International Journal of Sustainable Transportation最新文献

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E-bike crashes: Who they affect and which circumstances to avoid? 电动自行车事故：谁会受到影响，哪些情况需要避免？

IF 3.1 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-01-02 DOI: 10.1080/15568318.2024.2447993

Yuntong Zhou , Natalia Barbour , Mohamed Abdel-Aty , Xin Gu , Yanyan Chen

In the last decade, the popularity of e-bikes has increased as they have shown potential to relieve congestion and aid the environment. However, with the increase of their popularity, there has been also an increase in their traffic crashes. This study aims to understand factors playing a role in the e-bike crash injury outcomes. The analysis uses 1,351 records of e-bike crashes to estimate random parameters multinomial logit models with heterogeneity in the means and variances of random parameters in four groups. This paper also seeks to provide insights into e-bike crash injury severities across gender (female versus male) and lighting conditions (daytime and nighttime) specific models. Numerous likelihood ratio tests were performed to justify splitting the data. It was found that a variety of factors relating to the weather and road characteristics, crash type, and rider’s demographics play a role in crash outcomes. Particularly interesting are findings relating to the rollover crashes increasing the likelihood of severe outcomes as well as gender specific effects with, for example, male riders have a higher probability of severe injuries during peak traffic hours. The findings can be used to support e-bike safety as well as advocate for a more nuanced and inclusive approach relating to e-bike travel.

在过去的十年里，电动自行车越来越受欢迎，因为它们显示出了缓解拥堵和保护环境的潜力。然而，随着他们的普及，交通事故也有所增加。本研究旨在了解影响电动自行车碰撞损伤结果的因素。分析使用1351个电动自行车碰撞记录来估计四组随机参数均值和方差均存在异质性的随机参数多项logit模型。本文还试图提供不同性别（女性与男性）和光照条件（白天和夜间）特定车型的电动自行车碰撞损伤严重程度的见解。进行了许多似然比检验来证明拆分数据的合理性。研究发现，与天气和道路特征、碰撞类型和骑手的人口统计学相关的各种因素在碰撞结果中发挥作用。特别有趣的是，与侧翻事故有关的发现增加了严重后果的可能性，以及性别特定的影响，例如，男性骑手在交通高峰时段受重伤的可能性更高。研究结果可以用来支持电动自行车的安全性，并倡导一种更细致、更包容的电动自行车出行方式。

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引用次数: 0

Dynamic model to assess the impacts of government support for electric vehicles on the economy and environment sectors in Indonesia 动态模型，以评估印尼政府支持电动汽车对经济和环境部门的影响

IF 3.1 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-01-02 DOI: 10.1080/15568318.2024.2443825

Erma Suryani , M. S. Fadlillah , R. A. Hendrawan , Mudjahidin Mudjahidin , R. J. Pramundito , A. A. Zahra , S. Y. Chou , Anindhita Dewabharata , Z. U. Rizqi , Rafika Rahmawati

This research proposes a comprehensive analysis of the environmental and economic impacts of electric vehicle (EVs) adoption using system dynamics modeling. A system dynamics framework is utilized to integrate various aspects of EVs, economy, environment, and impact of policies on those sectors. Stock and flow diagrams were used to model and predict the impact of government support on electric vehicles based on the existing and future conditions through several proposed strategies. This research mainly contributes to providing causal relationships of variables and parameters influencing the number of EVs and their impact on the economy and environment, modeling and simulation of several sub-systems based on the existing condition, and scenario modeling to predict and improve the number of EV, economic value, and environmentally friendly in the future. This research examines how different policies for electric vehicles (EVs) might affect the numbers of people use them, the pollution caused, and the cost spent. They looked at total emissions, yearly budget, and the number of electric cars and motorcycles. The results show that continuing or increasing government help (scenarios SCN2 & SCN3) for EVs leads to the biggest pollution reduction. Focusing on developing new technologies and industries for EVs (SCN4) shows the biggest short-term pollution reduction. The key takeaway is that long-term support for EVs and technological advancements are essential for success. Finding a balance between the initial costs and the long-term benefits is crucial when designing policies for EVs.

本研究采用系统动力学模型对电动汽车的环境和经济影响进行了综合分析。系统动力学框架用于整合电动汽车、经济、环境和政策对这些部门的影响的各个方面。根据现有和未来的情况，通过几种拟议的策略，利用库存图和流程图来建模和预测政府支持电动汽车的影响。本研究主要提供影响电动汽车保有量的变量和参数及其对经济和环境影响的因果关系，基于现有条件对多个子系统进行建模和仿真，通过情景建模对未来的电动汽车保有量、经济价值和环境友好性进行预测和改进。这项研究考察了不同的电动汽车政策如何影响使用电动汽车的人数、造成的污染和花费的成本。他们考察了总排放量、年度预算以及电动汽车和摩托车的数量。结果表明，持续或增加政府援助(情景SCN2 &；电动汽车的SCN3是减少污染最多的。专注于开发新技术和新产业的电动汽车（SCN4）显示出最大的短期污染减少。关键是，对电动汽车的长期支持和技术进步是成功的关键。在设计电动汽车政策时，在初始成本和长期效益之间找到平衡至关重要。

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引用次数: 0

Oil tanker emissions: Measurement, factors, and future scenarios 油轮排放：测量、因素和未来情景

IF 3.1 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-01-02 DOI: 10.1080/15568318.2024.2443819

Suleyman Kose

This study measured emissions from 76 oil tankers at Eastern Black Sea petroleum terminals to determine their emission factors. Emissions of CO, CO₂, NO_X, and SO₂ were measured during cruise (C), maneuvering (M), and hotelling (H) activities of main engines (ME) and auxiliary engines (AE). Using an activity-based approach, emission factors were calculated from the collected data. Real-time data from 2013 to 2021 were utilized to determine total emissions for each year, while regression analysis forecasted emissions until 2040 under various scenarios. Weighted emission factors for ME were determined as 1.1 ± 0.22 g/kWh for CO, 654 ± 13 g/kWh for CO₂, 13.95 ± 2.75 g/kWh for NO_X, and 11.45± g/kWh for SO₂, and for AE, 1.1 ± 0.21 g/kWh for CO, 706 ± 15 g/kWh for CO₂, 15.3 ± 1.4 g/kWh for NO_X, and 11.15 ± 2.25 g/kWh. Average load factors were as follows: C (ME): 67%, C (AE): 35%, M (ME): 34%, M (AE): 53%, H (ME): 76%, H (AE): 62%. Total emissions from oil tankers in 2022 were projected to be 235 tons for CO, 151580 tons for CO₂, 3018 tons for NO_X, and 2251 tons for SO₂. Future scenarios indicate these amounts could increase by 3, 5, and 12 times by 2040 under optimistic, normal, and pessimistic scenarios, respectively.

本研究测量了东黑海石油码头76艘油轮的排放量，以确定其排放因子。测量了主、辅发动机巡航(C)、机动(M)和制动(H)活动时CO、CO2、NOX和SO2的排放。采用基于活动的方法，根据收集的数据计算排放因子。利用2013年至2021年的实时数据确定每年的总排放量，而回归分析预测了各种情景下到2040年的排放量。ME的加权排放系数分别为CO 1.1±0.22 g/kWh、CO2 654±13 g/kWh、NOX 13.95±2.75 g/kWh、SO2 11.45±g/kWh； AE的加权排放系数分别为CO 1.1±0.21 g/kWh、CO2 706±15 g/kWh、NOX 15.3±1.4 g/kWh、11.15±2.25 g/kWh。平均载客率为：C (ME): 67%, C (AE): 35%, M (ME): 34%, M (AE): 53%, H (ME): 76%, H (AE): 62%。2022年，油轮的总排放量预计为CO 235吨，CO2 151580吨，NOX 3018吨，SO2 2251吨。未来情景表明，到2040年，在乐观、正常和悲观情景下，这些数量可能分别增加3倍、5倍和12倍。

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引用次数: 0