AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth, safe, and green transportation. Therefore, effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management concepts. In contrast to classical traffic detection solutions, this study investigates the correlation between travelers' social activities and road traffic. The s's primary goal is to investigate the presence of the relationship between social activity and road traffic, which might allow an infrastructure-independent traffic monitoring technique as well. People's general activities at Point of Interest (POI) locations (measured as occupancy parameter) are correlated with traffic data so that, finally, proper proxys can be defined for link-level average traffic speed estimation. The method is tested and evaluated using real-world traffic and POI occupancy data from Budapest (District XI.). The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic, which holds promise for future practical applicability.
Alessandretti, L., Aslak, U., Lehmann, S., 2020. The scales of human mobility. Nature 402–407.
Alomari, E., Katib, I., Mehmood, R., 2020. Iktishaf: a big data road-traffic event detection tool using Twitter and spark machine learning. Mobile Network. Appl. 1–16.
Artusi, R., Verderio, P., Marubini, E., 2002. Bravais-pearson and spearman correlation coefficients: meaning, test of hypothesis and confidence interval. Int. J. Biol. Markers 17, 148–151.
Balsa-Barreiro, J., Menendez, M., Morales, A.J., 2022. Scale, context, and heterogeneity: the complexity of the social space. Sci. Rep. 12, 9037.
Byun, S., Shin, I.K., Moon, J., Kang, J., Choi, S.I., 2021. Road traffic monitoring from UAV images using deep learning networks. Rem. Sens. 13.
Cao, D., Wang, S., Lin, D., 2018. Chinese microblog users' sentiment-based traffic condition analysis. Soft Comput. 22, 7005–7014.
Carr, J., 2014. An introduction to genetic algorithms. Senior Project 1, 7.
Chai, T., Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. (GMDD) 7, 1525–1534.
Cherrett, T., Bell, H., McDonald, M., 2000. Traffic management parameters from single inductive loop detectors. Transport. Res. Rec. 1719, 112–120.
D'Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F., 2015. Real-time detection of traffic from Twitter stream analysis. IEEE Trans. Intell. Transport. Syst. 16, 2269–2283.
De Winter, J.C., Gosling, S.D., Potter, J., 2016. Comparing the pearson and spearman correlation coefficients across distributions and sample sizes: a tutorial using simulations and empirical data. Psychol. Methods 21, 273.
Di Bucchianico, A., 2008. Coefficient of Determination (R 2). Encyclopedia of Statistics in Quality and Reliability 1.
Eichberger, A., Szalay, Z., Fellendorf, M., Liu, H., 2022. Advances in automated driving systems. Energies 15.
Fekih, M., Bonnetain, L., Furno, A., Bonnel, P., Smoreda, Z., Galland, S., Bellemans, T., 2021. Potential of cellular signaling data for time-of-day estimation and spatial classification of travel demand: a large-scale comparative study with travel survey and land use data. Transportation Letters 1–19, 0.
Hruboš, M., Nemec, D., Bubeníková, E., Holečko, P., Spalek, J., Mihálik, M., Bujňák, M., Andel, J., Tichý, T., 2021. Model-based predictive detector of a fire inside the road tunnel for intelligent vehicles. J. Adv. Transport. 2021, 6634944.
Jain, N.K., Saini, R., Mittal, P., 2019. A review on traffic monitoring system techniques. Soft Comput.: Theories and Applications 569–577.
Kovács, A., Leelőssy, Ádám, Tettamanti, T., Esztergár-Kiss, D., Mészáros, R., Lagzi, I., 2021. Coupling traffic originated urban air pollution estimation with an atmospheric chemistry model. Urban Clim. 37, 100868.
Lipfert, F.W., Wyzga, R.E., 2008. On exposure and response relationships for health effects associated with exposure to vehicular traffic. J. Expo. Sci. Environ. Epidemiol. 18, 588–599.
Mariani, M., Styven, M.E., Ayeh, J.K., 2019. Using facebook for travel decision-making: an international study of antecedents. Int. J. Contemp. Hospit. Manag. 31, 1021–1044.
Möhring, M., Keller, B., Schmidt, R., Dacko, S., 2021. Google popular times: towards a better understanding of tourist customer patronage behavior. Tourism Rev. 76, 533–569.
Moyano, A., Stçpniak, M., Moya-Gómez, B., García-Palomares, J.C., 2021. Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods. Transportation 48, 3301–3324.
Nowoświat, A., Żuchowski, R., Pudełko, B., 2022. Effectiveness of the simulation of acoustic protection for a specific urban situation. Period. Polytech. Transp. Eng. 50, 227–234.
Osorio-Arjona, J., García-Palomares, J.C., 2019. Social media and urban mobility: using twitter to calculate home-work travel matrices. Cities 89, 268–280.
Papageorgiou, M., 2004. Overview of road traffic control strategies. IFAC Proc. Vol. 37, 29–40.
Pourebrahim, N., Sultana, S., Niakanlahiji, A., Thill, J.C., 2019. Trip distribution modeling with Twitter data. Comput. Environ. Urban Syst. 77, 101354.
Rathore, M.M., Attique Shah, S., Awad, A., Shukla, D., Vimal, S., Paul, A., 2021. A cyber-physical system and graph-based approach for transportation management in smart cities. Sustainability 13. https://doi.org/10.3390/su13147606.
Shahwani, H., Attique Shah, S., Ashraf, M., Akram, M., Jeong, J.P., Shin, J., 2022. A comprehensive survey on data dissemination in vehicular ad hoc networks. Vehicular Communications 34, 100420.
Tettamanti, T., Horváth, M., Varga, I., 2014. Road traffic measurement and related data fusion methodology for traffic estimation. Transport and Telecommunication 15, 269–279.
Thonhofer, E., Palau, T., Kuhn, A., Jakubek, S., Kozek, M., 2018. Macroscopic traffic model for large scale urban traffic network design. Simulat. Model. Pract. Theor. 80, 32–49.
Zhang, Z., He, Q., Zhu, S., 2017. Potentials of using social media to infer the longitudinal travel behavior: a sequential model-based clustering method. Transport. Res. C Emerg. Technol. 85, 396–414.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
