Transport Economics and Management最新文献

AI-driven scenarios for urban mobility: Quantifying the role of ODE models and scenario planning in reducing traffic congestion

Transport Economics and Management

Pub Date : 2025-02-19 DOI: 10.1016/j.team.2025.02.002

Katsiaryna Bahamazava

Urbanization and technological advancements are reshaping urban mobility, presenting both challenges and opportunities. This paper investigates how Artificial Intelligence (AI)-driven technologies can impact traffic congestion dynamics and explores their potential to enhance transportation systems’ efficiency. Specifically, we assess the role of AI innovations, such as autonomous vehicles and intelligent traffic management, in mitigating congestion under varying regulatory frameworks. Autonomous vehicles reduce congestion through optimized traffic flow, real-time route adjustments, and decreased human errors.

The study employs Ordinary Differential Equations (ODEs) to model the dynamic relationship between AI adoption rates and traffic congestion, capturing systemic feedback loops. Quantitative outputs include threshold levels of AI adoption needed to achieve significant congestion reduction, while qualitative insights stem from scenario planning exploring regulatory and societal conditions. This dual-method approach offers actionable strategies for policymakers to create efficient, sustainable, and equitable urban transportation systems. While safety implications of AI are acknowledged, this study primarily focuses on congestion reduction dynamics.

城市化和技术进步正在重塑城市交通，带来了挑战和机遇。本文研究了人工智能（AI）驱动的技术如何影响交通拥堵动态，并探讨了这些技术提高交通系统效率的潜力。具体而言，我们将评估人工智能创新技术（如自动驾驶汽车和智能交通管理）在不同监管框架下缓解交通拥堵的作用。自动驾驶汽车通过优化交通流量、实时调整路线和减少人为错误来减少拥堵。该研究采用常微分方程（ODE）来模拟人工智能采用率与交通拥堵之间的动态关系，从而捕捉系统反馈回路。定量输出包括实现显著减少拥堵所需的人工智能采用阈值水平，而定性见解则来自于探索监管和社会条件的情景规划。这种双重方法为政策制定者提供了可行的策略，以创建高效、可持续和公平的城市交通系统。虽然人工智能对安全的影响已得到认可，但本研究主要侧重于减少拥堵的动态效果。

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引用次数: 0

Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models 预测基加利市的骑车偏好：传统统计模型与集合机器学习模型的比较研究

Transport Economics and Management

Pub Date : 2025-02-14 DOI: 10.1016/j.team.2025.02.003

Jean Marie Vianney Ntamwiza , Hannibal Bwire

This research enhanced the prediction of biking preferences in the City of Kigali, Rwanda and informed transportation management and economic policy. Specifically, it compared the performance of traditional statistical models—logistic regression, support vector machine (SVM), Naïve Bayes, and k-Nearest Neighbours (KNN)—with ensemble models including eXtreme Gradient Boosting (XGBoost), Light GBM, Random Forest, and stacking classifiers. This research used a dataset of 6386 observations incorporated weather and air quality variables and applied correlation-based and iterative model-based feature selection techniques to improve predictive accuracy. Results indicate that ensemble models, particularly XGBoost and Random Forest, outperform traditional statistical models, with an accuracy of 99 % and 98 %, respectively. Traditional statistical models underperformed, with 42 % and 82 % accuracy, in the logistic and SVM models. Ensemble models classified better biking preferences (shared, non-shared, and both categories), significantly improving precision and recall across all three groups. Feature importance indicated that day and month are critical factors in bike preference prediction, reflecting significant daily and seasonal patterns. Air quality factors (high ozone and PM2.5) and weather factors (temperature and rainfall) impacted the preferences. It is better to maintain bikes during the rainy season and rebalance bikes during high temperatures for efficient biking. To improve the air quality in the city, the government should increase car-free corridors to improve the air quality and motivate bike users to be comfortable. In a city with extreme weather, shaded bike lanes should be provided to encourage riders during the extreme weather.

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引用次数: 0

Analyzing the acceptance of new public transportation pricing schemes: A case study of a developing country

Transport Economics and Management

Pub Date : 2025-02-11 DOI: 10.1016/j.team.2025.02.004

Hamed Razeghifar , Amirhossein Baghestani , Mohammadhossein Abbasi , Majid Asadi

Globally, efforts are underway to encourage the use of sustainable transportation modes over private cars. Well-designed transit fare settings can significantly contribute to the development of sustainable, equitable, and efficient public transportation systems. This study aims to: (1) Assess respondents’ acceptance of a new transit pricing scheme, (2) Analyze changes in travel behavior resulting from various fare elements (e.g., flat, distance-based, or time-based fares), and (3) Identify influential factors using discrete choice models and stated preference surveys. Based on 808 observations, the binary logit model's estimation reveals that socioeconomic factors (such as car ownership, driving license possession, gender, age, and occupation) and travel-related factors (such as departure time, trip purpose, travel cost, waiting time, and number of stations traveled by transit) significantly affect the acceptability of the new pricing scheme. Additionally, the multinomial logit model indicates how the new pricing scheme influences travel behavior (modal shift, no modal shift, and changes in departure time or destination) and identifies significant determinants. The insights gained from this research can help policymakers develop more effective and sustainable transportation policies tailored to the needs of diverse urban populations.

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引用次数: 0

Who are MaaS avoiders, wanderers or enthusiasts and what drives their intentions to adopt MaaS?

Transport Economics and Management

Pub Date : 2025-02-06 DOI: 10.1016/j.team.2025.02.001

Zuoxian Gan , Wentao Li

This study integrates the Unified Theory of Technology Acceptance and Use (UTAUT) and Status Quo Bias (SQB) theories and draws on survey data covering nine dimensions: performance expectancy, effort expectancy, social influence, individual innovation, transition costs, sunk costs, inertia and resistance to use. The aim is to explore the underlying reasons and disparities that influence people's adoption of MaaS from both a facilitator and inhibitor perspective. To mitigate the confounding effect of group heterogeneity on the MaaS acceptance mechanism, the latent class clustering method was employed to naturally categorize respondents into three distinct groups: MaaS avoiders, wanderers and enthusiasts. A structural equation model was then developed to delineate the path of influence of users' intention to use and to contrast the differences in path coefficients between the different groups. The results show that individuals who are most dependent on public transport are not necessarily the most willing to use MaaS, while those who have used car-sharing services are more likely to adopt MaaS. It also highlights that there is no one-size-fits-all approach to promoting MaaS adoption, as different groups of people have different preferences, needs and concerns about the service. MaaS avoiders are predominantly middle-aged and older individuals with lower incomes, whose reluctance to switch stems from the associated transition costs, which create inertia. To encourage this group to adopt MaaS, operators should develop a gradual and user-friendly transition plan that minimizes complexity and addresses potential challenges such as unfamiliarity with the system and resistance to service changes. Conversely, MaaS wanderers’ willingness to engage with the service is strongly influenced by social influence and performance expectancy. Operators can increase their social media presence and raise awareness of the practicality of MaaS, helping to build an early customer base. In addition, the innovative mindset of MaaS enthusiasts plays a key role in their willingness to adopt the service, although operators must also be vigilant about privacy concerns and the risk of data breaches. Overall, this study enriches our understanding of the factors that shape MaaS adoption and provides actionable insights for improving services across different market segments.

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引用次数: 0

Dissecting climate adaptation strategies and planning of ports from different theoretical angles

Transport Economics and Management

Pub Date : 2025-01-31 DOI: 10.1016/j.team.2025.01.001

Adolf K.Y. NG , Mark C.P. POO , Tianni WANG , Austin BECKER , Yui-yip LAU , Tina Ziting XU , Zaili YANG

As the key nodes of globalization and international business, ports are exposed to the impacts of climate change, mainly because of their locations, including low-lying areas, coastal zones, and deltas. While there is increasing research on climate adaptation strategies and planning of ports, there is a lack of works that explain how scholars address the topic from different theoretical angles. This paper fills this gap by dissecting climate adaptation strategies and planning of ports from four main perspectives, including institutional systems, path dependence, supply chain risk management, and stakeholder management. It is a germane reminder to port decision-makers that effective climate adaptation is not limited to engineering technicalities but is an ideological issue that requires shifting existing political, economic, and social paradigms. Towards the end, we propose a process of effective adaptation planning to climate change impacts by ports.

引用次数: 0

Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda

Transport Economics and Management

Pub Date : 2024-12-24 DOI: 10.1016/j.team.2024.12.001

Jean Marie Vianney Ntamwiza , Hannibal Bwire

Over the past years, bike-sharing programs have evolved and passed through various developmental stages since 1965, becoming a significant part of urban mobility worldwide. Researchers conducted numerous studies to examine the usage of bike-sharing systems. While earlier research has highlighted the benefits of bike-sharing, limited attention has been given to changes in docked bike-share systems and the use of machine learning algorithms to predict docked bike-sharing usage. This research investigated the effectiveness of machine learning models in predicting docked bike-sharing station usage in Kigali City. Descriptive statistics are analysed to reveal user characteristics by Gender, education, age, and occupation. The Random Forest Model effectively classified docked bike-sharing users and non-users, achieving a balanced accuracy of 84 %. With a sensitivity of 75 % and an F1 score of 82.5 %, it demonstrated strong user identification while balancing precision and recall and a positive predictive value of 91.6 %. The study also examined the factors influencing program usage. Results indicated that Gender positively affects docked bike-sharing, with a slightly higher impact from male users. Specific stations are popular among students, while others attract non-students. Corridor analysis revealed that the Central Business District positively impacts docked bike-sharing usage. Temporal and spatial trends indicate higher usage during school months, with younger riders dominating the age distribution of users. Demand also varies by season. This study provides valuable insights to support the optimisation of docked bike-sharing operations and to guide city planners in developing relevant infrastructure and policies.

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引用次数: 0

Air travel-induced COVID-19 risk and mortality across US counties 美国各县航空旅行诱发 COVID-19 的风险和死亡率

Transport Economics and Management

Pub Date : 2024-11-23 DOI: 10.1016/j.team.2024.11.002

Jules Yimga

This study investigates the impact of air travel-induced COVID-19 importation risk on COVID-related mortality across US counties from January 2020 to December 2021. We construct a novel measure of relative risk of COVID-19 importation for each destination county, based on passenger flows from origin counties, the severity of local outbreaks, and the presence of active cases. Using county-level data on COVID-19 mortality, vaccination rates, demographic characteristics, and socioeconomic factors, we find a significant association between higher importation risk and increased COVID-19 mortality. The results suggest that air travel plays a crucial role in shaping the spatial distribution of COVID-19 mortality, underlining the need for targeted public health interventions in high-risk areas. Moreover, we conduct robustness checks using an alternative measure of mortality, confirming the consistency of these results.

本研究调查了 2020 年 1 月至 2021 年 12 月期间美国各县因航空旅行引起的 COVID-19 输入风险对 COVID 相关死亡率的影响。我们根据来自原籍县的客流、当地疫情的严重程度以及活跃病例的存在情况，为每个目的地县构建了一种新的 COVID-19 输入相对风险衡量标准。利用有关 COVID-19 死亡率、疫苗接种率、人口特征和社会经济因素的县级数据，我们发现较高的输入风险与 COVID-19 死亡率增加之间存在显著关联。结果表明，航空旅行在形成 COVID-19 死亡率的空间分布方面起着至关重要的作用，强调了在高风险地区采取有针对性的公共卫生干预措施的必要性。此外，我们还使用另一种死亡率测量方法进行了稳健性检验，证实了这些结果的一致性。

引用次数: 0

Workforce development in the trucking industry: A comprehensive analysis of truck driver training entities 卡车运输行业的劳动力发展：卡车司机培训实体的综合分析

Transport Economics and Management

Pub Date : 2024-11-22 DOI: 10.1016/j.team.2024.11.003

Sicheng Wang , Elizabeth A. Mack , Nidhi Kalani , Chu-Hsiang Chang , Shelia R. Cotten

The transformation of transportation technologies, economic structures, and social lifestyles is changing the truck-driving workforce. Recognizing the trends and challenges of the job is essential for proactive planning to address potential disruptions in the trucking industry and the broader economy. Despite the importance of truck drivers, the research community has little information about the entities involved in training truck drivers. These entities are critical in creating a pipeline of drivers to address the driver shortage issue and respond to the changing requirements of drivers. To address this knowledge gap, we utilize institutional theory as a framework to disentangle the factors that affect entities' considerations behind the design and delivery of driver training programs. Using explanatory sequential mixed methods, we collect and analyze multiple sources of data about driver training, including information about the entities providing training, as well as information about funding and federal regulations. In-depth interviews with these entities provide additional insights into the process of training drivers and how it varies between different types of training entities. Analytical results indicate that regulatory changes have impacted the number and types of entities providing driver training. A qualitative analysis of the interviews reveals different business models for training drivers, as well as the advantages and disadvantages of these models in terms of cost to the trainee, time to completion, and coordination costs. Finally, we discuss the implications of our findings for policymaking, including workforce development, transportation safety, and preparation for technological change.

运输技术、经济结构和社会生活方式的转变正在改变卡车司机的工作。认识到这项工作的趋势和挑战，对于积极规划应对卡车运输业和更广泛经济领域的潜在干扰至关重要。尽管卡车司机很重要，但研究界对培训卡车司机的实体知之甚少。这些实体在创建驱动程序管道以解决驱动程序短缺问题和响应驱动程序不断变化的需求方面至关重要。为了解决这一知识差距，我们利用制度理论作为框架来理清影响实体在驾驶员培训计划设计和交付背后考虑的因素。使用解释性顺序混合方法，我们收集和分析了有关驾驶员培训的多个数据来源，包括提供培训的实体信息，以及有关资金和联邦法规的信息。与这些实体的深入访谈提供了对培训驾驶员过程的额外见解，以及不同类型的培训实体之间的差异。分析结果表明，监管变化影响了提供驾驶员培训的实体的数量和类型。访谈的定性分析揭示了培训司机的不同商业模式，以及这些模式在培训人员成本、完成时间和协调成本方面的优缺点。最后，我们讨论了我们的研究结果对政策制定的影响，包括劳动力发展、运输安全以及为技术变革做准备。

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引用次数: 0

The effectiveness of autonomous public transport systems in densely populated urban areas 人口稠密城市地区自主公共交通系统的有效性

Transport Economics and Management

Pub Date : 2024-11-20 DOI: 10.1016/j.team.2024.11.004

Maksim Gusev, Shane Gilroy

This paper presents an in-depth analysis and comparison of technologies for autonomous transport systems, focusing on logistical considerations, economic factors, and overall outcomes. The study evaluates various scenarios, including manned and autonomous ground vehicles, as well as infrastructure solutions, to provide insights into their operational efficiency and economic viability.

In the logistical comparison, findings indicate that autonomous and manned ground vehicles operate within similar logistical frameworks but offer different operational flexibilities. Autonomous systems demonstrate potential advantages in adaptability to changing passenger needs and risk reduction through sensor-based navigation. Additionally, deploying autonomous infrastructure solutions shows promising results in reducing cycle time (-43 % to ground manned/autonomous vehicles) and increasing technical speed (+57 % to ground manned/autonomous vehicles), especially in strained infrastructure environments.

The economic comparison reveals challenges in assessing the cost-effectiveness of autonomous solutions due to a lack of pricing data. While the automation of public transport vehicles incurs higher capital (e.g. 33 % increase for autonomous buses vs manned) and operational expenditures (e.g. 60 % increase for autonomous buses vs manned), autonomous systems offer benefits such as continuous operation and reduced idle time. Investments in infrastructure solutions present opportunities to diversify traffic flow and enhance the overall transportation system.

In conclusion, the autonomous transport system market requires increased transparency in pricing structures and technical maturity. Successful deployment depends on thorough demand studies and compatibility analyses with existing infrastructure. Innovative approaches like autonomous monorails or suspended Light Rail Transit (LRT) offer scalability, efficiency, and reduced environmental impact. Integrating predictive maintenance systems and advanced fleet management enhances reliability and service quality. Fostering transparency, embracing innovation, and implementing robust management systems are crucial for the successful integration of autonomous transport systems into urban environments.

本文对自主运输系统技术进行了深入分析和比较，重点关注后勤考虑因素、经济因素和总体结果。研究评估了各种方案，包括有人驾驶和自动驾驶地面车辆，以及基础设施解决方案，以深入了解其运营效率和经济可行性。在后勤比较方面，研究结果表明，自动驾驶和有人驾驶地面车辆在类似的后勤框架内运行，但具有不同的运营灵活性。自主系统在适应不断变化的乘客需求和通过传感器导航降低风险方面具有潜在优势。此外，部署自主基础设施解决方案在减少周期时间（与地面载人/自主车辆相比减少 43%）和提高技术速度（与地面载人/自主车辆相比提高 57%）方面显示出良好的效果，尤其是在基础设施紧张的环境中。由于缺乏定价数据，经济比较显示出在评估自主解决方案的成本效益方面存在挑战。虽然公共交通车辆的自动化会产生更高的资本支出（例如，自动驾驶公交车与有人驾驶公交车相比增加了 33%）和运营支出（例如，自动驾驶公交车与有人驾驶公交车相比增加了 60%），但自动驾驶系统具有连续运行和减少空闲时间等优点。总之，自主运输系统市场需要提高定价结构和技术成熟度的透明度。成功部署取决于全面的需求研究和与现有基础设施的兼容性分析。自主单轨或悬挂式轻轨（LRT）等创新方法具有可扩展性、高效性并可减少对环境的影响。整合预测性维护系统和先进的车队管理可提高可靠性和服务质量。提高透明度、拥抱创新和实施强大的管理系统对于将自主运输系统成功融入城市环境至关重要。

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引用次数: 0

Adoption of electric vehicles: An empirical study of consumers’ intentions 电动汽车的采用：消费者意向实证研究

Transport Economics and Management

Pub Date : 2024-11-04 DOI: 10.1016/j.team.2024.11.001

Apurva Pamidimukkala , Sharareh Kermanshachi , Jay Michael Rosenberger , Greg Hladik

Electric vehicles (EVs) are widely recognized within the transportation industry as highly promising green technology that mitigates carbon dioxide emissions and high energy usage. The public, however, has demonstrated great hesitancy in embracing EVs, and it’s important to learn why this is so. This study employed an integrative approach to personality traits, beliefs, and intentions to understand consumers’ willingness or unwillingness to adopt EVs. The first step in this endeavor was to develop and distribute a survey to gain insight into what potential consumers see as EVs’ major advantages and disadvantages. Structural equation modeling (SEM) was performed on the 743 responses that were collected from the survey, and the results show that personal innovativeness, usefulness, and ease of use positively impact individual’s intentions to acquire an EV; risk was shown to be the most negative influence. A mediation analysis indicated that a person’s level of innovativeness influences their decision about whether or not to adopt an EV. These findings will add to the body of research on sustainable mobility and will equip policymakers and marketers with a deeper understanding of the public’s perceptions of EVs that will enable them to design effective marketing tools that will result in increased sales.

电动汽车（EV）是交通行业公认的极具前景的绿色技术，可以减少二氧化碳排放，降低能源消耗。然而，公众在接受电动汽车方面却表现出极大的犹豫，了解其中的原因非常重要。本研究采用人格特质、信念和意向的综合方法来了解消费者是否愿意采用电动汽车。这项工作的第一步是编制和分发一份调查问卷，以深入了解潜在消费者眼中电动汽车的主要优势和劣势。结果表明，个人创新性、实用性和易用性对个人购买电动汽车的意愿有积极影响；而风险则是最消极的影响因素。中介分析表明，个人的创新水平会影响其是否采用电动汽车的决定。这些研究结果将为有关可持续交通的研究增添新的内容，并使政策制定者和营销人员更深入地了解公众对电动汽车的看法，从而设计出有效的营销工具，提高电动汽车的销量。

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引用次数: 0