Simulation Of Chittenden County Transportation Network With TRANSIMS

Yi Yang, Christopher M. Danforth, Peter Sheridan Dodds, and Jun YuDepartment of Mathematics and Statistics

College of Engineering and Mathematical Sciences, The University of Vermont

Simulation Of Chittenden County Transportation Network With TRANSIMS

Yi Yang, Christopher M. Danforth, Peter Sheridan Dodds, and Jun YuDepartment of Mathematics and Statistics

College of Engineering and Mathematical Sciences, The University of Vermont

Background1. Our research is part A of the UVM Transportation Research

Center (TRC) Signature Research Project #1: Integrated Land-use, Transportation and Environmental Modeling: Complex Systems Approaches and Advanced Policy Applications.

2. Long term objective of the project: Develop, evaluate, calibrate, and validate an integrated framework of agent-based land use model, UrbanSim, and transportation model, TRANSIMS, for Chittenden County.

3. Our group’s objective: Test the sensitivity of the combined model to the level of model complexity through comparative variation in different dynamic processes, submodels, variables, etc. and assess how the appropriate level of complexity varies by practical application.

Background1. Our research is part A of the UVM Transportation Research

Center (TRC) Signature Research Project #1: Integrated Land-use, Transportation and Environmental Modeling: Complex Systems Approaches and Advanced Policy Applications.

2. Long term objective of the project: Develop, evaluate, calibrate, and validate an integrated framework of agent-based land use model, UrbanSim, and transportation model, TRANSIMS, for Chittenden County.

3. Our group’s objective: Test the sensitivity of the combined model to the level of model complexity through comparative variation in different dynamic processes, submodels, variables, etc. and assess how the appropriate level of complexity varies by practical application.

TRANSIMS

TRANSIMS is an agent-based simulation system capable of simulating the second-by-second movements of every person and every vehicle through a regional transportation network.

TRANSIMS will offer transportation planning agencies more accurate and more sensitive travel forecasts and more detailed vehicle-emissions estimates.

Conceptual Model of TRANSIMS

TRANSIMS

TRANSIMS is an agent-based simulation system capable of simulating the second-by-second movements of every person and every vehicle through a regional transportation network.

TRANSIMS will offer transportation planning agencies more accurate and more sensitive travel forecasts and more detailed vehicle-emissions estimates.

Conceptual Model of TRANSIMS

Simulation ResultsSimulation Results

Fig. 5: Observation by CCMPO on July 31, 2003, and TRANSIMS simulation for Link 829. Agreement between observation and TRANSIMS simulation is good, although peak hours associated with the observation data for a summer day are a little earlier.

Sensitivity StudySensitivity Study

Conclusion

•TRANSIMS is a powerful tool for evaluating performance of transportation system, as demonstrated by our study for the Chittenden County. The simulation was able to capture realistic events such as congestion sites, as well as observed event atchosen site.

•In general, comparing with variation of parameters (such as driver reaction time), variation in transportation demand has stronger influence on performance of the transportation network.

•Actuated signal devices improve performance of the transportation system only for off-peak hour period.

Acknowledgement

We would like to thank Dr. Adel Sadek and Mr. Shan Huang for their helpful introduction and comments. This work is supported by UVM Transportation Research Center.

Conclusion

•TRANSIMS is a powerful tool for evaluating performance of transportation system, as demonstrated by our study for the Chittenden County. The simulation was able to capture realistic events such as congestion sites, as well as observed event atchosen site.

•In general, comparing with variation of parameters (such as driver reaction time), variation in transportation demand has stronger influence on performance of the transportation network.

•Actuated signal devices improve performance of the transportation system only for off-peak hour period.

Acknowledgement

We would like to thank Dr. Adel Sadek and Mr. Shan Huang for their helpful introduction and comments. This work is supported by UVM Transportation Research Center.

Abstract

The Transportation Analysis and Simulation System (TRANSIMS), is an integrated set of tools developed to conduct regional transportation system analyses. The most recent version of the TRANSIMS was implemented at the Vermont Advanced Computing Center (VACC), and used to study the transportation network of Chittenden County, Vermont. The agreement between observation and TRANSIMS simulation is good at test sites, withwell-known congestion sites identified from spatial analysis of the simulation result. We also carried out sensitivity study for various parameters, and found that replacing timed traffic signals withactuated ones leads to better performance of the transportation system during off-peak hours, while it has virtually no positive effect to the traffic network during the rush hours.

Abstract

The Transportation Analysis and Simulation System (TRANSIMS), is an integrated set of tools developed to conduct regional transportation system analyses. The most recent version of the TRANSIMS was implemented at the Vermont Advanced Computing Center (VACC), and used to study the transportation network of Chittenden County, Vermont. The agreement between observation and TRANSIMS simulation is good at test sites, withwell-known congestion sites identified from spatial analysis of the simulation result. We also carried out sensitivity study for various parameters, and found that replacing timed traffic signals withactuated ones leads to better performance of the transportation system during off-peak hours, while it has virtually no positive effect to the traffic network during the rush hours.

Synthetic PopulationGenerator

Activity Generator

Router

Micro-simulator

Emissions Estimator

Input Data

Feed

back

Con

trolle

r

Fig. 1: Transportation network of Chittenden County, prepared for TRANSIMS simulation. The whole simulation area has 535 nodes and 779 links, and is divided into 367 subregions, with total 431406 trips assigned between the subregions.

Fig. 1: Transportation network of Chittenden County, prepared for TRANSIMS simulation. The whole simulation area has 535 nodes and 779 links, and is divided into 367 subregions, with total 431406 trips assigned between the subregions.

Fig. 2: Temporal evolution of link averaged speed and density. Peak hour periods are around 8:00 am and 5:00PM.Fig. 2: Temporal evolution of link averaged speed and density. Peak hour periods are around 8:00 am and 5:00PM.

Fig. 3: Spatial distribution of time averaged speed and density.Speed peaks: Link #15, #1110 and #1632 are part of Interstate 89. Density peaks: Link #235, # 829 and #883 are well recognized as congestion sites. Link #235 is close to the intersection of I-189 and Shelburne road; Link #883 is close to the intersection of Williston Road and Dorset street.

Fig. 3: Spatial distribution of time averaged speed and density.Speed peaks: Link #15, #1110 and #1632 are part of Interstate 89. Density peaks: Link #235, # 829 and #883 are well recognized as congestion sites. Link #235 is close to the intersection of I-189 and Shelburne road; Link #883 is close to the intersection of Williston Road and Dorset street.

Fig. 4: A photo on the website of CCMPO (Chittenden County Metropolitan Planning Organization), illustrating jughandelsaround the intersection of US 2 / Spear St. / East Ave, South Burlington. Link #829 is a section of US 2 from 1A to 2A. CCMPO conducted a traffic study for this area on July 31, 2003.

Fig. 6: Difference between cases with different driver reaction time: 0.5s, 0.7s, 1.0s. Gap between vehicles is equal to driver reaction time multiplied by vehicle speed. Reducing driver reaction time leads to a increase of speed, but the effect is small.

Fig. 7: Difference between cases with different percentage of actuated signal device: 0, 10 and 100. During off-peak hour period, more actuated signal devices could lead to a increase of average speed through reducing unnecessary waiting time. During peak hour period, actuated signal devices have no advantage over timed signal devices in responding to demand of huge amount of vehicles.

Fig. 8:Difference between future case (20 years after) and current case, assuming a 1% annually increase of trip demand. The same transportation network facing increased transportation demand results in a decrease of average speed.