Pub Date : 2025-09-01Epub Date: 2025-03-07DOI: 10.1016/j.multra.2025.100221
Ittirit Mohamad
Seatbelt use significantly reduces the severity of injuries and fatalities in vehicular accidents. This study leverages the Random Forest algorithm to evaluate the impact of seatbelt usage on fatality probabilities in Thailand, with a novel focus on drivers who caused the accidents. The model demonstrated high accuracy, correctly identifying 95.10 % of non-fatal cases and 91.60 % of fatal cases, though some misclassifications were observed. A key contribution of this research is the identification of hidden risk factors influencing fatality rates, including temporal patterns that revealed a surge in fatalities after 17:00, with increased risks associated with non-seatbelt use during late evening and early morning hours. Younger drivers, particularly active at night, were found to exhibit higher rates of non-seatbelt usage and were more likely to be involved in severe accidents. These findings highlight the critical need for targeted seatbelt enforcement and safety interventions during high-risk periods, especially among younger drivers who are at fault in accidents.
{"title":"Quantifying the life-saving impact of seatbelt usage: A random forest analysis of unobserved heterogeneity and latent risk factors in vehicular fatalities","authors":"Ittirit Mohamad","doi":"10.1016/j.multra.2025.100221","DOIUrl":"10.1016/j.multra.2025.100221","url":null,"abstract":"<div><div>Seatbelt use significantly reduces the severity of injuries and fatalities in vehicular accidents. This study leverages the Random Forest algorithm to evaluate the impact of seatbelt usage on fatality probabilities in Thailand, with a novel focus on drivers who caused the accidents. The model demonstrated high accuracy, correctly identifying 95.10 % of non-fatal cases and 91.60 % of fatal cases, though some misclassifications were observed. A key contribution of this research is the identification of hidden risk factors influencing fatality rates, including temporal patterns that revealed a surge in fatalities after 17:00, with increased risks associated with non-seatbelt use during late evening and early morning hours. Younger drivers, particularly active at night, were found to exhibit higher rates of non-seatbelt usage and were more likely to be involved in severe accidents. These findings highlight the critical need for targeted seatbelt enforcement and safety interventions during high-risk periods, especially among younger drivers who are at fault in accidents.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 3","pages":"Article 100221"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705819","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}
Pub Date : 2025-09-01Epub Date: 2025-03-19DOI: 10.1016/j.multra.2025.100223
Shanqi Zhang , Maju Sadagopan , Xiao Qin
With over 50 % of the world's population now living in the cities and the number continuing to grow, cities are increasingly responsible for delivering services to people and businesses. Recent developments in volunteered geographic information (VGI) have provided new opportunities for improving city services by enabling citizens instantly and collectively share and report issues. However, the usefulness of VGI for such use has not been evaluated from a citizen perspective. This paper aims to bridge this research gap through a case study that innovatively uses geosocial media, as an example of VGI, for reporting accessibility issues to local governments and for providing customized navigation services to the general public. Particularly, a study website was developed that allows citizen participants to evaluate the usefulness of geosocial media for issue reporting and for pedestrian navigation. The results suggest that citizens consider geosocial media useful for helping them maneuver dynamic urban environments and for providing a convenient tool for issue reporting. These results suggest that citizens evaluate the usefulness of VGI differently from government officials and that VGI can facilitate government-citizen communication as well as the provision of customized public services, both of which are important to the development of smart cities.
{"title":"Evaluating the usefulness of VGI for citizen co-producing city services from citizen perspective: A case study of crowdsourcing pedestrian navigation","authors":"Shanqi Zhang , Maju Sadagopan , Xiao Qin","doi":"10.1016/j.multra.2025.100223","DOIUrl":"10.1016/j.multra.2025.100223","url":null,"abstract":"<div><div>With over 50 % of the world's population now living in the cities and the number continuing to grow, cities are increasingly responsible for delivering services to people and businesses. Recent developments in volunteered geographic information (VGI) have provided new opportunities for improving city services by enabling citizens instantly and collectively share and report issues. However, the usefulness of VGI for such use has not been evaluated from a citizen perspective. This paper aims to bridge this research gap through a case study that innovatively uses geosocial media, as an example of VGI, for reporting accessibility issues to local governments and for providing customized navigation services to the general public. Particularly, a study website was developed that allows citizen participants to evaluate the usefulness of geosocial media for issue reporting and for pedestrian navigation. The results suggest that citizens consider geosocial media useful for helping them maneuver dynamic urban environments and for providing a convenient tool for issue reporting. These results suggest that citizens evaluate the usefulness of VGI differently from government officials and that VGI can facilitate government-citizen communication as well as the provision of customized public services, both of which are important to the development of smart cities.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 3","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738695","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}
Pub Date : 2025-09-01Epub Date: 2025-03-05DOI: 10.1016/j.multra.2025.100211
Rui Chen , Jing Zhang , Hua Wang
Automated horizontal transportation in container terminals represents a significant advancement in the field of autonomous commercial vehicles. Traditionally, these systems rely on the individual intelligence of each vehicle, similar to autonomous passenger vehicles. However, recent uses of automated technology in select container terminals have demonstrated the benefits of integrating vehicles with a centralized Autonomous Fleet Management System (AFMS). This collaboration not only mitigates information silos but also enhances operational efficiency, safety, and fosters fleet cluster intelligence. This study examines current applications in automated container terminals, analyses practical scenarios, and identifies the essential characteristics of an effective AFMS to support horizontal transportation management. The insights from this comprehensive analysis assist port operators in designing and operating their systems and help scholars better understand and define research questions in this field.
{"title":"Autonomous fleet management system in smart ports: Practical design and analytical considerations","authors":"Rui Chen , Jing Zhang , Hua Wang","doi":"10.1016/j.multra.2025.100211","DOIUrl":"10.1016/j.multra.2025.100211","url":null,"abstract":"<div><div>Automated horizontal transportation in container terminals represents a significant advancement in the field of autonomous commercial vehicles. Traditionally, these systems rely on the individual intelligence of each vehicle, similar to autonomous passenger vehicles. However, recent uses of automated technology in select container terminals have demonstrated the benefits of integrating vehicles with a centralized Autonomous Fleet Management System (AFMS). This collaboration not only mitigates information silos but also enhances operational efficiency, safety, and fosters fleet cluster intelligence. This study examines current applications in automated container terminals, analyses practical scenarios, and identifies the essential characteristics of an effective AFMS to support horizontal transportation management. The insights from this comprehensive analysis assist port operators in designing and operating their systems and help scholars better understand and define research questions in this field.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 3","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600762","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}
Pub Date : 2025-09-01Epub Date: 2025-04-22DOI: 10.1016/j.multra.2025.100227
Yagnik M. Bhavsar , Mazad S. Zaveri , Mehul S. Raval , Shaheriar B. Zaveri
While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. We observed that around 48 % of the vehicles did not follow defensive driving practices. In our vehicle inter-class interaction analyses, we also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. Our methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on https://www.youtube.com/@YagnikBhavsar.
{"title":"Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout","authors":"Yagnik M. Bhavsar , Mazad S. Zaveri , Mehul S. Raval , Shaheriar B. Zaveri","doi":"10.1016/j.multra.2025.100227","DOIUrl":"10.1016/j.multra.2025.100227","url":null,"abstract":"<div><div>While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. We observed that around 48 % of the vehicles did not follow defensive driving practices. In our vehicle inter-class interaction analyses, we also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. Our methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on <span><span>https://www.youtube.com/@YagnikBhavsar</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 3","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-02-20DOI: 10.1016/j.multra.2025.100210
Rahul Tanwar, Pradeep Kumar Agarwal
This study explores the opportunities and challenges of advancing multimodal integration for sustainable urban mobility in India. With rapid urbanization and increasing motorization, Indian cities face issues of congestion, air pollution, and social inequity. Multimodal integration, the seamless integration of different transportation modes, is a promising approach to address these challenges. The study assesses the current state of urban mobility in India, examines the concepts and benefits of multimodal integration, and identifies key opportunities, including supportive policies, technological advancements, and public-private partnerships. It also discusses challenges such as institutional barriers, financial constraints, and the need for behavioral change. Case studies of successful initiatives in Delhi and Ahmedabad demonstrate the potential benefits of integrated transport systems. The study proposes recommendations for advancing multimodal integration, focusing on policy reforms, infrastructure development, capacity building, and stakeholder engagement. It concludes by summarizing key findings and identifying future research directions, emphasizing the need for further investigation into long-term impacts, innovative funding mechanisms, emerging technologies, comparative policy analysis, and social and behavioral aspects of sustainable urban mobility. This research contributes to the growing knowledge on multimodal integration and sustainable urban mobility in India, providing valuable insights for policymakers, urban planners, and transportation professionals working towards creating more sustainable, efficient, and inclusive cities.
{"title":"Multimodal integration in India: Opportunities, challenges, and strategies for sustainable urban mobility","authors":"Rahul Tanwar, Pradeep Kumar Agarwal","doi":"10.1016/j.multra.2025.100210","DOIUrl":"10.1016/j.multra.2025.100210","url":null,"abstract":"<div><div>This study explores the opportunities and challenges of advancing multimodal integration for sustainable urban mobility in India. With rapid urbanization and increasing motorization, Indian cities face issues of congestion, air pollution, and social inequity. Multimodal integration, the seamless integration of different transportation modes, is a promising approach to address these challenges. The study assesses the current state of urban mobility in India, examines the concepts and benefits of multimodal integration, and identifies key opportunities, including supportive policies, technological advancements, and public-private partnerships. It also discusses challenges such as institutional barriers, financial constraints, and the need for behavioral change. Case studies of successful initiatives in Delhi and Ahmedabad demonstrate the potential benefits of integrated transport systems. The study proposes recommendations for advancing multimodal integration, focusing on policy reforms, infrastructure development, capacity building, and stakeholder engagement. It concludes by summarizing key findings and identifying future research directions, emphasizing the need for further investigation into long-term impacts, innovative funding mechanisms, emerging technologies, comparative policy analysis, and social and behavioral aspects of sustainable urban mobility. This research contributes to the growing knowledge on multimodal integration and sustainable urban mobility in India, providing valuable insights for policymakers, urban planners, and transportation professionals working towards creating more sustainable, efficient, and inclusive cities.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579851","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}
Recognizing the importance of road safety modeling, the study explores Deep Neural Networks (DNN) with features like hidden layers, batch normalization, Rectified Linear Unit (ReLU) activation, and dropout to predict crash severity, interpreting decisions using SHapley Additive exPlanations (SHAP) for crashes involving older pedestrians. The objective is to understand features influencing crashes involving older pedestrians, including vehicle attributes, road and environmental conditions, and temporal parameters. The analysis focused on 1808 pedestrian crashes involving individuals aged 65 and over at intersections in Victoria, Australia. This dataset comprises 6.14% fatalities, 52.38% serious injuries, and 41.48% incidents with other injuries. The study evaluated three DNN models for crash severity prediction, with the two hidden layers DNN model excelling in precision metrics and achieving a perfect Area Under the Receiver Operating Characteristics curve for fatalities. Compared to XGBoost, the DNN models demonstrated superior performance in predicting severe outcomes. SHAP analysis on the two hidden layers DNN model highlighted key factors influencing crash severity, offering insights into the nuanced relationships between features and predictions. The analysis highlighted the significance of variables like Traffic Control, Vehicle Type, and Movement in predicting fatalities and serious injuries. This study emphasizes the importance of considering Road and Vehicle Types to understand their roles in accident severity and identify interventions to reduce risks. Neglecting these factors may lead to incomplete or biased conclusions about crash outcomes. This research provides valuable insights for improving road safety, highlighting the effectiveness of SHAP force plots, bars, beeswarm plots, and dependency plots in enhancing clarity and understanding of DNN model predictions. These tools help identify the impact of features on crash severity.
{"title":"Analyzing feature importance for older pedestrian crash severity: A comparative study of DNN models, emphasizing road and vehicle types with SHAP interpretation","authors":"Rocksana Akter , Susilawati Susilawati , Hamza Zubair , Wai Tong Chor","doi":"10.1016/j.multra.2025.100203","DOIUrl":"10.1016/j.multra.2025.100203","url":null,"abstract":"<div><div>Recognizing the importance of road safety modeling, the study explores Deep Neural Networks (DNN) with features like hidden layers, batch normalization, Rectified Linear Unit (ReLU) activation, and dropout to predict crash severity, interpreting decisions using SHapley Additive exPlanations (SHAP) for crashes involving older pedestrians. The objective is to understand features influencing crashes involving older pedestrians, including vehicle attributes, road and environmental conditions, and temporal parameters. The analysis focused on 1808 pedestrian crashes involving individuals aged 65 and over at intersections in Victoria, Australia. This dataset comprises 6.14% fatalities, 52.38% serious injuries, and 41.48% incidents with other injuries. The study evaluated three DNN models for crash severity prediction, with the two hidden layers DNN model excelling in precision metrics and achieving a perfect Area Under the Receiver Operating Characteristics curve for fatalities. Compared to XGBoost, the DNN models demonstrated superior performance in predicting severe outcomes. SHAP analysis on the two hidden layers DNN model highlighted key factors influencing crash severity, offering insights into the nuanced relationships between features and predictions. The analysis highlighted the significance of variables like Traffic Control, Vehicle Type, and Movement in predicting fatalities and serious injuries. This study emphasizes the importance of considering Road and Vehicle Types to understand their roles in accident severity and identify interventions to reduce risks. Neglecting these factors may lead to incomplete or biased conclusions about crash outcomes. This research provides valuable insights for improving road safety, highlighting the effectiveness of SHAP force plots, bars, beeswarm plots, and dependency plots in enhancing clarity and understanding of DNN model predictions. These tools help identify the impact of features on crash severity.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696674","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}
Pub Date : 2025-06-01Epub Date: 2025-01-23DOI: 10.1016/j.multra.2025.100202
Mustafa Gadah , Xuesong Zhou , Mohammad Abbasi , Vamshi Yellisetty
This paper introduces an innovative approach to enhancing active transportation analysis and decision support by addressing the notable research gap of integrating traffic flow analysis, spatio-temporal trajectory models, and an input-output (moving queue) diagram. We establish a unique four-stage method for assessing bike-vehicle traffic interaction on designated road links: 1) Given the input of volume, we convert it to speed and density using the fundamental diagram and Q-K curves under different congestion conditions. 2) We analyze vehicle trajectories and utilize an input-output (moving queue) diagram to calculate the total exposures between bikes and vehicles as a function of speed difference and the product of bike and vehicle volume, ensuring the balance equations for both vehicle and bike exposure individually. 3) Beginning at the moment a vehicle enters a shared facility, we apply an illustrative method to determine the duration of individual exposure time, adjusting Newell’s car-following model to accommodate for various phases of driver reactions, transitioning from anticipation to overtaking/yield phase. 4) We measure the overall impact of exposure on mobility and safety using a multimodal semi-dynamic traffic assignment that focuses on both delay and exposure-based utility across various facility types and development scenarios. Our research underscores that controlling the flow of bikes and vehicles is a pivotal factor in determining the relative bike exposure to risk, offering valuable insights for the future development of transportation models and safety improvement strategies using a case study from Gilbert, AZ.
{"title":"Traffic flow theory-based modeling of bike-vehicle interactions for enhanced safety and mobility","authors":"Mustafa Gadah , Xuesong Zhou , Mohammad Abbasi , Vamshi Yellisetty","doi":"10.1016/j.multra.2025.100202","DOIUrl":"10.1016/j.multra.2025.100202","url":null,"abstract":"<div><div>This paper introduces an innovative approach to enhancing active transportation analysis and decision support by addressing the notable research gap of integrating traffic flow analysis, spatio-temporal trajectory models, and an input-output (moving queue) diagram. We establish a unique four-stage method for assessing bike-vehicle traffic interaction on designated road links: 1) Given the input of volume, we convert it to speed and density using the fundamental diagram and Q-K curves under different congestion conditions. 2) We analyze vehicle trajectories and utilize an input-output (moving queue) diagram to calculate the total exposures between bikes and vehicles as a function of speed difference and the product of bike and vehicle volume, ensuring the balance equations for both vehicle and bike exposure individually. 3) Beginning at the moment a vehicle enters a shared facility, we apply an illustrative method to determine the duration of individual exposure time, adjusting Newell’s car-following model to accommodate for various phases of driver reactions, transitioning from anticipation to overtaking/yield phase. 4) We measure the overall impact of exposure on mobility and safety using a multimodal semi-dynamic traffic assignment that focuses on both delay and exposure-based utility across various facility types and development scenarios. Our research underscores that controlling the flow of bikes and vehicles is a pivotal factor in determining the relative bike exposure to risk, offering valuable insights for the future development of transportation models and safety improvement strategies using a case study from Gilbert, AZ.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715708","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}
Pub Date : 2025-06-01Epub Date: 2025-01-17DOI: 10.1016/j.multra.2025.100200
Sangen Hu , Zikang Huang , Ke Wang , Haiyuan Lin , Mingyang Pei
Flying cars, a symbol of Urban Air Mobility (UAM), signify a pivotal step in revolutionizing urban transportation and play a pivotal role in shaping future transport systems. To enhance travelers' willingness to accept flying cars and promote the widespread adoption of this novel transportation mode, this study develops a comprehensive model to explore key factors determining the public's acceptance of flying cars by integrating the Technology Acceptance Model, Risk Perception Theory, and Trust Theory. The validity of the model was confirmed through a rigorous structure equation modeling analysis, utilizing 553 sample data collected from a network questionnaire survey across a diverse demographic of the Chinese market. Results revealed significant associations between the intention to use flying cars and various factors, including attitudes towards usage, perceived usefulness, and personal innovativeness. Heterogeneity analysis further uncovered how demographic factors (such as age, gender, education, and possession of a driver's license) impacted perceptions and acceptance. As the study concludes, despite general optimism, public acceptance of flying cars is strongly influenced by factors such as cost, safety, and privacy concerns play crucial roles in public acceptance. The insights from this study provide valuable implications for manufacturers, policymakers, and marketers in strategizing the introduction and promotion of flying cars.
{"title":"Modeling the adoption of urban air mobility based on technology acceptance and risk perception theories: A case study on flying cars","authors":"Sangen Hu , Zikang Huang , Ke Wang , Haiyuan Lin , Mingyang Pei","doi":"10.1016/j.multra.2025.100200","DOIUrl":"10.1016/j.multra.2025.100200","url":null,"abstract":"<div><div>Flying cars, a symbol of Urban Air Mobility (UAM), signify a pivotal step in revolutionizing urban transportation and play a pivotal role in shaping future transport systems. To enhance travelers' willingness to accept flying cars and promote the widespread adoption of this novel transportation mode, this study develops a comprehensive model to explore key factors determining the public's acceptance of flying cars by integrating the Technology Acceptance Model, Risk Perception Theory, and Trust Theory. The validity of the model was confirmed through a rigorous structure equation modeling analysis, utilizing 553 sample data collected from a network questionnaire survey across a diverse demographic of the Chinese market. Results revealed significant associations between the intention to use flying cars and various factors, including attitudes towards usage, perceived usefulness, and personal innovativeness. Heterogeneity analysis further uncovered how demographic factors (such as age, gender, education, and possession of a driver's license) impacted perceptions and acceptance. As the study concludes, despite general optimism, public acceptance of flying cars is strongly influenced by factors such as cost, safety, and privacy concerns play crucial roles in public acceptance. The insights from this study provide valuable implications for manufacturers, policymakers, and marketers in strategizing the introduction and promotion of flying cars.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099537","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}
Pub Date : 2025-06-01Epub Date: 2025-02-20DOI: 10.1016/j.multra.2025.100209
Khaled Hamad , Emran Alotaibi , Waleed Zeiada , Ghazi Al-Khateeb , Saleh Abu Dabous , Maher Omar , Bharadwaj R.K. Mantha , Mohamed G. Arab , Tarek Merabtene
Efficient management of traffic incidents is a focal point in traffic management, with direct implications for road safety, congestion, and the environment. Traditional models have grappled with the unpredictability inherent in traffic incidents, often failing to capture the multifaceted influences on incident durations. This study introduces an application of Explainable Artificial Intelligence (XAI) using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to analyze the complexities of traffic incident duration prediction. Utilizing a substantial dataset of over 366,000 records from the Houston traffic management center, the study innovates in the domain of traffic analytics by predicting incident durations and revealing the contribution of each predictive variable. The XGBoost algorithm's ability to handle multi-dimensional datasets was employed to identify crucial variables affecting incident durations. Meanwhile, SHAP values offered transparency into the model's decision-making process, clarifying the roles of over fifty parameters. The study's results demonstrate that variables such as the involvement of heavy trucks and blockage of main lanes are essential in influencing incident durations, aligning with findings from previous literature. The SHAP analysis further revealed time-sensitive patterns, with time of day and day of the week exhibiting considerable effects on predictions. The beeswarm plots of SHAP provided a detailed visualization of these effects, differentiating between high and low values effects for each variable. The model's high accuracy, with a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 21.2 min, indicates the potential of XAI in enhancing traffic management systems.
{"title":"Explainable artificial intelligence visions on incident duration using eXtreme Gradient Boosting and SHapley Additive exPlanations","authors":"Khaled Hamad , Emran Alotaibi , Waleed Zeiada , Ghazi Al-Khateeb , Saleh Abu Dabous , Maher Omar , Bharadwaj R.K. Mantha , Mohamed G. Arab , Tarek Merabtene","doi":"10.1016/j.multra.2025.100209","DOIUrl":"10.1016/j.multra.2025.100209","url":null,"abstract":"<div><div>Efficient management of traffic incidents is a focal point in traffic management, with direct implications for road safety, congestion, and the environment. Traditional models have grappled with the unpredictability inherent in traffic incidents, often failing to capture the multifaceted influences on incident durations. This study introduces an application of Explainable Artificial Intelligence (XAI) using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to analyze the complexities of traffic incident duration prediction. Utilizing a substantial dataset of over 366,000 records from the Houston traffic management center, the study innovates in the domain of traffic analytics by predicting incident durations and revealing the contribution of each predictive variable. The XGBoost algorithm's ability to handle multi-dimensional datasets was employed to identify crucial variables affecting incident durations. Meanwhile, SHAP values offered transparency into the model's decision-making process, clarifying the roles of over fifty parameters. The study's results demonstrate that variables such as the involvement of heavy trucks and blockage of main lanes are essential in influencing incident durations, aligning with findings from previous literature. The SHAP analysis further revealed time-sensitive patterns, with time of day and day of the week exhibiting considerable effects on predictions. The beeswarm plots of SHAP provided a detailed visualization of these effects, differentiating between high and low values effects for each variable. The model's high accuracy, with a coefficient of determination (R<sup>2</sup>) of 0.72 and a root mean square error (RMSE) of 21.2 min, indicates the potential of XAI in enhancing traffic management systems.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621291","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}
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