Pub Date : 2025-03-01DOI: 10.1016/j.multra.2025.100195
Mason D. Gemar , Shidong Pan , Zhanmin Zhang , Randy B. Machemehl
Over the past decade, many municipalities have begun to replace incandescent lamps in their traffic signals with light emitting diode (LED) arrays. While LED technology boasts longer lifetimes and superior performance over their counterparts, there are many limitations involved in both testing and evaluating their reliability. As such, the methodology and subsequent analysis procedures used to evaluate the reliability of traffic signal lamps along a corridor is proposed. To accomplish this task, the progression of the reliability assessment from individual lamp to the entire signal light system for a corridor is demonstrated. Furthermore, due to the nature of these systems and reliability assessment strategies, it is suggested that fuzzy, or more specifically, profust reliability theory could be applied to effectively analyze LED arrays, as well as corridor-wide signal light systems. Preliminary case study results, coupled with field observations of partially burned-out LED arrays, support this hypothesis.
{"title":"Fuzzy reliability theory analysis of traffic signal lamp performance","authors":"Mason D. Gemar , Shidong Pan , Zhanmin Zhang , Randy B. Machemehl","doi":"10.1016/j.multra.2025.100195","DOIUrl":"10.1016/j.multra.2025.100195","url":null,"abstract":"<div><div>Over the past decade, many municipalities have begun to replace incandescent lamps in their traffic signals with light emitting diode (LED) arrays. While LED technology boasts longer lifetimes and superior performance over their counterparts, there are many limitations involved in both testing and evaluating their reliability. As such, the methodology and subsequent analysis procedures used to evaluate the reliability of traffic signal lamps along a corridor is proposed. To accomplish this task, the progression of the reliability assessment from individual lamp to the entire signal light system for a corridor is demonstrated. Furthermore, due to the nature of these systems and reliability assessment strategies, it is suggested that fuzzy, or more specifically, profust reliability theory could be applied to effectively analyze LED arrays, as well as corridor-wide signal light systems. Preliminary case study results, coupled with field observations of partially burned-out LED arrays, support this hypothesis.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509645","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":"2025-02-25","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 : 2025-02-20DOI: 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":"2025-02-20","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}
Pub 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":"2025-02-20","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}
Pub Date : 2025-02-13DOI: 10.1016/j.multra.2025.100208
Lalit Swami, Mokaddes Ali Ahmed, Suprava Jena
As shared mobility options like ridesourcing services continue to reshape urban transportation systems globally, their impact on airport ground access has become increasingly significant. This study investigates the changing dynamics of airport access at Lokpriya Gopinath Bordoloi International Airport (LGBI) in Guwahati, India, amidst the growing presence of ridesourcing services. A total of 700 air passengers were surveyed using a random sampling technique over 15 consecutive days, providing comprehensive data for the analysis. A multinomial logit (MNL) model was employed to examine factors influencing mode choice, considering variables such as age, residential status, group size, car ownership, luggage, safety, and convenience. The model explains 48.2 % to 57.2 % of the variation in mode choice. The results reveal that younger passengers (aged 21–30) are 2.14 times more likely to choose ridesourcing services. Additionally, visitors are significantly more inclined to use ridesourcing services compared to locals, with an odds ratio of 2.56. While passengers with car ownership are 5.43 times more likely to prefer private vehicles. The study underscores the growing significance of ridesourcing services in airport ground access and highlights the need for transportation planning and policymaking to adapt to these evolving trends.
{"title":"The evolving dynamics of airport ground access: A multinomial logit analysis of mode choice at Guwahati Airport, India","authors":"Lalit Swami, Mokaddes Ali Ahmed, Suprava Jena","doi":"10.1016/j.multra.2025.100208","DOIUrl":"10.1016/j.multra.2025.100208","url":null,"abstract":"<div><div>As shared mobility options like ridesourcing services continue to reshape urban transportation systems globally, their impact on airport ground access has become increasingly significant. This study investigates the changing dynamics of airport access at Lokpriya Gopinath Bordoloi International Airport (LGBI) in Guwahati, India, amidst the growing presence of ridesourcing services. A total of 700 air passengers were surveyed using a random sampling technique over 15 consecutive days, providing comprehensive data for the analysis. A multinomial logit (MNL) model was employed to examine factors influencing mode choice, considering variables such as age, residential status, group size, car ownership, luggage, safety, and convenience. The model explains 48.2 % to 57.2 % of the variation in mode choice. The results reveal that younger passengers (aged 21–30) are 2.14 times more likely to choose ridesourcing services. Additionally, visitors are significantly more inclined to use ridesourcing services compared to locals, with an odds ratio of 2.56. While passengers with car ownership are 5.43 times more likely to prefer private vehicles. The study underscores the growing significance of ridesourcing services in airport ground access and highlights the need for transportation planning and policymaking to adapt to these evolving trends.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527555","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 : 2025-02-12DOI: 10.1016/j.multra.2025.100207
Yuyan Annie Pan , Fuliang Li , Anran Li , Zhiqiang Niu , Zhen Liu
Accurate traffic flow forecasting at urban intersections is critical for optimizing transportation infrastructure and reducing congestion. This manuscript introduces a novel framework, the Physics-Guided Spatio-Temporal Graph Neural Network (PG-STGNN), specifically designed for traffic flow prediction. By integrating the principles of traffic flow physics with advanced spatio-temporal graph neural network algorithms, the framework captures complex spatio-temporal dependencies in traffic networks. PG-STGNN adopts a stepwise approach, addressing key performance metrics like queue formation and signal timing complexities at intersections. To validate its effectiveness, the model was applied to real-world traffic data from the Yizhuang District of Beijing. Compared to traditional models such as ARIMA, KNN, and Random Forest, PG-STGNN significantly improves prediction accuracy, achieving MAPE reductions of 19.9 %, 18.6 %, 6.1 %, 20.7 %, 5.0 %, 1.8 %, and 1.1 % against KNN, ARIMA, RF, BP, T-GCN, STGCN, and ST-ED-RMGC, respectively. With the lowest MAPE (9.452 %), MAE (2.485), and RMSE (4.364), PG-STGNN demonstrates superior prediction performance. These results underscore its potential to provide reliable short-term traffic forecasts, offering essential insights for the strategic planning and management of urban intelligent transportation systems.
{"title":"Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms","authors":"Yuyan Annie Pan , Fuliang Li , Anran Li , Zhiqiang Niu , Zhen Liu","doi":"10.1016/j.multra.2025.100207","DOIUrl":"10.1016/j.multra.2025.100207","url":null,"abstract":"<div><div>Accurate traffic flow forecasting at urban intersections is critical for optimizing transportation infrastructure and reducing congestion. This manuscript introduces a novel framework, the Physics-Guided Spatio-Temporal Graph Neural Network (PG-STGNN), specifically designed for traffic flow prediction. By integrating the principles of traffic flow physics with advanced spatio-temporal graph neural network algorithms, the framework captures complex spatio-temporal dependencies in traffic networks. PG-STGNN adopts a stepwise approach, addressing key performance metrics like queue formation and signal timing complexities at intersections. To validate its effectiveness, the model was applied to real-world traffic data from the Yizhuang District of Beijing. Compared to traditional models such as ARIMA, KNN, and Random Forest, PG-STGNN significantly improves prediction accuracy, achieving MAPE reductions of 19.9 %, 18.6 %, 6.1 %, 20.7 %, 5.0 %, 1.8 %, and 1.1 % against KNN, ARIMA, RF, BP, T-GCN, STGCN, and ST-ED-RMGC, respectively. With the lowest MAPE (9.452 %), MAE (2.485), and RMSE (4.364), PG-STGNN demonstrates superior prediction performance. These results underscore its potential to provide reliable short-term traffic forecasts, offering essential insights for the strategic planning and management of urban intelligent transportation systems.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479107","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 : 2025-02-12DOI: 10.1016/j.multra.2025.100206
Sixuan Xu , Xinbo Xie , Chen Wang , Junyi Yan
Among the factors related to traffic crash risk, the speed characteristics are crucial. Most studies on the safety effect of speed characteristics focused on highways and rural roads, whereas the investigations on urban roads are not comprehensive. Urban arterials operate at higher speeds during off-peak hours, which may possibly lead to more serious crashes. Hence, this study focuses on the correlation between speed characteristics and serious crash occurrence (i.e., injuries and fatalities) on urban arterials during off-peak hours, while considering the interaction between speed and road/traffic characteristics. The spatial autocorrelation and intrinsic correlation of injury and fatal crashes are analyzed by using multivariate conditional autoregressive model (MVCAR) from 12 urban arterials in a district in Ningbo, China. Research findings include: (1) speed characteristics, including the percentage of speeding vehicles, mean speed, speed standard deviation, speed skewness, were found as significant and the inclusion of interaction terms of speed characteristics improved the model fit; (2) the interaction terms of percentage of speeding vehicles with the presence of median and access density, speed skewness with access density showed significant effects; (3) the interaction term of mean speed and access density are positively correlated with crash risk; (4) Speed standard deviation is positively correlated with crash risk. The findings can provide guidance for improving urban speed management and safety.
{"title":"On the safety effects of off-peak hour speed characteristics of urban arterials","authors":"Sixuan Xu , Xinbo Xie , Chen Wang , Junyi Yan","doi":"10.1016/j.multra.2025.100206","DOIUrl":"10.1016/j.multra.2025.100206","url":null,"abstract":"<div><div>Among the factors related to traffic crash risk, the speed characteristics are crucial. Most studies on the safety effect of speed characteristics focused on highways and rural roads, whereas the investigations on urban roads are not comprehensive. Urban arterials operate at higher speeds during off-peak hours, which may possibly lead to more serious crashes. Hence, this study focuses on the correlation between speed characteristics and serious crash occurrence (i.e., injuries and fatalities) on urban arterials during off-peak hours, while considering the interaction between speed and road/traffic characteristics. The spatial autocorrelation and intrinsic correlation of injury and fatal crashes are analyzed by using multivariate conditional autoregressive model (MVCAR) from 12 urban arterials in a district in Ningbo, China. Research findings include: (1) speed characteristics, including the percentage of speeding vehicles, mean speed, speed standard deviation, speed skewness, were found as significant and the inclusion of interaction terms of speed characteristics improved the model fit; (2) the interaction terms of percentage of speeding vehicles with the presence of median and access density, speed skewness with access density showed significant effects; (3) the interaction term of mean speed and access density are positively correlated with crash risk; (4) Speed standard deviation is positively correlated with crash risk. The findings can provide guidance for improving urban speed management and safety.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552975","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 : 2025-02-11DOI: 10.1016/j.multra.2025.100205
Graham Currie , Alexa Delbosc , Ryan Cox , Mahesha Jayawardhena , James Reynolds
This paper explores how public transport and shared e-scooter travel interact. Trip end travel patterns of shared e-scooter users are explored in relation to Public Transport service levels. An index measuring transit service level is developed. This is compared to spatial and temporal patterns of e-scooter trip ends to explore the hypothesis that e-scooter use is stronger in areas where inner area transit offers a poorer quality service i.e. are e-scooters acting as a ‘gap filler’ to transit providing first-last mile access to transit?
Analysis methodologies including comparative spatial and temporal mapping of service level and trip end concentrations supported by statistical tests. A novel approach is also adopted to compare PT service level at each e-scooter trip end which identifies potential first-last mile and gap filling e-scooter trips from a large trip end database.
Results show e-scooter trip ends are concentrated in areas and at times when transit service levels are highest. This suggests that shared e-scooters may be competing with transit service rather than filling service gaps. We therefore conclude that the hypothesis that e-scooters act as a ‘gap filler’ for areas of low transit use is not supported.
Nevertheless, we have found limited and specific evidence of times and areas where ‘gap filling’ and first-last mile trips are apparent. Night time, early morning and weekend e-scooter travel volume is high when transit service levels are low. We also found limited evidence of spatial gaps in transit where first-last mile rail access was occurring and some evidence that rail-linked e-scooter travel was from lower service level trip ends and that these patterns increased with e-scooter trip distance.
{"title":"Exploring shared e-scooter trip patterns and links to public transport service level","authors":"Graham Currie , Alexa Delbosc , Ryan Cox , Mahesha Jayawardhena , James Reynolds","doi":"10.1016/j.multra.2025.100205","DOIUrl":"10.1016/j.multra.2025.100205","url":null,"abstract":"<div><div>This paper explores how public transport and shared e-scooter travel interact. Trip end travel patterns of shared e-scooter users are explored in relation to Public Transport service levels. An index measuring transit service level is developed. This is compared to spatial and temporal patterns of e-scooter trip ends to explore the hypothesis that e-scooter use is stronger in areas where inner area transit offers a poorer quality service i.e. are e-scooters acting as a ‘gap filler’ to transit providing first-last mile access to transit?</div><div>Analysis methodologies including comparative spatial and temporal mapping of service level and trip end concentrations supported by statistical tests. A novel approach is also adopted to compare PT service level at each e-scooter trip end which identifies potential first-last mile and gap filling e-scooter trips from a large trip end database.</div><div>Results show e-scooter trip ends are concentrated in areas and at times when transit service levels are highest. This suggests that shared e-scooters may be competing with transit service rather than filling service gaps. We therefore conclude that the hypothesis that e-scooters act as a ‘gap filler’ for areas of low transit use is not supported.</div><div>Nevertheless, we have found limited and specific evidence of times and areas where ‘gap filling’ and first-last mile trips are apparent. Night time, early morning and weekend e-scooter travel volume is high when transit service levels are low. We also found limited evidence of spatial gaps in transit where first-last mile rail access was occurring and some evidence that rail-linked e-scooter travel was from lower service level trip ends and that these patterns increased with e-scooter trip distance.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444355","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 : 2025-01-29DOI: 10.1016/j.multra.2025.100204
Sofia Suárez , Eloisa Macedo , Gennaro Ciccarelli , Jorge M. Bandeira
Mobility-as-a-Service (MaaS) is viewed as a prospective approach to encourage sustainable mobility. To ensure the effectiveness of MaaS subscription plans, continuous feedback and communication with users are crucial. The objective of this study is to develop a methodology for designing region-focused MaaS bundles and assess their viability through end-user's feedback and its potential for increasing users’ uptake of more sustainable travel. To explore user willingness to adopt the suggested MaaS bundles and estimate net changes in carbon dioxide (CO2) and nitrogen oxide (NOx) emissions, stated preference surveys (SPS) were conducted in the Portuguese cities of Aveiro and Coimbra. Results suggest a preference for bundles offering unlimited travel on public transport and, due to the efficient public transport network in Coimbra, the willingness to use such bundles was higher than for Aveiro. In an optimistic scenario, average emission savings of 35 % for CO2 and 30 % for NOx emissions, specifically for the most frequent trips, were found. In a realistic scenario with values adjusted to revealed preferences, these reductions drop to 5 % for CO2 and 4 % for NOx. Overall, our research highlights the complexities associated with behavioural changes and underscores the importance of policies that consider the intricacies of human behaviour. Furthermore, the findings regarding the introduction of MaaS bundles emphasize the pivotal role of a robust PT system in driving changes in travel behaviour among the population, contributing to mitigating the negative effects of unsustainable, carbon-dependent travel choices.
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