Evaluating the contribution of individual and spatial factors in the method of intra-city travel in the Case study of Rasht city

Document Type : Research Paper

Authors

Department of Urban Planning, Faculty of Architecture and Art, Gilan University, Rasht, Iran

Abstract

A B S T R A C T
Different ways of travelling are the basis of the economic and social activities of every city due to the costs of transportation and access, and the flow of people's travel in the city to access activities and services turns it into a living organism. The current research aims to investigate the internal relationships affecting individual and spatial factors on the way of intra-city travel in Rasht city. The present research method is considered to be applied in terms of purpose and in terms of descriptive-survey data collection. The current research aims to investigate the internal relationships affecting individual and spatial factors on the way of intra-city travel in Rasht city. The statistical population of this research consists of 21 urban planning and urban transportation experts. The data collection tool is in the form of a questionnaire with 31 factors documented and presented from the background table of the research. The subjects of the research are coded and in the next step, internal relationships of the effective factors are identified and determined through the Dimel technique, finally, by forming a group decision matrix, a better understanding of the internal effective factors is created. Also, the results obtained from the Dimtel technique showed that the accessibility according to travel costs (1.695), the distance between stations (1.549), the accessibility according to the distance from the city centre (0.628), the accessibility according to traffic (0.254), willingness to use public transportation (0.166), the distance between home and important city service-commercial centres (0.123) were the most influential indicators in evaluating the ways of intra-city travel in Rasht city
Extended Abstract
Introduction
In the cities of the third world, due to the characteristics and chaotic structure resulting from excessive population growth, it is more tangible and requires basic and infrastructure planning from the city managers. During the past three decades, the city of Rasht, as the largest urban center in the southern margin of the Caspian Sea, has witnessed significant changes in the urban structure and, as a result, changes in transportation and trip methods. Changes in communication networks by expanding and opening new axes in the central part and changing the disorderly and chaotic street network of the past into a semi-radial network, replacing single-unit buildings with multi-storey buildings, changing the architectural pattern of buildings, building residential complexes in Inside and around the city and the high growth of construction are considered to be important changes in the urban structure in Rasht. In addition, like some other cities in the country, Rasht has faced two categories of rapid population growth and an increase in the car ownership rate. Due to the inappropriate structure of the communication networks, the traffic tolerance capacity of this city has now reached the saturation stage, and the development measures of the communication networks are in no way equal to the rate of increase in automobiles. Therefore, today the city of Rasht is considered as one of the metropolises of the country, which is not separated from this issue due to the increasing trend of car production and use.  In general, Rasht city has two types of textures that are completely different from each other in terms of spatial structure. On the one hand, it has a very compact and dense texture in the central core and old neighborhoods of the city, and on the other hand, it has a completely wide and heterogeneous texture outside this area.   In each of these two types of urban texture, the time, cost, length and way of trip of citizens are different from each other under the influence of density and the way of replacing land uses. Therefore, the aim of the current research is to investigate the internal relationships affecting individual and spatial factors on the way of intra-city trip of Rasht. Thus, the main research question is as follows:
-At what level is the contribution of spatial and individual factors in determining the way of intra-city trip of citizens?
 
Methodology
The method of the present research is considered to be applied in terms of purpose and in terms of descriptive-survey data collection. The statistical population of this research consists of 21 urban planning and urban transportation experts. The data collection tool is in the form of a questionnaire with 31 factors that have been documented and proposed from the research background. The items in question of the research are coded and in the next step, internal relationships of the effective factors are identified and determined through the DEMATEL technique, and finally, by forming a group decision matrix, a better understanding of the internal effective factors is created. The DEMATEL technique is a comprehensive method for building and analyzing a structural model of relationships between complex and multiple factors on the research target variable. In the DEMATEL technique, quantitative relationships between multiple factors of a problem and the effect of each of them on the other are calculated. In order to determine the intensity of the direct and indirect influence of the factors on each other, it is measured. In order to determine the intensity of the influence of the elements on each other, it is necessary to determine how to score the determined indicators. For this purpose, a questionnaire was designed and experts' judgments regarding the severity of the effects of elements on each other were questioned. In this research, the intensity of the effects of the elements was ranked from 0 to 4.
DEMATEL's method was first presented by two researchers named Fontela and Gabos in 1976. This technique is based on pairwise comparisons and is a decision-making tool based on graph theory. This method may confirm the relationships between variables or limit the relationships in a developmental and systematic process.  In other words, this technique determines their influence and importance numerically by examining the mutual relationship between the criteria. The most important feature of DEMATEL's method is multi-criteria decision-making and its performance in creating relationships and structure between factors. This technique, in addition to converting causal and normal relationships into a structural-visual model, is also able to identify internal dependencies between factors and make them understandable.
 
Results and discussion
The results of the classification of individual and spatial factors in the way of intra-city trips have shown that factors C4, C6, C9, C10, C11, and C13 require a high level of concentration compared to factors C1, C2, C3, C5, C7, C8, C12, C14. The factors of the first group interact a lot with other factors and are more important. Also, in this group, which is also called the cause group, the factors of accessibility are the most important factors according to the travel costs and the distance between the stations, respectively. In the other group, the effect group, the least important factor compared to other factors is the accessibility according to the distance from the city center. Identifying and classifying these factors can be effective in making decisions and taking executive action to improve the process of intra-city trips by determining the impact of a decision on other parts. Also, with these categories, it is possible to recognize implementation measures with similar and overlapping results and use them for better decision-making.
Also, according to the results obtained from the DEMATEL technique, accessibility according to travel costs (1.695), the distance between stations (1.549), accessibility according to distance from the city center (0.628), accessibility according to traffic (0.254), the degree of willingness to use public transportation (0.166), the distance between home and important urban service-commercial centers (0.123) have been the most influential indicators in the evaluation of intra-city trip methods in Rasht city.
 
Conclusion
The introduction of automobiles into urban communities, along with facilitating and increasing the speed of travel, has led to the development of urban spaces, the construction of various uses, and the construction of roads and communication networks as the main structure of the city. Willingly or not, this development has led to an increase in demand and the amount of travel. In cases where it is not accompanied by development control and management policies, it has intensified the superficial expansion of the city. The current research aims to investigate the internal relationships affecting individual and spatial factors on the way of intra-city trips of Rasht. The main results of the research show that, in general, individual and spatial factors play a role in various travel goals, each with a different but effective contribution. In the meantime, the accessibility is more pronounced according to the distance from the city center as an effective spatial index. However, in this regard, it is impossible to ignore the influence of public transportation system performance factors such as the distance between stations and the degree of willingness to use public transportation for other trip purposes.
Based on the results of this research and consistent with the results of other ones, it is important to pay attention to the environmental characteristics effective in trip behavior, including the urban structure and form (compaction, dispersion, etc.) and the level of urbanization (city center, growth center, urban suburbs). The spatial structure of cities has a significant impact on the way of intra-city trips in such a way that dense structures with a more compact distribution of population and activity in the urban space and, as a result of the relative reduction of distances, provide grounds for the establishment of travel methods and pedestrian access, which is valid for dispersed structures. According to the results, the residents of the outer and middle areas were more inclined to use private cars in their trip methods. The high dependence on personal cars in intra-city trips significantly differs from urban areas in outer areas further away from the center of work and activity. As a result, the excessive use of private cars in Rasht city is caused by the weakness of the spatial organization of the city and its main functions at the neighborhood level. In addition to that, the sprawl shape of the city, along with the weakness of financial interests and technical facilities, has affected the behavior of citizens in a long-term process. Furthermore, it has changed the transportation culture and traffic behavior of the people.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved thecontent of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.

Keywords


  1. Abane, A. M. (2011). Travel behaviour in Ghana: Empirical observations from four metropolitan areas. Journal of Transport Geography, 19(2), 313–322.
  2. Alizadeh, T., Azimi, J., Motevalli, S., & Sarvar, R. (2022). An Analysis of the Future of Public Transport Systems in the Framework of Environmental Sustainability the Case Study of Tehran Metropolitan. Journal of Sustainable city5(3), 71-88. [In Persian].
  3. Aloulou, F. (2018). The Application of Discrete Choice Models in Transport. Statistics: Growing Data Sets and Growing Demand for Statistics. London: IntechOpen.
  4. Bates, J. (2000). History of demand modeling. Handbook of Transport Modeling, 1, 11–33.
  5. Birago, D., Mensah, S. O., & Sharma, S. (2017). Level of service delivery of public transport and mode choice in Accra, Ghana. Transportation research part F: traffic psychology and behaviour, 46, 284–300.
  6. Bukhsh, Z., Saeed, A., Stipanovic, I., & Doree, A. (2019). Predictive maintenance using tree-based classification techniques: A case of railway switches. Transportation Research Part C: Emerging Technologies, 101, 35–54.
  7. Choi, K. (2018). The influence of the built environment on household vehicle travel by the urban typology in calgary, Canada. Cities, 75, 101–110.
  8. Ding, C., Liu, C., Zhang, Y., Yang, J., & Wang, Y. (2017). Investigating the impacts of built environment on vehicle miles traveled and energy consumption: Differences between commuting and non-commuting trips. Cities, 68, 25–36.
  9. Ding, L., & Zhang, N. (2016). A Travel Mode Choice Model Using Individual Grouping Based on Cluster Analysis. Procedia Engineering, 137, 786–795.
  10. Esson, J., Gough, K. V., Simon, D., Amankwaa, E. F., Ninot, O., & Yankson, P. W. (2016). Livelihoods in motion: Linking transport, mobility and income-generating activities. Journal of Transport Geography, 55, 182–188.
  11. Ghadami, M., & Nabinezhad Kenari, M. (2012). An Investigation into the Effects of Individual and Spatial Factors on the Urban Trips Case Study: Babol City. Geography and Environmental Sustainability1(1), 79-94. [In Persian].
  12. Ghorbani, R., Asghari Zamani, A., & Gholamhosseini, R. (2023). The Analysis of Urban Form Elements Effect on the Behaviour of the travel and to Develop low-carbon City Case Study: Tabriz Metropolitan. Journal of applied research of geographical sciences, 23 (71):123-136. [In Persian].
  13. Hamadneh, J., & Esztergár-Kiss, D. (2022). The preference of onboard activities in a new age of automated driving. European Transport Research Review, 14(1), 15.
  14. Hauber, A. B., González, J. M., Groothuis-Oudshoorn, C. G., Prior, T., Marshall, D. A., Cunningham, C., & Bridges, J. F. (2016). Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value in Health, 19(4), 300-315.
  15. Heidari, A., & Shojaie, A. (2017). Ranking of Passenger Transportation Modes by Using TOPSIS Method. Journal of Transportation Research14(3), 159-168. [In Persian].
  16. Hosseini, A. & Bahrami, Y. (2012). The effect of the spatial structure of the city on the travel behavior of citizens, a case study Rasht city. Journal of applied research of geographical sciences, 13(28), 243-267. [In Persian].
  17. Jaber, A., Baker, L. A., & Csonka, B. (2022). The Influence of Public Transportation Stops on Bike-Sharing Destination Trips: Spatial Analysis of Budapest City. Future Transportation, 2(3), 688–697.
  18. Jain, D., & Tiwari, G. (2019). Explaining travel behaviour with limited socio-economic data: Case study of Vishakhapatnam, India. Travel Behaviour and Society, 15, 44–53.
  19. Kadkhodaei, M., & shad, R. (2020). Ranking of traffic congestion pricing schemes in tourist metropolises: Case study of Mashhad. Motaleate Shahri, 9(33), 27-38. [In Persian].
  20. Mahpour, A. (2022). Identifying and modeling the latent individuals’ variables affecting intracity rail mode choice. Journal of Transportation Research19(4), 103-116. [In Persian].
  21. Mirzaei, E., Kheyroddin, R., Behzadfar, M., Mignot, D., & Mohamadi, M. (2019). An Analysis of the Intraurban Trip Distance Using the Time Geography Framework; Influenced by Individual Constraints or Spatial Opportunities. The Monthly Scientific Journal of Bagh-e Nazar, 16(78), 41-52. [In Persian].
  22. Muniz, I., & Garcia-Lopez, M.A. (2019). Urban form and spatial structure as determinants of the ecological footprint of commuting. Transportation Research Part D: Transport and Environment, 67, 334–350.
  23. Nagavi, M., divsalar, A., & reiahi, V. (2018). Measuring quality of life in coastal cities make decisions based on the scale test model DEMATEL (Case coastal city ofnoor). Geography and Development16(52), 211-226. [In Persian].
  24. Nastaran, M., Nouri, M. J., & Rikhtehgaran, F. (2018). Explaining and Evaluating the Criteria of Comfort and Convenience in Urban Public Transport Trips: A Case Study of the 28th Bus Line of Isfahan Metropolis. Urban Planning Knowledge2(1), 105-121. [In Persian].
  25. Oña, R., & Oña, J. (2015). Analysis of transit quality of service through segmentation and classification tree techniques. Transportmetrica A: Transport Science, 11(5), 365–387.
  26. Paredes, M., Hemberg, E., O’Reilly, U. M., & Zegras, C. (2017). Machine learning or discrete choice models for car ownership demand estimation and prediction?. In 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), (pp. 780–785).
  27. Paulssen, M., Temme, D., Vij, A., & Walker, J. L. (2014). Values, attitudes and travel behavior: A hierarchical latent variable mixed logit model of travel mode choice. Transportation, 41(4), 873–888.
  28. Sarvar, H. (2019). identification worn-out urban textures Based on the physical parameters Case Study: Region One  Tabriz city. Journal of Sustainable city2(1), 1-14. [In Persian].
  29. Soltani, A., & Shariati, S. (2012). Investigating the incentives and barriers to using bicycles in urban transportation (case study of Isfahan city). Journal of Iranian Architecture & Urbanism, 4(1), 63-73. [In Persian].
  30. Spinney, J. E., Maoh, H., & Millward, H. (2018). Factors affecting mode choice for the home–elementary school journey: Evidence from Halifax, Canada. The Canadian Geographer /Le Géographe canadien, 63(2), 254–266.
  31. Tandise, M., & Rezaei, M.R. (2013). Strategic Planning of Civil Persistent Transportation in Metropolises of Iran (Case Study: City of Mashhad).  Journal of Transportation Engineering5(1), 1-18. [In Persian].
  32. Vij, A., Carrel, A., & Walker, J. L. (2013). Incorporating the influence of latent modal preferences on travel mode choice behavior. Transportation Research Part A: Policy and Practice, 54, 164–178.
  33. Ye, R., & Titheridge, H. (2017). Satisfaction with the commute: The role of travel mode choice, built environment and attitudes. Transportation Research Part D: Transport and Environment, 52, 535–547.
  34. Zhan, G., Yan, X., Zhu, S., & Wang, Y. (2016). Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China. Transport Policy, 45, 55–65.
  35. Zhao, P. (2010). Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat International, 34(2), 236–243.
  36. Zhao, X., Yan, X., Yu, A., & Hentenryck, P. V. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22–35.
  37. Zhong, C., Schläpfer, M., Müller Arisona, S., Batty, M., Ratti, C., & Schmitt, G. (2017). Revealing centrality in the spatial structure of cities from human activity patterns. Urban Studies, 54(2), 437-455.