TY - JOUR
T1 - Spatiotemporal Implications of Population Downscaling
T2 - 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops
AU - Ben-Dor, Golan
AU - Ben-Elia, Eran
AU - Benenson, Itzhak
N1 - Funding Information:
Funding for this research was provided by the Israeli Ministry of Science, Cooperation with China Research Grant “Mobility as a Service: From Rigid to Smart Evolving Public Transport”. Special thanks to M. Maciejewski (TU Berlin). Golan Ben-Dor is partially supported by a scholarship from the Shlomo Shmeltzer Institute for Smart Transportation in Tel-Aviv University, and by the Dr. Etel Friedman Memorial Scholarship, Tel-Aviv Municipality.
Publisher Copyright:
© 2020 The Authors. Published by Elsevier B.V.All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Computer hardware is steadily advancing; however, simulating real urban transportation systems that serve millions of individual travelers is still a difficult task. The Multi-Agent Transportation Simulation (MATSim) is the only agent-based traffic model that includes intrinsic downscaling - procedures of changing network parameters in order to simulate the dynamics of the system as a whole while activating only a fraction k of travelers. In this paper, we present the MATSim's downscaling procedure and compare the dynamics of car traffic in the downscaled and full-scaled scenarios of the Sioux Falls test case. We compare aggregate and disaggregate statistics that represent Sioux Falls daily traffic, focusing on the morning peak. We conclude that downscaling up to k = 0.25 preserves all major statistics of urban traffic, within the interval of k between [0.1, 0.25]. Some of the statistics replicate well the statistics of the full-scaled runs, while downscaling below k = 0.1 can easily result in substantial deviations from the dynamics of the full-scale model.
AB - Computer hardware is steadily advancing; however, simulating real urban transportation systems that serve millions of individual travelers is still a difficult task. The Multi-Agent Transportation Simulation (MATSim) is the only agent-based traffic model that includes intrinsic downscaling - procedures of changing network parameters in order to simulate the dynamics of the system as a whole while activating only a fraction k of travelers. In this paper, we present the MATSim's downscaling procedure and compare the dynamics of car traffic in the downscaled and full-scaled scenarios of the Sioux Falls test case. We compare aggregate and disaggregate statistics that represent Sioux Falls daily traffic, focusing on the morning peak. We conclude that downscaling up to k = 0.25 preserves all major statistics of urban traffic, within the interval of k between [0.1, 0.25]. Some of the statistics replicate well the statistics of the full-scaled runs, while downscaling below k = 0.1 can easily result in substantial deviations from the dynamics of the full-scale model.
KW - Agent-Based simulation
KW - Car traffic
KW - Downscaling
KW - MATSim
KW - Morning Peak
UR - http://www.scopus.com/inward/record.url?scp=85085549337&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2020.03.165
DO - 10.1016/j.procs.2020.03.165
M3 - Conference article
AN - SCOPUS:85085549337
SN - 1877-0509
VL - 170
SP - 720
EP - 725
JO - Procedia Computer Science
JF - Procedia Computer Science
Y2 - 6 April 2020 through 9 April 2020
ER -
