TIME PREDICTION ALGORITHM BASED ON DISTANCE AND REAL-WORLD CONDITIONS
Prediction algorithms have seen a rise in popularity in various applications. These algorithms are frequently implemented in applications to assist in making data-driven decisions based on the predicted output. Time prediction algorithms are often used to predict the travel time between two distances that allow better planning and anticipation. However, the non-linear situation of urban traffic has undermined the accuracy of these predictions. With the increased application of such algorithms in urban settings, it is important to conduct research to further improve the accuracy of current algorithms. The factors affecting travel time are researched to develop an algorithm that includes these factors into consideration during calculation.
"The Definitive Glossary of Higher Mathematical Jargon.," Math Vault, [Online]. Available: https://mathvault.ca/math-glossary/#algo. [Accessed 9 February 2019].
J. Brogan, "Defining Algorithms—a Conversational Explainer," Slate Magazine, 2 February 2016. [Online]. Available: https://slate.com/technology/2016/02/whats-the-deal-with-algorithms.html. [Accessed 9 February 2020].
"A Basic Introduction To Neural Networks," The University of Wisconsin Madison, [Online]. Available: http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html. [Accessed 9 February 2020].
K. Gurney, An introduction to neural networks, London: UCL Press, 1997.
M. Deng and S. Qu, "Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations.," Mathematical Problems in Engineering, pp. 1-14, 2016.
C. Bai, Z. Peng, Q. Lu and J. Sun, "Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes," Computational Intelligence and Neuroscience, pp. 1-9, 2015.
J. Ma, J. Chan, G. Ristanoski, S. Rajasegarar and C. Leckie, "Bus travel time prediction with real-time traffic information," Transportation Research Part C: Emerging Technologies, vol. 105, pp. 536-549, 2019.
M. Abdollahi, T. Khaleghi and K. Yang, "An integrated feature learning approach using deep learning for travel time prediction," Expert Systems with Applications, vol. 139, 2020.
E. J. Schmitt and H. Jula, "On the Limitations of Linear Models in Predicting Travel Times," in 2007 IEEE Intelligent Transportation Systems Conference, Seattle, 2007.
H. van Lint and C. van Hinsbergen, "Short-Term Traffic and Travel Time Prediction Models," Artificial Intelligence Applications to Critical Transportation Issues, 2012.
L. Masiero, M. Casanova and M. de Carvalho, "Travel time prediction using machine learning," in 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2011.
C. van Hinsbergen, J. van Lint, H. van Zuylen and A. Hegyi, "Bayesian neural networks for the prediction of stochastic travel times in urban networks," IET Intelligent Transport Systems, vol. 5, no. 4, pp. 259-265, 2011.
I. Russell, "Neural Networks in the Undergraduate Curriculum," Journal of Computing Sciences in Colleges, pp. 92-97, 1991.
M. Raju, R. Srivastava, D. Bisht, H. Sharma and A. Kumar, "Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge," Advances in Artificial Intelligence, pp. 1-11, 2011.
D. Yoon, "Artificial Neural Network Technology," Defense Advanced Research Projects Agency, 1989.
T. Kolarik and G. Rudorfer, "Time Series Forecasting Using Neural Networks," in APL : the language and its applications, 1994.
C. Heinze, M. Leodolter, H. Koller and D. Bauer, "Transferring urban traveling speed model fits across cities," European Transport Research Review, vol. 8, no. 3, 2016.
Z. Liang and Y. Wakahara, "Real-time urban traffic amount prediction models for dynamic route guidance systems," EURASIP Journal on Wireless Communications and Networking, vol. 2014, no. 1, 2014.
D. Tiesyte and C. Jensen, "Similarity-Based Prediction of Travel Times for Vehicles Traveling on Known Routes," in 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, 2008.
N. Wu, J. Wang, W. Zhao and Y. Jin, "Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation," in 28th ACM International Conference on Information and Knowledge Management, 2019.
M. Wang, W. Lee, T. Fu and G. Yu, "Learning Embeddings of Intersections on Road Networks," in 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019.
M. a. M. T. As, "Dynamic Bus Travel Time Prediction Using an ANN-based Model," in 12th International Conference on Ubiquitous Information Management and Communication, 2018.
D. Vora, S. Ramesh, S. Rathod, K. Parthasarathy and D. Elias, "Traffic Analysis and Prediction using a Committee of Experts," in 2nd International Conference on Perception and Machine Intelligence, 2015.
F. Lin, Y. Xu, Y. Yang and H. Ma, "A Spatial-Temporal Hybrid Model for Short-Term Traffic Prediction," in Mathematical Problems in Engineering, 2019.
C. Bauer, "On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running Tracking Applications," in MoMM2013, 2013.
"The 3 Most Common Observation Research Methods.," Fuel Cycle, 22 August 2019. [Online]. Available: https://fuelcycle.com/blog/the-3-most-common-observation-research-methods/. [Accessed 30 January 2020].
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