research-article

SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator

Authors:

Kotagiri Ramamohanarao,

Shanika Karunasekera,

Eman Bin KhunaynAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 2

Article No.: 26, Pages 1 - 22

https://doi.org/10.1145/2898363

Published: 06 December 2016 Publication History

Abstract

Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We have developed the Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS), a distributed microscopic traffic simulator that can utilize multiple independent processes in parallel. SMARTS can perform fast large-scale simulations. For example, when simulating 1 million vehicles in an area the size of Melbourne, the system runs 1.14 times faster than real time with 30 computing nodes and 0.2s simulation timestep. SMARTS supports various driver models and traffic rules, such as the car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate, and monitor simulations. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1% accuracy in predicting traffic on a 10km freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.

References

[1]

J. Barceló, E. Codina, J. Casas, J. L. Ferrer, and D. García. 2005. Microscopic traffic simulation: A tool for the design, analysis and evaluation of intelligent transport systems. J. Intell. Robot. Syst. 41, 2--3 (2005), 173--203.

Digital Library

[2]

T. Brinkhoff. 2002. A framework for generating network-based moving objects. Geoinformatica 6, 2 (2002), 153--180.

Digital Library

[3]

E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numer. Math. 1, 1 (1959), 269--271.

Digital Library

[4]

C. Düntgen, T. Behr, and R. H. Güting. 2009. BerlinMOD: A benchmark for moving object databases. VLDB J. 18, 6 (2009), 1335--1368.

Digital Library

[5]

P. A. M. Ehlert and L. J. M. Rothkrantz. 2001. Microscopic traffic simulation with reactive driving agents. In Intelligent Transportation Systems. IEEE, 860--865.

[6]

N. Geroliminis and C. F. Daganzo. 2008. Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transport. Res. B: Method. 42, 9 (2008), 759--770.

[7]

P. Hidas. 2002. Modelling lane changing and merging in microscopic traffic simulation. Transport. Res. C: Emerg. Technol. 10, 5 (2002), 351--371.

[8]

R. Jacob, M. Marathe, and K. Nagel. 1999. A computational study of routing algorithms for realistic transportation networks. J. Exp. Algor. 4 (1999), 6.

Digital Library

[9]

A. Kesting, M. Treiber, and D. Helbing. 2007. General lane-changing model MOBIL for car-following models. J. Transport. Res. Board 1999, 1 (2007), 86--94.

[10]

A. Kesting, M. Treiber, and D. Helbing. 2010. Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philos. Trans. Roy. Soc. Lond. A 368, 1928 (2010), 4585--4605.

[11]

R. Klefstad, Y. Zhang, M. Lai, R. Jayakrishnan, and R. Lavanya. 2005. A distributed, scalable, and synchronized framework for large-scale microscopic traffic simulation. In Intelligent Transportation Systems. IEEE, 813--818.

[12]

D. Krajzewicz, G. Hertkorn, C. Rössel, and P. Wagner. 2002. SUMO (simulation of urban mobility) -- an open--source traffic simulation. In Middle East Symposium on Simulation and Modelling. DLR, 183--187.

[13]

D. H. Lee and P. Chandrasekar. 2002. A framework for parallel traffic simulation using multiple instancing of a simulation program. Intell. Transport. Syst. 7, 3--4 (2002), 279--294.

[14]

G. McArdle, E. Furey, A. Lawlor, and A. Pozdnoukhov. 2014. Using digital footprints for a city-scale traffic simulation. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 41.

Digital Library

[15]

M. F. Mokbel, L. Alarabi, J. Bao, A. Eldawy, A. Magdy, M. Sarwat, E. Waytas, and S. Yackel. 2013. MNTG: An extensible web-based traffic generator. In Advances in Spatial and Temporal Databases. Lecture Notes in Computer Science, Vol. 8098. Springer, 38--55.

[16]

K. Nagel and M. Rickert. 2001. Parallel implementation of the TRANSIMS micro-simulation. Parallel Comput. 27, 12 (2001), 1611--1639.

[17]

U. T. V. Nguyen, S. Karunasekera, L. Kulik, E. Tanin, R. Zhang, H. Zhang, H. Xie, and K. Ramamohanarao. 2015. Randomized path routing algorithm for decentralized route allocation in transportation networks. IWCTS (2015), 6.

[18]

T. Potuzak and P. Herout. 2007. Use of distributed traffic simulation in the JUTS project. In International Conference on Computer as a Tool. IEEE, 2250--2255.

[19]

PTV Group. 2015. PTV Vissim. Retrieved from http://vision-traffic.ptvgroup.com/en-uk/products/ptv-vissim.

[20]

SMARTS Team. 2015. Video demo of SMARTS. Retrieved from https://youtu.be/sUMfHXOBCE8.

[21]

Syncfusion. 2015. Syncfusion Metro Studio. Retireved from https://www.syncfusion.com/downloads/metrostudio.

[22]

S. Taori and A. Rathi. 1996. Comparison of NETSIM, NETFLO I, and NETFLO II traffic simulation models for fixed-time signal control. J. Transport. Res. Board 1566 (1996), 20--30.

[23]

TomTom. 2015. Online Navigation Services. Retrieved from http://developer.tomtom.com.

[24]

Transport for New South Wales. 2014. Household travel survey report: Sydney 2012-13. Retrieved from http://www.bts.nsw.gov.au/ArticleDocuments/79/r2014-11-hts-summary-report_12-13.pdf.aspx.

[25]

Victorian Government. 2010. Melbourne Metro One Business Case Draft: public transport patronage forecasts. Retrieved from http://ptv.vic.gov.au.

[26]

X. Wang, C. Leckie, H. Xie, and T. Vaithianathan. 2015. Discovering the impact of urban traffic interventions using contrast mining on vehicle trajectory data. In Pacific Asia Conference on Knowledge Discovery and Data Mining, Vol. 1. Springer, 486--497.

[27]

R. A. Waraich, D. Charypar, M. Balmer, and K. W. Axhausen. 2015. Performance improvements for large-scale traffic simulation in MATSim. In Computational Approaches for Urban Environments. Springer, 211--233.

[28]

Q. Yang and H. N. Koutsopoulos. 1996. A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transport. Res. C: Emerg. Technol. 4, 3 (1996), 113--129.

[29]

Y. Zheng. 2015. Trajectory data mining: An overview. ACM Trans. Intell Syst. Technol. 6, 3 (2015), 29.

Digital Library

[30]

Y. Zheng, L. Capra, O. Wolfson, and H. Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 38.

Digital Library

Cited By

Potuzak T(2024)Improved methodology for assessment of communication protocols for distributed road traffic simulationSimulation10.1177/00375497221130097100:1(91-109)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/00375497221130097
Xu ZLi LZhang MXu YZhou X(2024)Managing the Future: Route Planning Influence Evaluation in Transportation Systems2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00347(4558-4572)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00347
Da LChu CZhang WWei H(2024)CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning ModelsMachine Learning and Knowledge Discovery in Databases. Research Track and Demo Track10.1007/978-3-031-70371-3_22(368-373)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70371-3_22
Show More Cited By

Index Terms

SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Distributed simulation

Recommendations

A framework for simulation of surrounding vehicles in driving simulators

This article describes a framework for generation and simulation of surrounding vehicles in a driving simulator. The proposed framework generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between ...
Fast-Forwarding of Vehicle Clusters in Microscopic Traffic Simulations
SIGSIM-PADS '20: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

State fast-forwarding has been proposed as a method to reduce the computational cost of microscopic traffic simulations while retaining per-vehicle trajectories. However, since fast-forwarding relies on vehicles isolated on the road, its benefits extend ...
Comparative evaluation of microscopic car-following behavior

Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 2

Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers

March 2017

407 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3004291

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2016

Accepted: 01 February 2016

Revised: 01 November 2015

Received: 01 August 2015

Published in TIST Volume 8, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Linkage Project
Discovery Project

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
462
Total Downloads

Downloads (Last 12 months)47
Downloads (Last 6 weeks)6

Reflects downloads up to 24 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Potuzak T(2024)Improved methodology for assessment of communication protocols for distributed road traffic simulationSimulation10.1177/00375497221130097100:1(91-109)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1177/00375497221130097
Xu ZLi LZhang MXu YZhou X(2024)Managing the Future: Route Planning Influence Evaluation in Transportation Systems2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00347(4558-4572)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00347
Da LChu CZhang WWei H(2024)CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning ModelsMachine Learning and Knowledge Discovery in Databases. Research Track and Demo Track10.1007/978-3-031-70371-3_22(368-373)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70371-3_22
Abdul Razak SYogarayan SAzman AAbdullah MMuhamad Amin ASalleh M(2023)Simulation framework for connected vehicles: a scoping reviewF1000Research10.12688/f1000research.73398.210(1265)Online publication date: 16-Feb-2023
https://doi.org/10.12688/f1000research.73398.2
Bin Khunayn EXie HKarunasekera SRamamohanarao K(2023)Dynamic Straggler Mitigation for Large-Scale Spatial SimulationsACM Transactions on Spatial Algorithms and Systems10.1145/35789339:2(1-34)Online publication date: 6-Jan-2023
https://dl.acm.org/doi/10.1145/3578933
Lu YFu JTucker GPan XBronstein ERoelofs RSapp BWhite BFaust AWhiteson SAnguelov DLevine S(2023)Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10342038(7553-7560)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10342038
Wan LYin GWang JBen-Dor GOgulenko AHuang Z(2023)PATRIC: A high performance parallel urban transport simulation framework based on traffic clusteringSimulation Modelling Practice and Theory10.1016/j.simpat.2023.102775126(102775)Online publication date: Jul-2023
https://doi.org/10.1016/j.simpat.2023.102775
Barauskas ABrilingaitė ABukauskas LČeikutė VČivilis AŠaltenis S(2023)Test-data generation and integration for long-distance e-vehicle routingGeoinformatica10.1007/s10707-022-00485-y27:4(737-758)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10707-022-00485-y
Muthugama LXie HTanin EKarunasekera S(2023)Real-time road safety optimization through network-level data managementGeoinformatica10.1007/s10707-022-00473-227:3(491-523)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s10707-022-00473-2
Muthugama LXie HTanin EKarunasekera SGunarathna URenz MSarwat M(2022)Concurrent optimization of safety and traffic flow using deep reinforcement learning for autonomous intersection managementProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3561018(1-12)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3561018
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents