SimMobility – Integrated Simulation Platform

Project Managers: 

Kakali Basak (kakali@smart.mit.edu)
Carlos Miguel Lima de Azevedo (cami@mit.edu)

Sponsor: 

Singapore National Research Foundation

Team: 

Moshe Ben-Akiva, Kakali Basak, Francisco C. Pereira, Carlos M. Lima Azevedo, Ravi Seshadri, Arun Akkinepally, Andre Romano Alho,Bat-hen Nahmias-Biran, Zhang Huai Peng, Neeraj Deshmukh, Tai Chu Yaw, Balakumar Marimuthu, Tomer Toledo, Maya Abou-Zeid, Constantinos Antoniou, Song Gao, Jing Ding, Monique Stinson

Start Date: 

January 2011

Research Highlights: 

  1. SimMoblity is a next-generation urban simulation platform.
  2. SimMobility integrates various mobility-sensitive behavioral models in a multi-scale simulation platform.
  3. SimMobility implements the activity-based modeling paradigm.
  4. SimMobility is based on theagent-based or microsimulationapproach

Abstract: 

SimMoblity is the simulation platform of the Future Urban Mobility Research Group at the Singapore-MIT Alliance for Research and Technology (SMART) that aims to serve as the nexus of Future Mobility research evaluations. It integrate various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. The platform enables the simulation of the effects of a portfolio of technology, policy and investment options under alternative future scenarios. Specifically, SimMobility encompasses the modeling of millions of agents, from pedestrians to drivers, from phones and traffic lights to GPS, from cars to buses and trains, from second-by-second to year-by-year simulations, across entire countries.

Description: 

SimMobility is being designed to be: activity-based; multi-modal; multi-scale; fully modular; and consistent across levels. Below we further explain each of these concepts.

Activity-based - In SimMobility, representation of individuals as agents in the model is necessary for simulating how people will react in the uncertain future. The decision process of the agents is modeled by an activity-based approach. Activity-based modeling improves on single-trip modeling by combining multiple trips and activities into the schedule that drives the demand for transportation networks.

Multi-modal - One of the promising approaches to current mobility problems such as congestion, high accessibility demand at certain places, environmental impacts and energy consumption is considering the transport system from a multi-modal viewpoint. To support multi-modality SimMobility explicitly simulates private traffic, public transit, pedestrian traffic as well as freight transportation, and allows agents to switch between these modes over the course of a given day.

Multi-scale - The high-level design of SimMobility is shown in Figure 1. SimMobility comprises three primary modules differentiated by the timeframe in which we consider the behaviour of an urban system. The short-term model functions at the operational level; it simulates movement of agents at a microscopic granularity (within day). It synthesizes driving and travel behavior in detail and also interacts with a communication simulator that models the impact of device to device communication on these behaviours. The mid-term (day-to-day) simulator handles transportation demand for passengers and goods; it simulates agents’ behavior which includes their activity and travel patterns. The mid term represents moving vehicles in aggregate, and routes are generated by behavior-based demand models. The long-term (year-to-year) model captures land use and economic activity, with special emphasis on accessibility. It predicts the evolution of land use and property development and use, determines the associated life cycle decisions of agents, and accounts for interactions among individuals and firms.

Fully modular -  Although we have been presenting our project as simultaneously having the three levels, of short-term, mid-term, and long-term, it would not be practical or even necessary to tightly couple them all the time. In fact, we apply the opposite concept: each level should be able to work independently and only needs to access others when necessary (e.g. if an update on accessibilities is needed for the long term for specific area or time frame, it calls the mid-term).

Consistent across all levels - The key to multi-scale integration in SimMobility is a single database model that is shared across all levels. Every agent exists and is recognized at all levels simultaneously, and information is used according to each level’s needs (e.g. the long-term model doesn’t need to know reaction times from the short-term model). In this way, the behaviours will remain consistent and, even if run separately, the impacts from one level’s model will be propagated to the others gracefully.

References: 

Journal papers

  1. Rui Tan*, Muhammad Adnan, Der-Horng Lee, Moshe E. Ben-Akiva.  A New Path Size Formulation in Path Size Logit for Route Choice Modeling in Public Transport Networks. Transportation Research Record. 2015
  2. Ana Alves, Filipe Rodrigues, Evgheni Polisciuc, Shan Jiang, Francisco C. Pereira and Joseph Ferreira (2013), "Estimating disaggregated employment size from Points-of-Interest and census data: From mining the web to model implementation and visualization". International Journal On Advances in Intelligent Systems , 6(1 and 2), 41-52.
  3. Guevara, C.A., and Ben-Akiva, M. (2013), 'Sampling of alternatives in Logit Mixture models', Transportation Research Part B (forthcoming)
  4. Guevara, C.A., and Ben-Akiva, M. (2013), 'Sampling of alternatives in Multivariate Extreme Value (MEV) models', Transportation Research Part B, Vol. 48, pp. 31-52
  5. Guevara, C.A., and Ben-Akiva, M. (2012), 'Change of scale and forecasting with the control-function method in Logit models', Transportation Science, Vol. 46, No. 3, pp. 425-437
  6. XuYan*(SMART), Xiao Song*(Beihang Univ), Zhiyong Weng(SMART), Gary Tan(NUS) “An Entry Time-based Supply Framework (ETSF) for Mesoscopic Traffic Simulations”. Published on ScienceDirect
  7. Ana Alves (UC), Filipe Rodrigues (UC), Shan Jiang*(MIT), Francisco C. Pereira**(SMART) and Joseph Ferreira (MIT/SMART). "Point-of-Interest mining from social networks for urban land-use classification". Published on ScienceDirect.

 

Journal papers under review

  1. Tan, R.*, Robinson, S., S., Lee, D.H., Ben-Akiva, M., Comparative and Comprehensive Evaluation of Choice Set Generation Algorithms for Modeling Route Choice with Smart Card Data in Large-Scale Public Transportation Network. Transportation Research Part A

Peer Reviewed Book Chapters:

  1. Alves, A. O. (UC) and Pereira, F. C. **(SMART) ''Lightweight Ontologies to Represent Semantically Enriched Places". In Perspectives of Ontology Learning. Johana Volker and Jens Lehman. IOS Press. 2014
  2. Lima Azevedo, C., Cardoso, J. L., Ben-Akiva, M., Costeira, J. P., Marques, M. (2014). Automatic Vehicle Trajectory Extraction by Aerial Remote Sensing. Procedia - Social and Behavioral Sciences, Volume 111, Pages 849-858

Conference papers

  1. Lu Y. (SMART), Adnan, M. (SMART), Basak, K. (SMART), F. Pereira (SMART), Carrion, C. (SMART), Saber, VH. ( SMART), Loganathan, H. (SMART), Ben-Akiva, M. (MIT),“SimMobility Mid-Term Simulator: A State of the Art Integrated Agent Based Demand and Supply Model”. Transportation Research Board 93st Annual Meeting. Washington DC. January 2015
  2. Rui Tan*, Muhammad Adnan, Der-Horng Lee, Moshe E. Ben-Akiva.  A New Path Size Formulation in Path Size Logit for Route Choice Modeling in Public Transport Networks. Transportation Research Board 94th Annual Meeting, Washington, D.C., United States of America. January 2015
  3. Rui Tan*, Der-Horng Lee, Moshe E. Ben-Akiva. Analysis on Value Of Journey Travel Time Reliability Using Automated Fare Collection Data. Accepted to the 6th International Symposium on Transportation Network Reliability, Nara, Japan, 2-3 August, 2015
  4. Rui Tan*, Der-Horng Lee, Moshe E. Ben-Akiva. Value of journey travel time reliability using smart card data: application to Singapore. Accepted to the 22nd Intelligent Transportation System World Congress. Bordeaux, France, 5-9 October 2015
  5. “Dealing with uncertainty in detailed calibration of traffic simulation models for safety assessment”. Lima Azevedo, C., Ciuffo, B., Cardoso, J., Ben-Akiva, M. E. Transportation Research Part C., forthcoming.
  6. “W–SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models”. Antoniou, C., Lima Azevedo, C., Lu, L., Pereira, F., Ben-Akiva, M. E. Accepted to the 21st International Symposium on Transportation and Traffic Theory. Kobe, Japan, 5-7 August 2015.
  7. "Modeling Crash Probability in large traffic simulators" Lima Azevedo, C., Cardoso, J., Ben-Akiva, M. E. Accpeted for presentation at the 5th International Symposium of Highway Geometric Design. June 22 - 24, 2015,  Vancouver, British Columbia, Canada
  8. Lima Azevedo, C. (SMART), Cardoso, J. (LNEC), Ben-Akiva, M. (MIT) "Probabilistic Safety Analysis using Traffic Microscopic Simulation". Transportation Research Board 93rd Annual Meeting. Washington DC. January 2015.
  9. Lima Azevedo, C. (SMART), Ciuffo, B. (JRC), Moura, F. (IST), Cardoso, J. (LNEC), Ben-Akiva, M. (MIT) "Dealing with uncertainty in detailed calibration of traffic simulation models for safety assessment". Transportation Research Board 93rd Annual Meeting. Washington DC. January 2015.
  10. Lima Azevedo, C., Cardoso, J., Ben-Akiva, M. (2014). Applying graph theory to automatic vehicle tracking by remote sensing. Transportation Research Board. 12-18 January 2014, Washington D.C., EUA
  11. XuYan*(SMART), Xiaosong Li*(NTU), Gary Tan(NUS) “Mesoscopic Traffic Simulation on CPU/GPU”.ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS), 2014, Accepted.
  12. XuYan*(SMART), Xiaosong Li*(NTU), Gary Tan(NUS). “Sim-Tree: Indexing Moving Objects in Large-Scale Parallel Microscopic Traffic Simulation”, ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS), 2014, Accepted.
  13. Tan, R.*, Robinson, S., S., Lee, D.H., Ben-Akiva, M., Evaluation of Choice Set Generation Algorithms for Modeling Route Choice with Smart Card Data in Large-Scale Public Transportation Network, ITS Asia Forum 2014. Auckland, New Zealand, April 2014
  14. Tan, R.*, Robinson, S., S., Lee, D.H., Ben-Akiva, M.,  Time Saving of Information - Evolution of At-Stop real time passenger information system in Public Transport Network, ITS Asia Forum 2014. Auckland, New Zealand, April 2014
  15. Li, S.*, Carrion, C., Abou-Zeid, M., Ben-Akiva, M. (2013) “Activity-based Travel Demand Models for Singapore: Application and Innovations”, 18th International Conference of Hong Kong Society for Transportation Studies, Hong Kong (December 2013)
  16. Basak, K., Hetu, S., Li, Z., Azevedo, C., Loganathan, H., Toledo, T., Yan, X., Xu, R., Peh, L. and Ben-Akiva, M. "Modeling reaction time within a traffic simulation model". Poster presentation at 2013 TRB Annual Meeting, January 2013
  17. Basak, K., Pereira, F., Hetu, S., Peh, L., Zegras, C. and Ben-Akiva, M.. “SimMobility: Integrated activity based modeling”. ITS World Congress 2013, Tokyo, Japan
  18. Basak, K. (SMART), Hetu, S.**(SMART), Li, Z.*(SMART), Azevedo, C. (LNEC), Loganathan, H. *(SMART), Toledo, T. (TECHNION), Yan, X.* (SMART), Xu, R. *(MIT), Peh, L. (MIT/SMART) and Ben-Akiva, M. (MIT). "Modeling reaction time within a traffic simulation model". Nominated for best paper award for IEEE Intelligent Transportation Systems Society Conference Management System, 2013.
  19. Guevara, C.A., Chorus, C.G., and Ben-Akiva, M. (2013), 'Sampling of alternatives in Random Regret Minimization models', 92nd Annual Meeting of the Transportation Research Board, Washington, D.C
  20. Pattabhiraman, V.* (MIT), Ben-Akiva, M. (MIT), and Abou-Zeid, M. (MIT) (2013) “A Needs-Based Approach to Activity Generation for Travel Demand Analysis”, abstract accepted for presentation at the 13th World Conference on Transportation Research, Rio de Janeiro, Brazil, July 2013.
  21. Enam, A.* (MIT), Li, S.* (NUS), Abou-Zeid, M. (MIT),  and Ben-Akiva, M. (MIT) (2013) “Travel Time Modeling with GPS and Household Survey Data”, paper submitted for presentation at the 92nd Annual Meeting of the Transportation Research Board, Washington, DC (January 2013) and publication in the journal Transportation Research Record.
  22. XuYan* (SMART), Gary Tan (NUS). "Workload Estimation Algorithms in Parallel Traffic Simulation" by poster presentation to IEEE International Conference on Parallel and Distributed System, 2013.
  23. Li, S.*(SMA3_NUS), Carrion, C.**(SMART), Abou-Zeid, M. (MIT), Ben-Akiva, M. (MIT) (2013) “Activity-based Travel Demand Models for Singapore”, at LTA-UITP Singapore International Transport Conference and Exhibition, Singapore (October 2013).
  24. Li, S.*(SMA3_NUS), Carrion, C.**(SMART), Abou-Zeid, M. (MIT), Ben-Akiva, M. (MIT) (2013) “Activity-based Travel Demand Models for Singapore: Application and Innovations”, at the 18th International Conference of Hong Kong Society for Transportation Studies, Hong Kong (December 2013)
  25. Rodrigues, F., Alves A. O., Pereira, F. C., Shan Jiang, Joseph Ferreira. “Automatic Classification of Points-of-Interest for Land-use Analysis”. The Fourth International Conference on Advanced Geographic Information Systems, Applications, and Services (GeoProcessing 2012). Valencia, Spain, February, 2012

Other submitted papers/abstracts

  1. Marczuk, K. A. , Soh, H., Lima Azevedo, C., Frazzoli, E. and Lee, D. H.  "Autonomous Mobility on Demand in SimMobility: Case Study of the Central Business District in Singapore. Submitted to the 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the 7th IEEE International Conference on Robotics, Automation and Mechatronics (RAM) to be held between 15-17 July, 2015 at Angkor Wat, Cambodia
  2. Lovrić, M. (SMART), Adnan, M. (SMART), Raveau, S. (SMART), Basak, K. (SMART), Pereira, F. C. (SMART) and Ben-Akiva, M. E. (MIT). “Evaluating Pricing Strategies in Public Transportation Using SimMobility Mid-term Modeling Framework”, submitted to the 4th Symposium of the European Association for Research in Transportation (hEART), Copenhagen, Denmark  (September, 2015).

The SimMobility and Behavioral Models are related, to see more information about Behavioral Models, click here Behavioral Models.