# Transport Safety Digitalization Plan

> **Version**: V1.0
> **Date**: ____ / __ / __
> **Prepared by**: _________
> **Reviewed by**: _________
> **Approved by**: _________

---

## Executive Summary

### Project Positioning
The [City/Region Name] Transport Safety Digitalization Programme is built on the philosophy of "technology-enabled safety" and oriented toward **Vision Zero** (the goal of zero traffic fatalities and serious injuries). It establishes a holistic digital safety prevention and control system founded on full-coverage sensing, active warning, rapid response, and precision remediation.

### Objectives
By [Target Year], achieve:
- Road traffic fatalities: reduce by ____%
- Traffic fatalities per 10,000 registered vehicles: decrease from ____ to ____
- Total crash count: reduce by ____%
- High-risk blackspot remediation rate: reach ____%
- Emergency response time: shorten from ____ minutes to ____ minutes
- Public satisfaction with road safety: increase to ____ points

### Investment Overview
| Item | Value |
|------|------|
| Total estimated investment | $____ million |
| Construction period | ____ months |
| Estimated annual safety benefit (crash loss avoided) | $____ million / year |

---

## 1. Current State Analysis of Transport Safety

### 1.1 Crash Statistics (last 3–5 years)
| Year | Total crashes | Fatalities | Injuries | Direct economic loss | Fatalities / 10k vehicles | Fatalities / 100k pop. |
|:----:|:-------:|:-------:|:-------:|:----------:|:--------:|:----------:|
| ____ | | | | | | |
| ____ | | | | | | |
| ____ | | | | | | |
| ____ | | | | | | |
| ____ | | | | | | |
| **Trend** | [up / down / flat] | | | | | |

### 1.2 Crash Characteristic Analysis
| Dimension | Characteristic |
|----------|----------|
| Crash type | TOP 3: rear-end ____% / side-impact ____% / pedestrian ____% |
| Peak time-of-day | [time-of-day distribution] |
| Peak weather | [weather distribution] |
| Peak road class | [road class distribution] |
| Peak vehicle type | [vehicle type distribution] |
| Crash causation | TOP 3: [Cause 1] ____% / [Cause 2] ____% / [Cause 3] ____% |

### 1.3 High-Risk Blackspot Distribution
| No. | Location | Crashes (3-yr) | Fatalities | Main crash type | Measures taken | Effectiveness |
|:---:|----------|:-------:|:-------:|-------------|-----------|:--------:|
| 1 | [location] | | | | | |
| 2 | [location] | | | | | |
| ... | | | | | | |

### 1.4 Assessment of Existing Safety Systems
| System / Facility | Current state | Gap | Benchmark comparison |
|-----------|------|------|---------------|
| Crash collection & analysis | [description] | [gap] | [benchmark] |
| Video surveillance coverage | [description] | [gap] | [benchmark] |
| Speed / red-light enforcement | [description] | [gap] | [benchmark] |
| Hazardous-section warning | [description] | [gap] | [benchmark] |
| Emergency response | [description] | [gap] | [benchmark] |
| Safety education | [description] | [gap] | [benchmark] |

---

## 2. Vision and Objectives

### 2.1 Overall Vision
Guided by **Vision Zero** as the long-term direction and following the **Safe System Approach**, build an integrated, proactive safety prevention and control system covering ____, ____, ____, and ____.

### 2.2 Safe System Principles
| Principle | Explanation | Implementation measures |
|------|------|----------|
| People make mistakes | The system must be forgiving; do not assume humans will not err | Forgiving road design + vehicle safety + speed management |
| Human fragility | The human body tolerates only limited crash forces | Limit speeds to human tolerance thresholds (30 km/h pedestrian / 50 km/h side / 70 km/h frontal) |
| Shared responsibility | Safety is the shared duty of designers, operators, and users | Cross-agency coordination + industry participation + public education |
| Redundancy | Single-point failure must not cause death | Multilayer protection: road + vehicle + speed + response |
| Proactive prevention | Shift from reactive remediation to proactive prevention | Prediction & early warning + root-cause blackspot treatment + safety-by-design |

### 2.3 Quantitative Targets
| Indicator | Baseline | Phase 1 | Phase 2 | Long-term (Vision Zero) |
|------|:---:|:------:|:------:|:------:|
| Fatalities | | -____% | -____% | 0 |
| Serious injuries | | -____% | -____% | 0 |
| Fatalities / 10k vehicles | | | | < 1.0 |
| Blackspot remediation rate | | 50% | 100% | 100% |
| Hazardous-section warning coverage | | 60% | 100% | 100% |
| Emergency response time (min) | | | | < 5 min |
| Public safety awareness rate | | | | > 90% |

---

## 3. Overall Architecture

```
┌──────────────────────────────────────────────────────────────┐
│  Layer 5  Application & Decision Layer                         │
│  ┌──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┐  │
│  │Black-│Safety │Emerg.│Vuln. │Work │Post- │Safety│Safety│  │
│  │spot  │Warn. │Resp. │Road  │Zone │Crash │Dash- │Edu.  │  │
│  │Treat.│      │      │User  │Safe │Learn │board │Comm. │  │
│  └──────┴──────┴──────┴──────┴──────┴──────┴──────┴──────┘  │
├──────────────────────────────────────────────────────────────┤
│  Layer 4  Data & AI Platform Layer                            │
│  ┌──────────┬──────────┬──────────┬──────────┬────────────┐  │
│  │Safety    │Crash     │Risk      │AI Video  │Knowledge   │  │
│  │Data Lake │Analysis  │Forecast  │Analytics │Graph      │  │
│  │Multi-src │Causal    │Spatial-  │Risky     │Safety KB  │  │
│  │Fusion    │Inference │Temporal  │Driving   │Reg/Case/  │  │
│  │Clean/Gov │Rules     │Hotspots  │Detection │Solution   │  │
│  └──────────┴──────────┴──────────┴──────────┴────────────┘  │
├──────────────────────────────────────────────────────────────┤
│  Layer 3  Communications Layer                                 │
│  ┌──────────┬──────────┬──────────┬────────────────────────┐  │
│  │5G/4G     │C-V2X/    │Fiber/    │LoRa/NB-IoT            │  │
│  │          │DSRC      │Dedicated │                        │  │
│  └──────────┴──────────┴──────────┴────────────────────────┘  │
├──────────────────────────────────────────────────────────────┤
│  Layer 2  Sensing Layer                                        │
│  ┌──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┐  │
│  │Video │mmWave│LiDAR │Weather│Pave- │Slope │Bridge│Vehicle│  │
│  │AI    │Radar │Radar │Sens. │Cond. │Mon.  │Mon.  │Sens.  │  │
│  └──────┴──────┴──────┴──────┴──────┴──────┴──────┴──────┘  │
├──────────────────────────────────────────────────────────────┤
│  Layer 1  Data Source Layer                                    │
│  ┌──────┬──────┬──────┬──────┬──────┬──────┬──────┬──────┐  │
│  │Crash │Viola-│ANPR/ │Emerg.│Emerg.│GPS   │Road- │Weather│  │
│  │Rec.  │tion  │Toll  │Call  │Med.  │Traj. │Side  │Env.   │  │
│  │      │Rec.  │Pass  │Center│Serv. │      │Sense │        │  │
│  └──────┴──────┴──────┴──────┴──────┴──────┴──────┴──────┘  │
└──────────────────────────────────────────────────────────────┘
```

---

## 4. Subsystem Detailed Design

### 4.1 Comprehensive Sensing

#### 4.1.1 Sensing System
| Sensing means | Deployment location | Monitored content | Coverage target | Refresh |
|----------|----------|----------|:--------:|:--------:|
| Video AI | Key intersections / corridors / tunnels / bridges | Traffic events (crash / congestion / stoppage / wrong-way / pedestrian / debris) + flow parameters | 100% of arterials | Real-time |
| mmWave radar | Motorway / expressway / trunk roads | Vehicle trajectory / speed / position (all-weather) | Critical segments | 50 ms |
| LiDAR | Major intersections / interchanges | Full 3D perception (pedestrian / micromobility fine detection) | Priority junctions | 100 ms |
| ANPR / enforcement | Intersections / corridors / boundaries | Vehicle passage / plate / speed / class / micromobility / pedestrian | Arterial junctions | Real-time |
| Weather sensors | Motorway / bridges / mountainous areas | Visibility / rainfall / wind / pavement temp. / surface state (dry / wet / ice / snow) | Weather-prone segments | 10 min |
| Pavement state | Motorway / bridges / tunnel portals | Friction coefficient / ponding depth / ice thickness | Critical segments | 10 min |

#### 4.1.2 AI Event Detection
| Event type | Detection technique | Target detection rate | Target false-alarm rate | Alarm latency |
|-------------|----------|:--------:|:--------:|:--------:|
| Crash (collision / rollover / barrier strike) | Video AI + radar | > 95% | < 5% | < 5 s |
| Abnormal stoppage | Video AI + radar + trajectory analysis | > 95% | < 5% | < 10 s |
| Pedestrian / micromobility on motorway | Video AI | > 90% | < 10% | < 10 s |
| Wrong-way driving | Video AI + trajectory direction | > 99% | < 1% | < 5 s |
| Congestion / queuing | Video AI + radar | > 95% | < 5% | < 30 s |
| Debris / spillage | Video AI | > 80% | < 15% | < 30 s |
| Smoke / fire | Video AI + thermal | > 90% | < 10% | < 10 s |
| Pavement flooding / icing | Video AI + sensors | > 85% | < 10% | < 5 min |
| Infrastructure damage | Video AI + inspection | > 80% | < 15% | < 1 min |

### 4.2 Active Safety Warning

#### 4.2.1 Warning System
| Warning type | Trigger | Delivery | Audience | Linked systems |
|----------|----------|----------|----------|----------|
| Upstream crash warning | AI detects upstream crash | VMS + broadcast + app + V2X (RSU→OBU) | Upstream drivers | Signal + diversion |
| Adverse weather warning | Weather threshold exceeded (fog / rain / snow / ice) | VMS speed limit + in-vehicle warning + app + broadcast | All corridor drivers | Speed limit + fog lamps |
| Work-zone warning | N km upstream of work zone | VMS alert + reduced-limit signage + crash cushion truck | Approaching vehicles | Temporary speed limit |
| Sharp curve / steep grade warning | Map-based hazardous location | VMS + roadside LED + rumble strips | Approaching vehicles | |
| Pedestrian crossing warning | Video AI detects high crossing frequency | In-pavement lighting + in-vehicle (V2I) | Approaching vehicles | |
| Red-light running warning | Signal state + vehicle trajectory analysis | Signal linkage + in-vehicle (V2I) | Red-light risk vehicles | |
| "Surprise" pedestrian warning | Roadside sensor detects occluded pedestrian | RSU→OBU warning | Approaching vehicles | |

#### 4.2.2 V2X Active Safety
| Scenario | Technical requirement | Condition | Expected safety effect |
|------|----------|----------|:----------:|
| Intersection Collision Warning (ICW) | RSU + V2I PC5 | RSU at key junctions | Junction crashes -30% |
| Emergency Electronic Brake Light (EEBL) | V2V PC5 + OBU | Factory-fit / aftermarket OBU | Rear-end crashes -50% |
| Vulnerable Road User warning (VRU) | Roadside sensing + V2P | Priority segments | Pedestrian crashes -30% |
| Speed Limit Warning (SLW) | RSU + V2I + dynamic limit | Variable speed limit segments | Speeding crashes -25% |
| Hazardous Location Warning (HLW) | RSU + V2I | Hazardous segments | Single-vehicle crashes -30% |

### 4.3 Blackspot Identification and Remediation

#### 4.3.1 Automated Blackspot Identification
| Method | Explanation | Input data | Output |
|------|------|----------|------|
| Crash-count method | Rank TOP-N by crash count | Crash data (≥3 yrs) | Blackspot list |
| Crash-rate method | Rank by crashes per million vehicle-km | Crash + traffic volume | Blackspot list |
| Equivalent-crash method | Weight fatalities / serious injuries | Crash data (with severity weights) | Blackspot list |
| AI spatiotemporal clustering | Density-based automatic clustering | Crash + network + volume | Dynamic blackspot heatmap |
| Bayesian prediction | Safety-level estimate accounting for regression-to-mean | Crash + segment attributes + volume | Safety-level ranking |

#### 4.3.2 Blackspot Remediation Generation
| Step | Content |
|:---:|------|
| 1 | Identify blackspot → GIS annotation → crash profile (time / weather / type / vehicle / cause) |
| 2 | Field survey (road condition / traffic facilities / sight distance / environment) |
| 3 | Causation analysis (human / vehicle / road / environment / management factors) |
| 4 | Option generation (AI recommendation + expert knowledge base + similar-case matching) |
| 5 | Option comparison (cost-benefit / construction difficulty / schedule / traffic impact) |
| 6 | Implement → effectiveness tracking (before-after + regression-to-mean correction) |
| 7 | Dynamically update blackspot database (add / clear / downgrade) |

#### 4.3.3 Remediation Measure Library (examples)
| Crash type | Engineering | Management | Technical |
|----------|----------|----------|----------|
| Rear-end | Add lane / improve sight distance / anti-skid surfacing | Speed limit / no-overtaking | V2V warning / variable speed limit |
| Junction collision | Roundabout / channelization + protected left phase | Signal optimization / all-red clearance | Red-light enforcement / V2I warning |
| Pedestrian | Footbridge / refuge island / raised crossing | 30 km/h zone | Video detection / V2P warning / in-pavement lighting |
| Run-off-road (curve) | Superelevation / barrier / anti-skid | Curve speed limit | Curve warning (VMS + V2I) / rumble strips |
| Head-on | Median / separating barrier | No-overtaking | Wrong-way detection + warning |

### 4.4 Emergency Response System

#### 4.4.1 Response Workflow
```
AI event detection / public emergency call (112 / 911-style) / vehicle eCall
    → Event confirmation (video / CCTV auto-linkage)
    → Event classification (minor / moderate / major / severe)
    → Plan matching (AI recommendation + historical similar events)
    → One-click dispatch (police / medical / fire / highway authority / tow)
    → Resource scheduling (optimal route + congestion avoidance)
    → On-site handling (mobile app + video return + remote expert support)
    → Clearance → traffic recovery → event archival + debrief
```

#### 4.4.2 Response KPIs
| Indicator | Target | Measurement |
|------|:------:|----------|
| Detection → confirmation | < 30 s | System log |
| Confirmation → dispatch | < 1 min | System log |
| Police on scene | [urban < 10 min / motorway < 15 min] | GPS trajectory + timestamp |
| On-site handling | [minor < 15 min / moderate < 30 min / major < 2 h] | System log |
| All lanes recovered | [minor < 30 min / moderate < 1 h / major < 4 h] | CCTV / sensor verification |

### 4.5 Digital Safety Assessment & Review

#### 4.5.1 Digital Safety Assessment & Review
| Stage | Review content | Digital tool | Output |
|----------|----------|-----------|------|
| Feasibility | Preliminary design safety screening | AI design review + BIM clash detection + code knowledge base | Review report |
| Preliminary design | Detailed geometry / sight distance / conflict points | CAD / 3D sim. + safety performance eval. software | Review report + issue list |
| Detailed design | Traffic facilities / signing / lighting / drainage | BIM + AI drawing review | Review report |
| Construction | Work-zone safety / temporary traffic safety facilities | Video AI patrol + IoT sensors | Inspection report |
| Pre-opening | Comprehensive pre-opening safety check (incl. night / rain) | Mobile checklist app + photo / video evidence | Check report + remediation list |
| Operation | Periodic operational safety assessment | Crash data analysis + video AI patrol + road condition detection | Assessment report |

#### 4.5.2 Safety Assessment Knowledge Base
| Knowledge type | Content | Update mechanism |
|----------|------|----------|
| Regulations & standards | Road safety national / international / local codes | Automatic standards-change monitoring |
| Design codes | Alignment / junction / facilities / lighting / signing design | Periodic update |
| Crash case library | Local / national / international classic cases + causes + measures | Continuous accumulation |
| Remediation library | Effective remediation by blackspot type (engineering + management + tech) | Effectiveness tracking → optimization |

### 4.6 Vulnerable Road User Protection

#### 4.6.1 Pedestrian Safety
| Measure | Technical solution | Deployment principle |
|------|----------|----------|
| Pedestrian detection & warning | Video AI + radar fusion → crossing warning (VMS / in-pavement / V2P) | High footfall / high-crash junctions |
| Smart zebra crossing | Illuminated crossing + auto-activation on detection + audio prompt | Unsignalized crossings |
| 30 km/h zone | Speed limit signs + speed tables / raised crossings / lane narrowing | Schools / hospitals / residential |
| Crossing aid system | Push-button / sensor signal + countdown + audio prompt | Areas with elderly / children |
| Median refuge island | Centre refuge on wide roads | Roads with ≥ 4 lanes per direction |

#### 4.6.2 Micromobility (bicycle / e-bike) Safety
| Measure | Technical solution |
|------|----------|
| Dedicated lane separation | Segregated micromobility lane + hard separation (barrier / greenbelt / raised) + coloured surfacing |
| Junction protection | Dedicated micromobility phase + advanced stop line + waiting area + right-turn blind-spot warning |
| Helmet detection | Video AI detects helmet use by e-bike riders → links to education / enforcement |
| Speed management | E-bike e-plates + RFID / video speed → over-speed warning |
| Safety education | App / mini-program safety learning + points + incentives |

### 4.7 Work-Zone Safety Management

#### 4.7.1 Work-Zone Safety Plan
| Stage | Safety measure | Technical solution |
|------|----------|----------|
| Pre-construction | Work-zone traffic management plan + safety assessment | AI-assisted work-zone design + traffic impact simulation |
| During | Work-zone warning | Upstream VMS + speed limit + crash cushion truck + in-vehicle / roadside V2X warning |
| During | Work-zone monitoring | CCTV + video AI (intrusion / congestion / facility collapse) + drone patrol |
| During | Worker safety | Smart helmet (GPS + SOS) + geo-fence + AI PPE detection (vest / helmet) |
| Post | Clearance safety check | Clearance checklist app + photo evidence |

### 4.8 Post-Crash Analysis and Learning

#### 4.8.1 In-Depth Crash Analysis
| Step | Content | Tool / Method |
|----------|------|----------|
| Data collection | Police crash report + scene photo / video + dashcam + CCTV + vehicle EDR | Multi-source data aggregation platform |
| Reconstruction | 1:1 3D reconstruction of crash process (trajectory / impact point / injury mechanism) | Reconstruction sim. (PC-Crash / HVE) |
| Causation analysis | Human-vehicle-road-environment-management five-factor analysis | Fishbone + FTA + 5-Why + AI analysis |
| Measure recommendation | Short / medium / long-term measures to prevent recurrence | Knowledge base matching + expert review |
| Closed-loop tracking | Measure implementation → effectiveness → feedback & correction | Task management + KPI tracking |

#### 4.8.2 Safety Learning System
| Mechanism | Content | Audience |
|----------|------|------|
| Crash bulletin | Rapid notice + cause + lessons of major / typical crashes | City-wide traffic management / operators |
| Case library | Typical crashes (3D reconstruction + full replay + analysis + measures) | All relevant staff |
| Experience sharing | Successful remediation + innovations + data comparison | District traffic units |
| Training & drill | Tabletop + live drills based on real cases | Emergency response personnel |

### 4.9 Safety Performance Dashboard

#### 4.9.1 KPI Dashboard
| KPI dimension | Indicator | Refresh | Benchmark |
|----------|------|:--------:|------|
| Crash outcome | Fatal / serious / slight injuries / total / fatalities per 10k vehicles | Monthly | National / peer cities |
| Crash trend | YoY / MoM change rate | Monthly | |
| Blackspot remediation | Total / remediated / before-after comparison | Monthly | |
| Enforcement effect | Violations detected / key-violation trend / enforcement-crash correlation | Monthly | |
| Emergency response | Avg. response / handling / recovery time | Monthly | |
| Safety facilities | Facility integrity / warning system online rate | Real-time | |
| Public perception | Safety satisfaction / awareness rate | Quarterly / Annual | |

#### 4.9.2 Safety Cockpit (video wall)
| Display area | Content |
|----------|------|
| Main screen | GIS map + real-time crash / violation / blackspot annotation + warnings |
| Metric cards | Today / month crashes (fatal / injured) / fatalities per 10k vehicles / blackspots remediated |
| Trend charts | Monthly crash trend (YoY / MoM) / TOP 10 causes / TOP 10 segments |
| Video linkage | Auto-retrieve surrounding CCTV on crash occurrence |
| Handling tracking | Status of in-progress emergency tasks |

### 4.10 Public Education and Communication

#### 4.10.1 Audience-Specific Education
| Audience | Content | Channel | Frequency |
|------|----------|------|:----:|
| Children (3–12) | Basic road safety (signals / crossings / seat belts) | School / animation / games / VR | Per semester |
| Youth (13–18) | Cycling / e-bike / drink-driving risks | School / short video / social practice | Per semester |
| New drivers | Defensive driving / complex conditions / typical crashes | Driving school / app / VR sim. | Licensing + yearly |
| Older drivers | Age-related risk / awareness / medical check importance | Community / TV / radio | Yearly |
| Professional drivers | Fatigue / distraction / blind spots / adverse weather / regulation updates | In-company / online learning | Monthly |
| E-bike users | Helmet / red-light / wrong-way / modification risks | Short video / community / roadside | Ongoing |
| Pedestrians / passengers | Crossing / seat belts / child seats / bus safety | Public ads / app / community | Ongoing |

#### 4.10.2 Multi-Channel Communication Matrix
| Channel | Format | Strategy |
|------|----------|----------|
| Short-video platforms (TikTok / Reels / Shorts) | 15–60 s safety clips (warning / education / enforcement live) | Fast pace / hook titles / emotional resonance |
| Social / messaging (WeChat-style / WhatsApp / Facebook) | In-depth articles + cases + regulation explainers | Scheduled push + sharing |
| Microblog-style (X / Twitter) | Real-time alerts + hot-topic interaction + live | Timeliness + interactivity |
| Radio | Driving tips + crash bulletins + weather alerts | Peaks + motorway broadcast |
| Outdoor / VMS | Safety slogans / crash data / weather alerts | Continuous / dynamic refresh |

---

## 5. Implementation Plan

| Phase | Time | Content | Investment |
|------|------|------|:----:|
| Phase 1 (Sensing & Warning) | ____ / __ – __ | Full sensing (video AI + radar + weather) + active warning (VMS + V2X pilot) + blackspot ID system + emergency response 1.0 | $___ M |
| Phase 2 (Remediation & Control) | ____ / __ – __ | Full blackspot remediation + VRU protection + work-zone safety + digital safety review + safety dashboard | $___ M |
| Phase 3 (Learning & Optimization) | ____ / __ – __ | Post-crash deep learning + safety knowledge graph + public education platform + AI prediction + full V2X safety scenarios | $___ M |

---

## 6. Investment and Benefits

### 6.1 Investment Estimate
| No. | Item | Estimate ($M) |
|:---:|------|:----------:|
| 1 | Sensing devices (video AI / radar / weather / pavement sensors) | $____ |
| 2 | Active warning devices (VMS / RSU / roadside LED / audio-visual alarm) | $____ |
| 3 | Blackspot remediation works (road modification / facility addition) | $____ |
| 4 | AI & data analytics platform | $____ |
| 5 | Emergency response system | $____ |
| 6 | Safety assessment & review + knowledge base | $____ |
| 7 | Safety education & communication | $____ |
| 8 | Network & communications | $____ |
| 9 | O&M (3 years) | $____ |
| 10 | Contingency | $____ |
| | **Total** | **$____** |

### 6.2 Safety Benefit Assessment
| Benefit item | Quantification method | Expected annual benefit |
|--------|----------|:----------:|
| Fatalities avoided | Expected fatality reduction × Value of Statistical Life (VSL) | $____ M |
| Injuries avoided | Expected injury reduction × medical + lost-work + disability cost | $____ M |
| Property loss avoided | Expected crash reduction × avg. property loss per crash | $____ M |
| Congestion loss avoided | Faster clearance × value of time | $____ M |
| Emergency cost avoided | Precise prevention reduces unnecessary dispatches | $____ M |

> **Safety benefit note**: Transport safety benefits are typically measured by **Value of Statistical Life (VSL)** and **comprehensive crash cost**. Under internationally referenced guidance, the societal benefit of averting one fatality is on the order of **$[3–6] million**. Even without monetization, saving lives is the paramount benefit.

---

> **Usage note**: This template is for city / regional transport safety digitalization plans. Road safety is a matter of life and death — the plan must consistently apply the Safe System methodology and the Vision Zero vision. Replace `[placeholder]` content with actual project data.

> **Legal notice**: This template is protected by copyright and related laws. It is provided for individual study and reference only; commercial use requires the author's written authorization.

> **Disclaimer**: This template is for study and reference only and does not constitute professional advice. Transport safety affects human life; any implementation must be reviewed by road-safety experts and comply with applicable national / sector / local regulations and standards. The author accepts no liability for any loss arising from the use of or reliance on this template.

> **Note**: The core value of transport safety digitalization is saving lives. Every blackspot remediated and every warning system deployed protects the lives of road users. Behind every data point is a human life, and behind every metric is a family. Only by treating safety with reverence can technology truly serve the primacy of life.

> **Author**: yinjianheng | yinjianheng@foxmail.com
