Providing a model for selecting a vehicle by citizens in intra-city trips (Tehran metropolis, Zone 6)

Document Type : Original Article

Authors

1 M.Sc., Grad., Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Iran.

2 Assistant Professor, Department of Civil Engineering, Malard Branch, Islamic Azad University, Malard, Iran.

Abstract

Promoting the use of public transport instead of using private vehicles is the most practical, effective, and least cost-effective way to reduce intra-city traffic problems in a country. Encouraging citizens to use public transport instead of private vehicles requires the various facilities for people when using these vehicles. Lack of sufficient attention to transport and traffic problems in recent years has led to huge social, economic, and environmental costs, the most important of which are risks to energy resources, high fuel consumption, increased fatalities and injuries, air pollution, traffic, and environmental damage. According to published statistics, every passenger in a private vehicle consumes twelve times as much fuel as a passenger in a bus and produces fifteen and a half times as much pollution. This study aims to investigate the vehicles selected by citizens in intra-city travels in District 6 of Tehran. Hence, using a questionnaire, we have focused on gathering information and modeling the travel model selected by citizens with various travel purposes. The results show that the purpose in travel for majority of the citizens is business, most of which have chosen private vehicles as travel mode. Then, for modeling the travel mode selected by citizens, the logistic regression model for each travel purpose (business, leisure, education, shopping) and the neural network model were used. By observing the R square derived from logistic and neural network models, it is found that the neural network model is a more appropriate model for selecting the travel mode. In this regard, the neural network model was used to forecast the travel mode selection. The validation results in the neural network model show the proximity of the prediction value to the actual value.

Keywords

Main Subjects

References

فلاح تفتی، م. شهابی، س. و تقی زاده، ی.، (1397)، "مدل سازی رفتار انتخاب وسیله کاربران وسایل نقلیه شخصی در قبال اعمال سیاست­های مدیریت تقاضای سفر
(مطالعه موردی: شهر یزد)".
-Achour, H., & Belloumi, M., (2016), "Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia", Renewable and Sustainable Energy Reviews, 56, pp.988-998.
-Bento, A. M., Hughes, J. E., & Kaffine, D., (2013), "Carpooling and driver responses to fuel price changes: Evidence from traffic flows in Los Angeles", Journal of Urban Economics, 77, pp.41-56.
-Bharadwaj, S., Ballare, S., & Chandel, M. K., (2017), "Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region", Transportation Research Procedia, 25, pp.3538-3551.
-Bullard, R. D., (2018), "Transportation matters: stranded on the side of the road before and after disasters strike", Routledge.
In Race, Place, and Environmental Justice After Hurricane Katrina, pp. 85-108.
-Chai, J., Yang, Y., Wang, S., & Lai, K. K., (2016), "Fuel efficiency and emission in China's road transport sector: Induced effect and rebound effect", Technological Forecasting and Social Change, pp.188-197.
-Chi, G., Porter, J. R., Cosby, A. G., & Levinson, D., (2013), "The impact of gasoline price changes on traffic safety: a time geography explanation", Journal of Transport Geography, 28, pp.1-11.
-Clewlow, R. R., & Mishra, G. S., (2017), "Disruptive transportation: The adoption, utilization, and impacts of ride-hailing in the United States".
-Ercan, T., Onat, N. C., & Tatari, O., (2016), "Investigating carbon footprint reduction potential of public transportation in United States: A system dynamics approach", Journal of cleaner production, 133,
pp.1260-1276.
-Geels, F. W., (2012), "A socio-technical analysis of low-carbon transitions: introducing the multi-level perspective into transport studies", Journal of transport geography, 24, pp.471-482.
-Gruel, W., & Stanford, J. M., (2016), "Assessing the long-term effects of autonomous vehicles: a speculative approach", Transportation Research Procedia, 13, pp.18-29.
-He, L.-Y., Yang, S., & Chang, D., (2017), "Oil price uncertainty, transport fuel demand and public health", International journal of environmental research and public health, 14(3), 245.
-Javid, R. J., Nejat, A., & Hayhoe, K., (2014), "Selection of CO2 mitigation strategies for road transportation in the United States using a multi-criteria approach", Renewable and Sustainable Energy Reviews, 38, pp.960-972.
-Kalupová, B., & Hlavoň, I., (2016), "Intelligent transport systems in the management of road transportation", Open Engineering, 6,1.
-Kim, D., Ko, J., & Park, Y., (2015), "Factors affecting electric vehicle sharing program participants", attitudes about car ownership and program participation", Transportation Research Part D: Transport and Environment, 36, pp.96-106.
-Larson, W., Liu, F., & Yezer, A., (2012), "Energy footprint of the city: Effects of urban land use and transportation policies", Journal of Urban Economics, 72(2-3), pp.147-159.
-López-Mosquera, N., Lera-López, F., & Sánchez, M., (2015), "Key factors to explain recycling, car use and environmentally responsible purchase behaviors: a comparative perspective", Resources, Conservation and Recycling, 99, pp.29-39.
-Macmillan, A., Connor, J., Witten, K., Kearns, R., Rees, D., & Woodward, A., (2014), "The societal costs and benefits of commuter bicycling: simulating the effects of specific policies using system dynamics modeling", Environmental health perspectives, 122(4), pp.335-344.
-Molloy, R., & Shan, H., (2013), "The effect of gasoline prices on household location", Review of Economics and Statistics, 95(4), pp.1212-1221.
-Nanaki, E., Koroneos, C., Roset, J., Susca, T., Christensen, T. H., Hurtado, S. D. G., López-Jiménez, P. A., (2017), "Environmental assessment of 9 European public bus transportation systems", Sustainable cities and society, 28, pp.42-52.
-Power, A., (2012), "Social inequality, disadvantaged neighbourhoods and transport deprivation: an assessment of the historical influence of housing policies", Journal of Transport Geography, 21, pp.39-48.
-Rachele, J. N., Learnihan, V., Badland, H. M., Mavoa, S., Turrell, G., & Giles-Corti, B., (2017), "Neighbourhood socioeconomic and transport disadvantage: The potential to reduce social inequities in health through transport", Journal of Transport & Health, 7, pp.256-263.
-Shen, Q., Chen, P., & Pan, H., (2016), "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city", Transportation Research Part A: Policy and Practice, 94, pp.31-44.
-Tang, L., & Thakuriah, P. V., (2012), "Ridership effects of real-time bus information system: A case study in the City of Chicago", Transportation Research Part C: Emerging Technologies, 22, pp.146-161.

Files

Share

How to cite

Statistics