不同交通資訊來源對城際通勤者路線選擇行為影響之研究

Abstract

近年來世界各國均致力於利用先進科技，充分且均衡地使用現有運輸系統之容量，以解決交通擁擠問題。智慧型運輸系統的先進旅行者資訊系統藉由資訊的提供，協助用路者作最有效的旅運決策，進而提高運輸系統的整體效率。交通部於民國八十九年公開「北部地區高速公路即時路況資訊」，希望透過提供正確的交通路況資訊，導引高速公路壅塞的車流，合理地分配道路資源，以有效解決交通擁擠問題。本研究探討小汽車城際通勤者如何利用不同交通路況資訊及其對通勤路線選擇行為之影響，以提供相關單位改善交通路況資訊之參考。 本研究設計顯示性偏好問卷，調查基隆至新竹間的小汽車城際通勤者，共計302份有效問卷。調查項目包括 (1) 個人基本資料，如所得、性別、年齡、共乘與否、上班簽到規定等，(2) 旅次特性資料，如旅行時間、旅行成本、行駛距離等，(3) 駕駛人認知特性資料，如交通擁擠狀況、路線熟悉度、行前及路途中之移轉傾向等，(4) 不同交通資訊來源的使用情形，如行前及路途中使用各種交通資訊之頻率，及不常使用的原因等。問卷回收後，先以統計方法分析城際通勤者的特性及通勤路線的選擇情形，再分別以多項羅吉特、巢式羅吉特模式、及更具彈性的成對組合羅吉特與一般化巢式羅吉特模式探討不同交通資訊來源對城際通勤者路線選擇之影響。以排序普羅比模式構建不同交通資訊之使用頻率模式。最後為有效表現不同特性的城際通勤者對交通資訊使用頻率之感受差異，將不同交通資訊使用頻率模式所獲得之預測機率帶入路線選擇模式，構建整合模式。 實證結果發現，旅次特性(如行駛距離、旅行成本、路線類型)、駕駛人認知特性(如路線熟悉度、交通擁擠度、行前轉移傾向)、個人社經特性(如行駛經驗、年齡、性別)、不同交通資訊(如廣播、電視、網際網路、電話語音)的使用頻率等皆為影響城際通勤路線選擇的重要變數。經統計檢定發現，巢式羅吉特路線選擇模式的解釋能力顯著優於多項羅吉特模式，表示通勤路線彼此因有部分路段重疊，所以替選路線間具有相似特性；一般化巢式羅吉特路線選擇模式的解釋能力又顯著優於成對組合羅吉特模式及最佳的巢式羅吉特模式，表示一般化巢式羅吉特模式與實際的路線選擇行為特性最相符，模式最具彈性。而由排序普羅比模式的校估結果顯示，旅次特性、駕駛人認知特性、個人社經特性是影響通勤者對不同交通資訊使用頻率的重要變數。最後本研究提出交通路況資訊的改善策略，以供相關主管單位參考。

Recently, every country works for using advanced technology to solve traffic congestion. Advanced traveler information systems included intelligent transportation systems can provide help for drivers to make an effective decision, and then to improve the entire efficiency of transportation systems by supplying enough and suitable traffic information. The Ministry of Communications proclaimed freeway real-time traffic information in the north region. To settle traffic congestion effectively, they hope to disperse the jammed traffic flow and deal out road resources fairly by supplying straight traffic information. This purpose of this study is to research into the effect of different traffic information on intercity commuter route choice behavior, and then to bring up some methods for improving information. The data used in this research are from a revealed preference survey of drivers commuting between Keelung to Hsindhu (302 interviews). The investigative events include that personal characteristics, trip characteristics, drivers’ perception and four levels of choices of traffic information usage (never, sometimes, frequently, and always), the reason of seldom usage. This study used multinomial logit (MNL) model, nested logit (NL) model, paired combinatorial logit (PCL) model, and generalized nested logit (GNL) model to construct route choice models. Ordered probit model of the drivers’ usage in the effectiveness of different traffic information is then developed. Finally, the combined model that the traffic information usage probability is used as an input to route choice models is constructed, so as to exhibit the perceptive difference to intercity commuters effectively. The results indicate that trip characteristics (distance, travel cost, and types of roads), drivers’ perception (familiar with multiple routes, traffic congestion, and pre-trip diversion), personal characteristics (driving experience, age, and sex), and the frequency of different traffic information usage (radio, television, internet, and telephone) are important factors affecting route choice models; trip characteristics (distance, travel time, travel cost, types of roads, and times of substitute routes usage), drivers’ perception (familiar with multiple routes, traffic congestion, pre-trip and enroute diversion, and time pressure), personal characteristics (driving experience, age, sex, and the degree of education) are important factors affecting different traffic information usage models. At last, utilizing statistic tests we find that GNL is the best model for route choice model in this study because parts of sections of substitute routes overlap.