Project Objectives/Outcomes

Using TRANSIMS for On-line Transportation System Management during Emergencies Peer Exchange Meeting March 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 Presentation 07 1 Buffalo, NY March 15, 2012

What is TRANSIMS Initially developed at Los Alamos 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 to model traveler behavior Feedback Controller Input Data Synthetic Population Generator Activity Generator Router Micro-simulator Emissions Estimator TRANSIMS Model Model Refinement & Error Checking Calibration: Demand and Diurnal Distribution Model Validation Presentation 07 2 Buffalo, NY March 15, 2012

Network Refinement 1. Subarea Expansion Error-Checking List: Pocket lanes; Zigzag links; Signal locations; Major network bottlenecks; 5 Network Refinement 2. Missing left-turn pocket lane; the Intersection between John James Audubon Pkwy and N Forest Rd 6 Presentation 07 3 Buffalo, NY March 15, 2012

Network Refinement 3. Redundant signal: intersection between Erie Ave and Niagara Falls Blvd; 7 Network Refinement 4. Study the impact of the missing local roads; Capacity Loss 8 Presentation 07 4 Buffalo, NY March 15, 2012

Network Refinement 5. Study the impact of the missing local roads; Scenario Test: add Koenig Rd between Niagara Falls Blvd and Parker Blvd Volume Up: extra capacity attracts more traffic. Daily Volume Daily Queue Length 9 Network Refinement 6. 20% Missing Trips Almost 20% problem trips in the last run; Much more vehicles on the network than the simplified network could handle; Reduced Demand might fix it. 10 Presentation 07 5 Buffalo, NY March 15, 2012

Demand & Diurnal Calibration 1. Review Micro-simulator reports on the problem trips 11 Calibration 2. Parameter Sensitivity Study Router END_TIME_CONSTRAINT Micro-Simulator MINIMUM_WAITING_TIME MAXIMUM_WAITING_TIME MAX_ ARRIVAL_ TIME_ VARIANCE MAX_DEPARTURE_TIME_VARIANCE 12 Presentation 07 6 Buffalo, NY March 15, 2012

Calibration 2. 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 parameter is defined in minutes. The default is zero. 13 Calibration 2. Parameter Sensitivity Study MINIMUM_WAITING_TIME Arrival Time Problems 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 waiting 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. 14 Presentation 07 7 Buffalo, NY March 15, 2012

Calibration 2. Parameter Sensitivity Study MAXIMUM_WAITING_TIME Waiting Time Problems Arrival Time Problems 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. 15 Calibration 2. Parameter Sensitivity Study MAX_ARRIVAL_TIME_VARIANCE Arrival Time Problems 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 arrival time problem message is posted in the problem file. The default value for this parameter is 60 minutes. 16 Presentation 07 8 Buffalo, NY March 15, 2012

Calibration 2. Parameter Sensitivity Study MAX_DEPARTURE_TIME_VARIANCE Departure Time Problems 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 problem message is generated. The default value for this parameter is 60 minutes. 17 Calibration 2. Parameter Sensitivity Study Five Scenarios with Different Parameter Settings 18 Presentation 07 9 Buffalo, NY March 15, 2012

Calibration 3. Demand Study Waiting time and arrival time problems indicates that the number of the vehicles on the network has exceeded network capacity. 193 Count Stations Tuesday, Wednesday and Thursday Only the locations of the count stations 19 Calibration 3. Demand Study 70%, 80% and 90% 1 st Scenario: 80% Demand Hr NYSDOT MSim MicErr 0 20224.5 11321-44% 1 11679.66667 14330 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 91828.1666716667 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% Hr NYSDOT MSim Router MicErr RtrErr 0 20224.5 9217 9178-54% -55% 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 91828.22 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 20 Presentation 07 10 Buffalo, NY March 15, 2012

Calibration 3. Demand Study 2 nd Scenario : 77% demand and new Diurnal Distribution, time-consuming adaptive process; 21 Calibration 3. Demand Study Problem trips is as low as 0.1%. Trip Distribution of Scenario Three 22 Presentation 07 11 Buffalo, NY March 15, 2012

Calibration 3. Demand Study Demand Reduction Step by Step Starting 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, 254 2.54 million trips 23 Validation 1. Mean Absolute Error (MAE) Hr Field Simulation MAE 7 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% 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% 24 Presentation 07 12 Buffalo, NY March 15, 2012

Validation 25 Validation 2. Regression Analysis 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; 26 Presentation 07 13 Buffalo, NY March 15, 2012

Validation 2. Regression Analysis (Simulation vs. Field) R 2 =0.958 R 2 =0.998 27 Weather Impact on Driving Behavior & Demand Motivation Purpose & Scope Literature Review Methodology Conclusions Next Steps Presentation 07 14 Buffalo, NY March 15, 2012

Motivation In the U.S., More than 25% of the annual 1,561,000 vehicle crashes are weather-related t Weather-related crashes kill 7,400 people killed, and injure more than 673,000 annually State 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 is applicable at: Macroscopic level - average speed Microscopic level speed of an individual vehicle Presentation 07 15 Buffalo, NY March 15, 2012

Literature Review Macroscopic Impact Studies Capacity 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) HCM 2000: FFS light rain: 2~14%, heavy: 5~17% Light snow: 3~10%, heavy: 20~35% Kyte et al. (2001) R 2 = 40% Microscopic (limited) Methodology Data Collection & Processing Weather Data TRANSMIT Speed Data Probe Vehicle Data Weather Indexing Framework Regression Model Development Microscopic Traffic Simulation Presentation 07 16 Buffalo, NY March 15, 2012

Weather Indexing Framework Visibility_ Index 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 degrees WindSpeed_Index Precipitation_Index cumulative precipitation (update 12 p.m. daily) Regression Model Development 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 R 2 = 56.1% Presentation 07 17 Buffalo, NY March 15, 2012

Microscopic Traffic Simulation TRANSIMS open-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 and deceleration rate) and lane changing (e.g. look ahead distance); Feedback Controlle er Input Data Synthetic Population Generator Activity Generator Router Micro-simulator Emissions Estimator Microscopic Traffic Simulation Probe Vehicle Speed & Acceleration on Dry & Snowy days December 6, 2010 (Snowy) December 9, 2010 (Dry) Presentation 07 18 Buffalo, NY March 15, 2012

December 9, 2010 (Dry) December 6, 2010 (Snowy) Presentation 07 19 Buffalo, NY March 15, 2012

Microscopic Traffic Simulation TRANSIMS Model Parameters for Base and Inclement Weather Cases 2.50 meter/second 2 Base (Dec 9) Inclement weather (Dec 6) Probe vehicle Max accel 4.36 meter/second 2 Max decel 4.26 meter/second 2 2.75 meter/second 2 PLAN_FOLLOWING_DISTANCE 1000 1500 DRIVER_REACTION_TIME 0.7 1.4 SLOW_DOWN_PROBABILITY 10% 30% SLOW_DOWN_PERCENTAGE 10% 30% LOOK_AHEAD_DISTANCE 260 260 LOOK_AHEAD_LANE_FACTOR 4.0 8.0 LOOK_AHEAD_TIME_FACTOR 1.0 0.5 Microscopic Traffic Simulation TRANSIMS 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 mph TRANSIMS Model 57.2 mph 42.9 mph Presentation 07 20 Buffalo, NY March 15, 2012

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/dec significantly lower than under dry weather; TRANSIMS model can simulate freeway traffic under the inclement weather 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 Presentation 07 21 Buffalo, NY March 15, 2012

Using Transportation Models for Systems Management 8I Incident tscenarios 2 Incident Times (9-10:00 am vs. 12-13:00 pm) 2 Incident Severities (1 vs. 2 lanes) 2 Management Strategies (Information Dissemination (VMS) vs. None) 1. Travel Time Impact (1-lane Peak vs. Off Peak) No significant impact from 1-lane Incident, both peak and off-peak Presentation 07 22 Buffalo, NY March 15, 2012

2. Travel Time Impact (2-lane Peak vs. Off Peak) Huge delays if 2-lane incident during rush hours 3. Information Dissemination (VMS) Reroute travellers therefore reduce congestions Presentation 07 23 Buffalo, NY March 15, 2012

4. Information Dissemination (VMS) 5. Volume, Speed & Density Presentation 07 24 Buffalo, NY March 15, 2012

INCLEMENT WEATHER SCENARIOS Methodology Parameters of micro-simulator CA model modified 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. accordingly. Presentation 07 25 Buffalo, NY March 15, 2012

Microscopic Traffic Simulation 2.9 miles I-90 Travel Time & Speed Inclement Weather Dry Sustainability on Traffic Demand Nu umber of Incomplete Trips 100000 90000 80000 70000 60000 50000 40000 30000 20000 10000 0 Number of Incomplete Trips Ave Travel Time (min) 100% 95% 90% 88% Demand Percentage 14 13.5 13 12.5 12 11.5 11 10.5 10 Ave Travel Time (min) Presentation 07 26 Buffalo, NY March 15, 2012

Conclusions 88% of the typical traffic demand is sustainable under inclement 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 Presentation 07 27 Buffalo, NY March 15, 2012

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 Presentation 07 28 Buffalo, NY March 15, 2012

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) Prototype t implementation ti motivated t 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: speed Incident Log, Help Log Device history Border crossing delay GBNRTC: Turning movement counts ATR counts: volume Presentation 07 29 Buffalo, NY March 15, 2012

Initial Data (cont.) NYSDOT: 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 & 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 Presentation 07 30 Buffalo, NY March 15, 2012

ITS Data Warehouse Architecture 61 Prototype Development Outline Task 1: Data Warehouse Schema Design 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 Presentation 07 31 Buffalo, NY March 15, 2012

Task 1: DB Schema Design Presentation 07 32 Buffalo, NY March 15, 2012

Schema Intersection Counts Table DIRECTION DIRECTI ON_COD E DESCRIPTION DIRECTI ON_CO DE DESCRIPTION R To Right E East Bound T L O Thru To Left ROR N W S North Bound West Bound South Bound NE North East SE South East SW South West NW North West Presentation 07 33 Buffalo, NY March 15, 2012

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 access. For example: To upload Incident Log XML files, all the user has to do is - Copy Incident Logs to F:/its_data/incidentlogs Presentation 07 34 Buffalo, NY March 15, 2012

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. Presentation 07 35 Buffalo, NY March 15, 2012

ITS Data Warehouse http://128.205.19.55:8082/datawarehouse/home.html Map User Interface for Querying Data Warehouse Data Warehouse Applications Performance Measurement: Travel Time reliability measures Congestion duration and extent measures ITS Device reliability Accident frequency, distribution and duration Border crossing delay analysis Presentation 07 36 Buffalo, NY March 15, 2012

Data Warehouse Applications Transportation and Extreme Weather Data Warehouse Applications Regional Transportation Planning Model Applications: Better Diurnal Distributions ib ti Updating origin-destination information Event-related traffic patterns Presentation 07 37 Buffalo, NY March 15, 2012

Data warehouse Applications Simulation Model Development and Calibration: THANK YOU! QUESTIONS! Presentation 07 38 Buffalo, NY March 15, 2012