台灣交通安全地圖｜全國面量圖 · Taiwan A1 Choropleth 2016

線上手冊 · 點我閱讀Online Handbook · Read now 【2026 雙齊零街道願景手冊】2026 Dual-Zero Street Vision Handbook 交通零死亡 × 碳排歸零 — 完整願景與五大訴求,一次讀懂Zero road deaths × net-zero carbon — the full vision & Five Demands 開啟手冊 →Open handbook → 📍 我家附近安全嗎?📍 How safe is my area? 輸入你住的鄉鎮市區,看十年死亡、全國排名與最危險路段Type your district — deaths, national rank & most dangerous roads 立即查詢 →Check now →

01 — Decade Overview 全國十年總覽01 — Decade Overview

17,175^人deaths

十年內全台道路 A1 死亡人數A1 road deaths nationwide, last 10 years

Lives lost on Taiwan's roads · 2016 — 2025

364 個鄉鎮市區 · 22 個縣市 · 平均每年 1,718 人364 districts · 22 counties · ~1,718 deaths per year

Events

A1 死亡事故件數A1 fatal crash events

16,713

Most Victims

機車為受害最多的用路人Motorcyclists — most victims

—%

Pedestrian Victim

行人受害Pedestrian victims

—%

Most Deaths · County

十年累計第一Highest 10-yr total

台南市 1,785

A1 死亡，只是冰山一角 A1 deaths are the tip of the iceberg

死亡Deaths

17,175

事件Events

3,543,840

受傷Injured

4,738,069

每 1 人死亡，背後對應 ~206 件 A2 事件、~276 人受傷。A1 為事後或當場 24 小時內死亡；A2 為事後死亡或受傷，傷亡規模約為 A1 的 200 倍以上。 For every road death, ~206 A2 events and ~276 injuries lie underneath. A1 = death on the scene or within 24 h; A2 = injury or post-24h death. A2 casualties exceed A1 by more than 200×.

02 — Casualty Trend 傷亡趨勢02 — Casualty Trend

—

十年間，A1 死亡略降，A2 傷亡件數卻持續上升 A1 deaths edged down, but A2 casualties kept climbing

死亡（A1）的改善被「受傷與長期失能」的擴大遮蓋。當 A2 件數成長，意味著危險路口、過快車速、混合車流的暴露面持續擴大 — 治理重點應從「降死亡」延伸至「降傷亡規模」。 Improvement on the death count masks a growing tide of injuries and long-term disability. Rising A2 events means dangerous junctions, excess speed, and mixed traffic are still expanding exposure. Policy must shift from "reduce deaths" to "reduce total harm".

A1 死亡（左軸）A1 Deaths (left axis) A2 事件（右軸）A2 Events (right axis) A2 受傷（右軸）A2 Injured (right axis)

03 — 22 Counties 縣市深度地圖03 — 22 Counties

Explore by County

點選任一縣市，進入該地的完整互動地圖（事故點位、熱力圖、優先治理路段、幹線疊圖、逐年趨勢、行政區面量）Tap any county to open its full interactive map — crash points, heatmap, priority road segments, highway overlay, yearly trend, district choropleth

04 — District-Level Choropleth 行政區面量圖04 — District-Level Choropleth

Quintile · 0 → max

District Detail — 行政區明細

點選任一行政區（區/鄉/鎮/市）以查看分布 Click a district on the map to inspect Click a district on the map

05 — County Choropleth 縣市熱力圖05 — County Choropleth

Quintile

Ranking · 全 22 縣市 · all 22 counties

點選縣市進入完整地圖 · 顏色＝當前指標、長條＝相對比例Tap a county for its full map · colour = current metric, bar = relative scale

Rate · deaths per 100,000 residents (2013 population, MOI dataset 8410)
標 N<30 之縣市樣本數過小，每十萬人率波動大、僅供參考Counties tagged N<30 have too few deaths for a stable per-100k rate — interpret with caution

06 — Age × Accident Type 年齡層與事故態樣06 — Age × Accident Type

—

BY AGE

BY AGE × MODE

BY AGE × ACCIDENT TYPE

TOP COMBINATIONS

最常見的（年齡 × 運具 × 態樣）組合 — 排行越前面，越值得針對性對策 Most-frequent age × mode × type combinations — top-ranked combos warrant targeted intervention

資料覆蓋說明：本切面僅涵蓋有完整當事人欄位之年份（2018–2020、2022–2025），共 — 件（占 A1 全資料 —）；2016、2017、2021 為簡式欄位無年齡與事故態樣。
「年齡」採事件中最脆弱當事人之年齡作為死亡年齡之代理（與 victim-mode 邏輯一致），可能與實際死者略有偏差。 Coverage note: this slice covers only full-schema years (2018–2020, 2022–2025): — events (— of all A1). 2016, 2017, 2021 use a simplified schema without age or accident-type fields.
"Age" uses the most-vulnerable party's age as a proxy for the deceased (consistent with victim-mode logic), which may differ from the actual victim in multi-party events.

07 — Structure 結構的代價07 — The Price of Structure

r = 0.66 · 22 counties · 17 years · 3 scenarios

公共運輸愈不足,死傷率愈高Less transit, more casualties

街道願景白皮書互動數據:22 縣市市佔×死傷散佈、17 年死亡率賽跑、2050 三情境推估 — 結構決定死傷,選擇決定未來。Interactive whitepaper data: the share-casualty scatter, a 17-year county race, and three scenarios to 2050 — structure decides casualties; choices decide the future.

看數據 →Explore →

08 — International 國際比較08 — International

Japan 2.21 · Korea 4.88 · Taiwan 11.84 / 100k

同樣的東亞,5.4 倍的死亡率Same East Asia, 5.4× the death rate

日本 47 都道府縣 × 韓國 17 廣域市道 × 臺灣 22 縣市,86 個行政區的每十萬人死亡率比較。東京 0.97 vs 屏東 23.92 — 差異不在地理,而在基礎設施投資。86 first-level divisions across Japan, Korea and Taiwan. Tokyo 0.97 vs Pingtung 23.92 — the difference is infrastructure, not geography.

看比較 →Compare →

09 — Climate Evidence 臺灣升溫的真相09 — Climate Evidence

Taipei · Taichung · Kaohsiung · 1990–2025 · p < 0.05

臺灣升溫的真相 — 這不是體感Taiwan is warming — and it's not a feeling

三大都市 35 年夏季升溫,全數通過統計顯著性檢定(Mann-Kendall · p < 0.05)。從升溫證據、都市熱島機制到機動車輛的局部加劇 — 氣候與交通,從來是同一件事。35 years of summer warming across three metros, all statistically significant (Mann-Kendall, p < 0.05). From the evidence to the urban heat island and the role of motor vehicles — climate and transport were always one story.

看證據 →See the evidence →

10 — Dual-Zero Vision 雙齊零願景10 — Dual-Zero Vision

Carbon 0 × Vision Zero 0

交通零死亡的雙齊零願景The Dual-Zero Vision

氣候危機與道路死亡共享同一個根源——也共享同一個解方。「交通習慣移轉的複利效應」互動模型 × 「五大訴求」行動方案,已整合至專頁。The climate crisis and road deaths share one root cause — and one solution. The Compound Effect interactive model × the Five Demands, on a dedicated page.

前往專頁 →Open the page →

11 — Notes 資料說明11 — Notes

關於本面量圖About this choropleth

本面量圖整理自內政部警政署 A1 級道路交通事故公開資料，涵蓋年度民國 105 至 114 年（2016–2025），共 16,713 件事件、17,175 人死亡，分布於 364 個鄉鎮市區（22 個縣市）。

Built from the National Police Agency A1 (fatal) road-crash open dataset, covering 2016–2025 — 16,713 events, 17,175 deaths, across 364 districts (22 counties).

受害者運具邏輯：每筆事件以其中最脆弱用路人之運具為類別（優先序：行人 > 自行車/慢車 > 機車 > 汽/貨車）。例如左轉小客車撞死行人，在原資料 P1 為駕駛、運具為汽車，本地圖會歸類為「行人」。

Victim-mode logic: each event is classified by its most-vulnerable road user (priority: pedestrian > bicycle/slow vehicle > motorcycle > car/truck). E.g. a left-turning car that kills a pedestrian is recorded with the driver as party-1 (a car) in the raw data, but is classified here as “pedestrian”.

每萬人率：採用戶政司 dataset 8410 民國 102 年之人口數作為分母，可呈現「不只看絕對數量，而是相對風險」之治理優先序。2013 vs 2025 期間人口分布變化有限，適用於相對比較。

Per-capita rates use population from MOI Household-Registration dataset 8410 (2013) as the denominator, surfacing relative risk rather than raw counts. Population shifts 2013→2025 are modest, so the comparison holds.

邊界圖資：依 g0v/twgeojson 之鄉鎮市區界（1982 年版，整併至 2014 年後行政地理）。連江縣東引鄉與台東縣綠島鄉等少數島嶼之邊界與資料皆有侷限。

Boundaries from g0v/twgeojson district polygons (1982 edition, normalised to post-2014 administrative geography). A few small islands (Lienchiang–Dongyin, Taitung–Green Island) have limited boundary/data coverage.

Sources · National Police Agency (A1) · MOI Household Registration dataset 8410 · g0v/twgeojson district boundaries

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