Abstract
Generally, in biological and ecological disciplines, the concept of adaptation refers to ecosystems evolutionarily changing as system perturbations. This paper extends these concepts to road transportation networks and focuses on the potential for adaptability of commuters to traffic disruptions. Specifically, we apply an Ecosystem Network Analysis (ENA) based upon an information-theoretic framework to demonstrate the potential, calculated as the number of alternate options available throughout a given network, for adaptive routing optimization on road networks. An initial assessment of balanced metrics of resilience and efficiency, calculated from 13 Metropolitan Statistical Area (MSA) road networks, is performed. These metrics are then compared with their respective commuter delay levels, which indicates a correlation between the balance of resilient and efficient networks and the annual commuter delay of each MSA. Whereas road network topologies that demonstrate either a highly efficient or highly resilient network structure show a tendency for higher commuter delay levels, road networks that balance efficiency and resilience suggest a tendency for lower commuter delay levels. This study's novel implication explicitly considers the road network topology as a driver of traffic delay patterns, isolated from commuter decisions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fu, L.: An adaptive routing algorithm for in-vehicle route guidance systems with real-time information (2021). https://doi.org/10.1016/s0191-2615(00)00019-9
Ritzinger, U., Puchinger, J., Hartl, R.F.: A survey on dynamic and stochastic vehicle routing problems. Int. J. Prod. Res. 54(1), 215–231 (2016). https://doi.org/10.1080/00207543.2015.1043403
Chabini, I.: Discrete dynamic shortest path problems in transportation applications: complexity and algorithms with optimal run time. Transp. Res. Rec. J. Transp. Res. Board 1645(1), 170–175 (2007). https://doi.org/10.3141/1645-21
Schulz, A.S., Stier-Moses, N.E., Jahn, O., Mohring, R.H.: System-optimal routing of traffic flows with user constraints in networks with congestion. Oper. Res. 53(4) (2005)
Hoang, N.H., Vu, H.L., Lo, H.K.: An informed user equilibrium dynamic traffic assignment problem in a multiple origin-destination stochastic network. https://doi.org/10.1016/j.trb.2018.07.007
Isaac Engel, J., MartÃn, J., Barco, R.: A low-complexity vision-based system for real-time traffic monitoring. https://doi.org/10.1109/tits.2016.2603069
Afrin, T., Yodo, N.: probabilistic estimation of traffic congestion using Bayesian network. https://doi.org/10.1016/j.measurement.2021.109051
Zaidi, A.A., Kulcsár, B., Wymeersch, H.: Back-Pressure traffic signal control with fixed and adaptive routing for urban vehicular networks. https://doi.org/10.1109/tits.2016.2521424
Alizadeh, H., Bourbonnais, P.-L., Morency, C., Farooq, B., Saunier, N.: An online survey to enhance the understanding of car drivers route choices. https://doi.org/10.1016/j.trpro.2018.10.042
Peeta, S., Whon, J.Y.: A hybrid model for driver route choice incorporating en-route attributes and real-time information effects. Springer Science + Business Media, Inc. Manufactured in The Netherlands (2005)
Ulanowicz R.E.: Quantitative methods for ecological network analysis. https://doi.org/10.1016/j.compbiolchem.2004.09.001
Shannon, C.E.: A Mathematical Theory of Communication (1948)
Goerner, S.J., Lietaer, B., Ulanowicz, R.E.: Quantifying economic sustainability: implications for free-enterprise theory, policy and practice. https://doi.org/10.1016/j.ecolecon.2009.07.018
Cui, D., Zeng, W., Ma, B., Zhuo, Y., Xie, Y.: Ecological network analysis of an urban water metabolic system: Integrated metabolic processes of physical and virtual water. Sci. Total Environ. 787 (2021). https://doi.org/10.1016/j.scitotenv.2021.147432
Morris, Z.B., Weissburg, M., Bras, B.: Ecological network analysis of urban–industrial ecosystems. https://doi.org/10.1111/jiec.13043
Dunne, J.A.: Food-web structure and network theory: the role of connectance and size
Dunne, J., Yeakel, J.: Modern lessons from ancient food webs. Am. Sci. 103(3), 188 (2015). https://doi.org/10.1511/2015.114.188
Ulanowicz, R.E.: Quantifying the complexity of flow networks: how many roles are there?
Ulanowicz, R.E.: The balance between adaptability and adaptation. https://doi.org/10.1016/s0303-2647(01)00170-8
Ulanowicz, R.E., Goerner, S.J., Lietaer, B., Gomez, R.: Quantifying sustainability: resilience, efficiency and the return of information theory. https://doi.org/10.1016/j.ecocom.2008.10.005
Boeing, G.: OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street networks. https://doi.org/10.2139/ssrn.2865501
Report, U.A., et al.: Performance Measure Summary—101 Area Sum The Mobility Data for 101 Area Sum, vol. 2014 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Logan, M., Goodwell, A. (2023). Adaptive Routing Potential in Road Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-21127-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21126-3
Online ISBN: 978-3-031-21127-0
eBook Packages: EngineeringEngineering (R0)