Pub Date : 2024-09-18DOI: 10.1186/s12544-024-00677-7
Yihan Tian, Wei Zhu, Fangqing Song
Route choice modelling is a critical aspect of analysing urban rail transit (URT) networks and provides a foundation for URT planning and operation. Unlike in a free-flow road network, the consideration set for route choice decisions in a URT network does not depend purely on the physical connectivity of the network and decision makers’characteristics. Instead, it is also contingent on the train schedules. This paper delves into the evolution of research on route choices in URT networks, encompassing both probabilistic route choice modelling derived from utility maximisation theory and logit curve with physical connectivity, and retrospective route choice modelling based on travel time chaining along with comprehensive transport data. The former is noted for its conciseness, simplicity, and interpretability in real-world applications, even though the methodologies may not be cutting-edge. The latter incorporates dynamic temporal information to understand activities of passengers in URT networks. Enhancements of each genres are also examined. However, these improvements might not fully address the inherent limitations of models relating to a dependency on the quality of parameters, experience of experts, and calculation efficiency. In addition, novel research adopting contemporary data mining techniques instead of classical models are introduced. The historical development of research on URT network route choices underscores the importance of amalgamating independent information networks such as surveillance networks and social networks to establish a comprehensive multi-dimensional network. Such an approach integrates passenger attributes across networks, offering a multi-dimensional understanding of passengers’ route choice behaviours. Our review work aims to present not only a systematic conceptual framework for route choices in URT networks but also a novel path for transport researchers and practitioners to decipher the travel behaviours of passengers.
{"title":"Route choice modelling for an urban rail transit network: past, recent progress and future prospects","authors":"Yihan Tian, Wei Zhu, Fangqing Song","doi":"10.1186/s12544-024-00677-7","DOIUrl":"https://doi.org/10.1186/s12544-024-00677-7","url":null,"abstract":"Route choice modelling is a critical aspect of analysing urban rail transit (URT) networks and provides a foundation for URT planning and operation. Unlike in a free-flow road network, the consideration set for route choice decisions in a URT network does not depend purely on the physical connectivity of the network and decision makers’characteristics. Instead, it is also contingent on the train schedules. This paper delves into the evolution of research on route choices in URT networks, encompassing both probabilistic route choice modelling derived from utility maximisation theory and logit curve with physical connectivity, and retrospective route choice modelling based on travel time chaining along with comprehensive transport data. The former is noted for its conciseness, simplicity, and interpretability in real-world applications, even though the methodologies may not be cutting-edge. The latter incorporates dynamic temporal information to understand activities of passengers in URT networks. Enhancements of each genres are also examined. However, these improvements might not fully address the inherent limitations of models relating to a dependency on the quality of parameters, experience of experts, and calculation efficiency. In addition, novel research adopting contemporary data mining techniques instead of classical models are introduced. The historical development of research on URT network route choices underscores the importance of amalgamating independent information networks such as surveillance networks and social networks to establish a comprehensive multi-dimensional network. Such an approach integrates passenger attributes across networks, offering a multi-dimensional understanding of passengers’ route choice behaviours. Our review work aims to present not only a systematic conceptual framework for route choices in URT networks but also a novel path for transport researchers and practitioners to decipher the travel behaviours of passengers.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"93 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1186/s12544-024-00665-x
Shaghayegh Rahnama, Adriana Cortez, Andres Monzon
Buses are among the most accessible and frequently used means of transport. Due to its importance, road safety analysis is frequently conducted to reduce accidents. This paper studied the relationship between weather conditions and the causes of accidents to improve road safety, focusing on long-distance services between Madrid and Bilbao (Spain). We employed Latent Class Clustering (LCC) and Hierarchical Ordered Logit models to identify these factors’ relationships. Additionally, Kaplan-Meier survival analysis was adopted to provide temporal insights into accident occurrences. The main results show a downward trend in accidents since 2019, with manoeuvres being the most frequent cause. LCC reveals that “manoeuvres and car invading lanes in the opposite direction” in “clear and cloudy weather” has the highest probability of occurrence (63%). The hierarchical-ordered logit model indicates that rainy weather significantly affects all accident causes. Kaplan-Meier survival analysis reveals a vertical initial decline in survival probability within the first ten days, emphasizing a high initial accident risk. The integrated approach used in this work provides a thorough understanding of accident hazards, which is its main contribution. By integrating LCC, Hierarchical Ordered Logit models and Kaplan-Meier survival analysis; we could offer a comprehensive and nuanced interpretation of the connection between weather and bus accidents. The findings highlight the need for rapid and sustained safety interventions, enhancing robustness and providing actionable insights for improving bus safety.
{"title":"Investigating key explanatory factors for safer long-distance bus services","authors":"Shaghayegh Rahnama, Adriana Cortez, Andres Monzon","doi":"10.1186/s12544-024-00665-x","DOIUrl":"https://doi.org/10.1186/s12544-024-00665-x","url":null,"abstract":"Buses are among the most accessible and frequently used means of transport. Due to its importance, road safety analysis is frequently conducted to reduce accidents. This paper studied the relationship between weather conditions and the causes of accidents to improve road safety, focusing on long-distance services between Madrid and Bilbao (Spain). We employed Latent Class Clustering (LCC) and Hierarchical Ordered Logit models to identify these factors’ relationships. Additionally, Kaplan-Meier survival analysis was adopted to provide temporal insights into accident occurrences. The main results show a downward trend in accidents since 2019, with manoeuvres being the most frequent cause. LCC reveals that “manoeuvres and car invading lanes in the opposite direction” in “clear and cloudy weather” has the highest probability of occurrence (63%). The hierarchical-ordered logit model indicates that rainy weather significantly affects all accident causes. Kaplan-Meier survival analysis reveals a vertical initial decline in survival probability within the first ten days, emphasizing a high initial accident risk. The integrated approach used in this work provides a thorough understanding of accident hazards, which is its main contribution. By integrating LCC, Hierarchical Ordered Logit models and Kaplan-Meier survival analysis; we could offer a comprehensive and nuanced interpretation of the connection between weather and bus accidents. The findings highlight the need for rapid and sustained safety interventions, enhancing robustness and providing actionable insights for improving bus safety.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"31 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1186/s12544-024-00663-z
Alberica Bozzi
Digital nomadism (DN), an emerging lifestyle based on remote working, digital technology, and leisure travels, has grown from a marginal phenomenon concerning isolated individuals in the early 2000s to a flourishing movement in less than two decades. In the post COVID-19 context of mainstream remote work, the number of digital nomads has risen sharply. Digital nomads are professionals who live, travel, and work online from multiple destinations with a reliable internet connection. In response to this rapid growth, both public and private actors have launched new initiatives targeting digital nomads (e.g. dedicated visa schemes and coliving spaces). Despite the proliferation of these initiatives, there is a lot to discover on digital nomads, including their demographics, travel patterns, and impacts. Based on a systematic literature review covering the last decade, this article explores DN from the perspective of places and mobilities. Findings show that places and mobilities largely shape definitions of DN, how nomads perceive their identity, and how they select travel destinations. Moreover, DN has many impacts on mobilities and places by creating demand for specific infrastructures and contributing to gentrification. In the post-pandemic context, research on DN is needed to address the challenges raised by new mobile lifestyles and remote work practices.
{"title":"Digital nomadism from the perspective of places and mobilities: a literature review","authors":"Alberica Bozzi","doi":"10.1186/s12544-024-00663-z","DOIUrl":"https://doi.org/10.1186/s12544-024-00663-z","url":null,"abstract":"Digital nomadism (DN), an emerging lifestyle based on remote working, digital technology, and leisure travels, has grown from a marginal phenomenon concerning isolated individuals in the early 2000s to a flourishing movement in less than two decades. In the post COVID-19 context of mainstream remote work, the number of digital nomads has risen sharply. Digital nomads are professionals who live, travel, and work online from multiple destinations with a reliable internet connection. In response to this rapid growth, both public and private actors have launched new initiatives targeting digital nomads (e.g. dedicated visa schemes and coliving spaces). Despite the proliferation of these initiatives, there is a lot to discover on digital nomads, including their demographics, travel patterns, and impacts. Based on a systematic literature review covering the last decade, this article explores DN from the perspective of places and mobilities. Findings show that places and mobilities largely shape definitions of DN, how nomads perceive their identity, and how they select travel destinations. Moreover, DN has many impacts on mobilities and places by creating demand for specific infrastructures and contributing to gentrification. In the post-pandemic context, research on DN is needed to address the challenges raised by new mobile lifestyles and remote work practices.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"20 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1186/s12544-024-00675-9
María Díez-Gutiérrez, Sahar Babri, Erlend Dahl, Olav Kåre Malmin
Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.
{"title":"Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway","authors":"María Díez-Gutiérrez, Sahar Babri, Erlend Dahl, Olav Kåre Malmin","doi":"10.1186/s12544-024-00675-9","DOIUrl":"https://doi.org/10.1186/s12544-024-00675-9","url":null,"abstract":"Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"2 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1186/s12544-024-00674-w
Luis Cespedes, Mercedes Ayuso, Miguel Santolino
The concentration of population in cities and processes of rural depopulation coupled with the generational shift to older societies represent new challenges in road safety. Here, we examine the severity of injuries suffered by the occupants of motor vehicles involved in a crash based on the population density of the area in which the crash occurs, the driver’s age and the density of their place of residence. We conduct the study in Spain, a country with one of the highest levels of elderly population concentrated in rural areas in Europe. Relational methods are used to match Eurostat’s urbanization classifications with the accident database of Spain’s Directorate General of Traffic so as to correlate each crash with the population density of the place where it occurred. A set of generalized linear models with random effects is fitted to analyze the relationship between population density and the bodily injury severity of the occupants of the vehicle(s) involved in a crash, measuring the effect of drivers’ relocation and aging by geographical area. Independence of injury severity and the degree of urbanization was rejected at the 5% significance level. While 53.8% of the Spanish population is living in densely populated areas and only 13.5% in rural areas, the latter concentrates most crashes with fatalities: 2.3 times more than in urban areas (43.5 and 18.6%, respectively). Drivers living in rural areas are more likely to be associated with serious or fatal injuries when involved in a crash in urban and intermediate areas. Moreover, drivers aged over 75 are significantly more likely to be associated with serious and fatal injuries, especially when the crash occurred in urban areas. Recent research alerts on the implications for rural (often elderly) residents of concentrating public services, particularly healthcare, in densely populated areas. Our study shows that motor crashes in more densely populated areas are also a rural health concern. Policy decision-makers need to address this issue to reduce the number of victims and their bodily injury severity.
{"title":"Effect of population density in aging societies and severity of motor vehicle crash injuries: the case of Spain","authors":"Luis Cespedes, Mercedes Ayuso, Miguel Santolino","doi":"10.1186/s12544-024-00674-w","DOIUrl":"https://doi.org/10.1186/s12544-024-00674-w","url":null,"abstract":"The concentration of population in cities and processes of rural depopulation coupled with the generational shift to older societies represent new challenges in road safety. Here, we examine the severity of injuries suffered by the occupants of motor vehicles involved in a crash based on the population density of the area in which the crash occurs, the driver’s age and the density of their place of residence. We conduct the study in Spain, a country with one of the highest levels of elderly population concentrated in rural areas in Europe. Relational methods are used to match Eurostat’s urbanization classifications with the accident database of Spain’s Directorate General of Traffic so as to correlate each crash with the population density of the place where it occurred. A set of generalized linear models with random effects is fitted to analyze the relationship between population density and the bodily injury severity of the occupants of the vehicle(s) involved in a crash, measuring the effect of drivers’ relocation and aging by geographical area. Independence of injury severity and the degree of urbanization was rejected at the 5% significance level. While 53.8% of the Spanish population is living in densely populated areas and only 13.5% in rural areas, the latter concentrates most crashes with fatalities: 2.3 times more than in urban areas (43.5 and 18.6%, respectively). Drivers living in rural areas are more likely to be associated with serious or fatal injuries when involved in a crash in urban and intermediate areas. Moreover, drivers aged over 75 are significantly more likely to be associated with serious and fatal injuries, especially when the crash occurred in urban areas. Recent research alerts on the implications for rural (often elderly) residents of concentrating public services, particularly healthcare, in densely populated areas. Our study shows that motor crashes in more densely populated areas are also a rural health concern. Policy decision-makers need to address this issue to reduce the number of victims and their bodily injury severity.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"29 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1186/s12544-024-00671-z
Pavlos Tafidis, Mehdi Gholamnia, Payam Sajadi, Sruthi Krishnan Vijayakrishnan, Francesco Pilla
Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.
{"title":"Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data","authors":"Pavlos Tafidis, Mehdi Gholamnia, Payam Sajadi, Sruthi Krishnan Vijayakrishnan, Francesco Pilla","doi":"10.1186/s12544-024-00671-z","DOIUrl":"https://doi.org/10.1186/s12544-024-00671-z","url":null,"abstract":"Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1186/s12544-024-00670-0
Anna Beatriz Espíndola de Oliveira, Ana Maria César Bastos Silva, Anabela Salgueiro Narciso Ribeiro
Observational studies, whether conducted on-site or through simulation, are practical options for investigating and comprehending pedestrian behaviour and validating the findings of similar studies that have already taken place on the same subject. Although they present difficulties in demographic characterisation and personal context, observation represents the adoption of actual behaviour, highlighting the different scenarios, contexts and characteristics of the environment and infrastructure in urban areas. However, with the growth in simulators, surveys, and data protection issues in Europe, field observation is less considered, and several studies have been left aside. Based on observation, this study answers behavioural and demographic questions, namely age and gender, using the data of six pedestrian crossings in Coimbra, Portugal. This experience occurred on different days of the week (the week before lockdown), registering blind (and non-blind) pedestrians' behaviour facing the electric vehicle in 6 crosswalks, 30 min in each crosswalk, morning or afternoon, with favourable weather conditions outside peak hours In total 180 (30*6) minutes were filmed and evaluated. The relationships between responsibility (when one pedestrian is guided by another) and group action in decision-making and crossing behaviour were also investigated. Another relevant aspect studied was the relationship between pedestrians and electric vehicles in different contexts. During the research, an electric vehicle was inserted in the observational experience, circulating in the streets to observe the pedestrians' reaction to the absence of noise. In this context, blind pedestrians were invited to perform crossings in these areas and identify the presence of the electric vehicle, to identify some differences in the behaviour of blind pedestrians and non-blind pedestrians. The results indicate that blind pedestrians tend to be more cautious than non-blind pedestrians, that younger and elderly pedestrians tend to have more dangerous and less calculated behaviours than other road users and that the differences are more perceptible in age than gender. It should be noted that when in a group, especially adolescents, they commit more traffic errors. Furthermore, regarding the absence of noise associated with the electric vehicle, in moments of pedestrian distraction, if the driver is not attentive, there is an increase in the risk of accidents.
{"title":"Insights into critical urban pedestrian dynamics: an observational study spotlighting a small sample of blind pedestrians and electric vehicles","authors":"Anna Beatriz Espíndola de Oliveira, Ana Maria César Bastos Silva, Anabela Salgueiro Narciso Ribeiro","doi":"10.1186/s12544-024-00670-0","DOIUrl":"https://doi.org/10.1186/s12544-024-00670-0","url":null,"abstract":"Observational studies, whether conducted on-site or through simulation, are practical options for investigating and comprehending pedestrian behaviour and validating the findings of similar studies that have already taken place on the same subject. Although they present difficulties in demographic characterisation and personal context, observation represents the adoption of actual behaviour, highlighting the different scenarios, contexts and characteristics of the environment and infrastructure in urban areas. However, with the growth in simulators, surveys, and data protection issues in Europe, field observation is less considered, and several studies have been left aside. Based on observation, this study answers behavioural and demographic questions, namely age and gender, using the data of six pedestrian crossings in Coimbra, Portugal. This experience occurred on different days of the week (the week before lockdown), registering blind (and non-blind) pedestrians' behaviour facing the electric vehicle in 6 crosswalks, 30 min in each crosswalk, morning or afternoon, with favourable weather conditions outside peak hours In total 180 (30*6) minutes were filmed and evaluated. The relationships between responsibility (when one pedestrian is guided by another) and group action in decision-making and crossing behaviour were also investigated. Another relevant aspect studied was the relationship between pedestrians and electric vehicles in different contexts. During the research, an electric vehicle was inserted in the observational experience, circulating in the streets to observe the pedestrians' reaction to the absence of noise. In this context, blind pedestrians were invited to perform crossings in these areas and identify the presence of the electric vehicle, to identify some differences in the behaviour of blind pedestrians and non-blind pedestrians. The results indicate that blind pedestrians tend to be more cautious than non-blind pedestrians, that younger and elderly pedestrians tend to have more dangerous and less calculated behaviours than other road users and that the differences are more perceptible in age than gender. It should be noted that when in a group, especially adolescents, they commit more traffic errors. Furthermore, regarding the absence of noise associated with the electric vehicle, in moments of pedestrian distraction, if the driver is not attentive, there is an increase in the risk of accidents.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"11 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available.
{"title":"Personalizing travel behaviour change interventions using the trans-theoretical model and multimodality data","authors":"Warnakulasooriya Umesh Ashen Lowe, Leonhard Lades, Páraic Carroll","doi":"10.1186/s12544-024-00666-w","DOIUrl":"https://doi.org/10.1186/s12544-024-00666-w","url":null,"abstract":"Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"38 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-23DOI: 10.1186/s12544-024-00669-7
C. Castillo, J. Panadero, E. J. Alvarez-Palau, A. A. Juan
The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.
{"title":"Towards greener city logistics: an application of agile routing algorithms to optimize the distribution of micro-hubs in Barcelona","authors":"C. Castillo, J. Panadero, E. J. Alvarez-Palau, A. A. Juan","doi":"10.1186/s12544-024-00669-7","DOIUrl":"https://doi.org/10.1186/s12544-024-00669-7","url":null,"abstract":"The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"9 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-21DOI: 10.1186/s12544-024-00667-9
Ilona Rahnasto, Martijn Hollestelle
Destination choice modeling has long been dominated by theory-based discrete choice models. Simultaneously, machine learning has demonstrated improved predictive performance to other fields of discrete choice modeling. The objective of this research was to compare machine learning models and a multinomial logit model in predicting destination choice. The models were assessed on their predictive performance using metrics for both binary classification and probabilistic classification. The results indicate that machine learning models, especially a random forest model, could bring improvements in prediction accuracy. The more data was used in training the models, the better the machine learning models tended to perform compared to the multinomial logit model. With less data, the multinomial logit model performed comparatively well. The findings are relevant for the field of destination choice modeling, where evidence on the use of machine learning models is very limited. In addition, the unbalanced choice sets of destination choice models with multiple non-chosen alternatives increases the need for further research in model fit and parameter tuning.
{"title":"Comparing discrete choice and machine learning models in predicting destination choice","authors":"Ilona Rahnasto, Martijn Hollestelle","doi":"10.1186/s12544-024-00667-9","DOIUrl":"https://doi.org/10.1186/s12544-024-00667-9","url":null,"abstract":"Destination choice modeling has long been dominated by theory-based discrete choice models. Simultaneously, machine learning has demonstrated improved predictive performance to other fields of discrete choice modeling. The objective of this research was to compare machine learning models and a multinomial logit model in predicting destination choice. The models were assessed on their predictive performance using metrics for both binary classification and probabilistic classification. The results indicate that machine learning models, especially a random forest model, could bring improvements in prediction accuracy. The more data was used in training the models, the better the machine learning models tended to perform compared to the multinomial logit model. With less data, the multinomial logit model performed comparatively well. The findings are relevant for the field of destination choice modeling, where evidence on the use of machine learning models is very limited. In addition, the unbalanced choice sets of destination choice models with multiple non-chosen alternatives increases the need for further research in model fit and parameter tuning.","PeriodicalId":12079,"journal":{"name":"European Transport Research Review","volume":"120 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142177792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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