多元化計程車機場接送服務動態派車接機時間預測模型

A Simulation-based Policy Analysis for Railway Online Booking System: A Case Study of Taiwan Railways Administration

王中允(Chung-Yung Wang)；嚴國基(Kuo-Chi Yen)；劉基全(Ji-Chyuan Liou)；高增英(Chen-Ying Kao)；洪上智(Shang-Chih Hung)；林韋捷(Wei-Chieh Lin)；謝閔易(Min-Yi Hsieh)；張倉賓(Tsang-Pin Chang)

步行時間預測 ； 倒傳遞類神經網路 ； 機場服務績效 ； Walking time forecast ； Back-propagation neural network ； level of service of airport

都市交通

32卷1期（2017 / 06 / 01）

69 - 110

繁體中文

隨著政府開放計程車多元化營業的政策，多元化計程車經營策略的妥善規劃，將有助於計程車業者的獲利，而多元化計程車的經營策略，以無障礙計程車、觀光計程車及定點特殊接送計程車的經營最受業者重視，而機場接送服務則是定點特殊接送計程車業者重要的營運市場。然而就目前機場入境乘車空間的限制，僅能提供接送車輛暫停上下客的服務，不許可長時間的停滯等候，因此當旅客選擇多元化計程車做為機場接送的服務運具時，如何能準時的到達機場接送旅客，便成為重要的研究課題。本研究的進行，將建立多元化計程車機場接送服務動態派車接機時間預測模型，利用倒傳遞類神經網路，預測航班抵達至旅客搭乘接駁運具的時間。本研究共建立單隱藏層與雙隱藏層神經網路模型並進行預測經測試雙層隱藏層之倒傳遞神經網路模型預測步行時間誤差在5 分鐘內之比例均在60%以上。此外並與MATLAB所附的類神經網路進行數值測試，由結果發現，本研究發展之模型較為準確與穩定，藉由本模型的發展，預期將可提供多元化計程車業者進行機場接送時，能準確的預測旅客到達入境旅客候車處的時間，以提高業者的服務水準，並增加產業的競爭力。

With the opening diversification policy for taxi, the diversification of taxi business strategy will create the benefit of Taxi operator. The business is focused on barrier-free taxi, tourism taxi, and fixed-point special shuttle taxi. The airport passengers pick-up service is one of the most important business for diversification taxi service. Due to the waiting area space restrictions for pick-up vehicle on airport, when passengers choose a diversified taxi as an airport shuttle service, how the airport can avoid the airport congestion problem is the critical issue. In this paper, the back-propagation neural network is used to construct the prediction model. This model effectively predicts the time between the flights arrives and passengers arrive take the feeder, expecting to reduce the vehicle's stay at the airport and avoid crowding. In this study, the double hidden layer neural network model were established and predicted. The results show the error of prediction in 5 minutes of the proportion of is more than 60%.The model of this research is more accurate and stable than MATLAB. With the development of this model, it could enhance the service performance of Taoyuan International Airport and increase the competitiveness of the national tourism industry.

1. 汪志忠、黃國平、鄭雅云(2008)。臺灣地區汽車持有預測模式之建構與評估：ARMAX 之應用。運輸學刊，20(4)，405-424。
   連結：
2. 蔡宗憲、李治綱、魏健宏(2006)。短期列車旅運需求之類神經網路預測模式建構與評估。運輸計劃季刊，35(4)，475-505。
   連結：
3. 魏健宏、林士傑、李穎(2003)。高速公路客運車輛旅行時間預測之實證評析。運輸計劃季刊，32(4)，651-679。
   連結：
4. 桃園機場公司網站http://www.taoyuanairport.com.tw/company_ch/NewPassengerQuery
5. Abu-Lebdeh, G.,Singh, A. K.(2011).Modeling arterial travel time with limited traffic variables using conditional independence graphs and state-space neural networks.Procedia-Social and Behavioral Sciences,16,207-217.
6. Celikoglu, H. B.(2011).Travel time measure specification by functional approximation: application of radial basis function neural networks.Procedia-Social and Behavioral Sciences,20,613-620.
7. Chen, H.,Grant-Muller, S.,Mussone, L.,Montgomery, F.(2001).A study of hybrid neural network approaches and the effects of missing data on traffic forecasting.Neural Computing and Applications,10(3),277-286.
8. Chen, S.,Dai, Q.(2005).Discounted least squares-improved circular back-propogation neural networks with applications in time series prediction.Neural Computing and Applications,14(3),250-255.
9. Chu, C.-H.,Widjaja, D.(1994).Neural network system for forecasting method selection.Decision Support Systems,12(1),13-24.
10. Dharia, A.,Adeli, H.(2003).Neural network model for rapid forecasting of freeway link travel time.Engineering Applications of Artificial Intelligence,16(7),607-613.
11. Freeman, J. A.,Skapura, D. M.(1991).Algorithms, Applications, and Programming Techniques.USA:Addison-Wesley Publishing Company.
12. Ghazali, R.,Hussain, A. J.,Liatsis, P.,Tawfik, H.(2008).The application of ridge polynomial neural network to multi-step ahead financial time series prediction.Neural Computing and Applications,17(3),311-323.
13. Ghiassi, M.,Saidane, H.,Zimbra, D.(2005).A dynamic artificial neural network model for forecasting time series events.International Journal of Forecasting,21(2),341-362.
14. Guin, A.(2006).Travel time prediction using a seasonal autoregressive integrated moving average time series model.2006 IEEE Intelligent Transportation Systems Conference
15. Haykin, S. S.(2001).Neural networks: a comprehensive foundation.Tsinghua University Press.
16. Jiang, Z.,Zhang, C.,Xia, Y.(2014).Travel Time Prediction Model for Urban Road Network based on Multi-source Data.Procedia-Social and Behavioral Sciences,138,811-818.
17. Juri, N.,Unnikrishnan, A.,Waller, S.(2007).Integrated traffic simulation-statistical analysis framework for online prediction of freeway travel time.Journal of the Transportation Research Board,2039,24-31.
18. Li, C.-S.,Chen, M.-C.(2014).A data mining based approach for travel time prediction in freeway with non-recurrent congestion.Neurocomputing,133,74-83.
19. Li, R.,Rose, G.,Sarvi, M.(2007).Predicting travel time and its variability in the short term.14th World Congress on Intelligent Transport Systems-ITS for a Better Life
20. Lin, W.-H.,Kulkarni, A.,Mirchandani, P.(2004).Short-term arterial travel time prediction for advanced traveler information systems.Intelligent Transportation Systems
21. Liu, H.,Motoda, H.(2001).Instance selection and construction for data mining.Springer.
22. MATSUMURA, S. Y.(1998).Experimental verification of travel-time prediction method.Towards the New Horizon Together Proceedings of the 5thWorld Congress on Intelligent Transportation Systems,Seoul, KOREA:
23. Mazloumi, E.,Rose, G.,Currie, G.,Moridpour, S.(2011).Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction.Engineering Applications of Artificial Intelligence,24(3),534-542.
24. Mendes-Moreira, J.,Jorge, A. M.,de Sousa, J. F.,Soares, C.(2015).Improving the accuracy of long-term travel time prediction using heterogeneous ensembles.Neurocomputing,150,428-439.
25. Nelson, M.,Hill, T.,Remus, W.,O'Connor, M.(1999).Time series forecasting using neural networks: Should the data be deseasonalized first?.Journal of forecasting,18(5),359-367.
26. Oh, J.-S.,Jayakrishnan, R.,Recker, W.(2002).Section travel time estimation from point detection data.Center for Traffic Simulation Studies.
27. Qi, M.,Zhang, G. P.(2008).Trend time-series modeling and forecasting with neural networks.IEEE Transactions on Neural Networks,19(5),808-816.
28. Refenes, A.,Bentz, Y.,Bunn, D. W.,Burgess, A. N.,Zapranis, A. D.(1997).Financial time series modelling with discounted least squares backpropagation.Neurocomputing,14(2),123-138.
29. Shimodaira, H.(1995).A method of selecting similar learning data in the prediction of time series using neural networks.Proceedings of Neural Networks
30. Sohl, J. E.,Venkatachalam, A.(1995).A neural network approach to forecasting model selection.Information and Management,29(6),297-303.
31. van Hinsbergen, C. I.,Van Lint, J.,Van Zuylen, H.(2009).Bayesian committee of neural networks to predict travel times with confidence intervals.Transportation Research Part C: Emerging Technologies,17(5),498-509.
32. Van Lint, J.,Hoogendoorn, S.,van Zuylen, H. J.(2005).Accurate freeway travel time prediction with state-space neural networks under missing data.Transportation Research Part C: Emerging Technologies,13(5),347-369.
33. Venkatachalam, A.,Sohl, J. E.(1999).An intelligent model selection and forecasting system.Journal of Forecasting,18(3),167-180.
34. Wang, J.,Tsapakis, I.,Zhong, C.(2016).A space-time delay neural network model for travel time prediction.Engineering Applications of Artificial Intelligence,52,145-160.
35. Wang, J.,Zou, N.,Chang, G.(2008).Empirical analysis of missing data issues for ATIS applications: travel time prediction.Proceedings of the Transportation Research Board 87th Annual Meeting,Washington DC:
36. Wang, L.,Zuo, Z.,Fu, J.(2014).Bus Arrival Time Prediction Using RBF Neural Networks Adjusted by Online Data.Procedia-Social and Behavioral Sciences,138,67-75.
37. Zhang, G. P.,Qi, M.(2005).Neural network forecasting for seasonal and trend time series.European journal of operational research,160(2),501-514.
38. Zhang, G.,Patuwo, B. E.,Hu, M. Y.(1998).Forecasting with artificial neural networks:: The state of the art.International journal of forecasting,14(1),35-62.
39. Zheng, F.,Van Zuylen, H.(2013).Urban link travel time estimation based on sparse probe vehicle data.Transportation Research Part C: Emerging Technologies,31,145-157.
40. Zurada, J. M.(1992).Introduction to artificial neural systems.West St. Paul.:
41. 葉怡成(2003)。類神經網路模式應用與實作。儒林圖書公司。