1. Bi-Level Optimal Sequence and Schedule for Multiple Workzone Projects using WISE and Traffic Simulation By
Sabya Mishra, Khademul Haque, Mihalis Golias, and Tao Ma(University of Memphis)
Brad Freeze (Tennessee Department of Transportation)
Presentation at 2017 planning applications conference, Raleigh, May 16, 2017

2. Overview Motivation Goals and Tasks Rationale Pilot Project Experience
Challenges and Limitations
Initial Recommendations

3. Motivation to WISE (1)
Significant projects which are anticipated to cause sustained work zone impacts
Optimal multiple project coordination helps to reduce work zone related crashes
Obtain three pillars of benefit
Social
Economic
Environmental

4. Motivation to WISE (2) Need for coordinating work zone (WZ) projects
Type: construction, maintenance, utility, etc.
Pass through state
EDC-3 Smarter Work Zones as a initiative to help stimulate and support the improvement of work zone planning.
In TN
Approximately 500 WZ/ year on interstate/state routes
Highest type of WZ are construction
Include a map…..of TN

5. Goals and Tasks Goals of the pilot project
Assessment of WISE to identify limitations
Recommendations to improve WISE
Tasks of pilot project include
Collect network and WZ data,
Build
Planning (sequencing)
Operational (mesoscopic simulation for traffic diversion)
Model calibration and validation
Use WISE to optimize sequence of WZ projects
WZ data includes data such as start and end date, spatial location, hours of operation, work zone type

6. Rationale Work Zone Sequencing
1. Decision Maker Objective: e.g. Minimize Cost
2. Users Objective: Minimum cost path choice
3. Input: # of WZs, and their characteristics, Budget and other constraints
4. Input: Network, Origin-Destination, and Traffic Control
6. Output: Link flow, path flow, travel time, queuing
5. Output: Sequence of WZ activities
Planning
Operations

7. Pilot Project – Shelby County, Memphis, Tennessee
Shelby county: Located in
Memphis Metropolitan Area
SOURCE: Memphis Metropolitan Planning Organization (Interactive Map)

8. Pilot Project Network Overview
Total Length of Roadway Segments (miles)
Rural Interstates
2.1
Urban Interstates
60.3
Rural Principle Arterials
28.6
Urban Principle Arterials
232.7
Urban Freeways/Expressways
35.5
Rural Minor Arterials
13.6
Urban Minor Arterials
513.2
Rural Major collectors
7.7
Urban Collectors
383.3
Rural Minor Collector
81.7
Local Roads
3183.5

9. NeXTA – Study Area Network
Network Properties
# Nodes: 26,348
# Links: 38,606
# O/D pairs: 1,267
Limitation:
23,000 nodes

10. Pilot Project Work Zones
Work Zone Type
Frequency ( )
Completed/Ongoing
511
Proposed/Not Started 51
Total
562

11. Pilot Project Work Zone Characteristics (1)

12. Objective in WISE (Planning Mode)
Find a feasible work zone schedule and sequence such that the overall cost (Z) on the network is reduced to the minimized, where:
overall cost = user costs (traffic delay) + agency costs (Eq. 1)
Subject to constraints including link flow conservation, path flow, and OD pair demand, etc.

13. Solution algorithm WISE Scheduling Engine
Heuristic method, Tabu search to find optimal sequence of projects with defined starting month times, and construction mode (daytime, night time, or both)
K-SP: Network Wide Travel Time
k-shortest path (k-SP) algorithm to find alternative paths where travel time is less than the path going through the work zone *(b)
Assign diverge traffic from the work zone path to K-SP by equilibrium method
Evaluate network-wide travel time accounting for traffic diversion

14. Traffic Diversion Analysis (Operation Mode)
Estimate traffic diversion due to the capacity reduction *(a) from the external DTA model, DynusT
Isolates a single work zone project
Runs DTA for diversion analysis with the work zone in place
Operation Diversion = 1 - (((Base Volume - Project Volume)/(Base Volume)) X 100)
Operational Diversion is made available to the user in the Project Information Section once the Base and Project Scenarios have been run for a given Project.

15. Traffic Diversion Analysis (Operation Mode)
Projects: Lists all Projects Within the Planning Mode
Project Modified:
True if Project Information has been modified since Volume Data was last collected
False if Project has not been modified since Volume Data was last collected.
Base Volume: Nexta/Dynust Volume for Link without a Project:
Null if Nexta/DynusT has not been run for Base Scenario
Contains the Volume Collected from Nexta/DynusT when Base Scenario is executed
Project Volume: Nexta/Dynust Volume for Project Link:
Null if Nexta/DynusT has not been run forProject Scenario
Contains the Volume Collected from Nexta/DynusT when Project Scenario is executed
Run Selected Project Scenario: Runs Nexta/DynusT for a Single Selected Project in the “Projects” list and collects the “Project Volume” Data
Run Base Scenario: Runs Nexta/DynusT for Base Scenario, not containing any Projects the “Base Volume” Data for all Projects in the “Project” list.

16. Overall algorithm procedure
Step 1: Initialization
Set each project an initial feasible schedule.
Step 2: Neighborhood search
For in each construction mode: daytime only, nighttime only, and both:
For in each project:
For in each month:
If schedule project to start at month with a mode of is feasible:
Do traffic diversion.
Compute the objective of Eq. (1).
If it results in a reduced objective, schedule project starting from month with construction mode , update the corresponding solution;
Add triple into Tabu list, and restrict this solution in the next few iterations (user defined).
Otherwise: continue
Step 3: Check stop criteria
Stop if a predefined maximal iteration number is reached, or in continuous 5 iterations it does not find a solution with improved objective.
Step 4: Return the solution with the minimum objective that has been found.
Why didn’t they put DTA in the loop?
Need to redesign…

17. Complementary Comments
Compare with other methods, such as (can be more than the list below)
Distributed Genetic Algorithm (GA)
Simulated annealing
Particle swarm optimization
Simulation based optimization
What if *(a) and *(b) consider combinatorial projects in place?
Improve the method computing traffic diversion;
Improve K-SP;
Automate scheduling and traffic impact assessing procedure, batch run DTA?

18. Complementary Comments
Combinatorial work zone sequencing
1. Work zone set
(One at a time)
1. Work zone set and sequencing algorithm
(Potential combinations)
2. Network wide travel time
2. Network wide travel time
3. Sequencing after individual WZ are analyzed
Improved, relatively complex and realistic
Current, simplistic, and far from reality

19. WISE structure WISE interface Static Assignment Demand Converter
Scheduling Algorithm
DynusT
NEXTA

20. Development environment
Programming language:
C# for Interface of WISE
Python for modules including Demand Converter, Static Assignment, Scheduling Algorithm
C++ for NEXTA, traffic network editor and visualizer
Fortune for DynusT, dynamic traffic assignment engine

21. NeXTA – Simplified Study Area Network
Network Properties
# Nodes: 119
# Links: 348
# O/D pairs: 26

22. Scheduling Results WorkZone Link Capacity Reduction Original Speed
New Speed
Start Month
Duration (months)
From To 1 22 95
0.25 65 20
Apr-15 2 87
0.20 25
May-15 3
101
104 55
Mar-15 4 27 28
0.15 15 5 39 41
0.22 45 6 96 92
Jun-15 7 79
Jul-15 8 90 83
0.3 30

23. Challenges and Limitations
Data Preparation
Identify work zones to be analyzed
State, MPO and City (all have different databases!)
Sub-area selection if the network is bigger
WISE uses DynusT for DTA
Provide flexibility to include other software
Significant effort needed mesoscopic model calibration
Detail construction cost components not defined as input in WISE
Labor, materials, tools, schedule conflict, and other components

24. WISE Software Review Summary
Improvement in four dimensions
Inputs and output data flow in NexTA
Make it easier for practitioners
Importing features from existing DOT/MPO data formats
Standards for signal and control features
Improved processes
Engineering dimensions
Algorithms
Improved algorithms for both project sequencing, and traffic assignment
Single level versus bi-level
Consider combinatorial problems
Consider larger number of work zones
GUI
User friendly GUI for practitioners

25. Q & A Sabya Mishra, Ph.D, P.E. Assistant Professor
University of Memphis
Eric
Hands back to Nicole.