Analysis of altruistic and egoistic effects in transportation networks

Authors

DOI:

https://doi.org/10.33448/rsd-v10i14.22514

Keywords:

Agents; Transportation network; Altruism; Simulation; Congestion.

Abstract

Transport networks are fundamental infrastructure for the dynamics of large urban centers. These structures are subject to congestion, which have a strong social, economic and environmental impact. In this work, an agent-based simulation model was built in order to investigate how altruistic behavior in route selection can affect travel times and flow distribution in a transport network. Methods: OpenStreetMap open database was used to obtain the structure of the transport network. Complex network theory was used to perform the simulation and estimate the impacts of congestion. Simulating mobility flows, we analyzed how the agents' path selection criteria influence congestion levels, path lengths and travel times. Results: altruistic behavior significantly reduces the propagation of congestion and the formation of groupings of congested roads in the transport network, as well as reducing the average travel time between two points, but increasing the average distance traveled in a smaller proportion.

References

Auld, J., Verbas, O., & Stinson, M. (2019). Agent-based dynamic traffic assignment with information mixing. Procedia Computer Science, 151, 864-869.

Barrat, A., Barthelemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge university press.

Barthelemy, J., & Carletti, T. (2017). A dynamic behavioural traffic assignment model with strategic agents. Transportation Research Part C: Emerging Technologies, 85, 23-46.Ben-Akiva, M. E., Gao, S., Wei, Z., & Wen, Y. (2012). A dynamic traffic assignment model for highly congested urban networks. Transportation research part C: emerging technologies, 24, 62-82.

Ben-Akiva, M. E., Gao, S., Wei, Z., & Wen, Y. (2012). A dynamic traffic assignment model for highly congested urban networks. Transportation research part C: emerging technologies, 24, 62-82.Ben-Akiva, M. E., Gao, S., Wei, Z., & Wen, Y. (2012). A dynamic traffic assignment model for highly congested urban networks. Transportation research part C: emerging technologies, 24, 62-82.

Boeing, G. (2019). Urban spatial order: Street network orientation, configuration, and entropy. Applied Network Science, 4(1), 1-19.

Boeing, G. (2020). A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood. Environment and Planning B: Urban Analytics and City Science, 47(4), 590-608.

Casali, Y., & Heinimann, H. R. (2020). Robustness response of the Zurich road network under different disruption processes. Computers, Environment and Urban Systems, 81, 101460.

Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3), 036125.Eikenbroek, O. A., Still, G. J., & van Berkum, E. C. (2021). Improving the performance of a traffic system by fair rerouting of travelers. European Journal of Operational Research.

Das, A. K., & Rama Chilukuri, B. (2020). Link Cost Function and Link Capacity for Mixed Traffic Networks. Transportation Research Record, 2674(9), 38-50.

Ding, R., Yin, J., Dai, P., Jiao, L., Li, R., Li, T., & Wu, J. (2019). Optimal Topology of Multilayer Urban Traffic Networks. Complexity, 2019.

Eikenbroek, O. A Still, G. J., & van Berkum, E. C. (2021). Improving the performance of a traffic system by fair rerouting of travelers. European Journal of Operational Research.Eikenbroek, O. A., Still, G. J., & van Berkum, E. C. (2021). Improving the performance of a traffic system by fair rerouting of travelers. European Journal of Operational Research.Gao, J., Barzel, B., & Barabási, A. L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312.

Gao, J., Barzel, B., & Barabási, A. L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312.Gao, J., Barzel, B., & Barabási, A. L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312.

Helbing, D. (Ed.). (2012). Social self-organization: Agent-based simulations and experiments to study emergent social behavior. Springer.

Jia, H., Li, F., Yang, L., Luo, Q., & Li, Y. (2020). Dynamic Cascading Failure Analysis in Congested Urban Road Networks With Self-Organization. IEEE Access, 8, 17916-17925.Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system?. European Journal for Philosophy of Science, 3(1), 33-67.

Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system?. European Journal for Philosophy of Science, 3(1), 33-67.Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system?. European Journal for Philosophy of Science, 3(1), 33-67.

Levy, N., Klein, I., & Ben-Elia, E. (2018). Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach. Research in Transportation Economics, 68, 46-55.

Liu, W., & Song, Z. (2020). Review of studies on the resilience of urban critical infrastructure networks. Reliability Engineering & System Safety, 193, 106617.

Ma, D., Guo, R., Zheng, Y., Zhao, Z., He, F., & Zhu, W. (2020). Understanding Chinese urban form: the universal fractal pattern of street networks over 298 cities. ISPRS International Journal of Geo-Information, 9(4), 192.

Macal, C. M., & North, M. J. (2005). Validation of an agent-based model of deregulated electric power markets. In Proc. North American Computational Social and Organization Science (NAACSOS) 2005 Conference, South.

Manzo, S., Nielsen, O. A., & Prato, C. G. (2013). Investigating uncertainty in BPR formula parameters: a case study.

Melo Neto, O. de M., Santos, B. L. de F., Carvalho, F. do S. de S., Nascimento, A. M. V. do, & Silva, G. C. B. da. (2020). Urban planning: traffic feasibility near rotating through a software to improve flow of vehicles and pedestrians. Research, Society and Development, 9(7), e13973808. https://doi.org/10.33448/rsd-v9i7.3808

Moreira, L. de A., Santos, S. F. dos, Oliveira Neto, R. de, & Silva Junior, L. A. (2019). Bibliographic review of the mode of road transportation in Brazil. Research, Society and Development, 8(3), e2283728. https://doi.org/10.33448/rsd-v8i3.728

Perez, Y., & Pereira, F. H. (2021). Simulation of traffic light disruptions in street networks. Physica A: Statistical Mechanics and its Applications, 582, 126225.

Portugali, J. (2016). What makes cities complex?. In Complexity, cognition, urban planning and design (pp. 3-19). Springer, Cham.

Rambha, T., Boyles, S. D., Unnikrishnan, A., & Stone, P. (2018). Marginal cost pricing for system optimal traffic assignment with recourse under supply-side uncertainty. Transportation Research Part B: Methodological, 110, 104-121.

Salman, S., & Alaswad, S. (2018). Alleviating road network congestion: Traffic pattern optimization using Markov chain traffic assignment. Computers & Operations Research, 99, 191-205.

Serra, M., & Hillier, B. (2019). Angular and metric distance in road network analysis: A nationwide correlation study. Computers, Environment and Urban Systems, 74, 194-207.

Sharifi, A. (2019). Resilient urban forms: A macro-scale analysis. Cities, 85, 1-14.Sharifi, A. (2019). Resilient urban forms: A macro-scale analysis. Cities, 85, 1-14.Zilske, M., Neumann, A., & Nagel, K. (2015). OpenStreetMap for traffic simulation. Technische Universität Berlin.

Zilske, M., Neumann, A., & Nagel, K. (2015). OpenStreetMap for traffic simulation. Technische Universität Berlin.Zilske, M., Neumann, A., & Nagel, K. (2015). OpenStreetMap for traffic simulation. Technische Universität Berlin.

Published

13/11/2021

How to Cite

PEREZ, Y.; PEREIRA, F. H. . Analysis of altruistic and egoistic effects in transportation networks. Research, Society and Development, [S. l.], v. 10, n. 14, p. e546101422514, 2021. DOI: 10.33448/rsd-v10i14.22514. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/22514. Acesso em: 24 jul. 2024.

Issue

Section

Engineerings