1. Work Flow
July 21, 2014

2. Workflow Data Processing Model Components: FD / BF / SR / Signals
cc-network
cc-scenario
CTM Forward Simulation
cell density, velocity, flow
Our work flow follows the general practice on building engineering systems. The typical process is like this:
Data analysis, data cleaning
Calibration of the system parameters
Running the model
Analyzing/Aggregating the results into metrics
Validation of system against reality by comparing system metrics with real measurements
This picture explains the main components of the work flow to build a traffic simulation model.
Our simulation is based on cell transmission model (CTM). CTM models feature fast simulation by aggregating individual vehicles into macroscopic measures like flow, density, and speed. A CTM model requires the following components to simulate: a road network represented as links and nodes, signal timing at nodes that represent signalized intersection, inflows on the boundary of the network (which we call boundary flow, or BF), turn ratios at diverge nodes (which we call split ratio, or SR), and road network parameters like flow capacity and free flow speed (which we call fundamental diagram, or FD). The CTM simulation produces flow, density and speed over all links over the time period of interest.
The first step of the work flow is to clean and aggregate the various types of data we collected (e.g., loop detector data, traffic study data, signal timing sheets). We then use these data to calibrate the CTM model, which means to decide the best parameter values to use for model components such as boundary flows, split ratios and fundamental diagram. We then run the CTM simulation model, which produces flow, density and speed. Performance metrics like travel time, delay, level of service, vehicle hours traveled, and vehicle miles traveled are calculated from the simulation results. Last but not least, we compare travel time and flow from the simulation and those measured in the field. If we see significant discrepancies between simulation and field measurements, we iterate the calibration process until results pass sanity check.
There are some limitations to the current work flow.
First, the work flow only covers the baseline situation, which means no major accident. If there are accidents, we need to model them differently. Second, this process is currently for arterial network only. For a network that includes both freeway and arterial, the calibration processes are done separately and we “stitch” the model together. This obviously complicates the creation of the traffic simulation model, at least given the tools we have built so far. Third, the calibration process is static in nature, which means the parameters we obtain does not change with time. This may be OK for a small period of time on the order of minutes but not an extended period of time on the order of hours.
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow

3. Data Processing Data Processing Racetrack flow 2006 1
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Data Processing
Data Processing
Racetrack flow 2006 1
Racetrack SR 2006 2
Loop flow 2014 3
Raw Data
Loop SR 2014 4
Background of data:
* Note: all our data are aggregated for the evening peak period of weekdays.
Data types:
Flow (aka counts) in vehicle/hour; either on a link across all lanes (“On Huntington westbound between 1st and Santa Anita flow is 733 veh/hr”) , or for a specific movement at an intersection (“On Huntington westbound, 125 veh/hr turn left at Santa Anita”).
Split ratios in percentage (“On Huntington westbound, 17% of traffic turns left at Santa Anita”)
Travel Time in seconds between two locations. (“On Huntington westbound, the average travel between Gateway and Santa Clara is … seconds”)
Signal Timings: describing when the lights show red, yellow and green.
Data sources:
A traffic study carried out in 2006 around the racetrack in Arcadia (which we call the Racetrack study); movement flows were counted manually on a few days when the racetrack was in session.
Induction loops: loops are installed in the pavement on the freeway and on some arterial roads, which measure the flow continuously; [Fred: Please decide whether to keep the narrative about freeway loops, as this process only concerns arterial.]
Bluetooth: Bluetooth sensors mounted at certain intersections record the time and id of the nearby mobile Bluetooth devices. Travel time of Bluetooth devices is produced through the matching of id at different mounting locations.
Signal Timing Sheets are provided by the operator of the signalized intersections (e.g., the city or Caltrans). They range from 2012 to 2013.
Data processing is the act of filtering and extracting useful information form the vast amount of raw data.
Bluetooth Travel time 2014 5
Signal Timing 2012/13 6

4. Model Components Generate FD Fixed capacity Fixed jam density
Data Processing
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Model Components
Generate FD
Fixed capacity
Fixed jam density
Speed limit from
Nokia map
cc-network FD
SR/BF Inference
Racetrack flow 1
Racetrack SR 2
SR, BF
cc-scenario building
Loop flow 3
This picture describes the calibration process to create the model components for simulation.
Cc-scenario:
Cc-network + all model components (boundary flows, split ratios, fundamental diagrams, signal timing).
Cc-network:
With a map provided by Here.com, we generate a representation of the road network with links (each representing a road segment) and nodes (each connecting 2 or more road segments). We also define source links to be the links where traffic enters the network, and sink links where traffic leaves the network. FD
The fundamental diagram (FD) components describe roadway parameters such as free flow speed (the speed during light traffic conditions), flow capacity (maximum possible flow often seen at a recurrent bottleneck), jam density (number of cars per mile when traffic is standing still). They are created according to the Highway Capacity Manual. The only input it needs is the free flow speed, which we approximate with the speed limit we obtain from the Here.com map.
BF and SR
Boundary flows (BF) components describe the amount of traffic that enters the network at each source link. Split ratios (SR) components describe the fraction of traffic that turns at intersections and off-ramps (or any bifurcation in the network). The data available to us do not cover all the boundary flows and split ratios, due to limited instrumentation. So we use data measured elsewhere on the network to estimate the missing boundary flows and split ratios. We did this by building a inference model that takes in the flow and split ratio data from arterial loop detectors and the racetrack traffic study. The core of the inference model is an optimization that matches measured flows with flows estimated from the principle of mass conservation.
Signals
We convert the signal timing sheets obtained from jurisdictions into fixed traffic signal plans.
Loop SR 4
Signal Timing 6
cc-scenario

5. CTM Forward Simulation
Data Processing
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
CTM Forward Simulation
CTM Forward Simulation
Cell density,
velocity, flow
cc-scenario
Main features (advantages)
1. CTM simulation is dynamic in nature, i.e., both the input (such as boundary flows) and output (such as the simulated flow, density, and speed) change with time. We choose dynamic simulation models due to the time-varying nature of some of our bottlenecks, such as incidents.
2. CTM simulation is one of the macroscopic traffic simulations. Different from the microscopic (and some mesoscopic) simulation that keeps track of individual vehicles, CTM abstract out individual vehicles into aggregate measures like flow, density and speed. With fewer things to keep track of, the simulation runs much faster. This approximation is acceptable if the desired level of granularity is on the order of dozens of vehicles instead of individual vehicles.
Key assumptions
1. CTM assumes mass conservation, which means vehicles can only appear at source links and disappear at sink links.
2. Vehicles flow according to the fundamental diagram (the relation between flow and density).
Limitations
1. It is less intuitive than microscopic models, since traffic is not represented as individual vehicles
2. There is no explicit vehicle route; rerouting vehicles requires recalculation of split ratios.
3. Macroscopic traffic simulation software in general is less developed and used less frequently in industry compared with microscopic simulations.

6. Metrics Travel Time Delay Cell density, velocity, flow
Data Processing
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Metrics
Travel Time
Delay
Cell density, velocity, flow
Level of Service
The CTM simulation results are then used to calculate various performance measures such as
vehicle hours traveled
vehicle miles traveled
travel time
delay
level of service
Meaning
vehicle hours traveled: total time spent by all the vehicles in the specified area and time period
vehicle miles traveled: total distance traveled by all the vehicles in the specified area and time period
travel time: average experienced travel time along specified path over specified time period
Delay: average extra travel time (compared with free flow speed) along specified path over specified time period
level of service: a letter grade from A (best) to F (worst) indicating the performance of the facility (in our case, signalized intersections)
Why important
vehicle hours traveled, vehicle miles traveled: network performance metrics from the system operator’s point of view. VMT indicates the amount of vehicles using the network, or network load; VHT indicates the level of congestion in the network, or network performance.
travel time, delay: performance metrics relevant to and commonly used by individual drivers
level of service: metric for transportation facility performance
VHT
VMT

7. Comparison Data Processing Validation Data Set Calibration Data Set
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Comparison
Data Processing
Validation Data Set
Calibration Data Set
Model Components
CTM Forward Simulation
Metric Calculation
Bluetooth Travel time 5
Loop flow 3
The last step here is to compare the performance measures from the simulation with those data measurements.
[Fred: The note at end bottom of the slide is obsolete. We have done such analysis after the August demo.]
We have selected 21 days from March to May in year 2014 that mimics the baseline situation that we are modeling here. Of these 21 days, we used data from 15 days from March and April in the calibration process. When making comparisons, we compare the simulated flow and travel time with measured flow and travel time from the 6 days in May, according to the FHWA model calibration criteria. Technically, making comparisons this way prevents us from over-fitting data to our model. Intuitively, you may imagine current time is the end of April, we can use data from the past to calibrate the model, and we want to predict future traffic status, at least of the baseline situation.
Comparison: Travel Time, Flow
NOTE, we still have to do this splitting into two data sets. Currently we are committing the crime of using the same data set for calibration and validation

8. Data Processing Data Processing Racetrack flow 2006 1
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Data Processing
Data Processing
Racetrack flow 2006 1
Racetrack SR 2006 2
Loop flow 2014 3
Raw Data
Loop SR 2014 4
Bluetooth Travel time 2014 5
Signal Timing 2012/13 6

9. Data Processing 1: Racetrack 2006
Raw Data: Flows per movement per intersection
Process: Calculate flows per approach and split ratios (SR)
qN = qNL + qNT + qNR
qE = …
qS = …
qW = …
βNL = qNL / qN
βNT = qNT / qN
βNR = qNR / qN ….
qER
qNR
qNT
qNL
qET
Racetrack flow 1
qEL
qWL
qWT
qSL
qST
qSR
qWR
Racetrack SR 2
qWR
Direction: Left, Through, Right
Racetrack flow
where loop is available
at the same location 1a 1
Static flow
Approach: East, South, West, North
Racetrack flow
where loop is not available at the same location 1b

10. Racetrack Flows 2006 1 I210W 867 552 2004 Colorado Blvd 1680 224 854
Michillinda
Baldwin / Oxford
Colorado Pl
510
Huntington
Colorado Pl
905
417
356
555
Colorado
Label the region that we scale up / down
297
1238
1283
752
809
1059
1138
1100
1116
1933
1705
1258
1571
1397
96 9
497
Huntington
Santa Clara
Santa Anita
1st
2nd
Gateway
5th
I210E
I210W

11. Racetrack Split Ratios 2006
I210W
Racetrack Split Ratios 2006 2
49%
24%
58%
18%
44% 7%
11%
Colorado Blvd
55%
45%
93%
77% 7%
Colorado Blvd
12% 7%
71%
22%
Colorado Pl
94% 6%
Michillinda
Baldwin / Oxford
Huntington
73%
19%
82%
11%
72%
17% 8%
18%
74%
26%
Colorado Pl
100% 8%
92%
10% 6%
23% 2%
73%
75%
10%
35%
Colorado
77%
98%
17%
19%
90%
65% 5% 5% 3%
100%
10%
95%
68%
91%
90%
27% 6%
Huntington 7%
37%
56%
17%
64%
19% 8%
51%
41%
Santa Clara
Santa Anita
1st
2nd
Gateway
5th
I210E
I210W

12. Data Processing 2: Arcadia Loops 2014
Raw Data: Flows per approach and per left-turn per intersection (measured upstream of turnbay opening)
Process: Calculate typical working day PM peak flow
q̄W = meant { mediand { qWL(d,t) } }
β̄WL = q̄WL / q̄W
qEL(d,t)
qN(d,t)
qE(d,t)
d in { 88 day, Mon-Thu }
t in { 16:00 – 18:00 }
qWL(d,t)
qW(d,t)
qS(d,t)
Flow
Loop flow 3
qW(d,t)
Time of day
Date
16:00
Time
18:00
Measured loop flow (5-min)
Approach: East, South, West, North
Loop SR 4

13. Loop Flows 2014 3 I210W Colorado Blvd Colorado Blvd Michillinda
Baldwin / Oxford
Colorado Pl
Huntington
Colorado Pl
654
221
Colorado
Label the region that we scale up / down
316
1187
803
733
788
965
362
814
742
1079
1331
544
Huntington
411
355
Santa Clara
Santa Anita
1st
2nd
Gateway
5th
I210E
I210W

14. Data Processing 3: Travel Time 2014
Raw Data: Individual travel time between Gateway and Santa Clara
Process: Calculate typical working day PM peak travel time
T̄W = meant { mediand { TW(d,t) } }
TW(d,t)
d in { 88 day, Mon-Thu }
t in { 16:00 – 18:00 }
TE(d,t)
Bluetooth Travel time 5
TW(d,t)
Time of day
Date
Measured travel time (individual)
Direction: East, West

15. Data Processing 4: Signals 2012/13
Raw Data: Timing sheets per each intersection
Process: manually adapt according to simulation capability, default: each phase used maximum allowable green time
Signal Timing 6

16. CTM Forward Simulation
Data Processing
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
CTM Forward Simulation
CTM Forward Simulation
Cell density,
velocity, flow
cc-scenario

17. High-Level Explanation of CTM
“Forward Simulation”
Use a dynamic model with realistic traffic behavior to simulate traffic conditions
Model used: Cell-Transmission-Model
Reproduction of traffic conditions of large networks
Intuitive parameters / input:
Saturation flow
Speed limit
Split ratios, boundary flows
Signal Timings
Model output
Density contour, Speed contour, Congestion contour,
VHT, VMT
Vehicle Trajectories, Travel Time, Delay

18. Why not use a micro model?
CTM abstracts from microscopic behavior that is irrelevant for corridor-wide management:
Lane change behavior
Gap-acceptance behavior
Slow acceleration/deceleration
Instead, direct modeling of quantities that are relevant for corridors
Where is traffic right now:  density
What is the throughput at intersection X:  flow
How fast is traffic moving:  speed

19. Further advantages of CTM
Average traffic behavior
One run is sufficient to get a meaningful average
Deterministic Model
Same input  same output
Reproducible results
Suitable for model-predictive control (MPC)
State-space of constant size
Computation during congestion as fast as during light load

20. Metrics Travel Time Delay Cell density, velocity, flow
Data Processing
Model Components: FD / BF / SR / Signals
CTM Forward Simulation
Metrics: TT, Delay, LOS, VHT, VMT
Comparison: Travel Time, Flow
Metrics
Travel Time
Delay
Cell density, velocity, flow
Level of Service
VHT
VMT

21. Metrics TODO clarify notation Travel Time Delay
Vehicle Delay = Experienced Travel Time – Free-flow travel time
LOS
Based on average vehicle delay

22. Metrics TODO clarify notation Vehicles Miles Traveled
Vehicle Hours Traveled
Total Delay
All these performance values can be computed at run time of the dynamical system.