Sunsetting skim matrices: A trajectory-mining approach to derive travel time skim matrix in dynamic traffic assignment for activity-base model integration

Ye Tian

Tongji University

http://orcid.org/0000-0002-2225-7566

Yi-Chang Chiu

University of Arizona

Jian Sun

Tongji University

Chen Chai

Tongji University

DOI: https://doi.org/10.5198/jtlu.2020.1551

Keywords: Skim matrix, Vehicle trajectories, Dynamic traffic assignment, Activity-based models, Integrated models


Abstract

The travel impedance skim matrix is one of the most essential intermediate products within transportation forecasting models and is a fundamental input for activity-based transportation forecasting models. It reflects interzonal travel time, travel time reliability, travel costs, etc. by time of day. The traditional method to obtain skim matrices is to execute multiple times of time-dependent, shortest-path calculations. However, the computational and memory use burden can easily increase to an intractable level when dealing with mega-scale networks, such as those with thousands of traffic-analysis zones. This research proposes two new approaches to extract the interzonal travel impedance information from the already existing vehicle trajectory data. Vehicle trajectories store travel impedance information in a more compact format when compared to time-dependent link performance profiles. The numerical experiments highlight huge potential advantages of the proposed approaches in terms of saving both memory and CPU time.


References

Ben-Akiva, M., Bierlaire, M., Koutsopoulos, H., & Mishalani, R. (1998). DynaMIT: a simulation-based system for traffic prediction. Paper presented at the DACCORD Short Term Forecasting Workshop, Delft, Netherlands.

Boyce, D., O’Neill, C., & Scherr, W. (2008). Solving the sequential travel forecasting procedure with feedback. Transportation Research Record, 2077, 129–135. https://doi.10.3141/2077-17

Boyce, D., Zhang, Y., & Lupa, M. (1994). Introducing “feeback” into four-step travel forecasting procedure versus equilibrium solution of combined model. Transportation Research Record, 1443, 65–74.

Bradley, M. A., Bowman, J. L., & Griesenbeck, B. (2007). Development and application of the SACSIM activity-based model system. Paper presented at the 11th World Conference on Transportation Research, Berkeley, CA.

Caliper. (2011). Traffic simulation models. Retrieved from http://www.caliper.com/TransModeler/Simulation.htm

Castiglione, J., Bradley, M., & Gliebe, J. (2015). Activity-based travel demand models: A primer. Washington, DC: Transportation Research Board.

Chiu, Y.-C., Bottom, J., Mahut, M., Paz, A., Balakrishna, R., Waller, T., & Hicks, J. (2011). Dynamic traffic assignment: A primer. Washington, DC: Transportation Research Board.

Chiu, Y.-C., Nava, E., Zheng, H., & Bustillos, B. (2011). DynusT user’s manual. Retrieved from http://dynust.net/wikibin/doku.php.

Davidson, W., Donnelly, R., Vovsha, P., Freedman, J., Ruegg, S., Hicks, J., . . . Picado, R. (2007). Synthesis of first practices and operational research approaches in activity-based travel demand modeling. Transportation Research Part A: Policy and Practice, 41(5), 464–488. https://doi.http://dx.doi.org/10.1016/j.tra.2006.09.003

Davidson, W., Vovsha, P., Freedman, J., & Donnelly, R. (2010). CT-RAMP family of activity-based models. Paper presented at the 33rd Australasian Transport Research Forum (ATRF), Canberra, Australia.

Florian, M. A., Mahut, M., & Tremblay, N. (2006). A simulation-based dynamic traffic assignment model: Dynameq. Montreal, Canada: Centre for Research on Transportation.

Hao, J., Hatzopoulou, M., & Miller, E. (2010). Integrating an activity-based travel demand model with dynamic traffic assignment and emission models. Transportation Research Record, 2176, 1–13. https://doi.10.3141/2176-01

Isard, W. (1956). Location and space-economy. New York: Wiley.

Lam, W. H. K., & Yin, Y. (2001). An activity-based time-dependent traffic assignment model. Transportation Research Part B: Methodological, 35(6), 549–574. https://doi.org/10.1016/S0191-2615(00)00010-2

Levinson, D. M., & Kumar, A. (1994). A multi-modal trip distribution model. Transportation Research Record, 1466, 124–131. Retrieved from SSRN 1091827.

Li, Y., Zhu, L., Sun, J., & Tian, Y. (2019). Generating a spatiotemporal dynamic map for traffic analysis using macroscopic fundamental diagram. Journal of Advanced Transportation, 2019. Retrieved from https://doi.org/10.1155/2019/9540386

Lin, D.-Y., Eluru, N., Waller, S., & Bhat, C. (2008). Integration of activity-based modeling and dynamic traffic assignment. Transportation Research Record, 2076, 52–61. https://doi.10.3141/2076-06

Loudon, W., Parameswaran, J., & Gardner, B. (1997). Incorporating feedback in travel forecasting. Transportation Research Record, 1607, 185–195. https://doi.10.3141/1607-25

Mahmassani, H. S., & Khaled, F. A. (2003). Dynasmart-ip: Dynamic traffic assignment meso-simulator for intermodal networks. In Advanced modeling for transit operations and service planning (200–229). Amsterdam: Elsevier.

Meyer, M. D. (2009). Transporation planning handbook (Third edition). Washington, DC: Institute of Transportation Engineers.

Mitchell, R. B., & Rapkin, C. (1954). Urban traffic: A function of land use. New York, NY: Columbia University Press.

Park, J. I., Lee, Y. P., & Yoon, S. (2013). Partition-based hybrid MIMO decoding schemes with vombined depth- and breath-first search. Applied Mechanics and Materials, 284–287, 2652–2656.

Peeta, S., & Ziliaskopoulos, A. K. (2001). Foundations of dynamic traffic assginment: The past, the present and the future. Networks and Spatial Economics, 1, 233–265.

Reinhart, C. F. (2006). Tutorial on the use of daysim simulations for sustainable design. Ottawa, Ontario: Institute for Research in Construction, National Research Council Canada.

Tian, Y., & Chiu, Y.-C. (2014). A variable time-discretization strategies-based, time-dependent, shortest-path algorithm for dynamic traffic assignment. Journal of Intelligent Transportation Systems, 18(4), 339–351. https://doi.10.1080/15472450.2013.806753

Tian, Y., Chiu, Y.-C., & Sun, J. (2019). Understanding behavioral effects of tradable mobility credit scheme: An experimental economics approach. Transport Policy, 81, 1–11. https://doi.org/10.1016/j.tranpol.2019.05.019

Zhou, X., Taylor, J., & Pratico, F. (2014). DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration. Cogent Engineering 1, 1. https://doi.org/10.1080/23311916.2014.961345