Pub Date : 2026-01-20DOI: 10.1016/j.trip.2026.101868
Alexander Schuler , Travis Waller , Riccardo Bassoli , Nikola Bešinović
The demand for rail transport is expected to increase due to growing mobility needs and shifts in transport policies. This growth will lead to a higher capacity utilization of existing networks, making disturbances more frequent. To minimize their impact, dispatchers engage in real-time traffic management by adjusting the timetable to resolve the arising conflicts. Given the limited time to identify high-quality solutions, developing computer-based optimization methods to support dispatchers can significantly enhance rail transport efficiency. However, this is a challenging task with high computational complexity particularly in large and dense railway networks. Quantum computing offers a possible research direction, as it may offer advantages over classical computing due to its unique properties. This paper introduces the fundamentals of quantum computing and explores its challenges and opportunities for real-time railway traffic management. For this purpose, the paper reviews existing approaches that use quantum computing in railway operations and related fields, and highlights open challenges. Finally, several further research directions are identified, ranging from detailed benchmarking to new algorithm development.
{"title":"Quantum computing for railway rescheduling: Literature review and future directions","authors":"Alexander Schuler , Travis Waller , Riccardo Bassoli , Nikola Bešinović","doi":"10.1016/j.trip.2026.101868","DOIUrl":"10.1016/j.trip.2026.101868","url":null,"abstract":"<div><div>The demand for rail transport is expected to increase due to growing mobility needs and shifts in transport policies. This growth will lead to a higher capacity utilization of existing networks, making disturbances more frequent. To minimize their impact, dispatchers engage in real-time traffic management by adjusting the timetable to resolve the arising conflicts. Given the limited time to identify high-quality solutions, developing computer-based optimization methods to support dispatchers can significantly enhance rail transport efficiency. However, this is a challenging task with high computational complexity particularly in large and dense railway networks. Quantum computing offers a possible research direction, as it may offer advantages over classical computing due to its unique properties. This paper introduces the fundamentals of quantum computing and explores its challenges and opportunities for real-time railway traffic management. For this purpose, the paper reviews existing approaches that use quantum computing in railway operations and related fields, and highlights open challenges. Finally, several further research directions are identified, ranging from detailed benchmarking to new algorithm development.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101868"},"PeriodicalIF":3.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024106","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}
Road safety is a function of multiple factors that affect roads, and these factors compromise road safety. To Evaluate the Pedestrian Behavior Criteria related to road safety, the Analytic Hierarchy Process (AHP) approach with Triangular Fuzzy Sets (TFS) was used in this study to analyze the holistic model. In this study, pedestrian behavior criteria were studied using an analytical hierarchy process and Kendall’s correlation in a fuzzy environment. In this study, factors causing pedestrian behavior are weighted with triangular fuzzy numbers for pairwise comparisons (PCs). These weighted and fuzzy linguistic variables were used to evaluate these factors according to the work system. For level 1, the results showed that ‘psychological’ was the most significant factor affecting pedestrian safety for older and younger pedestrian groups. For Level 2, the results showed that ’Hurried” was the most significant factor affecting pedestrian safety for both groups. Kendall’s correlation test was conducted to check for similarity between the old and young groups. The results showed a high degree of similarity between the two groups for the main factors compared to the sub-factors. This study will help to identify the most significant criteria for pedestrian behavior for road safety.
{"title":"Evaluating pedestrian behavior factors related to road safety using analytic hierarchy process and Kendall’s correlation in a fuzzy environment","authors":"Sarbast Moslem , Danish Farooq , Francesco Pilla , Domokos Esztergár-Kiss , Asim Farooq , Rana Faisal Tufail , Beatriz Martinez-Pastor , Ulviyya Rzayeva","doi":"10.1016/j.trip.2026.101850","DOIUrl":"10.1016/j.trip.2026.101850","url":null,"abstract":"<div><div>Road safety is a function of multiple factors that affect roads, and these factors compromise road safety. To Evaluate the Pedestrian Behavior Criteria related to road safety, the Analytic Hierarchy Process (AHP) approach with Triangular Fuzzy Sets (TFS) was used in this study to analyze the holistic model. In this study, pedestrian behavior criteria were studied using an analytical hierarchy process and Kendall’s correlation in a fuzzy environment. In this study, factors causing pedestrian behavior are weighted with triangular fuzzy numbers for pairwise comparisons (PCs). These weighted and fuzzy linguistic variables were used to evaluate these factors according to the work system. For level 1, the results showed that ‘psychological’ was the most significant factor affecting pedestrian safety for older and younger pedestrian groups. For Level 2, the results showed that ’Hurried” was the most significant factor affecting pedestrian safety for both groups. Kendall’s correlation test was conducted to check for similarity between the old and young groups. The results showed a high degree of similarity between the two groups for the main factors compared to the sub-factors. This study will help to identify the most significant criteria for pedestrian behavior for road safety.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101850"},"PeriodicalIF":3.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024115","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 : 2026-01-19DOI: 10.1016/j.trip.2026.101862
Haruka Kato
Demand-responsive transport (DRT) is expected to be a transport mode that solves first- and last-mile mobility problems. This study aimed to investigate the potential association of multimodal transport (buses or railways) with DRT on daily walking steps. Additionally, the reasons were examined in terms of changes in the frequency of multimodal trips and the reasons for choosing multiple modes. Senboku-NT was analyzed as an example of a DRT operation area. This study employed a quasi-experimental approach integrating the propensity score matching and difference-in-differences analysis. As a result, this study revealed that DRT is associated with daily walking steps through multimodal transport, including buses. Specifically, the average increase in daily walking steps was 1,730.74 [95% CI: 130.52, 3,330.96] steps/day. The increase was shown to be significantly greater than that of DRT alone. In addition, the frequency of bus use increased significantly from before to during the DRT operation period. Moreover, few participants reported using multimodal transport combined with DRT for the purpose of improving their health. These results suggest that multimodal transport, combining buses and DRT, may be highly effective in promoting physical activity based on the population approach, by inducing more frequent public transport journeys regardless of users’ health intentions. In contrast, the multimodal transportation of railway and DRT did not significantly increase the number of daily walking steps and use frequency. Therefore, this study suggests that the potential synergistic associations of multimodal transport, including buses and DRTs, may be significantly greater for first- and last-mile mobility.
{"title":"Synergistic association between multimodal transport and demand-responsive transportation on daily walking steps: Quasi-experimental study in Senboku New-Town","authors":"Haruka Kato","doi":"10.1016/j.trip.2026.101862","DOIUrl":"10.1016/j.trip.2026.101862","url":null,"abstract":"<div><div>Demand-responsive transport (DRT) is expected to be a transport mode that solves first- and last-mile mobility problems. This study aimed to investigate the potential association of multimodal transport (buses or railways) with DRT on daily walking steps. Additionally, the reasons were examined in terms of changes in the frequency of multimodal trips and the reasons for choosing multiple modes. Senboku-NT was analyzed as an example of a DRT operation area. This study employed a quasi-experimental approach integrating the propensity score matching and difference-in-differences analysis. As a result, this study revealed that DRT is associated with daily walking steps through multimodal transport, including buses. Specifically, the average increase in daily walking steps was 1,730.74 [95% CI: 130.52, 3,330.96] steps/day. The increase was shown to be significantly greater than that of DRT alone. In addition, the frequency of bus use increased significantly from before to during the DRT operation period. Moreover, few participants reported using multimodal transport combined with DRT for the purpose of improving their health. These results suggest that multimodal transport, combining buses and DRT, may be highly effective in promoting physical activity based on the population approach, by inducing more frequent public transport journeys regardless of users’ health intentions. In contrast, the multimodal transportation of railway and DRT did not significantly increase the number of daily walking steps and use frequency. Therefore, this study suggests that the potential synergistic associations of multimodal transport, including buses and DRTs, may be significantly greater for first- and last-mile mobility.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101862"},"PeriodicalIF":3.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023528","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 : 2026-01-19DOI: 10.1016/j.trip.2026.101841
Taryn Waite , Siwa Msangi , Ying Zhang , Molly French , Nazar Kholod , Jae Edmonds , Stephanie T. Morris
We explore impacts of sea ice thinning and evolutions in the energy sector on future use of the Northern Sea Route (NSR) versus the Suez Canal Route (SCR), using a case study of shipping oil extracted from the offshore Russian Arctic to China. We combine an integrated human-Earth system model with a shipping cost model to incorporate impacts on both oil production and shipping costs under internally consistent scenarios. We find that the NSR could become cost-competitive with the SCR as sea ice thickness declines, especially in an RCP8.5 scenario, due to decreasing fuel and icebreaker escort costs. In a global energy evolution scenario consistent with RCP2.6, high emissions costs on the longer SCR may outweigh the costs associated with thicker sea ice on the NSR. Our novel framework provides integrated projections of NSR shipping traffic and associated emissions driven by potential Arctic offshore oil production and exports.
{"title":"Arctic shipping under global change: A case study of offshore oil exports","authors":"Taryn Waite , Siwa Msangi , Ying Zhang , Molly French , Nazar Kholod , Jae Edmonds , Stephanie T. Morris","doi":"10.1016/j.trip.2026.101841","DOIUrl":"10.1016/j.trip.2026.101841","url":null,"abstract":"<div><div>We explore impacts of sea ice thinning and evolutions in the energy sector on future use of the Northern Sea Route (NSR) versus the Suez Canal Route (SCR), using a case study of shipping oil extracted from the offshore Russian Arctic to China. We combine an integrated human-Earth system model with a shipping cost model to incorporate impacts on both oil production and shipping costs under internally consistent scenarios. We find that the NSR could become cost-competitive with the SCR as sea ice thickness declines, especially in an RCP8.5 scenario, due to decreasing fuel and icebreaker escort costs. In a global energy evolution scenario consistent with RCP2.6, high emissions costs on the longer SCR may outweigh the costs associated with thicker sea ice on the NSR. Our novel framework provides integrated projections of NSR shipping traffic and associated emissions driven by potential Arctic offshore oil production and exports.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101841"},"PeriodicalIF":3.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024112","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 : 2026-01-19DOI: 10.1016/j.trip.2026.101859
Alejandro Sánchez-Atondo, Leonel García, Manuel Gutiérrez, Alejandro Mungaray-Moctezuma, Marco Montoya-Alcaraz, Julio Calderón-Ramírez
Despite global trends to promote sustainable urban mobility, Public Transport (PT) services in most small and medium-sized cities of the Global South still operate in a non-integrated scheme that promotes unsustainable mobility and negatively affects quality of life. Generally, these cities lack data for detailed analysis, so transport planning based on modeling and simulation is difficult to implement. This study presents a methodological proposal for restructuring PT systems in such contexts, with the aim of improving accessibility for the population with the lowest quality of life, while acknowledging data and resource limitations. The proposal is structured as a four-phase workflow, whose core contribution is a three-module method for route restructuring: i) definition of trunk routes, ii) optimization of sinuosity, overlay, and route lengths, and iii) definition of frequency and stop points. The method relies on information obtainable from official sources or generated with minimal effort, making it applicable to other urban areas. The method was applied to the case of Mexicali, Mexico, and the resulting network was evaluated through modeling to assess its impacts on both all users and those with a lower quality of life. Results show that the restructured network reduces travel, walking, and waiting times, with greater benefits for users with a lower quality of life. Overall, the findings indicate that in Global South cities with non-integrated PT systems, the proposed method is feasible and can enhance system efficiency, benefiting all users, particularly those with a lower quality of life.
{"title":"Reorganization of public transport systems in Global South cities and its relation with quality of life: A case study of Mexicali, Mexico","authors":"Alejandro Sánchez-Atondo, Leonel García, Manuel Gutiérrez, Alejandro Mungaray-Moctezuma, Marco Montoya-Alcaraz, Julio Calderón-Ramírez","doi":"10.1016/j.trip.2026.101859","DOIUrl":"10.1016/j.trip.2026.101859","url":null,"abstract":"<div><div>Despite global trends to promote sustainable urban mobility, Public Transport (PT) services in most small and medium-sized cities of the Global South still operate in a non-integrated scheme that promotes unsustainable mobility and negatively affects quality of life. Generally, these cities lack data for detailed analysis, so transport planning based on modeling and simulation is difficult to implement. This study presents a methodological proposal for restructuring PT systems in such contexts, with the aim of improving accessibility for the population with the lowest quality of life, while acknowledging data and resource limitations. The proposal is structured as a four-phase workflow, whose core contribution is a three-module method for route restructuring: i) definition of trunk routes, ii) optimization of sinuosity, overlay, and route lengths, and iii) definition of frequency and stop points. The method relies on information obtainable from official sources or generated with minimal effort, making it applicable to other urban areas. The method was applied to the case of Mexicali, Mexico, and the resulting network was evaluated through modeling to assess its impacts on both all users and those with a lower quality of life. Results show that the restructured network reduces travel, walking, and waiting times, with greater benefits for users with a lower quality of life. Overall, the findings indicate that in Global South cities with non-integrated PT systems, the proposed method is feasible and can enhance system efficiency, benefiting all users, particularly those with a lower quality of life.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101859"},"PeriodicalIF":3.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023526","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}
As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate remaining travel time and reliability scores for route planning. DQN learns optimal charging policies by determining when to charge, how much to charge (partial charging at 25%, 50%, 75%, or 100% levels), and which route to take based on battery state, traffic patterns, and available stationary charging stations (SCSs) and mobile charging infrastructure—including Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). DQN training uses simulation-based learning from actual traffic patterns of the Washington, DC metropolitan region, allowing the agent to explore charging-route pairs and discover efficient solutions through trial and error. To accommodate heterogeneous user preferences, the system calculates multiple Pareto-optimal solutions that trade off travel time, charging cost, battery safety, and route reliability, enabling users to select alternatives that match their current priorities without specifying preference weights in advance.
{"title":"Adaptive electric vehicle routing and charging with deep reinforcement learning","authors":"Mandana Farhang Ghahfarokhi , Hyoshin Park , Venktesh Pandey , Gyugeun Yoon","doi":"10.1016/j.trip.2025.101795","DOIUrl":"10.1016/j.trip.2025.101795","url":null,"abstract":"<div><div>As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate remaining travel time and reliability scores for route planning. DQN learns optimal charging policies by determining when to charge, how much to charge (partial charging at 25%, 50%, 75%, or 100% levels), and which route to take based on battery state, traffic patterns, and available stationary charging stations (SCSs) and mobile charging infrastructure—including Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). DQN training uses simulation-based learning from actual traffic patterns of the Washington, DC metropolitan region, allowing the agent to explore charging-route pairs and discover efficient solutions through trial and error. To accommodate heterogeneous user preferences, the system calculates multiple Pareto-optimal solutions that trade off travel time, charging cost, battery safety, and route reliability, enabling users to select alternatives that match their current priorities without specifying preference weights in advance.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101795"},"PeriodicalIF":3.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024111","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 : 2026-01-17DOI: 10.1016/j.trip.2026.101842
Saba Ayub, Yuting Hou
This systematic review explores the complex relationships between the built environment, transport systems, and travel behaviours, synthesising findings from 62 studies screened from the Scopus database. The review highlights how factors like infrastructure quality, transportation network connectivity, and land use diversity influence travel patterns. Key findings show that high-density bicycle networks and mixed-use urban developments promote active transportation, though their effectiveness varies by socio-economic and cultural contexts. Emerging mobility innovations, such as electric bicycles and dockless bike-sharing, further complicate these dynamics. The review also underscores the importance of subjective factors like perceived safety and comfort, alongside objective built environment attributes. Public transit systems, particularly rail networks, are crucial for facilitating multimodal travel and fostering urban development, but challenges related to equity and accessibility persist. Future research should focus on adaptive strategies that integrate advanced technologies, localised planning, and inclusive policies to enhance urban mobility, sustainability, and equity.
{"title":"Impact of the built environment on urban mobility patterns and advanced transport dynamics: a systematic review","authors":"Saba Ayub, Yuting Hou","doi":"10.1016/j.trip.2026.101842","DOIUrl":"10.1016/j.trip.2026.101842","url":null,"abstract":"<div><div>This systematic review explores the complex relationships between the built environment, transport systems, and travel behaviours, synthesising findings from 62 studies screened from the Scopus database. The review highlights how factors like infrastructure quality, transportation network connectivity, and land use diversity influence travel patterns. Key findings show that high-density bicycle networks and mixed-use urban developments promote active transportation, though their effectiveness varies by socio-economic and cultural contexts. Emerging mobility innovations, such as electric bicycles and dockless bike-sharing, further complicate these dynamics. The review also underscores the importance of subjective factors like perceived safety and comfort, alongside objective built environment attributes. Public transit systems, particularly rail networks, are crucial for facilitating multimodal travel and fostering urban development, but challenges related to equity and accessibility persist. Future research should focus on adaptive strategies that integrate advanced technologies, localised planning, and inclusive policies to enhance urban mobility, sustainability, and equity.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101842"},"PeriodicalIF":3.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024110","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}
What are the latent thematic communities driving transport decarbonisation research across road, rail, maritime, and crossmodal domains? To answer this central question, this work proposes an integrated network model that fuses bibliographic coupling with Sentence-BERT semantic similarities, followed by a statistical analysis using state-of-the-art community detection algorithms. We assess partitions via four complementary metrics, modularity, silhouette, density, and NF1, and compare weighted versus unweighted graphs. Our analysis of mode-specific corpora uncovers distinct clusters: road research splits into urban last-mile automation and rural logistics; rail coalesces around a synchromodal scheduling hub with biofuel and corridor electrification offshoots; maritime divides into green fuels, autonomous safety, and shore-power streams; and crossmodal studies form overlapping triads of electrification, data analytics, and blockchain. Weighted edge integration uniformly enhances thematic clarity without altering algorithm rankings. These findings yield actionable algorithm-selection heuristics and tie each community to specific SDG targets, transforming aspirational goals into a concrete, community-by-community policy roadmap for green logistics.
{"title":"Unveiling emerging communities: a network approach on transport decarbonisation technology","authors":"Joao Tiago Aparicio , Elisabete Arsenio , Rui Henriques","doi":"10.1016/j.trip.2025.101833","DOIUrl":"10.1016/j.trip.2025.101833","url":null,"abstract":"<div><div>What are the latent thematic communities driving transport decarbonisation research across road, rail, maritime, and crossmodal domains? To answer this central question, this work proposes an integrated network model that fuses bibliographic coupling with Sentence-BERT semantic similarities, followed by a statistical analysis using state-of-the-art community detection algorithms. We assess partitions via four complementary metrics, modularity, silhouette, density, and NF1, and compare weighted versus unweighted graphs. Our analysis of mode-specific corpora uncovers distinct clusters: road research splits into urban last-mile automation and rural logistics; rail coalesces around a synchromodal scheduling hub with biofuel and corridor electrification offshoots; maritime divides into green fuels, autonomous safety, and shore-power streams; and crossmodal studies form overlapping triads of electrification, data analytics, and blockchain. Weighted edge integration uniformly enhances thematic clarity without altering algorithm rankings. These findings yield actionable algorithm-selection heuristics and tie each community to specific SDG targets, transforming aspirational goals into a concrete, community-by-community policy roadmap for green logistics.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101833"},"PeriodicalIF":3.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023527","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}
In highly densely populated cities within low-income cultural contexts, a unique relationship emerges among free-flow speed (FFS), traffic flow, and driver behavior. These underline the impact of diverse road conditions on these dynamics. This study presents a comprehensive examination of statistical analysis, which highlights numerous factors impacting FFS and traffic flow on the national highways of Bangladesh, along with the level of service (LOS). The statistical test includes multiple linear regression, one-way ANOVA, and other correlation tests to support the features and ranks. In this research, 59,425 vehicles were observed to determine the traffic flow and LOS. Additionally, 443 (N = 443) field survey data, including FFS and driver perception, were analyzed to identify the factors influencing FFS. Employing methodologies linking FFS to driver responses, the study found features such as vehicle type, road geometry, and accidents and incidents as the top influential factors for impacting FFS. However, features like heterogeneous conditions, seasonal variations, etc., did not support the predictions but showed similar influences on the process. The regression model stands out with an R-squared of 0.698, and the analysis also indicates LOS falls from the range of “D” to “E”, reflecting moderate to high levels of congestion at the Kanchpur segment. This research suggests valuable insights into the dynamic nature of traffic over time, considering driving experience and factors with individual effects.
{"title":"Traffic analysis and driver behavior study in highly populated city of low-income economies: A statistical approach","authors":"Md. Iktekar Alam Imran , Soleman Rakib , Mohammad Nyme Uddin , Anisuzzaman Khan , Syeda Rezwana Jannat , Emon Talukder","doi":"10.1016/j.trip.2026.101838","DOIUrl":"10.1016/j.trip.2026.101838","url":null,"abstract":"<div><div>In highly densely populated cities within low-income cultural contexts, a unique relationship emerges among free-flow speed (FFS), traffic flow, and driver behavior. These underline the impact of diverse road conditions on these dynamics. This study presents a comprehensive examination of statistical analysis, which highlights numerous factors impacting FFS and traffic flow on the national highways of Bangladesh, along with the level of service (LOS). The statistical test includes multiple linear regression, one-way ANOVA, and other correlation tests to support the features and ranks. In this research, 59,425 vehicles were observed to determine the traffic flow and LOS. Additionally, 443 (N = 443) field survey data, including FFS and driver perception, were analyzed to identify the factors influencing FFS. Employing methodologies linking FFS to driver responses, the study found features such as vehicle type, road geometry, and accidents and incidents as the top influential factors for impacting FFS. However, features like heterogeneous conditions, seasonal variations, etc., did not support the predictions but showed similar influences on the process. The regression model stands out with an R-squared of 0.698, and the analysis also indicates LOS falls from the range of “D” to “E”, reflecting moderate to high levels of congestion at the Kanchpur segment. This research suggests valuable insights into the dynamic nature of traffic over time, considering driving experience and factors with individual effects.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"36 ","pages":"Article 101838"},"PeriodicalIF":3.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980319","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 : 2026-01-14DOI: 10.1016/j.trip.2026.101845
Abrar Hazoor, Giuseppe Marinelli, Özlem Simsekoglu
The automotive industry has significantly evolved with the integration of advanced technologies in car infotainment systems, leading to more interactive driving experiences. However, the usage of in-car touchscreen (ICT) interfaces poses challenges such as increased driver distraction. This study investigates the impact of instructor-led pre-drive short (PDS) training on the usage of ICT. The drivers’ performance and behaviour were assessed when using unfamiliar ICT interfaces in a driving simulation. Sixty participants divided into two groups (i.e., trained and untrained) were included and asked to perform multiple secondary tasks on the ICT while driving along a motorway section. We also measured drivers’ longitudinal and lateral behaviour in distracted and undistracted conditions. Contrary to the hypothesis that PDS training sessions would enhance driving performance through improved ICT interface proficiency, the study results show that while such training did increase task completion rates, it was not sufficient to improve overall driving performance. No statistically significant improvements were observed in longitudinal or lateral driving performance compared to untrained drivers. Nevertheless, the study found that driver performance, particularly in lateral vehicle control, improved with increased familiarity and experience. These results suggest that more extensive or alternative training methods may be necessary to address the complexities of ICT interfaces in vehicles. Future research should focus on exploring diverse training approaches and their long-term effects across different in-vehicle infotainment systems.
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