Transport Economics and Management最新文献

{"title":"AI-driven scenarios for urban mobility: Quantifying the role of ODE models and scenario planning in reducing traffic congestion","authors":"Katsiaryna Bahamazava","doi":"10.1016/j.team.2025.02.002","DOIUrl":"10.1016/j.team.2025.02.002","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 92-103"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models","authors":"Jean Marie Vianney Ntamwiza ,&nbsp;Hannibal Bwire","doi":"10.1016/j.team.2025.02.003","DOIUrl":"10.1016/j.team.2025.02.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 78-91"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the acceptance of new public transportation pricing schemes: A case study of a developing country","authors":"Hamed Razeghifar ,&nbsp;Amirhossein Baghestani ,&nbsp;Mohammadhossein Abbasi ,&nbsp;Majid Asadi","doi":"10.1016/j.team.2025.02.004","DOIUrl":"10.1016/j.team.2025.02.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 70-77"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who are MaaS avoiders, wanderers or enthusiasts and what drives their intentions to adopt MaaS?","authors":"Zuoxian Gan ,&nbsp;Wentao Li","doi":"10.1016/j.team.2025.02.001","DOIUrl":"10.1016/j.team.2025.02.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 57-69"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dissecting climate adaptation strategies and planning of ports from different theoretical angles","authors":"Adolf K.Y. NG ,&nbsp;Mark C.P. POO ,&nbsp;Tianni WANG ,&nbsp;Austin BECKER ,&nbsp;Yui-yip LAU ,&nbsp;Tina Ziting XU ,&nbsp;Zaili YANG","doi":"10.1016/j.team.2025.01.001","DOIUrl":"10.1016/j.team.2025.01.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 46-56"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143311021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda","authors":"Jean Marie Vianney Ntamwiza ,&nbsp;Hannibal Bwire","doi":"10.1016/j.team.2024.12.001","DOIUrl":"10.1016/j.team.2024.12.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 35-45"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Air travel-induced COVID-19 risk and mortality across US counties","authors":"Jules Yimga","doi":"10.1016/j.team.2024.11.002","DOIUrl":"10.1016/j.team.2024.11.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 9-22"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Workforce development in the trucking industry: A comprehensive analysis of truck driver training entities","authors":"Sicheng Wang ,&nbsp;Elizabeth A. Mack ,&nbsp;Nidhi Kalani ,&nbsp;Chu-Hsiang Chang ,&nbsp;Shelia R. Cotten","doi":"10.1016/j.team.2024.11.003","DOIUrl":"10.1016/j.team.2024.11.003","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 23-34"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effectiveness of autonomous public transport systems in densely populated urban areas","authors":"Maksim Gusev,&nbsp;Shane Gilroy","doi":"10.1016/j.team.2024.11.004","DOIUrl":"10.1016/j.team.2024.11.004","url":null,"abstract":"<div><div>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.</div><div>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.</div><div>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.</div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"3 ","pages":"Pages 1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adoption of electric vehicles: An empirical study of consumers’ intentions","authors":"Apurva Pamidimukkala ,&nbsp;Sharareh Kermanshachi ,&nbsp;Jay Michael Rosenberger ,&nbsp;Greg Hladik","doi":"10.1016/j.team.2024.11.001","DOIUrl":"10.1016/j.team.2024.11.001","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":101258,"journal":{"name":"Transport Economics and Management","volume":"2 ","pages":"Pages 359-366"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}