Multi-Modal Agent-based Path Finding in Public Transportation System

Document Type : Scientific - Research

Authors

1 MSc. Student, Department of Surveying Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

2 Assistant Professor, Department of Surveying Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

Abstract

The increasing importance of transportation in urban spaces, as well as the numerous problems it has created, have prompted decision-makers to encourage citizens to use public transportation as much as possible. Therefore, scientists and urban decision-makers are looking for a comprehensive system for more connections between transportation systems. On the other hand, the use of public transportation is very important and will create spatial justice, will bring proper access for all people. In this paper, a factor-based multiprocessor routing including taxis, buses and agencies was designed. Agents appear in the role of travelers who have the opportunity to choose any possible means of transportation to cross from origin to destination and also fill the gaps in their path by walking. The main criteria are choosing the path of time and cost along the way. Field data were collected to evaluate the results of the model. The results of analyzing the field data by multicriteria methods revealed that the priorities of people’s travelling mode are internet taxi, agency (called by telephone), bus and taxi (with predetermined rout and taking more than one passenger) with values of 0.4, 0.27, 0.19 and 0.14 respectively. The observations differed somewhat from the model output, which in fact represents optimal choices. This seems to be due to complex human decision-making in which many factors, including risk-taking and personality traits and habits and other factors, play a role.

Keywords


-Bandini, S., Manzoni, S. and Vizzari, G. (2009). "Agent based modeling and simulation: an informatics perspective". Journal of Artificial Societies and Social Simulation, Vol. 12, No. 4, pp. 1-4.
-Berryman, M. (2008). "Review of software platforms for agent based models." Land Operations Division DSTO Defence Science and Technology Organisation, Australia.
 
-Bezyak, J. L., Sabella, S., Hammel, J., McDonald, K., Jones, R. A., and Barton, D. (2020). "Community participation and public transportation barriers experienced by people with disabilities." Disability and rehabilitation, Vol. 42, No. 23, pp. 3275-3283.
 
-Bradshaw, J. M., Suri, N., Breedy, M., Cañas, A., Davis, R., Ford, K., Hoffman, R., Jeffers, and Reichherzer, T. (2001). "Terraforming cyberspace."  Computers, Vol. 34,  No. 7, pp. 48-56.
 
-Chen, J., Ni, J., Xi, C., Li, S., and Wang, J. (2017). "Determining intra-urban spatial accessibility disparities in multimodal public transport networks." Journal of Transport Geography, Vol. 65, pp. 123-133.
 
-Chen, S., Tan, J., Claramunt, C., and Ray, C. (2011). "Multi-scale and multi-modal GIS-T data model. " Journal of Transport Geography, Vol, 19, No. 1, pp. 147-161.
 
-Chow, A. H., Han, K., and Achuthan, K. (2016). "An agent-based analysis of transport network vulnerability and resilience with provision of travel information." 6 th International Symposium on Dynamic Traffic Assignment (Sydney, 28-30 June, 2016).
 
-Crooks, A. T., and Castle, C. J. (2012). "The integration of agent-based modelling and geographical information for geospatial simulation." In Agent-based models of geographical systems, (pp. 219-251): Springer.
-Delmelle, E. C., and Casas, I. (2012). "Evaluating the spatial equity of bus rapid transit-based accessibility patterns in a developing country: The case of Cali, Colombia." Transport Policy, Vol. 20, pp. 36-46.
 
-Farid, D. M., Zhang, L., Rahman, C. M., Hossain, M. A. and Strachan, R. (2014). "Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks." Expert Systems with Applications, Vol. 41, No. 4, pp. 1937-1946.
 
-Fletterman, M. (2008). "Designing multimodal public transport networks using metaheuristics." University of Pretoria,  South Africa.
 
-Genesereth, M. R. (1994). "Software Agents." Michael R. Genesereth Logic Group Computer Science Department, Stanford University.
 
-Hosseinali, F., Alesheikh, A. A., and Nourian, F. (2013). "Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city." Cities, Vol. 31, pp. 105-113.
 
-Hosseinali, F., and Azizkhani, M. (2016). "Developing an agent-based model for spatial simulation of pedestrian's behavior passing across the street and using the pedestrian bridges." Journal of Geospatial Information Technology, Vol. 4, No. 2, pp. 65-81.
 
-Macal, C., and North, M. (2005). "Tutorial on agent-based modeling and simulation." Paper presented at the Proceedings of the Winter Simulation Conference, 2005.
 
-Niger, M. (2019). "Rationalizing Public Transport system of Dhaka city: Proposal of Creating a Multimodal Hierarchical Transport Network to Reduce Traffic Congestion." Journal of Environmental Design and Planning, Vol. 16, pp. 1-14.
 
-Seaborn, C., Attanucci, J., and Wilson, N. H. M. (2009). "Analyzing multimodal public transport journeys in London with smart card fare payment data." Journal of the Transportation Research Board, Vol. 2121, No. 1, pp. 55-62.
 
-Tahmasbi, B., and Haghshenas, H. (2019). "Public transport accessibility measure based on weighted door to door travel time." Computers, Environment and Urban Systems, Vol. 76, pp. 163-177.
 
-Yen, B. T. H., Mulley, C., Tseng, W. C., and Chiou, Y. C. (2018). "Assessing interchange effects in public transport: A case study of South East Queensland, Australia." Case Studies on Transport Policy, Vol. 6, No. 3, pp. 364-375.
 
-Zhang, J, Liao, F., Arentze, T., and  Timmermans, H. (2011). "A multimodal transport network model for advanced traveler information systems." Procedia - Social and Behavioral Sciences, Vol. 20, pp. 313-322.