Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

  • Xiaobo Zhu The Key Laboratory of Road and Traffic Engineering (Ministry of Education), School of Transportation Engineering, Tongji University； Intelligent Transportation System Research Center, Southeast University
  • Jianhua Guo Intelligent Transportation System Research Center, Southeast University
  • Wei Huang Intelligent Transportation System Research Center, Southeast University
  • Fengquan Yu Intelligent Transportation System Research Center, Southeast University
  • Byungkyu Brian Park Department of Civil and Environmental Engineering, University of Virginia
DOI: https://doi.org/10.7307/ptt.v30i2.2388
Keywords: parking guidance system, remaining parking space, time series method, short-term forecasting method, neural network,

Abstract

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Xiaobo Zhu, The Key Laboratory of Road and Traffic Engineering (Ministry of Education), School of Transportation Engineering, Tongji University； Intelligent Transportation System Research Center, Southeast University

Xiaobo Zhu is a PHD student in School of Transportation Engineering, Tongji University (Shanghai, China). He received the M.S. degree from Southeast University. His research interests are in the areas of traffic flow theory of urban road network, intelligent transportation system and traffic control and management.

Jianhua Guo, Intelligent Transportation System Research Center, Southeast University
Dr. Guo, Jianhua is a professor in Transportation Engineering at the Intelligent Transportation System Research Center of the Southeast University, Nanjing, Jiangsu Province, P.R. China.  His major research fields include intelligent transportation system applications, traffic management and control, statistical time series analysis, and discrete choice modeling.  Prior to joining Southeast University, he worked in University of Virginia and Federal Highway Administration.  He received research funds from the Chinese Natural Science Foundation, Ministry of Science and Technology of China, and various provincial agencies.  He serves as reviewer for Chinese National Science Foundation and international journals. He received the Outstanding Reviewer Award for ASCE Journal of Transportation Engineering of the year 2012, and the Outstanding Reviewer Award for ASCE Journal of Computing in Civil Engineering of the years 2012, 2013, and 2014, consecutively.
Wei Huang, Intelligent Transportation System Research Center, Southeast University
Wei Huang is a distinguished professor in Civil Engineer at the Intelligent Transportation System Research Center of the Southeast University, Nanjing, Jiangsu Province, P.R. China.  He is a member of Chinese Academy of Engineering.  He enjoys the State Council special allowance and receives supports from the New Century Talent Program, the National Outstanding Mid-aged Experts Program, the National Talents Engineering Program, and the Yangtze Scholar Program from various agencies and organizations.  He is one of the forerunners in the research fields of long span steel bridge pavement and intelligent transportation systems of China. As the leading awardee, he receives 26 awards from both the national and provincial level.  He published 13 books.
Fengquan Yu, Intelligent Transportation System Research Center, Southeast University
Fengquan Yu is a Ph.D. student in School of Transportation, Southeast University (Nanjing, China). He received the M.S. degree from Southeast University. His research interests are intelligent transportation system and traffic data analysis and modeling.
Byungkyu Brian Park, Department of Civil and Environmental Engineering, University of Virginia

Brian Park is an Associate Professor of Civil and Environmental Engineering Department at the University of Virginia.Prior to joining the University of Virginia, he was a Research Fellow at the National Institute of Statistical Sciences and a Post-Doctoral Research Associate at North Carolina State University. Dr. Park received the B.S. and the M.S. from the Hanyang University and the Ph.D. from the Texas A&M University.  His research interests are in the areas of traffic control and management, intelligent transportation system and conneted vehicle technology. He has authored about 108 technical publications.


Dr. Park is a recipient of PTV America Best Paper Award, Outstanding Reviewer Award from the American Society of Civil Engineers, Jack H. Dillard Outstanding Paper Award from the Virginia Transportation Research Council and Charley V. Wootan Award (for best Ph.D. dissertation) from the Council of University Transportation Centers. He is an ASCE ExCEEd teaching fellow.


Dr. Park is an Editor in Chief of the International Journal of Transportation, an Associate Editor of the American Society of Civil Engineers Journal of Transportation Engineering, Journal of Intelligent Transportation Systems and the KSCE Journal of Civil Engineering, and an editorial board member of the International Journal of Sustainable Transportation. Furthermore, he is a member of TRB (a division of the National Academies) vehicle highway automation committee and statistical methods committee, and chair of the simulation subcommittee of the traffic signal systems.

References

Kurogo H, Takada K, Akiyama H. Concept of a Parking Guidance System and Its Effects. In: The Shinjuku Area-configuration, Performance, and Future Improvement of System. Proceedings of the 6th International Vehicle Navigation and Information Systems Conference, 30 July - 2 Aug 1995, Washington, USA.

Ristola T. Parking guidance system in Tapiola. Proceedings of the 6th International Conference on Road Traffic Monitoring and Control, 1992, London, England.

Spencer ME, West J. Parking Guidance System in San Jose, California. Transportation Research Record. 2004;1886: 34-39.

Yoo S, Chong PK, Kim T. PGS: Parking Guidance System Based on Wireless Sensor Network. The 3rd International Symposium on Wireless Pervasive Computing, 7-9 May 2008, Santorini, Greece. IEEE; 2008. p. 218-222.

Idris MYI, Tamil EM, Noor NM, Razak Z, Fong KW. Parking Guidance System Utilizing Wireless Sensor Network and Ultrasonic Sensor. Information Technology Journal. 2009;8(2): 138-146.

Jia JP. Statistics. Beijing: China Renmin University Press; 2000.

David A, Keller H. Event-driven Modeling of On-street Parking Probability. Proceedings of the 8th World Congress on ITS, 2001, Sydney, Australia. [8] Hu JJ. [Research on the Key Technologies of Parking Guidance System]. Dissertation, Jilin: Jilin University, 2006.

Caicedo F, Blazquez C, Miranda P. Prediction of Parking Space Availability in Real Time. Expert Systems with Applications. 2012;39(8): 7281-7290.

Guo F, Krishnan R, Polak J. A Computationally Efficient Two-stage Method for Short-term Traffic Prediction on Urban Roads. Transportation Planning and Technology. 2013;36(1): 62-75.

Cyprich O, Konecny V, Kilianova K. Short-term Passenger Demand Forecasting Using Univariate Time Series Theory. Promet – Traffic & Transportation. 2013;25(6): 533-541.

Zhang L, Li XY, Shi HJ. Study of Short-term Traffic Flow Forecasting Algorithms Based on ARIMA Model. Journal of Zhengzhou University of Light Industry. 2008;23(4): 89-92.

Lippi M, Bertini M, Frasconi P. Short-term Traffic Flow Forecasting: An Experimental Comparison of Time-series Analysis and Supervised Learning. IEEE Transaction Intelligent Transportation System. 2013;14(2): 871–882.

Brockwell P, Davis R. Introduction to time series and forecasting. Springer; 2011.

Guo X, Deng FQ. Short-term Prediction Of Intelligent Traffic Flow Based On BP Neural Network And ARIMA Model. International Conference on E-Product E-Service and E-Entertainment, 7-9 November, Henan, China. IEEE; 2010. p. 1-4.

Kumar SV, Vanajakshi L. Short-term Traffic Flow Prediction Using Seasonal ARIMA Model with Limited Input Data. European Transport Research Review. 2015;7(3): 21.

Lin J. Study on the Prediction of Urban Traffic Flow Based on ARIMA Model. 3rd International Conference on Engineering Technology and Application (ICETA 2016). 2016: 418-422.

Pandit SM and Wu SM. Time Series and system analysis with application. New York: John Wiley and Sons, Inc.; 1983.

Yang ZS, Liu HH, Wang XY. The Research on the Key Technologies for Improving Efficiency of Parking Guidance System. Intelligent Transportation Systems. 2003;2: 1177-1182.

Ji YJ, Wang W, Deng W. Available Parking Space Occupancy Change Characteristics and Short-term Forecasting Model. Journal of Southeast University (English Edition). 2007;23(4): 604-608.

Ji YJ, Tang DN, Guo WH. Forecasting Available Parking Space with Largest Lyapunov Exponents Method. Journal of Central South University. 2014;21: 1624-1632.

Sun Y, Cui ZM. Research and Implementation of Forecasting Model for Parking Guidance System Based on Neural Network. Computer Application and Software. 2007;24(11): 124-126.

Chen Q, Yan KF, Wang RT, Mo YK. Parking Space Information Prediction Based on Phrase Construction and Elman Neural Network. Journal of Tongji University (Natural Science). 2007;28 (4): 413-420.

Thompson RG, TakadK, Kobayakawa S. Optimization of Parking Guidance and Information Systems Display Configurations. Transportation Research, Part C. 2001;9(1): 79-85.

Box GEP, Jenkins GM, Reinsel GC. Time series analysis: forecasting and control. New York: Prentice Hall PTR Upper Saddle River; 1976.

Chen P. Applied mathematical statistics. Beijing: China Machine Press; 2008.

Dickey, DA, Fuller WA. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association. 1979;74(366): 427-431.

Ljung GM, Box GEP. On a Measure of Lack of Fit in Time Series Models. Biometrica. 1978;65(2): 297-303.

Ansley C, Newbold P. Finite Sample Properties of Estimators for Autoregressive Moving Average Models. Journal of Econometrics. 1980;13(2): 159-183.

Davidson J. Problems with the Estimation of Moving Average Models. Journal of Econometrics. 1981;16(3): 295-310.

Akaike H. A New Look at the Statistical Model Identification. Transaction on Automatic Control. 1974;19(6): 716-723.

Schwarz G. Estimating the Dimension of a Model. Annals of Statistics. 1978;6(2): 461-464.

Guo JH, Williams BM. Real-time Short Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters. Transportation Research Record. 2010;2175: 28-37.

Hagan MT, Demuth HB, Beale M. Neural network design. Boston: PWS Publishing Co.; 1996.

Smith BL, Demetsky MJ. Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering. 1997;123(4): 261-266.

Zheng WZ, Lee DH, Shi QX. Short-term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach. Journal of Transportation Engineering. 2006;132(2): 114-121.

Gao XP, Cong S. Comparative Study on Fast Learning Algorithms of BP Networks. Control and Decision. 2001;16(2): 167-171.

Minns AW, Hall MJ. Artificial Neural Networks as Rainfall-runoff Models. Hydrological Sciences Journal. 1996;41(3): 399-417.

Demuth HB, Beale M. Neural network toolbox – for use with MATLAB. Natick: The MathWorks, Inc.; 2004.

Ji YJ, Gao LP, Chen XS, Guo WH. Strategies for Multistep-ahead Available Parking Spaces Forecasting Based on Wavelet Transform. Journal of Central South University. 2017;24(6): 1503-1512.

Published
2018-04-20
How to Cite
1.
Zhu X, Guo J, Huang W, Yu F, Park BB. Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems. Promet [Internet]. 2018Apr.20 [cited 2024Nov.21];30(2):173-85. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2388
Issue
Vol 30 No 2 (2018)
Section
Articles

Copyright (c) 2018 PROMET - Traffic&Transportation

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).