用社会经济数据与智能交通系统，帮低密度城市加速电动车普及、减少行车里程。
Using Socio-economic Indicators, Smart Transit Systems, and Urban Simulator to Accelerate ZEV Adoption and Reduce VMT
- 结合社会经济指标与智能交通技术，构建政策评估框架。
- 在休斯顿模拟显示，优化公交与停车可使车均行驶里程降低20%。
- 适合关注低碳出行的城市规划者与交通政策制定者。
全球道路交通贡献了15%的温室气体排放，导致约38.5万人因细颗粒物（PM2.5）过早死亡。城市贡献了75%的能源相关温室气体排放，是实现气候目标的关键。以德克萨斯州休斯顿为例，道路运输占其气候行动计划（CAP）基线排放的48%，需在2014年基础上减排70%才能实现2050年净零目标，剩余30%由可再生能源抵消。但该市地广人稀、依赖汽车，90%以上的道路排放来自小汽车和轻型卡车，公共交通使用率低，且存在显著社会经济不平等，制约零排放车辆（ZEV）推广。策略聚焦于提升ZEV可及性，并通过改善交通系统和城市设计将车辆行驶里程（VMT）降低20%。本文提出基于社会经济指标与智能交通系统（ITS）的政策评估方法，支持智慧停车、公交激励、安全数据系统与电动车队管理，提升出行方式结构与系统可靠性。为辅助评估，开发了基于Unity 3D的动态城市出行仿真环境，可视化不同政策情景。对依赖汽车的低密度城市而言，这些指标、度量与技术可为其2050减排目标提供有效支撑。
原文摘要 · Abstract (English)
Globally, on-road transportation accounts for 15% of greenhouse gas (GHG) emissions and an estimated 385,000 premature deaths from PM2.5. Cities play a critical role in meeting IPCC targets, generating 75% of global energy-related GHG emissions. In Houston, Texas, on-road transportation represents 48% of baseline emissions in the Climate Action Plan (CAP). To reach net-zero by 2050, the CAP targets a 70% emissions reduction from a 2014 baseline, offset by 30% renewable energy. This goal is challenging because Houston is low-density and auto-dependent, with 89% of on-road emissions from cars and small trucks and limited public transit usage. Socio-economic disparities further constrain Zero Emissions Vehicle (ZEV) adoption. Strategies focus on expanding ZEV access and reducing Vehicle Miles Traveled (VMT) by 20% through transit improvements and city design. This paper presents methods for establishing an on-road emissions baseline and evaluating policies that leverage socio-economic indicators and Intelligent Transportation Systems (ITS) to accelerate ZEV adoption and reduce VMT. Smart parking, transit incentives, secure data systems, and ZEV fleet management support improvements in modal split and system reliability. Policy options are analyzed and potential actions identified. To support evaluation, a simulation environment was developed in Unity 3D, enabling dynamic modeling of urban mobility and visualization of policy scenarios. Auto-dependent cities aiming for 2050 emission targets can benefit from the indicators, metrics, and technologies discussed.
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