Parseh Designers Transportation Research Institute Quarterly Journal of Transportation Engineering 2008-6598 16 3 2025 03 21 Estimation of Intercity Trip Demand from Mobile Data (Case Study: Tehran and Shahriar Counties) Estimation of Intercity Trip Demand from Mobile Data (Case Study: Tehran and Shahriar Counties) 4605 4626 191836 10.22119/jte.2024.424005.2690 FA Siavash Taheri Navid M.Sc., Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran Afshin Shariat Mohaymany Professor, Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran 0000-0002-7344-2067 Mahdiyar Darvishzade Phd Student, Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran Journal Article 2023 11 07 Nowadays, various organizations and institutions are continuously recording and storing large amounts of data, which has led to the emergence of rich sources of big data. Utilizing big data in transportation planning has been becoming a popular trend among engineers. The continuity in time and space makes some of these data very appealing for trip extraction purposes. Meanwhile, the rapid development of telecommunication networks and the increasing penetration rate of mobile phones in recent years have provided valuable data sources on how people move. In this study, we developed a methodology to estimate the travel demand between the counties of Tehran and Shahriar for different purposes, using LU mobile phone data. Cosidering this goal, the methodology consists of utilizing spatio-temporal algorithms for determining origin and destination of trips with relevant time windows for identifying the main activity locations of users. One main contribution of our study is the use of a two-step approach to expand trips based on the population of cities and mobile phone penetration rate. To evaluate the results, the movement patterns obtained from the estimated demand between two counties of Tehran province were compared with real traffic counts. As a result, Pearson coefficients with the values more than 0.9 and P-values less than 0.05 were obtained, demonstrating a high value of correlation between the estimated matrix and real traffic counts. According to the obtained results, it is possible to use mobile phone data as an applicable source in transportation analysis, specifically the estimation of travel demand at macro levels with high reliability. Nowadays, various organizations and institutions are continuously recording and storing large amounts of data, which has led to the emergence of rich sources of big data. Utilizing big data in transportation planning has been becoming a popular trend among engineers. The continuity in time and space makes some of these data very appealing for trip extraction purposes. Meanwhile, the rapid development of telecommunication networks and the increasing penetration rate of mobile phones in recent years have provided valuable data sources on how people move. In this study, we developed a methodology to estimate the travel demand between the counties of Tehran and Shahriar for different purposes, using LU mobile phone data. Cosidering this goal, the methodology consists of utilizing spatio-temporal algorithms for determining origin and destination of trips with relevant time windows for identifying the main activity locations of users. One main contribution of our study is the use of a two-step approach to expand trips based on the population of cities and mobile phone penetration rate. To evaluate the results, the movement patterns obtained from the estimated demand between two counties of Tehran province were compared with real traffic counts. As a result, Pearson coefficients with the values more than 0.9 and P-values less than 0.05 were obtained, demonstrating a high value of correlation between the estimated matrix and real traffic counts. According to the obtained results, it is possible to use mobile phone data as an applicable source in transportation analysis, specifically the estimation of travel demand at macro levels with high reliability. https://jte.sinaweb.net/article_191836_b7147f8b5c3aa4bb660e8581094e408b.pdf