Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case
Abstract
:1. Introduction
2. Case of Study: Cartagena de Indias
- Most daily trips are made to/from the historic city center, since it is the locus of many institutions and enterprises. Moreover, in the city, we find a convergence of touristic circuits coming from the airport, the harbor, and the bus terminal. The influx of vehicles comes via the Pedro de Heredia Avenue.
- Another important flux in the network is formed by industrial workers traveling to the Mamonal Industrial Zone. It should be noted that it is in this area that cargo operation logistics are concentrated and large vehicles (cargo trucks) are therefore mixed with regular vehicles.
- The third type of flux is composed of interurban vehicles transporting workers from nearby municipalities (see Figure 1) including insular and rural areas.
3. Mathematical Methods
3.1. Network Analysis
3.2. Dynamics of Traffic Flow
- Scenario #1: Both departure and destination nodes are assigned randomly.
- Scenario #2: Departure and destination nodes are defined following a preferential assignment rule, seeking to emulate realistic commuting patterns:
- (i)
- 80% of the R agents created at each step are assigned a destination node within the historic city center and the Mamonal Industrial Zone, the main centers of gravity as described in Section 2. The other 20% are assigned randomly across the nongravity nodes.
- (ii)
- Similarly, 80% of the R agents created at each step are assigned a departure node in the peripheral area, namely nodes from the northern, eastern, and southernmost areas. The remaining 20% of departure nodes are assigned randomly across the nonperipheral nodes.
4. Results
4.1. Network Analysis
4.2. Dynamics of Traffic Flow
5. Conclusions
5.1. Summary and Discussion
5.2. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Evolution of the Urban Configuration of Cartagena
- The first and the oldest is the so-called historic center (see the orange region in Figure 2), established as a UNESCO World Heritage Site in 1988. It is the original outpost of the conquest and the colonial viceroyalty, between the XVI and XVIII centuries. It also served as the port of extraction of resources from New Granada, and the slave trade. This “old city” is segregated from the rest of the later urban expansion due to its walled defense architecture surrounded by the sea, lagoon bodies, and water pipes, which allowed its defense during the colony. Later, after the independence in the 19th century, the old city was linked to the interior of the country through a railroad. On this route, once the railway system was dismantled in 1930, the main road was built between 1969–1971, called Pedro de Heredia Avenue, until today the main and most congested road in the urban area [42].
- The second enclave is the Mamonal Industrial Complex born with the construction of the oil refinery in 1957 (light-violet region in Figure 2). The increasing number of facilities and docks (more than 36) evolved toward the consolidation of the “cargo corridor” absorbing some already consolidated urban routes to favor heavy cargo traffic that dangerously mixes with urban logistics.
- The third enclave is the touristic hotel area (yellow region in Figure 2), which, starting from the historic center, creates narrow strip of land with a peninsula. Since 1990, the tourism of the city grew toward the group of neighboring islands (Los Corales Natural Park) and more recently towards the north, following the coastal strip. It is appropriate to say that the aforementioned enclaves have occupied most of the coastal strip and low tide, and therefore access to the sea and inland water bodies.
References
- Pumain, D.; Saint-Julien, T. Análisis Espacial: Las Interacciones; Universidad de Concepción-Facultad de Arquitectura, Urbanisme y Geografía: Concepción, Chile, 2014. [Google Scholar]
- Barthélemy, M. Spatial networks. Phys. Rep. 2011, 499, 1–101. [Google Scholar] [CrossRef] [Green Version]
- Crucitti, P.; Latora, V.; Porta, S. Centrality measures in spatial networks of urban streets. Phys. Rev. E 2006, 73, 036125. [Google Scholar] [CrossRef] [Green Version]
- Crucitti, P.; Latora, V.; Porta, S. Centrality in networks of urban streets. Chaos Interdiscip. J. Nonlinear Sci. 2006, 16, 015113. [Google Scholar] [CrossRef] [PubMed]
- Duan, Y.; Lu, F. Robustness of city road networks at different granularities. Phys. A Stat. Mech. Appl. 2014, 411, 21–34. [Google Scholar] [CrossRef]
- Manley, E.; Cheng, T. Understanding road congestion as an emergent property of traffic networks. In Proceedings of the 14th WMSCI, Orlando, FL, USA, 29 June–2 July 2010. [Google Scholar]
- Nagel, K.; Paczuski, M. Emergent traffic jams. Phys. Rev. E 1995, 51, 2909. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jayasinghe, A.; Sano, K.; Nishiuchi, H. Explaining traffic flow patterns using centrality measures. Int. J. Traffic Transp. Eng. 2015, 5, 134–149. [Google Scholar] [CrossRef] [Green Version]
- Lämmer, S.; Gehlsen, B.; Helbing, D. Scaling laws in the spatial structure of urban road networks. Phys. A Stat. Mech. Appl. 2006, 363, 89–95. [Google Scholar] [CrossRef] [Green Version]
- Gallelli, V.; Perri, G.; Vaiana, R. Operational and Safety Management at Intersections: Can the Turbo-Roundabout Be an Effective Alternative to Conventional Solutions? Sustainability 2021, 13, 5103. [Google Scholar] [CrossRef]
- Macioszek, E. Roundabout Entry Capacity Calculation—A Case Study Based on Roundabouts in Tokyo, Japan, and Tokyo Surroundings. Sustainability 2020, 12, 1533. [Google Scholar] [CrossRef] [Green Version]
- Davidović, S.; Bogdanović, V.; Garunović, N.; Papić, Z.; Pamučar, D. Research on Speeds at Roundabouts for the Needs of Sustainable Traffic Management. Sustainability 2021, 13, 399. [Google Scholar] [CrossRef]
- Auttakorn, S. Assessment of traffic flow benefits of flyovers: A case study. J. Soc. Transp. Traffic Stud. (JSTS) 2013, 4, 1–9. [Google Scholar]
- Headrick, J.; Uddin, W. Traffic flow microsimulation for performance evaluation of roundabouts and stop-controlled intersections at highway overpass. Adv. Transp. Stud. 2014, 34, 7–18. [Google Scholar]
- Ran, B.; Boyce, D. Modeling Dynamic Transportation Networks: An Intelligent Transportation System Oriented Approach; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. [Google Scholar]
- Nagatani, T. The physics of traffic jams. Rep. Prog. Phys. 2002, 65, 1331. [Google Scholar] [CrossRef] [Green Version]
- Orosz, G.; Wilson, R.E.; Stépán, G. Traffic jams: Dynamics and control. Philos. Trans. R. Soc. A 2010, 368, 4455–4479. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- van Wageningen-Kessels, F.; Van Lint, H.; Vuik, K.; Hoogendoorn, S. Genealogy of traffic flow models. EURO J. Transp. Logist. 2015, 4, 445–473. [Google Scholar] [CrossRef] [Green Version]
- Bazzan, A.L.; Klügl, F. A review on agent-based technology for traffic and transportation. Knowl. Eng. Rev. 2014, 29, 375. [Google Scholar] [CrossRef]
- Krajzewicz, D.; Erdmann, J.; Behrisch, M.; Bieker, L. Recent development and applications of SUMO-Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 2012, 5, 128–138. [Google Scholar]
- Smith, L.; Beckman, R.; Baggerly, K. TRANSIMS: Transportation Analysis and Simulation System; Technical Report; Los Alamos National Lab.: Los Alamos, NM, USA, 1995. [Google Scholar]
- Horni, A.; Nagel, K.; Axhausen, K.W. The Multi-Agent Transport Simulation MATSim; Ubiquity Press: London, UK, 2016. [Google Scholar]
- Echenique, P.; Gómez-Gardenes, J.; Moreno, Y. Dynamics of jamming transitions in complex networks. EPL Europhys. Lett. 2005, 71, 325. [Google Scholar] [CrossRef] [Green Version]
- Sreenivasan, S.; Cohen, R.; López, E.; Toroczkai, Z.; Stanley, H.E. Structural bottlenecks for communication in networks. Phys. Rev. E 2007, 75, 036105. [Google Scholar] [CrossRef] [Green Version]
- De Martino, D.; Dall’Asta, L.; Bianconi, G.; Marsili, M. Congestion phenomena on complex networks. Phys. Rev. E 2009, 79, 015101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yan, G.; Zhou, T.; Hu, B.; Fu, Z.Q.; Wang, B.H. Efficient routing on complex networks. Phys. Rev. E 2006, 73, 046108. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Colak, S.; Schneider, C.M.; Wang, P.; González, M.C. On the role of spatial dynamics and topology on network flows. New J. Phys. 2013, 15, 113037. [Google Scholar] [CrossRef] [Green Version]
- Amezquita-Lopez, J. Competitividad y Sostenibilidad de Cartagena de Indias; InterNaciones: Guadalajara, Mexico, 2017; Volume 6. [Google Scholar]
- Porta, S.; Crucitti, P.; Latora, V. The network analysis of urban streets: A primal approach. Environ. Plan. B Plan. Des. 2006, 33, 705–725. [Google Scholar] [CrossRef] [Green Version]
- Xie, F.; Levinson, D. Measuring the structure of road networks. Geogr. Anal. 2007, 39, 336–356. [Google Scholar] [CrossRef]
- Zhao, L.; Lai, Y.C.; Park, K.; Ye, N. Onset of traffic congestion in complex networks. Phys. Rev. E 2005, 71, 026125. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Universidad de Cartagena and Alcaldía Distrital de Cartagena. Diagnóstico del Distrito de Cartagena en materia de ordenamiento territorial. In Documento de Seguimiento y Evaluación de los Resultados Obtenidos Respecto de los Objetivos Planteados en el Plan de Ordenamiento Territorial Vigente del Distrito de Cartagena; Universidad de Cartagena: Cartagena, Colombia, 2010. [Google Scholar]
- Barthelemy, M.; Bordin, P.; Berestycki, H.; Gribaudi, M. Self-organization versus top-down planning in the evolution of a city. Sci. Rep. 2013, 3, 1–8. [Google Scholar]
- Reynoso, C. Redes Sociales y Complejidad: Modelos Interdisciplinarios en la Gestión Sostenible de la Sociedad y la Cultura; Sb: Buenos Aires, Argentina, 2011. [Google Scholar]
- Gonzalez-Urango, H.; Inturri, G.; Le Pira, M.; García-Melón, M. Planning for Pedestrians with a participatory multicriteria approach. J. Urban Plan. Dev. 2020, 146, 05020007. [Google Scholar] [CrossRef]
- Gómez-Lobo, A. Transit reforms in intermediate cities of Colombia: An ex-post evaluation. Transp. Res. Part A Policy Pract. 2020, 132, 349–364. [Google Scholar] [CrossRef]
- Chen, S.; Huang, W.; Cattani, C.; Altieri, G. Traffic dynamics on complex networks: A survey. Math. Probl. Eng. 2012, 2012, 732698. [Google Scholar] [CrossRef]
- Ben-Akiva, M.; Bierlaire, M. Discrete choice models with applications to departure time and route choice. In Handbook of Transportation Science; Springer: Berlin/Heidelberg, Germany, 2003; pp. 7–37. [Google Scholar]
- Wardman, M.; Chintakayala, V.P.K.; de Jong, G. Values of travel time in Europe: Review and meta-analysis. Transp. Res. Part A Policy Pract. 2016, 94, 93–111. [Google Scholar] [CrossRef]
- Birolini, S.; Malighetti, P.; Redondi, R.; Deforza, P. Access mode choice to low-cost airports: Evaluation of new direct rail services at Milan-Bergamo airport. Transp. Policy 2019, 73, 113–124. [Google Scholar] [CrossRef]
- López, J.A. La competitividad en el marco de políticas para ciudades sostenibles: Caso Cartagena, Colombia. InterNaciones 2018, 13, 101–130. [Google Scholar]
- Ortiz, C.J. Un Diablo al que le Llaman Tren. El Ferrocarril Cartagena-Calamar; Fondo de Cultura Económica: Bogotá, Colombia, 2018. [Google Scholar]
- Mariaca, D.A.R.; Calán, C.A.G.; Molina, C.J. La accesibilidad terrestre a los puertos marítimos de Colombia. Una aproximación desde la equidad territorial. Entorno Geográfico 2018, 15, 8–47. [Google Scholar] [CrossRef]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Amézquita-López, J.; Valdés-Atencio, J.; Angulo-García, D. Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case. Infrastructures 2021, 6, 85. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6060085
Amézquita-López J, Valdés-Atencio J, Angulo-García D. Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case. Infrastructures. 2021; 6(6):85. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6060085
Chicago/Turabian StyleAmézquita-López, Julio, Jorge Valdés-Atencio, and David Angulo-García. 2021. "Understanding Traffic Congestion via Network Analysis, Agent Modeling, and the Trajectory of Urban Expansion: A Coastal City Case" Infrastructures 6, no. 6: 85. https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6060085