城市導入智慧交通的條件——以短途運輸為例

Abstract

智慧交通系統的推行，已成為各城市邁向永續發展與碳中和目標的重要手段之一，而共享單車作為短途出行工具，也成為智慧交通的代表方式之一。然不同城市因資源條件、治理能力與市民使用行為的差異，導入智慧交通的成效也有所不同。因此，本研究以中國 14 個不同規模與區域的城市為樣本，結合《2024 年度中國主要城市共享單車/電單車騎行報告》與各地統計資料，總計574筆資料，探討城市條件與共享單車騎行體驗之間的關聯性。現有研究多聚焦於人口結構、建成環境與氣候條件對共享單車使用之影響，指出城市規模、教育設施、捷運站密度與氣溫變化等因素均與騎乘量具有相關性。然而，多數研究以單一城市為樣本，欠缺跨城市比較與具普遍適用性的評估標準，亦較少納入治安、觀光等政府治理面向，且分析多限於單一維度，忽略多重因素交互作用下的整體條件影響。 為補足上述研究缺口，本研究以中國十四個城市為樣本，蒐集各維度共計574筆與共享單車出行相關之實證資料，建構涵蓋社會、政府、觀光與自然環境四大構面、八項一級指標的城市評分體系，結合城市統計年鑑與騎乘體驗數據，量化分析城市條件與共享單車使用行為之關聯性。實證結果顯示，城市整體條件分數與共享單車體驗呈現顯著正相關（r＝0.567***），其中以政府治理與觀光發展為推動力最為關鍵。個案分析以瀋陽市為例，顯示即使城市資源相對有限，透過制度創新與協作治理，亦可有效推動智慧交通之應用。 本研究進一步提出四項政策與管理建議：一、強化基礎設施與跨部門協調機制；二、完善觀光導向城市之整合性交通規劃；三、支持中西部與非一線城市透過策略聚焦與制度化治理實現突破；四、企業應深化數據導向的精準營運與「在地化」產品設計。The implementation of intelligent transportation systems has become a key strategy for cities aiming to achieve sustainable development and carbon neutrality. As a representative mode of short-distance mobility, bike-sharing systems play a crucial role in this transition. However, due to differences in resource conditions, governance capacity, and user behavior across cities, the effectiveness of implementing intelligent transportation varies significantly. This study selects 14 cities in China of varying scales and regions as its research sample, integrating the 2024 Annual Report on Shared Bicycle/E-bike Riding in Major Chinese Cities with regional statistical data to explore the relationship between urban conditions and the bike-sharing riding experience.Existing studies mainly focus on the influence of demographic structures, built environment, and climate conditions on bike-sharing usage, highlighting correlations with city size, educational facilities, metro station density, and temperature. However, most research is based on single-city cases, lacking comparative analysis and generalizable evaluation frameworks. Moreover, key governance dimensions such as public safety and tourism are often overlooked, and the interplay among multiple factors is rarely considered.To address these research gaps, this study collects a total of 574 empirical data points related to bike-sharing from 14 cities, constructing an urban evaluation framework that includes four dimensions—society, government, tourism, and natural environment—and eight primary indicators. By integrating city statistical yearbooks with bike-sharing user experience data, the study quantitatively analyzes the correlation between urban conditions and usage outcomes. Results indicate a significant positive correlation between overall city condition scores and the quality of bike-sharing experience (r = 0.567***), with government governance and tourism development identified as the most critical driving forces. A case study of Shenyang further demonstrates that even resource-limited cities can effectively implement intelligent transportation through institutional innovation and collaborative governance.Based on these findings, this study proposes four key policy and management recommendations: (1) strengthening infrastructure and interdepartmental coordination; (2) improving integrated mobility planning in tourism-oriented cities; (3) supporting central and non-tier-one cities through targeted governance strategies; and (4) encouraging data-driven and localized business models among operators.