peer exchange meeting - buffalomceer.buffalo.edu/.../transims/07_sadek.pdf · using transims for...

Using TRANSIMS for On-line Transportation System Management during Emergencies

Peer Exchange MeetingMarch 15, 2012

Project Objectives/Outcomes

• Further development of the Buffalo TRANSIMS model

• Modifying TRANSIMS to allow for modeling impact of inclement weather

• Simulating emergency scenarios in the Buffalo-Niagara area

FHWA Peer Exchange Meeting on Transportation Systems Management during Inclement Weather

Presentation 07 1 Buffalo, NY | March 15, 2012

What is TRANSIMS

• Initially developed at Los Alamos National Lab as representing the

Synthetic PopulationGenerator

Input Data

National Lab as representing the next generation of transportation models

• A person-based simulator which combines detailed modeling of traffic flow dynamics with the ability

Activity Generator

Router

Micro-simulator

Fee

db

ac

k C

ont

rolle

r

traffic flow dynamics with the ability to model traveler behavior Emissions Estimator

TRANSIMS Model

Model Refinement & Error Checking

Calibration: Demand and Diurnal Distribution

Model Validation



Network Refinement1. Subarea ExpansionError-Checking List:Error Checking List:

Pocket lanes;“Zigzag links;Signal locations;Major network bottlenecks;

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Network Refinement

2. Missing left-turn pocket lane;

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the Intersection between John James Audubon Pkwy and N Forest Rd



Network Refinement3. Redundant signal: intersection between Erie Ave

and Niagara Falls Blvd;

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Network Refinement4. Study the impact of the missing local roads;

Capacity Loss

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Network Refinement5. Study the impact of the missing local roads;

Scenario Test: add Koenig Rd between Niagara g gFalls Blvd and Parker BlvdVolume Up: extra capacity

attracts more traffic.

9Daily Volume

Daily Queue Length

Network Refinement6. 20% Missing Trips

Almost 20% problem trips in the last run;

Much more vehicles on the network than the simplified network could handle;

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Reduced Demand might fix it.



Demand & Diurnal Calibration1. Review

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Micro-simulator reports on the problem trips

Calibration2. Parameter Sensitivity StudyRouter

END_TIME_CONSTRAINT

Micro-SimulatorMINIMUM_WAITING_TIMEMAXIMUM_WAITING_TIMEMAX ARRIVAL TIME VARIANCE

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_ _ _MAX_DEPARTURE_TIME_VARIANCE



Calibration2. Parameter Sensitivity Study

END_TIME_CONSTRAINT

The end time constraint is optional and only applied if the IGNORE_TIME_CONSTRAINTS key is “false”. This parameter enables the user to add a time buffer to the end time of the trip to limit the time constraint errors to those instances where the travel exceeds the end time plus the end time constraint. The

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pparameter is defined in minutes. The default is zero.


MINIMUM_WAITING_TIME

If a vehicle does not move from a given cell for a prolonged period of time, it is likely to be stuck in a deadlock situation. To break deadlocks, the simulation can give priority to vehicles that have not moved for some time. The vehicle is placed in a priority queue if it has not moved for more than the minimum

Arrival Time Problems

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p y qwaiting time. This parameter defaults to 180 seconds. The vehicle remains in the priority queue until it moves or the maximum waiting time is reached. Vehicles in the priority queue are given first opportunity to make lane changes or forward movements at the beginning of each time step.




Waiting Time Problems

MAXIMUM_WAITING_TIME

The maximum waiting time defines when a vehicle is removed from the simulation. If the vehicle has not moved for this amount of time, a Waiting Time problem message is generate, the vehicle is removed from the link, and moved to the destination parking lot. The default value is 3600 seconds.


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p g


MAX_ARRIVAL_TIME_VARIANCE

Each travel plan includes the expected arrival time at the destination activity location. If the vehicle is still traveling on the network at a time equal to the scheduled arrival time plus the maximum arrival time variance, the vehicle is removed from the simulation and moved to the destination parking lot, and an


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p g ,arrival time problem message is posted in the problem file. The default value for this parameter is 60 minutes.




MAX_DEPARTURE_TIME_VARIANCE

If the vehicle is unable to leave the parking lot at the beginning of the trip before the scheduled departure time plus the maximum departure time variance, the trip is abandoned and the vehicle is moved to the destination parking lot and a

Departure Time Problems

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the vehicle is moved to the destination parking lot, and a departure time problem message is generated. The default value for this parameter is 60 minutes.


Five Scenarios with Different Parameter SettingsFive Scenarios with Different Parameter Settings

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Calibration3. Demand Study

Waiting time and arrival time problems indicates that the number of g pthe vehicles on the network has exceeded network capacity.

193 Count Stations

Tuesday, Wednesday and

19the locations of the count stations

Thursday Only

Calibration3. Demand Study 70%, 80% and 90%1st Scenario: 80% Demand

Hr NYSDOT MSim MicErr0 20224.5 11321 -44%1 11679.66667 14330 23%

Hr NYSDOT MSim Router MicErr RtrErr0 20224.5 9217 9178 -54% -55%1 11679.7 11844 11977 1% 3%23%

2 6417.166667 9988 56%3 4597.666667 7945 73%4 4415.5 10305 133%5 6448 32342 402%6 16826.83333 73071 334%7 46353.16667 117240 153%8 102009.1667 139033 36%9 100873.8333 132373 31%

10 78563.66667 111545 42%11 76581 98376 28%12 84315.66667 100313 19%13 94304 108491 15%14 91828 16667 115730 26%

1 11679.7 11844 11977 1% 3%2 6417.17 7715 7781 20% 21%3 4597.67 6381 6407 39% 39%4 4415.5 8230 8258 86% 87%5 6448 25343 25234 293% 291%6 16826.8 58110 57984 245% 245%7 46353.2 94107 96148 103% 107%8 102009 110374 115680 8% 13%9 100874 102842 108546 2% 8%

10 78563.7 86572 90441 10% 15%11 76581 76465 77819 0% 2%12 84315.7 79946 81502 -5% -3%13 94304 85358 87477 -9% -7%14 91828 2 94939 98113 3% 7%

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14 91828.16667 115730 26%15 99409 123207 24%16 114617.1667 128452 12%17 125961.6667 154875 23%18 126660.8333 158564 25%19 96055.5 143220 49%20 74223.66667 110952 49%21 62745.66667 86560 38%22 50576.83333 53250 5%23 32609.16667 38668 19%

SUM 1528297.5 2080151 65%

14 91828.2 94939 98113 3% 7%15 99409 112127 116595 13% 17%16 114617 129789 134317 13% 17%17 125962 125827 132256 0% 5%18 126661 97546 110839 -23% -12%19 96055.5 85502 79970 -11% -17%20 74223.7 67671 58185 -9% -22%21 62745.7 49357 47326 -21% -25%22 50576.8 38241 38455 -24% -24%23 32609.2 29748 30120 -9% -8%

SUM 1528298 1593251 1630608 28% 29%

100% Demand 80% Demand




2nd Scenario : 77% demand and new Diurnal2nd Scenario : 77% demand and new Diurnal Distribution, time-consuming adaptive process;

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Problem trips is as low as 0 1%Problem trips is as low as 0.1%.

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Trip Distribution of Scenario Three




D d R d ti St b StDemand Reduction Step by StepStarting with 3.7 million daily trips (from GBNRTC);Removing intrazonals, 3.3 million trips (Scott);Removing the short trips (via Reduction Factor), got 2.5 million trips;77%demand + new diurnal distribution 2 54

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77%demand + new diurnal distribution, 2.54 million trips

Validation1. Mean Absolute Error (MAE)Hr Field Simulation MAE7 46353 56879 23%8 102009 91827 10%9 100874 106302 5%10 78564 99408 27%14 91828 82228 10%15 99409 91411 8%16 114617 107494 6%17 125962 123089 2%

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18 126661 120440 5%19 96056 99665 4%20 74224 73728 1%21 62746 54954 12%22 50577 45334 10%23 32609 37023 14%SUM 1528298 1514125 22%



Validation

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Validation2. Regression Analysis

Wh i l i ?Why regression analysis?percentage error: exaggeration of errors at low traffic volumes;U-statistic: overly sensitive to differences in the temporal variations;GEH statistic: sensitive to volume variation;

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;



Validation2. Regression Analysis (Simulation vs. Field)R2=0.958 R2=0 998R =0.998

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Weather Impact on Driving Behavior & Demand

MotivationMotivationPurpose & ScopeLiterature ReviewMethodologyConclusionsNext StepsNext Steps



Motivation

In the U.S., More than 25% of the annual 1,561,000 hi l h th l t dvehicle crashes are weather-related

Weather-related crashes kill 7,400 people killed, and injure more than 673,000 annually

State DOTs spend between 20- 25% of their budgetState DOTs spend between 20 25% of their budget on winter road maintenance annually

Purpose & Scope

Impact of inclement weather on freeway traffic speed, at both the macroscopic and microscopic levels

Uses data from the Buffalo-Niagara metropolitan area in Western NY

Operating speed is a traffic flow parameter that isOperating speed is a traffic flow parameter that is applicable at:

• Macroscopic level - average speed• Microscopic level – speed of an individual vehicle



Literature ReviewMacroscopic Impact Studies• CapacityCapacity

• HCM 2000: heavy rain: 15%• Light snow: 5~10%, heavy: 25~30%• Agarwal et al. (2011)

• Volume• Hanbali & Kuemmel : daily volumes, peak hours

• Speed (Travel Time)• Speed (Travel Time)• HCM 2000: FFS light rain: 2~14%, heavy: 5~17%• Light snow: 3~10%, heavy: 20~35%• Kyte et al. (2001) R2 = 40%

Microscopic (limited)

Methodology

Data Collection & ProcessingData Collection & Processing• Weather Data• TRANSMIT Speed Data• Probe Vehicle Data

Weather Indexing FrameworkRegression Model DevelopmentMicroscopic Traffic Simulation



Weather Indexing Framework

Visibility Indexy_• Threshold: 4 miles

WeatherType_Index• e.g. weather type “+SN FG” is interpreted as heavy (-2)

snow (-3) and fog (-3), -8 in total

Temperature_Index• Threshold: 32 degreesg

WindSpeed_IndexPrecipitation_Index

• cumulative precipitation (update 12 p.m. daily)

Regression Model Development

Average Operating Speed = 7 23 + 0 770 * Visibility Index +Average Operating Speed = 7.23 + 0.770 Visibility_Index + 0.358 * WeatherType_Index + 0.132 * Temperature_Index -0.0469 * WindSpeed_Index - 1.92 * CumuPrecip_Index (Update12am) + 0.853 * Norm_Hr_Speed – 0.935 * Day_Index

R2 = 56.1%



Microscopic Traffic SimulationTRANSIMS

• open-source agent-based transportation

Synthetic PopulationGenerator

Input Data

eropen source agent based transportation

simulation model

• Four modules as shown in Figure

• Micro-simulator:• Based on a cellular automata (CA) model • Accounts for driver behavior: driver reaction

time vehicle dynamics (e g acceleration

Activity Generator

Router

Micro-simulator

Fee

db

ac

k C

ont

rolle

time, vehicle dynamics (e.g. acceleration and deceleration rate) and lane changing (e.g. look ahead distance);

Emissions Estimator

Microscopic Traffic Simulation

Probe Vehicle Speed & Acceleration on Dry & Snowy days

December 6, 2010 (Snowy) December 9, 2010 (Dry)



December 9, 2010 (Dry)

December 6, 2010 (Snowy)




TRANSIMS Model Parameters for Base and InclementTRANSIMS Model Parameters for Base and Inclement Weather Cases

Base (Dec 9) Inclement weather (Dec 6)Probe vehicle Max accel

Max decel4.36 meter/second2

4.26 meter/second22.50 meter/second2

2.75 meter/second2

PLAN_FOLLOWING_DISTANCE 1000 1500DRIVER_REACTION_TIME 0.7 1.4SLOW_DOWN_PROBABILITY 10% 30%SLOW_DOWN_PERCENTAGE 10% 30%LOOK_AHEAD_DISTANCE 260 260LOOK_AHEAD_LANE_FACTOR 4.0 8.0LOOK_AHEAD_TIME_FACTOR 1.0 0.5


TRANSIMS vs Probe Vehicle vs TRANSMIT DataTRANSIMS vs. Probe Vehicle vs. TRANSMIT Data

Base Case(6:50 AM; Dec 9, 2010)

Inclement Weather(6:50 AM; Dec6, 2010)

TRANSMIT 62 mph 40 mph Probe vehicle 61.3 mph 40.3 mphTRANSIMS Model 57.2 mph 42.9 mph



Conclusions

Weather indices offer good explanation power

Speed reduction: a function of visibility, weather type, precipitation& wind-speed. Temperature: not a significant predictor

At the microscopic level: driving under inclement weather shows a higher frequency of acceleration & deceleration; magnitude of acc/decsignificantly lower than under dry weather;

TRANSIMS model can simulate freeway traffic under the inclement yweather when model parameters are appropriately adjusted; a small cell size, however, is needed to achieve required speed resolution.

Future Research Directions

Investigate inclement impact weather on traffic volumes

Mine data from SHRP2 Naturalistic Driving experiment



Using Transportation Models for Systems Management

8 I id t S i8 Incident Scenarios2 Incident Times (9 -10:00 am

vs. 12-13:00 pm)2 Incident Severities (1 vs. 2 lanes)2 Management Strategies

(Information Dissemination (VMS)(Information Dissemination (VMS) vs. None)

1. Travel Time Impact (1-lane Peak vs. Off Peak)

No significant impact from 1-lane Incident both peakNo significant impact from 1 lane Incident, both peak and off-peak



2. Travel Time Impact (2-lane Peak vs. Off Peak)

Huge delays if 2-lane incident during rush hoursg y g

3. Information Dissemination (VMS)Reroute travellers therefore reduce congestions



4. Information Dissemination (VMS)

5. Volume, Speed & Density



INCLEMENT WEATHER SCENARIOS

Methodology• Parameters of micro-simulator CA model modified

M d l t l t f d i t• Model run to evaluate performance during snow events

• Answering: • Can impaired network sustain normal weather travel

demand ?• What is the likely increase in average travel time?

within the micro simulated area accordinglywithin the micro-simulated area. accordingly.




2.9 miles I-90 Travel Time & Speed

Inclement Weather Dry

Sustainability on Traffic Demand

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90000

100000

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11.5

12

12.5

13

13.5

20000

30000

40000

50000

60000

70000

80000

90000

Ave

Tra

vel

Tim

e (

min

)

um

be

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f In

com

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te T

rip

s

Number of Incomplete Trips

Ave Travel Time (min)

10

10.5

0

10000

100% 95% 90% 88%

Nu

Demand Percentage



Conclusions

• 88% of the typical traffic demand is sustainable under inclement weather simulation parameter settingsinclement weather simulation parameter settings parameters (snow event)

• Inclement weather resulted in an increase in average trip travel time from 9.48 to 13.50 minutes.

NITTEC-UB ITS Data Warehouse



Archived Data Management Systems (ADMS)

• ADMS archive, fuse, organize & analyze ITS and data

• Take full advantage of data collected by ITS:• Performance Measurement• Develop effective operational strategies (e.g. signal timing)• Planning for Operations & special events• Enhance traveler information systems (predictive capability)• Long-term planning and decision-making• Invaluable asset for research (model building, calibration, …etc.)

Regional Transportation Data Warehouse Vision



Transportation Data Ware-house• Logical next step for UB’s Transportation Lab

• Review of existing ITS warehouses shows majority developed through a DOT/Univ. partnership

• Caltrans PeMS (with UC Berkeley)• Virginia ADMS (with UVA)• Oregon’s PORTAL (with Portland State University)

P t t i l t ti ti t d b th TRANSIMS• Prototype implementation motivated by the TRANSIMS project and using our own resources

• Very grateful to UTRC for providing funding for the next phase of development and applications

Initial Data Used for Prototype

• NITTEC:• TRANSMIT: speedTRANSMIT: speed• Incident Log, Help Log• Device history• Border crossing delay

• GBNRTC:• Turning movement counts• ATR counts: volume



Initial Data (cont.)• NYSDOT:

• Erie & Niagara County volume counts• Erie & Niagara County volume counts

• NYS Thruway:• Continuous count stations (thanks to Chris Jones & Tom Pericak)• 22 sites on I-90 between interchanges 49 and 57• 32 sites on I-190

• Weather:• NCDC: Visibility, Temperature, Precipitation,…• wunderground.com: Snow, Precipitation, Wind,…

Prototype Development

• Open source GIS server & Database• GeoServer v 2.0.2 & MySQLGeoServer v 2.0.2 & MySQL

• JAVA + Spring and Hibernate •Lots of features and support from Developer community. Used for developing Enterprise-level applications.• Ease of development without worrying about trivial issues•‘ Hibernate’ is a Object-Relational Mapping (ORM) framework, which allows for easy migration from MySQL to ORACLE or some other database – With absolutely no code changes



ITS Data Warehouse Architecture

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Prototype Development Outline

• Task 1: Data Warehouse Schema Design• MySQL Open Source DB• MySQL – Open Source DB

• Task 2: Data Import Tools• Batch Programs = Java + Spring Framework + Quartz Scheduler

• Task 3: User Interface & Programming• GeoServer v 2.0.2• Java Server Pages (JSPs), Spring Framework, Hibernate, XML, AJAX, SQL



Task 1: DB Schema Design



Schema – Intersection Counts• Table “DIRECTION”

DIRECTION_CODE

DESCRIPTION

R To Right

T Thru

L To Left

O ROR

DIRECTION_CODE

DESCRIPTION

E East Bound

N North Bound

W West Bound

S South Bound

NE North East

SE South East

SW South West

NW North West



Task 2: Data Import – Batch Programs• Nightly Batch Jobs scheduled to run for each type of data to import into MySQL DB

• Each Batch Job will be scheduled to run at the particular time based on how frequent the data files come in.

• The administrator of each organization has to copy the XML/XLS files into a specified directory which has read & write accesswrite access.

• For example: To upload ‘Incident Log’ XML files, all the user has to do is

- Copy Incident Logs to ‘F:/its_data/incidentlogs’



Data Import – Batch Programs

Data Import – Batch Programs

• All Batch Programs are logged using Log4j logging framework.

• Refer to Log Files if anything needs to be tracked.- Where and When an Error in the program occurred.



ITS Data Warehousehttp://128.205.19.55:8082/datawarehouse/home.html

Map User Interface for Querying Data Warehouse

Data Warehouse Applications• Performance Measurement:

• Travel Time reliability measures• Travel Time reliability measures• Congestion duration and extent measures• ITS Device reliability• Accident frequency, distribution and duration• Border crossing delay analysis



Data Warehouse Applications• Transportation and Extreme Weather

Data Warehouse Applications

• Regional Transportation Planning Model Applications:B tt Di l Di t ib ti• Better Diurnal Distributions

• Updating origin-destination information• Event-related traffic patterns



Data warehouse Applications

• Simulation Model Development and Calibration:

THANK YOU !

QUESTIONS !



peer exchange meeting - buffalomceer.buffalo.edu/.../transims/07_sadek.pdf · using transims for...

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