safety: Two-way link, two-way volumes
Issue: Center divider = different prediction equations

33. Network Structure/Topology
Omitted intersections
Intersection definitions
Zonal connectors influence traffic volumes
Red dots: Crashes

34. Calibration/Validation
Further problems when trying to calibrate and validate our crash prediction models.

35. Crash Calendar: Time Resolution
Operations: Peak period, #N modeling periods
Safety: Annual crash frequency
Crashes are rare events
How can we simulate their occurrence?
20 Crashes/Year
0.05 Crashes/Day
0.01 Crashes/Peak Period

36. Time Between Crashes Simulated from Poisson Duration Model
Can we find crash rate equilibrium (long run-times)?
How many “days” do we need to simulate?

37. Summary Why Model Crashes? Crash Prediction
Network compatibility
Simulation Tools (Hybrid Method)
Balance trade offs between approaches
Safety Improvement Strategy Evaluation

38. Summary of Proposed Multi-day DTA Simulation Framework