Predictive Modeling of Urban Travel Demand Using Neural Networks and Regression Analysis

Muhammed Ali Çolak · Osman Ünsal Bayrak

5 citations0/0 files storedUrban Science

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Transportation Planning and Optimization Traffic Prediction and Management Techniques Urban Transport Systems Analysis

Abstract

Urban transportation systems are increasingly strained by population growth, changing mobility patterns, and the need for sustainable infrastructure planning. The accurate modeling of urban trip generation is critical for effective and sustainable transportation planning, especially in the context of rapidly growing urban populations and evolving travel behaviors. This study investigated the application of advanced statistical methods and artificial intelligence-based techniques for forecasting urban travel demand. Erzincan, with a population of approximately 200,000, serves as a representative mid-sized city, offering valuable insights for transportation planning and traffic management. Data collected from various user groups, including households and university students, provide a comprehensive understanding of local travel behavior. Four predictive modeling techniques, linear regression, Poisson regression, negative binomial regression, and artificial neural networks (ANNs), were applied to the dataset, followed by a comparative performance evaluation. Additionally, a macro-level simulation was conducted using VISUM (Release 18.2.22) software to evaluate the current transportation network and assess the potential impacts of proposed improvement scenarios. The results show that the ANN model provided the highest predictive accuracy for household-based data (R2 = 0.62), while the linear regression model yielded the best results for dormitory-based data (R2 = 0.95). Furthermore, Poisson regression proved most effective in estimating the minimum trip generation time, which was estimated to be 22.77 min under simulated conditions. The study offers practical insights for transport planners and policymakers by demonstrating how predictive analytics and simulation tools can be integrated to address urban mobility challenges.

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2017Inferring transportation modes from GPS trajectories using a convolutional neural network 2022Transportation planning for sustainable supply chain network using big data technology 2018Assessing the Impacts of Dedicated Bus Lanes on Urban Traffic Congestion and Modal Split with an Agent-Based Model 2023Deep trip generation with graph neural networks for bike sharing system expansion 2017Estimation and valuation of travel time reliability for transportation planning applications 2024Understanding spatial inequalities and stratification in transportation accessibility to social infrastructures in South Korea: multi-dimensional planning insights 2022Sustainable mobility transitions in suburbia – exploring (dis)connections between transport planning and daily mobility 2023Revealing social dimensions of urban mobility with big data: A timely dialogue

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