Journal of Transportation Research

Journal of Transportation Research

Determining the socio-economic characteristics of passengers in urban traffic using artificial intelligence

Document Type : Original Article

Authors
Shahriar Afandizadeh 1
Mahmoud Ahmadinejad 2
Mohammad Ghaedi 3
Hamid Bigdeli Rad 4
1 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Associate Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
3 M.Sc., Student, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
4 Ph.D., Candidate, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
10.22034/tri.2024.426481.3206
Abstract
Travel demand predicting models are one of the widely used models that are used by researchers all over the world, and in these models, people's socio-economic characteristics are used as input to the model, and these characteristics were collected through various statistical methods. With the growth of the passenger population, population-based big data is widely used in transportation planning, and traditional methods are not effective for collecting socio-economic characteristics of people. Therefore, the researchers decided to obtain these parameters by using the pattern of trips that people have already made. In this study, with the help of artificial intelligence models and especially deep learning models, it has been tried to extract these features from the travel patterns of people. The LSTM model is one of the powerful models used to remember past events. Age, income and gender are the most important inputs of travel demand predicting models. Different age, gender and income groups may have specific travel behavior preferences, which are tried to be discovered by examining their travel behaviors. One of the important sources for determining people's travel patterns is mobile phone data that shows their daily movements. Due to the unavailability of travelers' mobile phone data, the NHTS National Household Travel Survey survey data set was used in this research. Using the obtained results, with the help of mobile phone data of passengers, their socio-economic characteristics are obtained for use in travel demand models.
Keywords
Travel Demand Model
Socio-Economic Parameters
Deep Learning
LSTM Model
Subjects
-Afandizadeh, S., & Bigdeli Rad, H. (2021). Developing a model to determine the number of vehicles lane changing on freeways by Brownian motion method. Nonlinear Engineering, 10(1), 450-460.
-Bakhtiari, A., Mirzahossein, H., Kalantari, N., & Jin, X. (2023). Inferring Socioeconomic Characteristics from Travel Patterns. Journal of Regional and City Planning, 34(1), 122-136.
-Bing-quan, L. I. U., & Wei, D. U. (2020). Model and method for traffic demand management with parking controlled on bimodal network. Operations Research and Management Science, 29(9), 218.
-Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning, New York: Springer. Vol. 4, No. 4, 738—739.
 -Bulusu, V., Onat, E. B., Sengupta, R., Yedavalli, P., & Macfarlane, J. (2021). A traffic demand analysis method for urban air mobility. IEEE Transactions on Intelligent Transportation Systems, 22(9), 6039-6047.
-Carlsson-Kanyama, A., & Linden, A. L. (1999). Travel patterns and environmental effects now and in the future:: implications of differences in energy consumption among socio-economic groups. Ecological Economics, 30(3), 405-417.
-Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., & Li, Z. (2020, April). Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 04, 3414-3421.
-Chu, T., Wang, J., Codecà, L., & Li, Z. (2019). Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1086-1095.
-Collins, D., & Tisdell, C. (2002). Gender and differences in travel life cycles. Journal of Travel Research, 41(2), 133-143.
-Crane, R., & Takahashi, L. (2009). Sex changes everything: the recent narrowing and widening of travel differences by gender. Public Works Management & Policy, 13(4), 328-337.
-Gärling, T., & Schuitema, G. (2007). Travel demand management targeting reduced private car use: effectiveness, public acceptability and political feasibility. Journal of Social Issues, 63(1), 139-153.
-Gopalakrishna, D., Hsu, E., Kuhn, B., Schreffler, E., & Diggins, L. (2019). Strengthening Linkages between Transportation Demand Management and Traffic Management, No. FHWA-HOP-18-072. United States. Federal Highway Administration.
-Hörcher, D., Singh, R., & Graham, D. J. (2022). Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis.  Transportation, 49(2), 735-764.
 -Jakobsson, C., Fujii, S., & Gärling, T. (2000). Determinants of private car users' acceptance of road pricing. Transport Policy, 7(2), 153-158.
-Khatri, S., Vachhani, H., Shah, S., Bhatia, J., Chaturvedi, M., Tanwar, S., & Kumar, N. (2021). Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges. Peer-to-Peer Networking and Applications, 14, 1778-1805.
-Koushik, A. N., Manoj, M., & Nezamuddin, N. (2020). Machine learning applications in activity-travel behaviour research: a review. Transport Reviews, 40(3), 288-311.
May, A. D., Kelly, C., & Shepherd, S. (2006). The principles of integration in urban transport strategies. Transport Policy, 13(4), 319-327.
-NHTS Data User Guide.
-Sari Aslam, N., Ibrahim, M. R., Cheng, T., Chen, H., & Zhang, Y. (2021). Activity NET, Neural networks to predict public transport trip purposes from individual smart card data and POIs. Geo-spatial Information Science, 24(4), 711-721.
-Vuong, N. K., Liu, Y., Chan, S., Lau, C. T., Chen, Z., Wu, M., & Li, X. (2020, July). Deep learning with long short-term memory networks for classification of dementia related travel patterns. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)­, IEEE., 5563-5566.
-Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208.
-Zargari, S. A., & Rad, H. B. (2023). Development of a gray box system identification model to estimate the parameters affecting traffic accidents. Nonlinear Engineering, 12(1), 20220218.
-Zhang, Y., & Cheng, T. (2019). A deep learning approach to infer employment status of passengers by using smart card data. IEEE Transactions on Intelligent Transportation Systems, 21(2), 617-629.
-Zhong, G., Yin, T., Zhang, J., He, S., & Ran, B. (2019). Characteristics analysis for travel behavior of transportation hub passengers using mobile phone data. Transportation, 46,1713-1736.
-Zhou, Z., Lin, S., Du, W., & Liang, H. (2019). Integration of regional demand management and signals control for urban traffic networks. IEEE Access, 7, 20235-20248.
-Zhu, L., Gonder, J., & Lin, L. (2017). Prediction of individual social-demographic role based on travel behavior variability using long-term GPS data. Journal of Advanced Transportation.
Journal of Transportation Research
Volume 23, Issue 4
Autumn 2026
Pages 31-50