L04 Deployment Discussion

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)

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

Result from L04

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

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

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

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.

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)

How to Model Crashes? Crash Prediction Simulation Tools Safety Improvements

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

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

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

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

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)

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 1@ 8AM 30 0.3 10.0 2 Day 12 @8:30AM 0.23 6.9 3 Day 15@ 7AM 0.13 3.81 4 Day 15 @ 9AM 15 0.12 1.84 5 Day 20 @ 8AM 0.07 1.12

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

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

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

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.

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.

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.

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?

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.

Output Visualization: Crash Heat Map

Modeling Complications, Limitations

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

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

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

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

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?

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

Summary of Proposed Multi-day DTA Simulation Framework