Pub Date : 2025-12-12DOI: 10.1016/j.rtbm.2025.101576
Boyang Li , Xiaowen Sha , Yuhang Yang , Miao Su
The perception of traffic accidents is crucial for improving road safety. However, existing studies have limitations, including fragmented analysis of influencing factors, weak generalization of perception models, and the lack of a specific review framework in this field. This study proposes a traffic meta-analysis method to systematically review and quantify existing research on traffic accident perception, and ultimately identify which influencing factors and model structures can enhance the accuracy of traffic accident perception. Methodologically, traffic meta-analysis follows four key steps. First, it screens literature based on inclusion and exclusion criteria. Second, it scores the literature using literature quality assessment criteria. Third, it calculates the percentage improvement (enhancement rate) of the models proposed in the literature over the baseline in terms of accuracy. Finally, it evaluates the role of model structures and influencing factors in the literature by considering the weighted enhancement rate of literature scores, thereby comparing the performance of different perception models. This study constructs a dedicated analytical framework for traffic accident perception models and provides practical guidance for the application of artificial intelligence models in the field of traffic safety.
{"title":"A review of traffic accident perception models considering multiple influencing factors based on analysis framework optimization","authors":"Boyang Li , Xiaowen Sha , Yuhang Yang , Miao Su","doi":"10.1016/j.rtbm.2025.101576","DOIUrl":"10.1016/j.rtbm.2025.101576","url":null,"abstract":"<div><div>The perception of traffic accidents is crucial for improving road safety. However, existing studies have limitations, including fragmented analysis of influencing factors, weak generalization of perception models, and the lack of a specific review framework in this field. This study proposes a traffic meta-analysis method to systematically review and quantify existing research on traffic accident perception, and ultimately identify which influencing factors and model structures can enhance the accuracy of traffic accident perception. Methodologically, traffic meta-analysis follows four key steps. First, it screens literature based on inclusion and exclusion criteria. Second, it scores the literature using literature quality assessment criteria. Third, it calculates the percentage improvement (enhancement rate) of the models proposed in the literature over the baseline in terms of accuracy. Finally, it evaluates the role of model structures and influencing factors in the literature by considering the weighted enhancement rate of literature scores, thereby comparing the performance of different perception models. This study constructs a dedicated analytical framework for traffic accident perception models and provides practical guidance for the application of artificial intelligence models in the field of traffic safety.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"65 ","pages":"Article 101576"},"PeriodicalIF":4.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1016/j.rtbm.2025.101577
Marios Giouroukelis, Eleni Mantouka, Eleni I. Vlahogianni
The study presents an integrated, data-driven Decision Support Tool designed to facilitate consensus-building among multiple stakeholders in traffic management. It moves beyond conventional preference-fusion techniques by offering modular components that can simulate stakeholder opinion interactions prior to formal participation and support the decision-making phases thereafter. The framework explicitly models the steps from initial opinion collection and network construction (via Bayesian Networks) to determination of a shared consensus, incorporating Opinion Dynamics and Consensus Reaching Process models. The common issues of opinion inconsistency and multitude are addressed using a linear optimization and a fuzzy c-means clustering algorithm, respectively. An application of the methodology on a multi-stakeholder traffic management, namely the synchronization of a demand responsive transport (DRT) system to the backbone of the PT network, is presented, using opinion and interaction data elicited from multiple decision-makers from Athens (GR), Lisbon (PT), Manchester (UK) and Rennes (FR), using a structured questionnaire survey. Results indicate that network efficiency is the primary concern for DRT synchronization strategies, with recommendations emphasizing the importance of modelling stakeholder conflicts, coalition formation, and minority influence in consensus building.
{"title":"Achieving stakeholder consensus in transport: An integrated modelling approach","authors":"Marios Giouroukelis, Eleni Mantouka, Eleni I. Vlahogianni","doi":"10.1016/j.rtbm.2025.101577","DOIUrl":"10.1016/j.rtbm.2025.101577","url":null,"abstract":"<div><div>The study presents an integrated, data-driven Decision Support Tool designed to facilitate consensus-building among multiple stakeholders in traffic management. It moves beyond conventional preference-fusion techniques by offering modular components that can simulate stakeholder opinion interactions prior to formal participation and support the decision-making phases thereafter. The framework explicitly models the steps from initial opinion collection and network construction (via Bayesian Networks) to determination of a shared consensus, incorporating Opinion Dynamics and Consensus Reaching Process models. The common issues of opinion inconsistency and multitude are addressed using a linear optimization and a fuzzy c-means clustering algorithm, respectively. An application of the methodology on a multi-stakeholder traffic management, namely the synchronization of a demand responsive transport (DRT) system to the backbone of the PT network, is presented, using opinion and interaction data elicited from multiple decision-makers from Athens (GR), Lisbon (PT), Manchester (UK) and Rennes (FR), using a structured questionnaire survey. Results indicate that network efficiency is the primary concern for DRT synchronization strategies, with recommendations emphasizing the importance of modelling stakeholder conflicts, coalition formation, and minority influence in consensus building.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"65 ","pages":"Article 101577"},"PeriodicalIF":4.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Green breakthrough innovation represents a novel model for achieving harmonious environmental and economic development. However, the green innovation process often faces insufficient funding due to high risks and extended investment return cycles. This study examines the intrinsic relationship between supply chain finance and green breakthrough innovation, and also explores the influence of knowledge spillover effects. By establishing a green innovation ecosystem that encourages investment in green innovation and reduces risk uncertainty, supply chain finance exerts a substantial positive consequence on firms' green breakthrough innovation. Dynamic environmental changes moderate green innovation on both technological and market dimensions, while the benefits of knowledge spillovers are amplified throughout the supply chain system, benefiting collaborative firms in the supply chain. By integrating supply chain finance with a current sustainability innovation framework, this study extends the economic implications of supply chain finance.
{"title":"How does supply chain finance impact green breakthrough innovation in the supply chain? Knowledge spillover effect based on core firms","authors":"Rui Huang , Guihong Hua , Zhisong Chen , Jianhui Peng","doi":"10.1016/j.rtbm.2025.101582","DOIUrl":"10.1016/j.rtbm.2025.101582","url":null,"abstract":"<div><div>Green breakthrough innovation represents a novel model for achieving harmonious environmental and economic development. However, the green innovation process often faces insufficient funding due to high risks and extended investment return cycles. This study examines the intrinsic relationship between supply chain finance and green breakthrough innovation, and also explores the influence of knowledge spillover effects. By establishing a green innovation ecosystem that encourages investment in green innovation and reduces risk uncertainty, supply chain finance exerts a substantial positive consequence on firms' green breakthrough innovation. Dynamic environmental changes moderate green innovation on both technological and market dimensions, while the benefits of knowledge spillovers are amplified throughout the supply chain system, benefiting collaborative firms in the supply chain. By integrating supply chain finance with a current sustainability innovation framework, this study extends the economic implications of supply chain finance.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"65 ","pages":"Article 101582"},"PeriodicalIF":4.4,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145712274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.rtbm.2025.101567
Jyun-Han Tsai , Cheng-Chieh Chen (Frank)
Demand responsive transport system (DRTS) is a special type of public transport service. The high operating cost of promoting DRTS in rural areas requires a shift from the government's long-term subsidy mechanism to a more sustainable approach. This study starts from the perspective of stakeholder collaboration and draws on the concept of timebanking to introduce social synergies outside the scope of the traditional economic system, which is not only conducive to sustainable development but also has the potential to strengthen rural transportation services in a fairer and more economical way than the status quo. A successful time banking mechanism into the DRTS system requires the simultaneous strengthening of institutional trust, social mobilization and policy stability. The three elements complement each other to build a mutual assistance service model for rural shared transportation. First, the introduction of timebanks needs to be based on the “principle of reciprocity” and establish a trust mechanism for the platform through institutional designs, such as identity authentication and two-way evaluation. Secondly, satisfying passengers' privacy and drivers' social interaction, while integrating community organizations and corporate CSR participation, play important supporting roles for the timebank and DRTS system. Finally, local political forces and the public's trust and support for DRTS directly affect the effectiveness of system and provide institutional guarantees for promoting the sustainability of social innovation policies in transportation.
{"title":"Incorporating stakeholder perspectives on timebanking to develop a sustainable demand responsive transportation system for rural areas","authors":"Jyun-Han Tsai , Cheng-Chieh Chen (Frank)","doi":"10.1016/j.rtbm.2025.101567","DOIUrl":"10.1016/j.rtbm.2025.101567","url":null,"abstract":"<div><div>Demand responsive transport system (DRTS) is a special type of public transport service. The high operating cost of promoting DRTS in rural areas requires a shift from the government's long-term subsidy mechanism to a more sustainable approach. This study starts from the perspective of stakeholder collaboration and draws on the concept of timebanking to introduce social synergies outside the scope of the traditional economic system, which is not only conducive to sustainable development but also has the potential to strengthen rural transportation services in a fairer and more economical way than the status quo. A successful time banking mechanism into the DRTS system requires the simultaneous strengthening of institutional trust, social mobilization and policy stability. The three elements complement each other to build a mutual assistance service model for rural shared transportation. First, the introduction of timebanks needs to be based on the “principle of reciprocity” and establish a trust mechanism for the platform through institutional designs, such as identity authentication and two-way evaluation. Secondly, satisfying passengers' privacy and drivers' social interaction, while integrating community organizations and corporate CSR participation, play important supporting roles for the timebank and DRTS system. Finally, local political forces and the public's trust and support for DRTS directly affect the effectiveness of system and provide institutional guarantees for promoting the sustainability of social innovation policies in transportation.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101567"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-02DOI: 10.1016/j.rtbm.2025.101565
Vincenza Torrisi , Giovanni Calabrò , Thamires De Souza Oliveira , Giuseppe Inturri , Salvatore Cavalieri , Matteo Ignaccolo
Monitoring and predicting traffic conditions is a crucial task for transportation agencies. Recent technological advances and the rise of big data have enabled real-time, high-frequency traffic data collection through Floating Car Data (FCD), which offers broader coverage and lower costs compared to traditional methods like fixed sensors. However, FCD is limited as it represents only a sample of users with heterogeneous market shares and, in some cases, lacks vehicle classification information.
This study aims to assess the reliability of FCD through a comparative analysis using fixed radar sensors as a ground truth. The analysed variables include vehicle counts to measure FCD Penetration Rates (PRs) as a performance metric and vehicle speeds to assess possible bias phenomena. Additionally, we developed a PR prediction model, identifying the most influential variables through feature engineering and assessing the model's accuracy with Symmetric Mean Absolute Percentage Error (SMAPE). The case study focuses on the city of Catania, Italy, with sensor data obtained from a traffic monitoring system consisting of several counting sections installed along a cordon surrounding the urban area, while FCD were extracted from TomTom portal. Results show spatial and temporal variability in FCD coverage, particularly low PRs at night, and an underestimation of speeds by FCD. The developed predictive model uses widely available FCD data to estimate PRs, helping identify FCD's opportunities and limitations for a more comprehensive understanding of road network performance. Future research will extend the analysis period and integrate more data sources to enhance traffic prediction accuracy and reliability.
{"title":"Exploring the reliability of Floating Car Data (FCD) through penetration rate prediction in urban contexts","authors":"Vincenza Torrisi , Giovanni Calabrò , Thamires De Souza Oliveira , Giuseppe Inturri , Salvatore Cavalieri , Matteo Ignaccolo","doi":"10.1016/j.rtbm.2025.101565","DOIUrl":"10.1016/j.rtbm.2025.101565","url":null,"abstract":"<div><div>Monitoring and predicting traffic conditions is a crucial task for transportation agencies. Recent technological advances and the rise of big data have enabled real-time, high-frequency traffic data collection through Floating Car Data (FCD), which offers broader coverage and lower costs compared to traditional methods like fixed sensors. However, FCD is limited as it represents only a sample of users with heterogeneous market shares and, in some cases, lacks vehicle classification information.</div><div>This study aims to assess the reliability of FCD through a comparative analysis using fixed radar sensors as a ground truth. The analysed variables include vehicle counts to measure FCD Penetration Rates (PRs) as a performance metric and vehicle speeds to assess possible bias phenomena. Additionally, we developed a PR prediction model, identifying the most influential variables through feature engineering and assessing the model's accuracy with Symmetric Mean Absolute Percentage Error (SMAPE). The case study focuses on the city of Catania, Italy, with sensor data obtained from a traffic monitoring system consisting of several counting sections installed along a cordon surrounding the urban area, while FCD were extracted from TomTom portal. Results show spatial and temporal variability in FCD coverage, particularly low PRs at night, and an underestimation of speeds by FCD. The developed predictive model uses widely available FCD data to estimate PRs, helping identify FCD's opportunities and limitations for a more comprehensive understanding of road network performance. Future research will extend the analysis period and integrate more data sources to enhance traffic prediction accuracy and reliability.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101565"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-29DOI: 10.1016/j.rtbm.2025.101569
Zdeněk Tomeš , Vilém Pařil
Night trains in Europe have recently attracted attention as a potential sustainable alternative to air travel. However, their long-term viability remains uncertain. This paper focuses on the supply-side challenges that limit the broader adoption of night trains. Through interviews with operators in the Czech Republic and Slovakia, the paper identifies critical obstacles, including high investment costs, infrastructure bottlenecks, low profitability, operational risks, and cross-border complexities. These findings suggest that, despite their environmental benefits, night trains are likely to remain a niche market without significant public-policy support. To address this, measures such as reducing infrastructure charges, providing financial incentives for rolling stock investment, and prioritising night trains in capacity planning could be essential. However, without substantial and sustained public support, night trains may struggle to become a mainstream alternative to air travel. The findings offer valuable insights for policymakers seeking to promote more sustainable long-distance travel.
{"title":"Night trains – Sustainable alternative or niche market?","authors":"Zdeněk Tomeš , Vilém Pařil","doi":"10.1016/j.rtbm.2025.101569","DOIUrl":"10.1016/j.rtbm.2025.101569","url":null,"abstract":"<div><div>Night trains in Europe have recently attracted attention as a potential sustainable alternative to air travel. However, their long-term viability remains uncertain. This paper focuses on the supply-side challenges that limit the broader adoption of night trains. Through interviews with operators in the Czech Republic and Slovakia, the paper identifies critical obstacles, including high investment costs, infrastructure bottlenecks, low profitability, operational risks, and cross-border complexities. These findings suggest that, despite their environmental benefits, night trains are likely to remain a niche market without significant public-policy support. To address this, measures such as reducing infrastructure charges, providing financial incentives for rolling stock investment, and prioritising night trains in capacity planning could be essential. However, without substantial and sustained public support, night trains may struggle to become a mainstream alternative to air travel. The findings offer valuable insights for policymakers seeking to promote more sustainable long-distance travel.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101569"},"PeriodicalIF":4.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-29DOI: 10.1016/j.rtbm.2025.101563
Hongliang Ding , Caiyin Dong , Yang Cao , Tiantian Chen , Hyungchul Chung
High-speed rail (HSR) and low-cost carriers (LCCs) have emerged as increasingly prominent modes of intercity travel, particularly in rapidly urbanizing regions. Understanding the determinants of passengers' mode choices is essential for informing transportation policy, optimizing infrastructure investments, and enhancing the overall travel experience. This study employs a stated preference (SP) survey to investigate these determinants in two distinct urban contexts: Shanghai and Chengdu. A total of 494 valid responses were collected in Shanghai and 524 in Chengdu, capturing data on sociodemographic attributes, attitudinal dispositions, and travel-related characteristics. To analyze this dataset, we integrated machine learning techniques with the SHAP (Shapley Additive Explanations) algorithm, enabling both high predictive accuracy and interpretability. Three models—random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost)—were evaluated, with the RF model demonstrating superior performance. This model was subsequently used to interpret the relative importance of influencing factors. The findings reveal that factors associated with HSR travel, such as service frequency, ticket price, and in-vehicle travel time, play a vital role in passengers' mode choice. Regional contrasts also emerged: passengers in Shanghai exhibited a stronger preference for LCCs, while those in Chengdu were more inclined toward HSR, particularly among price-sensitive travelers. Interestingly, travelers who prioritize safety, comfort, and convenience tended to favor LCCs in both regions, suggesting a shifting perception of LCC quality and reliability. Finally, this study presents targeted recommendations for both government and operators, focusing on enhancing market transparency, maintaining fare stability, adopting region-specific strategies, and improving safety, comfort, and convenience. The findings offer theoretical insights into the mechanisms driving passengers' choices between HSR and LCCs, along with practical implications for policymaking and strategic optimization.
{"title":"Low-cost carriers vs. high-speed rail? Difference in passenger's travel preferences in Shanghai and Chengdu","authors":"Hongliang Ding , Caiyin Dong , Yang Cao , Tiantian Chen , Hyungchul Chung","doi":"10.1016/j.rtbm.2025.101563","DOIUrl":"10.1016/j.rtbm.2025.101563","url":null,"abstract":"<div><div>High-speed rail (HSR) and low-cost carriers (LCCs) have emerged as increasingly prominent modes of intercity travel, particularly in rapidly urbanizing regions. Understanding the determinants of passengers' mode choices is essential for informing transportation policy, optimizing infrastructure investments, and enhancing the overall travel experience. This study employs a stated preference (SP) survey to investigate these determinants in two distinct urban contexts: Shanghai and Chengdu. A total of 494 valid responses were collected in Shanghai and 524 in Chengdu, capturing data on sociodemographic attributes, attitudinal dispositions, and travel-related characteristics. To analyze this dataset, we integrated machine learning techniques with the SHAP (Shapley Additive Explanations) algorithm, enabling both high predictive accuracy and interpretability. Three models—random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost)—were evaluated, with the RF model demonstrating superior performance. This model was subsequently used to interpret the relative importance of influencing factors. The findings reveal that factors associated with HSR travel, such as service frequency, ticket price, and in-vehicle travel time, play a vital role in passengers' mode choice. Regional contrasts also emerged: passengers in Shanghai exhibited a stronger preference for LCCs, while those in Chengdu were more inclined toward HSR, particularly among price-sensitive travelers. Interestingly, travelers who prioritize safety, comfort, and convenience tended to favor LCCs in both regions, suggesting a shifting perception of LCC quality and reliability. Finally, this study presents targeted recommendations for both government and operators, focusing on enhancing market transparency, maintaining fare stability, adopting region-specific strategies, and improving safety, comfort, and convenience. The findings offer theoretical insights into the mechanisms driving passengers' choices between HSR and LCCs, along with practical implications for policymaking and strategic optimization.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101563"},"PeriodicalIF":4.4,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.rtbm.2025.101566
Miaojia Lu , Rui Liu , Gonçalo Homem de Almeida Correia , Kuldeep Kavta , Chengyuan Huang
With the rapid growth of instant delivery services in China, the number of couriers is rising due to low entry barriers such as minimal educational requirements, flexible hours, and competitive salaries. However, the industry faces challenges like excessive workloads and high accident rates, which could reduce couriers' job satisfaction. While the literature on couriers' job satisfaction is extensive, the application of holistic needs-based theories remains unexplored, particularly through advanced quantitative methods. This study operationalizes Maslow's Hierarchy of Needs Theory (MHNT) as a multi-dimensional construct and incorporates it into a Structural Equation Modeling (SEM) framework to examine its hierarchical impact on job satisfaction. Additionally, it explores the impact of physical health, occupational discrimination, and new technologies on couriers' job satisfaction. To test the framework and derive a nuanced understanding of factors influencing courier job satisfaction, data from 490 couriers in Shanghai, China, and nearby areas was collected. To account for differences in employment types, the survey data was split into full-time and part-time courier groups, with a multigroup analysis conducted using a structural equation model. The results show differing factors influencing job satisfaction. Part-time couriers are significantly affected by compensation and working environment, while full-time couriers are, besides compensation and working environment, also influenced by career development. These findings enhance the understanding of work conditions and motivators for couriers across different employment types within the instant delivery sector, offering key insights to enhance courier job satisfaction and promote sustainable development of this business.
{"title":"Examining couriers' job satisfaction in instant delivery services: A structural equation model with multi-group analysis based on Maslow's hierarchy of needs theory","authors":"Miaojia Lu , Rui Liu , Gonçalo Homem de Almeida Correia , Kuldeep Kavta , Chengyuan Huang","doi":"10.1016/j.rtbm.2025.101566","DOIUrl":"10.1016/j.rtbm.2025.101566","url":null,"abstract":"<div><div>With the rapid growth of instant delivery services in China, the number of couriers is rising due to low entry barriers such as minimal educational requirements, flexible hours, and competitive salaries. However, the industry faces challenges like excessive workloads and high accident rates, which could reduce couriers' job satisfaction. While the literature on couriers' job satisfaction is extensive, the application of holistic needs-based theories remains unexplored, particularly through advanced quantitative methods. This study operationalizes Maslow's Hierarchy of Needs Theory (MHNT) as a multi-dimensional construct and incorporates it into a Structural Equation Modeling (SEM) framework to examine its hierarchical impact on job satisfaction. Additionally, it explores the impact of physical health, occupational discrimination, and new technologies on couriers' job satisfaction. To test the framework and derive a nuanced understanding of factors influencing courier job satisfaction, data from 490 couriers in Shanghai, China, and nearby areas was collected. To account for differences in employment types, the survey data was split into full-time and part-time courier groups, with a multigroup analysis conducted using a structural equation model. The results show differing factors influencing job satisfaction. Part-time couriers are significantly affected by compensation and working environment, while full-time couriers are, besides compensation and working environment, also influenced by career development. These findings enhance the understanding of work conditions and motivators for couriers across different employment types within the instant delivery sector, offering key insights to enhance courier job satisfaction and promote sustainable development of this business.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101566"},"PeriodicalIF":4.4,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.rtbm.2025.101568
Han Gu, Yong Liu
Despite their potential to transform mobility, the large-scale adoption of autonomous vehicles (AVs) faces a critical hurdle: a lack of deep, dynamic understanding of public acceptance. Existing research, heavily reliant on surveys and hypothetical scenarios, often fails to capture the nuanced and evolving nature of public opinion. How can we truly listen to the public's unprompted voice? This study bridges this gap by turning to the real-world conversations of over 110,000 users on Chinese car forums and social media. Leveraging machine learning—including topic modelling and sentiment analysis—we move beyond static snapshots to reveal a compelling narrative: while public sentiment is increasingly positive, a significant chasm exists between high expectations for performance, price, and enjoyment and the current reality. By mapping these insights onto the UTAUT2 theoretical framework and employing Importance-Performance Analysis, we not only diagnose the core acceptance drivers but also provide a strategic action plan for industry and policymakers to close this expectation gap and accelerate the journey towards a driverless future.
{"title":"Public acceptance of autonomous vehicles: Fresh evidence from China","authors":"Han Gu, Yong Liu","doi":"10.1016/j.rtbm.2025.101568","DOIUrl":"10.1016/j.rtbm.2025.101568","url":null,"abstract":"<div><div>Despite their potential to transform mobility, the large-scale adoption of autonomous vehicles (AVs) faces a critical hurdle: a lack of deep, dynamic understanding of public acceptance. Existing research, heavily reliant on surveys and hypothetical scenarios, often fails to capture the nuanced and evolving nature of public opinion. How can we truly listen to the public's unprompted voice? This study bridges this gap by turning to the real-world conversations of over 110,000 users on Chinese car forums and social media. Leveraging machine learning—including topic modelling and sentiment analysis—we move beyond static snapshots to reveal a compelling narrative: while public sentiment is increasingly positive, a significant chasm exists between high expectations for performance, price, and enjoyment and the current reality. By mapping these insights onto the UTAUT2 theoretical framework and employing Importance-Performance Analysis, we not only diagnose the core acceptance drivers but also provide a strategic action plan for industry and policymakers to close this expectation gap and accelerate the journey towards a driverless future.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"64 ","pages":"Article 101568"},"PeriodicalIF":4.4,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-22DOI: 10.1016/j.rtbm.2025.101564
Zhipeng Peng , Hao Ji , Said M. Easa , Chenzhu Wang , Yonggang Wang , Yu Cao , Hanyi Yang , Xiazhi Zhang
The transportation of goods is essential for economic and social progress, but the presence of heavy-duty trucks (HDTs) can have adverse effects on urban traffic safety and living conditions. Addressing these impacts requires effective policies, yet current studies lack a comprehensive analysis of truck types, spatial and temporal distribution of freight activities, and influencing factors. To fill this gap, this study uses various urban data sources to examine the complex relationships and geographical variations among road density, freight hub accessibility, points of interest (POIs), demographic indicators, and freight activity in Xi'an, China. The study implements a spatial machine-learning (ML) framework and SHapley Additive exPlanations (SHAP). Notably, the study accounts for the unique characteristics of HDTs, considering differences in truck types and the effects of restricted and non-restricted periods. The key findings are as follows: (1) expressway density is pivotal in all scenarios, (2) the proximity of freight hubs significantly impacts freight activity, with variations based on truck type and period, (3) most variables exhibit nonlinear correlations with freight activity, showing less variation between restricted and non-restricted periods but significant variation across truck types, and (4) certain factors demonstrate distinct effects across periods, regions, and truck types. These findings can offer valuable theoretical insights for refining freight activity management in the transportation sector.
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