Transcript Model Endogenous Sources

SHRP 2 Project L04
Incorporating Reliability Performance Measures in Operations
and Planning Modeling Tools
Reliability Technical Coordinating Committee
Briefing
in partnership with
&
National Academy of Sciences
Irvine - April 8, 2010
1
Agenda
 Project Overview – Methodology
 Data and Candidate Networks
 Anticipated products of the research
 Work Program Discussion
2
Methodology Framework: Three Components to
Incorporate Reliability in Network Simulation Models
Exogenous
sources
Input
Scenario manager
Endogenous
sources
Improvements to existing simulation tools
Reliabilityintegrated
Simulation model
(meso, micro)
Performance
measures
Output
3
Demand
- Special events
- Day-to-day variation
- Visitors
- Closure of alternative modes
Demand
- Heterogeneity in Route Choice and User Responses to
Information and Control Measures
- Heterogeneity in vehicle type
Supply
- Incidents
- Work zones
- Adverse weather
Supply
- Flow breakdown and incidents
- Heterogeneity in driver behavior
(car following, lane changing…)
- Traffic control
- Dynamic pricing
Vehicle trajectory processor
- Travel time distribution
- Reliability performance indicators
- User-centric reliability measures
Integration in Planning Models
 Reliability-sensitive network
equilibrium models
– Reliability affects traveler’s mode,
departure time and route choice.
– Reliability measures are produced
from the simulation models and
fed back to the demand models.
– Iterate between demand models
and network simulation until
convergence to UE (or SUE).
– Output performance measures for
policy evaluation and network
planning/design.
4
Mode, Departure Time and Route Choice
Traffic Assignment
Stochastic Network Simulation Model
Reliability Measures
Model Exogenous Sources: Scenario Manager
 Scenario-based approach
– Construct discrete scenarios
– Conduct single-point estimation to produce results for each
“what if” scenario
 Monte Carlo sampling
– Randomize demand and/or supply side parameters and
establish the corresponding probability distribution functions.
– Conduct Monte Carlo simulation with regard to these random
parameters
 Scenarios involving equilibrium traffic assignment
– Perform iterative equilibrium assignment for scenarios involving
medium to long term changes in demand or capacity
5
Model Endogenous Sources:
Route Choice Behavior
 Route choice behavior and travel time reliability interact
– Reliability is a result of travel decisions
– Reliability affects route choice behavior
 Reliability in generalized cost function
GC  c  VOT  t  VOR  r
 Heterogeneity in route choice behavior
Value of time distribution
Value of reliability distribution
Traffic simulation model
Value of time of driver n
Value of reliability of driver n
Travel time, toll and
reliability information
Least generalized cost path for driver n
6
Model Endogenous Sources:
Heterogeneity in Driving Behavior
 Microscopic simulation models
– Vehicle-related parameters, e.g. length, maximum
acceleration/ deceleration, reaction time, safety distance,
desired speed, desired acceleration/deceleration,
Maximum give-way time
– Link-related characteristics, e.g. speed limits, visibility
distance at junctions, maximum turning speed, slope
(grade), reaction time variation
– Heterogeneity in car-following and lane-changing behavior,
especially in the presence of heavy vehicles
 Mesoscopic simulation models
– Heterogeneity in vehicle types
– Varying and context-dependent impact on traffic
performance
7
Model Endogenous Sources:
Flow Breakdown and Incidents
Stochastic network simulation model
 Characterize flow
breakdown as a
collective
phenomenon
– Probability of
breakdown
– Breakdown duration
 Characterize flow
breakdown and
incident through
individual decisions
– Describe driver
behavior under
extreme and incident
conditions
8
t=t+1
Prevailing flow rate (q)
Probability of flow breakdown p(q)
Random
number
generator r
r < p(q)?
Yes
Flow breakdown in
the next time interval
Hazard model
No
Breakdown duration
Flow sustain in the
next time interval
Model Endogenous Sources:
State-Dependent Traffic Control
 State-dependent traffic controls - dynamically adjust the
control variables based on the prevailing (or predicted)
traffic conditions, for more effective management.
 State-dependent controls may introduce another source
of unreliability/unpredictability to the system.
 Actuated signal control
 Ramp metering
 Variable message signs
 Dynamic pricing
9
Vehicle Trajectories: Unifying Framework for
Micro and Meso Simulation
 Vehicle (particle) trajectories in the output of a simulation
model enable
– construction of the path and O-D level travel time distributions
of interest
– extraction of link level distributions
 Vehicle trajectories could be obtained from both microand meso-level simulation models
 Trajectories also obtained from direct measurement in
actual networks, enabling consistent theoretical
development in connection with empirical validation.
10
Vehicle Trajectory Processor
Vehicle trajectories
Travel time by lane, link, path
and trip (O-D)
Preferred
arrival time
Experienced vehicle travel time
and actual departure time
Travel time distribution
Performance indicators:
•Travel time variance
•95th percentile travel time
•Buffer index
•Planning time index
•Frequency that congestion
exceeds some threshold
11
User-centric measures:
•Probability of on time arrival
•Schedule delay
•Volatility
Modeling Platform Requirements
– Model Types & Roles
Type of Model
Role in Framework
Planning
• Provide traffic demand input to simulation models
(demand forecasting
models)
• Demonstrate the use of reliability measures for route /
mode choices (and potentially departure time choice)
in an integrated demand-supply framework
Operations
• Incorporate parameters affecting travel time variability
at operations level (supply side)
(meso/micro-simulation
models)
• Interface with Scenario Manager to obtain input based
on exogenous sources / parameters
• Generate (trajectory-based) travel time output for
reliability assessment
• Interface with Trajectory Processor to provide output
for development of travel time distributions, reliability
performance indicators & user-centric measures
13
Planning Model Requirements
 Ability of planning model to use quantitative measures of travel
time variability in demand forecasting processes
(i.e., beyond the common practice of using average travel time and cost)
– expected travel time
– schedule delay
– travel time standard deviation (inferred vs experienced)
 Ability to achieve at least some consistency between simulationgenerated reliability measures and those used in mode / route /
departure time choice models
 Preference for activity-based planning models in order to
incorporate schedule delay and other micro-level, reliability-related
measures
14
Operations (simulation) Model Requirements
 Ability to address most typical urban/suburban type of traffic
conditions
– vehicle/particle-based computational approach & fidelity
– uninterrupted and interrupted flow with various types of facilities (incl.
managed lanes) and control (signalized, stop/yield, etc.)
– multi-vehicle classes (auto, truck, bus), preferably with varying
characteristics
– multi-simulation periods
 Ability of underlying submodels (route choice, lane choice, etc.) to
“endogenize” certain variability sources *
– route choice and driver behavior heterogeneity
– incident and flow breakdown characteristics
– state-dependent traffic control
 Ability to generate vehicle/particle-based trajectories
*
15
may require open-source models or access to code
Software Code Access / Modification
Requirements
 Ability to access / tweak programming code for endogenizing time
variability sources / factors
– some software developers would be keen to assist (depending on level of
effort involved)
 Open source sub-models (e.g., NGSim-developed lane change and
other models)
– already available in some software packages (Dynasmart, Aimsun, Vissim)
 Various forms of intervention through programming tools (API)
– available for most commonly used simulation platforms in North America
(Paramics, Vissim, Aimsun, Transmodeler, Dynasmart, Dynameq, Vista, etc.)
16
Data Requirements
 Traffic data for model adaptation / re-validation
 Ancillary data for parameterization of time variability sources
(endogenous & exogenous)
e.g., special events, incidents, weather …
 Travel time data for
– reliability analysis / concept confirmation
– model output verification / checking
17
Travel Time Data
 Trajectory-based by vehicle trip
(X, Y coordinates and time stamp)
 Capturing both recurring and non-recurring congestion on a
range of road facilities
(from freeways to arterial roads and possibly managed lanes)
 Sufficient sampling and time-series to allow statistically
meaningful analysis
 Ability to tie travel time data to “ancillary data” for time
variability sources
(to allow parameterization for simulation testing purposes)
18
Potential Data Sources / Inquiries made to date
GPS- and Cell-probe data provide most promising prospects for large
scale spatial and temporal coverage
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19
INRIX (national)
NAVTEQ (national)
MyGistics (Chicago region)
Google (national) -no response
ITIS and FCD for validation (Missouri)
Calmar truck data (California, New York, etc.)
Intellione (Toronto) -prelim. tests undertaken
major navigation services provider -prelim. tests undertaken
Preliminary Data Tests to date
Cell Probe Raw Data Format
ProbeID
35948502253992
35948502253992
35932201116385
35932201116385
10304139942743222
10304139942743222
10304139942743222
10304139942743222
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35948502253992
35932201116385
35932201116385
35932201116385
35932201116385
10304139942743222
35932201116385
35932201116385
35932201116385
DepartureDateAnd
Time
3/31/2009 22:00
3/31/2009 22:00
3/31/2009 22:08
3/31/2009 22:11
3/31/2009 22:14
3/31/2009 22:15
3/31/2009 22:17
3/31/2009 22:18
3/31/2009 22:22
3/31/2009 22:32
3/31/2009 22:33
3/31/2009 22:38
3/31/2009 22:40
3/31/2009 22:45
3/31/2009 22:47
3/31/2009 22:50
3/31/2009 22:52
3/31/2009 22:54
3/31/2009 22:56
20
3/31/2009 22:59
LinkID
743407983
743407983
743407985
743407985
743407985
743407985
743407985
743407985
743411775
743411775
743411775
743411775
743411777
743411777
743411777
743411777
743411777
743411777
743411778
743411778
743411778
743417546
743417546
743417547
743417547
743417547
ProbeID
Speedkmhr
TripDistance
30647052
9
28359911
90.5685738
12639.304
8.91222088
3
80.2395639
332.599
0.332599
1412.7998
1.4127998
6
88.5936305
2
77.4538931
1412.7998
1.4127998
4353.602
4.353602
6
57.2858494
6
79.4489917
862.801
0.862801
1873.7998
1.8737998
1
86.1885529
1
82.8689835
5911.5014
5.9115014
1412.7998
1.4127998
5
69.9313469
4
74.2830656
3689.2004
3.6892004
3088.7001
3.0887001
81.1116279
4
119.033638
1412.7998
7
92.1919363
2
66.8715829
9
42.6679104
6
97.9091111
6
27386500
0
28249609
5
36597247
3
36710769
6
28188617
5
31157524
7
34364859
3
30835790
2
39944874
0
28814374
2
27775273
6
43758407
4
27760539
0
35412696
7
34062679
8
27802664
5
27593967
1
31366572
2
TripDistance
(Km)
12.639304
Direction
of travel
2
2
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
Longitude Start
-80.6850899966042
-80.6858600225125
-80.6849100124862
-80.684290005487
-80.6866899788012
-80.6856799844734
-80.6849100124862
-80.684290005487
-80.6814100238178
-80.6819700013037
-80.6814100238178
-80.6819700013037
-80.6825899919003
-80.6830700233299
-80.68353000075
-80.6825899919003
-80.6830700233299
-80.68353000075
-80.6837300172132
-80.6832900085453
-80.6828399801028
-80.689979996436
-80.689979996436
-80.6897100112995
-80.6886700032165
-80.6876499783595
TripTime
TravelTimePerKm
502.398265
7
134.349946
6
63.3861779
5
57.4090851
39.74888694
45.31209173
61.3749446
7
189.916568
43.44206778
48.46326644
1.4127998
5
149.688495
9
62.7046874
7421.6004
7.4216004
7
224.455555
30.24355165
5459.101
5.459101
213.172262
39.04896831
2040.301
2.040301
53.83452641
101.9994
0.1019994
109.838638
1
8.6059485
166.551586
9
55.0443747
36.76879462
1
322.331381
8
49.6291471
35.08788462
4.5296994
1.4127998
9186.4011
9.1864011
6
102.481698
2
1412.7998
1.4127998
7
43.1325600021493
43.1312000168074
43.1354399869108
43.1369500002555
43.1324999825377
43.1339500247966
43.1354399869108
43.1369500002555
43.1425399935343
43.1410200225322
43.1425399935343
43.1410200225322
43.1394600295052
43.1381900087434
43.1369199950256
43.1394600295052
43.1381900087434
43.1369199950256
43.1382199696614
43.1394899987922
43.1407600261243
43.1277999760038
43.1277999760038
43.1288900126855
43.1299900225328
43.1311199711812
Mean Travel
Time per km
106.41854
Standard
Deviation
133.27384
46.093075
3.4917907
37.891933
5.8302098
47.358861
17.770679
44.8656476
8
84.9057884
2
246.916839
1412.7998
Latitude End
-80.6858600225125
-80.6868200003515
-80.684290005487
-80.6837300172132
-80.6856799844734
-80.6849100124862
-80.684290005487
-80.6837300172132
-80.6819700013037
-80.6825300178688
-80.6819700013037
-80.6825300178688
-80.6830700233299
-80.68353000075
-80.6840000090416
-80.6830700233299
-80.68353000075
-80.6840000090416
-80.6832900085453
-80.6828399801028
-80.682389981059
-80.6897100112995
-80.6897100112995
-80.6886700032165
-80.6876499783595
-80.6866899788012
40.63497544
46.47926467
4529.6994
43.1339800097549
43.1325600021493
43.1339500247966
43.1354399869108
43.1311199711812
43.1324999825377
43.1339500247966
43.1354399869108
43.1440699972467
43.1425399935343
43.1440699972467
43.1425399935343
43.1407399960802
43.1394600295052
43.1381900087434
43.1407399960802
43.1394600295052
43.1381900087434
43.1369500002555
43.1382199696614
43.1394899987922
43.1275700193056
43.1275700193056
43.1277999760038
43.1288900126855
43.1299900225328
Trajectory
Longitude End
403.9397191
7
202.352219
6
54.2207827
8
92.3996195
2
102.599516
Latitude Start
62.84274448
41.76888784
51.47905993
44.38327884
84.37254043
38.96119939
35.12822352
Link classification
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Time since
111970 sec
1256842898
1256842906
1256842867
1256842873
1256842889
1256842893
1256842897
1256842901
1256842832
1256842837
1256842844
1256842852
1256842842
1256842846
1256842850
1256842861
1256842867
1256842874
1256842877
1256842882
1256842886
1256842832
1256842873
1256842836
1256842839
1256842843
Travel time sec
7.93010162457449
7.94944228245065
5.29451989243751
5.31951772465551
4.29094891357159
4.26237531203243
4.26516922535072
4.28530702572597
4.91146210334099
4.88143914381964
8.22098847742744
8.17073492785502
4.1156666382753
4.07434404815423
4.0799424280729
6.88895634299567
6.81978905022318
6.82915982736353
4.45394634674168
4.46037755637271
4.46026431115965
0.876549764185665
0.829482435996493
3.84500182950044
3.84458566202761
3.84839424900487
Speed msec
21.4452066187493
21.4452066187493
32.7420969216286
32.7420969216286
40.6440341080312
40.6440341080312
40.6440341080312
40.6440341080312
35.8957867940915
35.8957867940915
21.4452066187493
21.4452066187493
35.8957867940915
35.8957867940915
35.8957867940915
21.4452066187493
21.4452066187493
21.4452066187493
32.7420969216286
32.7420969216286
32.7420969216286
38.461606856942
40.6440341080312
38.461606856942
38.461606856942
38.461606856942
Demo Site Selection Considerations
 large urban/suburban area
– typical congestion-related travel time variability characteristics
 existing models that meet L04 technical approach / simulation
functional requirements
– network size /configuration for meaningful measurement of time variability
– vehicle trajectories / time distributions
 data availability
– primarily trajectory travel times
 other considerations
– willingness of jurisdictional authority to participate in the project and/or provide data
and base model
– familiarity of research team staff with candidate network, data and model…
21
Potential Sites -
(best candidates so far noted with *)
 Atlanta
(trajectory data availability concerns)
 Baltimore - Washington DC area
 California (San Francisco / Bay Area)
 Chicago
(cost considerations may be prohibitive)
 New York City / Metro Area * *
(most model requirements already met, wide-area GPS data from various sources)
 Toronto *
(most models already in place or close to completion, wide-area GPS & cell probe data)
 Montreal
(models in place, GPS data can be arranged, institutional/jurisdictional concerns)
 other areas (Seattle, Phoenix, Detroit, Austin)
22
Project Products
 Reports
– Phase I reviews in detail fundamental approach, includes
supporting data, candidate networks, reliability measures
– Phase II reports the results of model calibration and validation,
includes guidelines and materials for full replication of phase II
– Phase III report incorporates reliability into travel models
 Outreach
–
–
–
–
23
Pilot demonstrations of the simulation model
Brochure, website, “how to” CD
Information sessions and demonstrations
Visualization tools
Product Audience for SHRP 2 L04
 Practitioners and researchers
 Software vendors and developers
 Operations managers, planners in transportation
agencies interested in practical implications
24