更深入的了解不同地區個人運具選擇行為是為協助我們制定交通運輸政策，進而減少交通壅塞，空氣汙染，並提供良好運輸服務的一種方法。隨機車運具選擇模式行為逐漸受到愈來愈多的重視，此研究將探討台灣地區個人層級與地區層級因素相較於參考模式(私人運具或機車)對於其他運具選擇之影響，提供運具模式選擇行為的研究議題一些參考資料。
過去已經有將多項羅吉特模型應用到模式選擇的研究。然而，忽略了模式選擇資料中潛在的層級結構關係可能會造成一些限制，反映在鑲嵌資料的估計上。而多層次模型則可以克服這些限制，降低統計偏誤，提高統計檢定力。因此，此研究利用多層次模型來檢視多層級的因素對於台灣模式選擇行為的影響。此研究樣本資料包含居住在台灣20個縣市的24,832位受訪者，經由交通部統計處於2012年收集而來。此研究中個人層級的資料來自交通部統計處的資料，地區層級資料則是從內政部統計處，國家發展局及公路總局收集而來。
研究結果顯示對於參考模式，不同層級影響運具選擇模式的因素的確存在許多差異性，同時也驗證以多項羅吉特方程式呈現的模式選擇變數亦適用於多層次模型分析。個人運具選擇模式行為確實會與個人特徵與居住地區特徵有關係。大多數個人層級變數會顯著影響模式選擇，而地區層級變數則是隨運具選擇模式之不同而有其不同之影響因素。最後，希望藉由這些發現對於政策制定者制定交通政策來改變用路人之運具選擇以減少交通壅塞，空氣汙染是有幫助的。

Getting more understanding for individual mode choice in different areas is an appropriate way to support us to make transportation policies to reduce traffic congestion, air pollution, and provide a friendly transportation service. As scooter mode has been received more attention, we investigated the impact of individual- and area-level determinants on mode choice relative to reference mode (private vehicle or scooter) in Taiwan to provide some reference information for individual mode choice behavior.
Multinomial logit model has been used to analyze mode choice data. However, neglecting the possible existence of hierarchical structures with nested data could reflect limitations for the estimation. Multilevel model can overcome these limitations, then reduce statistical bias and improve statistical power. Thus, this research used multilevel models to identify the hierarchical-level determinants of mode choice in Taiwan. The study sample included 24,832 respondents with living in 20 cities/counties and was collected by Taiwanese Ministry of Transportation and Communication (MOTC) in 2012. Data on individual-level were provided from the department of Statistic, MOTC, and city-level data were provided from the Statistics Department of Ministry of Interior, National Development Council, and Directorate General of Highways, MOTC.
Results show variations for hierarchical-level determinants of mode choices relative to reference mode. And it also verifies that the outcome variables expressed in multinomial logit model can also be used in multilevel model analysis. Individual mode choice behaviors are associated with individual characteristics and area features where he/she lives. Most variables at individual-level have significant influence on each mode choice relative to reference mode. Among variables at area-level, each mode choice will be influenced by different determinants relative to reference mode. In the end, we hope these finding may be useful for policy-maker to make transportation policies to change individual mode choice for reducing traffic congestion, air pollution.

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