應用自動車輛辨識預測高速公路路段旅行時間

NCU Institutional Repository > 工學院 > 土木工程研究所 > 博碩士論文 > Item 987654321/761

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题名:	應用自動車輛辨識預測高速公路路段旅行時間
作者:	張慶麟;Ching-Lin Chang
贡献者:	土木工程研究所
关键词:	自動車輛辨識;旅行時間預測;簡單指數平滑法;Holt's指數平滑法;自我迴歸移動平均整合模式;倒傳遞網路;Holt's Exponential Smoothing Method;Single Exponential Smoothing Method;Travel Time Forecasting;Automatic Vehicle Identification;Auto-Regressive Integrated Moving Average Method;Back-Propagation Network
日期:	2003-06-27
上传时间:	2009-09-18 17:11:53 (UTC+8)
出版者:	國立中央大學圖書館
摘要:	目前國道路網已逐漸完成，而在預測機制精準之前提下，旅行時間預測資訊不但能直接提供用路人瞭解本身即將遭遇到的車流情形，並且相關交管單位更可整合此訊息進行決策與建議，方便用路人針對其旅次需求目的判斷適當的路徑，以發揮未來高速公路路網之整理績效。但因國內高速公路路段尚未使用自動車輛辨識蒐集旅行時間資料，因此本研究針對平常日之車流情形，先行應用車流模擬方式考量不同資料輸出時距、佈設間距及AVI辨識率等產生相關資料；再配合簡單指數平滑法、Holt’s指數平滑法、自我迴歸移動平均整合模式及倒傳遞神經網路構建四種旅行時間預測模式，分別進行預測績效分析，以期提供實用且精準的旅行時間預測資訊，藉此作為用路人路徑選擇判斷之依據。經由多種條件組合之測試結果可知，針對本研究資料蒐集方式及預測對象之設定下，資料輸出時距5分鐘、佈設間距1公里易產生較佳的預測成效；此外，就四種旅行時間預測模式而言，Holt’s指數平滑法之預測績效最差，而其餘三者預測效果差異無多，但以倒傳遞網路易獲得較佳之預測績效。可作為相關交通規劃單位之參考。 Now, the network of freeway is being completed. In presupposition that forecasting results are exact. The travel time forecasting information not only can let passengers directly realize the situation of traffic flow they will in, but also can let the traffic management make a suitable decision to fit this situation and decide a proper route of passenger’s needs to make the best use of the network of freeway in the future. Because that our link of freeway hasn’t use Automatic Vehicle Identification system to collect travel time data, so this study focus on the weekday’s situation of traffic flow, using simulation to gain travel time data and considering all kinds of related influential factors, such as the updating time period of information, distance between two neighborly collecting stations, and AVI rate. Furthermore, Using four forecasting models, such as single exponential smoothing method, Holt’s exponential smoothing method, autoregressive integrated moving average method, and back-propagation network, to test and analyze if forecasting results are exact. Hoping to offer practicable and exact travel time forecasting information, and using it as the basis of how passengers choose their routes. From the result of all kinds of test, we can know that it can have better forecast effect when information update takes five minutes, keeps one kilometer between two neighborly collecting stations. Besides, in four travel time forecasting models. HES forecast effect is the worst, and other’s forecast effects are almost the same but we still can have better forecast effect much easily from BPN forecasting model. Lastly, we can regard the result of all kinds of test as reference which is related to the traffic management.
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