Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100135
Paul Redelmeier , Rodrigo Victoriano-Habit , Miles Crumley , Ahmed El-Geneidy
Bus Priority Interventions are small-scale changes that improve bus speed and reliability. These include changes to street geometry, bus stops, and traffic signals. Automated Vehicle Location-Automated Passenger Counting (AVL-APC) data can help transit agencies by providing insight into bus location, speed, and passenger volumes. This work proposes an end-to-end methodology for using AVL-APC data to create a concept design for bus priority interventions on a bus corridor. The proposed method is illustrated by analyzing a bus route in Portland, Oregon. This mixed-methods approach paired quantitative data analysis with site visits to identify what was causing delay on the route and suggest targeted interventions. Scenario analysis of historical trip data was employed to predict the impact of different interventions. Historical trips that fell into two different scenarios were compared: a delay scenario (where a specific delay-inducing event occurred, like a red light) and a non-delay scenario (where that event did not occur). This end-to-end methodology could be used by transit agencies and transportation planners to quickly assess different corridors and interventions, diagnose problems, and determine which projects would create the greatest customer and financial benefits. Employing this approach could help planners prioritize time and resources to ensure that the highest impact projects are pursued.
{"title":"Bit by bit: A method for using bus data to develop plan bus priority interventions in Portland, Oregon, USA","authors":"Paul Redelmeier , Rodrigo Victoriano-Habit , Miles Crumley , Ahmed El-Geneidy","doi":"10.1016/j.jpubtr.2025.100135","DOIUrl":"10.1016/j.jpubtr.2025.100135","url":null,"abstract":"<div><div>Bus Priority Interventions are small-scale changes that improve bus speed and reliability. These include changes to street geometry, bus stops, and traffic signals. Automated Vehicle Location-Automated Passenger Counting (AVL-APC) data can help transit agencies by providing insight into bus location, speed, and passenger volumes. This work proposes an end-to-end methodology for using AVL-APC data to create a concept design for bus priority interventions on a bus corridor. The proposed method is illustrated by analyzing a bus route in Portland, Oregon. This mixed-methods approach paired quantitative data analysis with site visits to identify what was causing delay on the route and suggest targeted interventions. Scenario analysis of historical trip data was employed to predict the impact of different interventions. Historical trips that fell into two different scenarios were compared: a delay scenario (where a specific delay-inducing event occurred, like a red light) and a non-delay scenario (where that event did not occur). This end-to-end methodology could be used by transit agencies and transportation planners to quickly assess different corridors and interventions, diagnose problems, and determine which projects would create the greatest customer and financial benefits. Employing this approach could help planners prioritize time and resources to ensure that the highest impact projects are pursued.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100135"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100131
Yanwen Yun , Jingtong Zhai
Previous studies have linked public transport accessibility to travel satisfaction, but most focus on local accessibility effects, with limited research comparing these to network accessibility effects. Using data from the Beijing Rail Transit System (BRTS) and a large-scale household satisfaction survey, this study applies a Bayesian multilevel approach to examine and compare the impacts of local and network rail transit accessibility on travel satisfaction. We also explore the nonlinear nature of this relationship and the influence of rail transit configurations. The results show that: 1) Both local and network accessibility have significant effects on travel satisfaction, including for commuting and non-commuting trips. Local accessibility has a stronger impact than network accessibility. 2) The effect is nonlinear, peaking at the fourth quintile, and from the second quintile onward, local accessibility has a clearly stronger positive effect than network accessibility. 3) Residents near ring lines or non-transfer stations tend to benefit more from accessibility improvements. These findings suggest that urban planners and policymakers should evaluate transit investments based on network accessibility beyond just station areas, while accounting for threshold effects and rail network design to promote transport equity and overall welfare.
{"title":"Re-evaluating the satisfaction effects of rail transit accessibility: A comparison of local and network perspectives","authors":"Yanwen Yun , Jingtong Zhai","doi":"10.1016/j.jpubtr.2025.100131","DOIUrl":"10.1016/j.jpubtr.2025.100131","url":null,"abstract":"<div><div>Previous studies have linked public transport accessibility to travel satisfaction, but most focus on local accessibility effects, with limited research comparing these to network accessibility effects. Using data from the Beijing Rail Transit System (BRTS) and a large-scale household satisfaction survey, this study applies a Bayesian multilevel approach to examine and compare the impacts of local and network rail transit accessibility on travel satisfaction. We also explore the nonlinear nature of this relationship and the influence of rail transit configurations. The results show that: 1) Both local and network accessibility have significant effects on travel satisfaction, including for commuting and non-commuting trips. Local accessibility has a stronger impact than network accessibility. 2) The effect is nonlinear, peaking at the fourth quintile, and from the second quintile onward, local accessibility has a clearly stronger positive effect than network accessibility. 3) Residents near ring lines or non-transfer stations tend to benefit more from accessibility improvements. These findings suggest that urban planners and policymakers should evaluate transit investments based on network accessibility beyond just station areas, while accounting for threshold effects and rail network design to promote transport equity and overall welfare.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100131"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enabled by the emergence of new technologies, demand-responsive transport (DRT) offers a flexible alternative to fixed-schedule public transport. In order to improve their public transport networks, cities are attempting to integrate new DRT services into their networks without reducing fixed-schedule public transport ridership. This paper addresses the question of how DRT integration into an existing public transport network affects mobility behaviour and network load in a medium-sized city. To this end, we ran two scenarios in an agent-based model (MATSim). First, we set up a ‘Feeder Scenario’, where the DRT represents a feeder to a reduced fixed-schedule public transport network; second, we developed a ‘Replacement Scenario’ in which the DRT completely replaced fixed-schedule public transport in the whole study area. The results show that both scenarios generate extra vehicle traffic compared to the ‘Status Quo’ (the unchanged calibration state, before any scenario is implemented) because DRT trips replace walking and cycling trips as well as bus trips with higher capacities and the reduction in car trips does not compensate for this. Overall, our configuration of the scenarios results in the Replacement Scenario being slightly better than the Feeder Scenario in terms of replacing car trips, total motorised mileage and total vehicle load on roads in the city.
{"title":"Integrating demand-responsive services into public transport networks – Results from agent-based simulation of demand-responsive transport scenarios for the city of Aachen","authors":"Niklas Höing , Pradeep Burla , Conny Louen , Carina Böhnen , Tobias Kuhnimhof","doi":"10.1016/j.jpubtr.2025.100143","DOIUrl":"10.1016/j.jpubtr.2025.100143","url":null,"abstract":"<div><div>Enabled by the emergence of new technologies, demand-responsive transport (DRT) offers a flexible alternative to fixed-schedule public transport. In order to improve their public transport networks, cities are attempting to integrate new DRT services into their networks without reducing fixed-schedule public transport ridership. This paper addresses the question of how DRT integration into an existing public transport network affects mobility behaviour and network load in a medium-sized city. To this end, we ran two scenarios in an agent-based model (MATSim). First, we set up a ‘Feeder Scenario’, where the DRT represents a feeder to a reduced fixed-schedule public transport network; second, we developed a ‘Replacement Scenario’ in which the DRT completely replaced fixed-schedule public transport in the whole study area. The results show that both scenarios generate extra vehicle traffic compared to the ‘Status Quo’ (the unchanged calibration state, before any scenario is implemented) because DRT trips replace walking and cycling trips as well as bus trips with higher capacities and the reduction in car trips does not compensate for this. Overall, our configuration of the scenarios results in the Replacement Scenario being slightly better than the Feeder Scenario in terms of replacing car trips, total motorised mileage and total vehicle load on roads in the city.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100143"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100137
Joanne Yuh-Jye Lin , Erik Jenelius , Matej Cebecauer
Crowding exposure in public transport vehicles has a serious negative impact on passengers’ travel experience. Especially during the COVID-19 pandemic, exposure to crowded conditions may also increase the risk of virus transmission among passengers. To mitigate the negative impact of crowding on public transport systems, there is an increasing need to understand how crowding exposure is distributed across the service area and passenger groups, and how it changes over time. This paper provides a methodology for monitoring the equity of crowding exposure over time using longitudinal smart card data. An objective measure is proposed to quantify crowding exposure: relative crowded travel time (rCTT). We apply Lorenz curves, Gini and Suits coefficients to assess horizontal equity (across the population) and vertical equity (considering income). In our case study of the Stockholm Region, we demonstrate our method by assessing the equity of crowding exposure during different stages of the COVID-19 pandemic: pre-COVID, COVID, and post-COVID. Our findings show that the pandemic negatively impacted both horizontal and vertical equity. During the pandemic, crowding exposure became increasingly uneven across the service area. While overall ridership and crowding exposure declined during the pandemic, reductions were not uniform across social groups. Lower-income riders showed smaller decreases in travel compared to higher-income riders, resulting in greater crowding exposure among disadvantaged groups and a shift from a progressive to a regressive distribution. These findings reinforce the importance of continued monitoring of crowding exposure, especially as travel behavior and policy contexts continue to evolve. The proposed framework can help identify and target the most critical equity gaps, enabling more focused and effective interventions.with lower-income travelers experiencing more crowding than their higher-income counterparts. However, by the post-COVID stage, the equity of crowding exposure has nearly returned to pre-COVID levels.
{"title":"Monitoring public transport crowding exposure: Stockholm before, during, and after the COVID-19 pandemic","authors":"Joanne Yuh-Jye Lin , Erik Jenelius , Matej Cebecauer","doi":"10.1016/j.jpubtr.2025.100137","DOIUrl":"10.1016/j.jpubtr.2025.100137","url":null,"abstract":"<div><div>Crowding exposure in public transport vehicles has a serious negative impact on passengers’ travel experience. Especially during the COVID-19 pandemic, exposure to crowded conditions may also increase the risk of virus transmission among passengers. To mitigate the negative impact of crowding on public transport systems, there is an increasing need to understand how crowding exposure is distributed across the service area and passenger groups, and how it changes over time. This paper provides a methodology for monitoring the equity of crowding exposure over time using longitudinal smart card data. An objective measure is proposed to quantify crowding exposure: relative crowded travel time (rCTT). We apply Lorenz curves, Gini and Suits coefficients to assess horizontal equity (across the population) and vertical equity (considering income). In our case study of the Stockholm Region, we demonstrate our method by assessing the equity of crowding exposure during different stages of the COVID-19 pandemic: pre-COVID, COVID, and post-COVID. Our findings show that the pandemic negatively impacted both horizontal and vertical equity. During the pandemic, crowding exposure became increasingly uneven across the service area. While overall ridership and crowding exposure declined during the pandemic, reductions were not uniform across social groups. Lower-income riders showed smaller decreases in travel compared to higher-income riders, resulting in greater crowding exposure among disadvantaged groups and a shift from a progressive to a regressive distribution. These findings reinforce the importance of continued monitoring of crowding exposure, especially as travel behavior and policy contexts continue to evolve. The proposed framework can help identify and target the most critical equity gaps, enabling more focused and effective interventions.with lower-income travelers experiencing more crowding than their higher-income counterparts. However, by the post-COVID stage, the equity of crowding exposure has nearly returned to pre-COVID levels.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100137"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100121
Ran Du , Fumitaka Kurauchi , Toshiyuki Nakamura , Masahiro Kuwahara
Public transportation in Japan currently faces serious challenges, including depopulation, dispersed low demand, and a shortage of drivers. To address these issues and cover wider areas with fewer drivers, flexible transport systems like demand-responsive transport (DRT) services are becoming increasingly popular, particularly in rural areas, thanks to recent advancements in Information and Communication Technologies (ICT). Despite the potential for reduced operational costs through more efficient service provision, overall costs often remain high due to increased operator and system costs. Improving the efficiency of services is crucial even for flexible transport systems. Understanding detailed traveler behaviors within these systems is essential for this purpose.
Flexible transport systems often incorporate online booking and vehicle assignment systems, allowing for the automatic collection of booking data. By analyzing this data, we can gain insights into the behaviors of travelers and the patterns of bus stop utilization. This study utilizes booking data to examine the interactions between passengers and bus stops in flexible transport systems, with a particular focus on understanding and discussing patterns of regularity and variability in both traveler behavior and bus stop usage.
The study uses nine years of booking data (2015–2023) from a mid-sized city in Gifu Prefecture, encompassing 845 passengers and 142,638 records. The analysis first explores the regularity of traveler behaviors and bus stop usage patterns, followed by a discussion on the flexibility or variability of vehicle movements.
The results show that vehicle movements are primarily driven by regular high-frequency travelers, who use the service for commuting and returning home. This dominance often excludes low-frequency random travelers from accessing the service. Additionally, it is suggested that minimizing total operational costs may not adequately assign travelers onto vehicles, and the implementation of monthly passes may further reinforce the dominance of high-frequency travelers.
These insights underscore the importance of considering service designs from various dimensions, including user behavior, spatial factors, and temporal patterns, for the effective optimization of flexible transport systems.
{"title":"How flexible transportation services work in reality?- some insights from real-world observations","authors":"Ran Du , Fumitaka Kurauchi , Toshiyuki Nakamura , Masahiro Kuwahara","doi":"10.1016/j.jpubtr.2025.100121","DOIUrl":"10.1016/j.jpubtr.2025.100121","url":null,"abstract":"<div><div>Public transportation in Japan currently faces serious challenges, including depopulation, dispersed low demand, and a shortage of drivers. To address these issues and cover wider areas with fewer drivers, flexible transport systems like demand-responsive transport (DRT) services are becoming increasingly popular, particularly in rural areas, thanks to recent advancements in Information and Communication Technologies (ICT). Despite the potential for reduced operational costs through more efficient service provision, overall costs often remain high due to increased operator and system costs. Improving the efficiency of services is crucial even for flexible transport systems. Understanding detailed traveler behaviors within these systems is essential for this purpose.</div><div>Flexible transport systems often incorporate online booking and vehicle assignment systems, allowing for the automatic collection of booking data. By analyzing this data, we can gain insights into the behaviors of travelers and the patterns of bus stop utilization. This study utilizes booking data to examine the interactions between passengers and bus stops in flexible transport systems, with a particular focus on understanding and discussing patterns of regularity and variability in both traveler behavior and bus stop usage.</div><div>The study uses nine years of booking data (2015–2023) from a mid-sized city in Gifu Prefecture, encompassing 845 passengers and 142,638 records. The analysis first explores the regularity of traveler behaviors and bus stop usage patterns, followed by a discussion on the flexibility or variability of vehicle movements.</div><div>The results show that vehicle movements are primarily driven by regular high-frequency travelers, who use the service for commuting and returning home. This dominance often excludes low-frequency random travelers from accessing the service. Additionally, it is suggested that minimizing total operational costs may not adequately assign travelers onto vehicles, and the implementation of monthly passes may further reinforce the dominance of high-frequency travelers.</div><div>These insights underscore the importance of considering service designs from various dimensions, including user behavior, spatial factors, and temporal patterns, for the effective optimization of flexible transport systems.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100121"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100116
Kai-Chieh Hu , Li-Hao Yang
The increasing prominence of autonomous buses in metropolitan transportation has sparked considerable interest. However, the absence of a comprehensive theoretical framework hinders the systematic exploration of factors influencing passengers’ behavioral intentions regarding autonomous buses. This study addresses this gap by employing the decomposed planning behavior theory to investigate the antecedents of passengers’ behavioral intentions. Additionally, the study examines the impact of travel anxiety and perceived risk on passengers’ attitudes. Data were collected through a questionnaire survey, and structural equation modeling was utilized to rigorously test the research model. The findings reveal that purchase intention is positively influenced by novelty seeking, subjective norm, and perceived behavioral control, while being negatively impacted by travel anxiety. Conversely, travel anxiety is negatively influenced by novelty seeking but positively affected by perceived risk. Moreover, interpersonal influence positively affects subjective norm, and self-efficacy has a positive influence on perceived behavioral control. This study offers valuable insights for current and potential bus operators and government entities seeking to advance the promotion of autonomous buses in metropolitan areas.
{"title":"Exploring antecedents of passengers’ behavioral intentions toward autonomous buses: A decomposed planning behavior approach","authors":"Kai-Chieh Hu , Li-Hao Yang","doi":"10.1016/j.jpubtr.2025.100116","DOIUrl":"10.1016/j.jpubtr.2025.100116","url":null,"abstract":"<div><div>The increasing prominence of autonomous buses in metropolitan transportation has sparked considerable interest. However, the absence of a comprehensive theoretical framework hinders the systematic exploration of factors influencing passengers’ behavioral intentions regarding autonomous buses. This study addresses this gap by employing the decomposed planning behavior theory to investigate the antecedents of passengers’ behavioral intentions. Additionally, the study examines the impact of travel anxiety and perceived risk on passengers’ attitudes. Data were collected through a questionnaire survey, and structural equation modeling was utilized to rigorously test the research model. The findings reveal that purchase intention is positively influenced by novelty seeking, subjective norm, and perceived behavioral control, while being negatively impacted by travel anxiety. Conversely, travel anxiety is negatively influenced by novelty seeking but positively affected by perceived risk. Moreover, interpersonal influence positively affects subjective norm, and self-efficacy has a positive influence on perceived behavioral control. This study offers valuable insights for current and potential bus operators and government entities seeking to advance the promotion of autonomous buses in metropolitan areas.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100116"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143319507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2024.100114
Yuanyuan Wu , Alex Markham , Leizhen Wang , Liam Solus , Zhenliang Ma
Behaviour modelling has been widely explored using both statistical and machine learning techniques, primarily relying on analyzing correlations to understand passenger responses under different conditions and scenarios. However, correlation alone does not imply causation. This paper introduces a data-driven causal behaviour modelling approach, comprising two phases: causal discovery and causal inference. Causal discovery phase uses Peter-Clark (PC) algorithm to learn a directed acyclic graph that captures the causal relationships among variables. Causal inference phase estimates the corresponding model parameters and infers (conditional) causal effects of interventions designed to influence user behaviour. The method is validated by comparing the results with those from conventional modelling approaches (logistic regression and expert knowledge) using smart card data from a real-world use case on a pre-peak fare discount incentive program in the Hong Kong Mass Transit Railway system. The results highlight that the purely data-driven causal discovery method can produce reasonable causal graph. The method can also quantify the behavioural impacts of the incentive, identify key influencing factors, and estimate the corresponding causal effects. The overall causal effect of the incentive is approximately 0.7 %, with about 3 % of the population changing behaviour from previous statistical analysis. Interestingly, passengers with the highest flexibility exhibit a negative response, while those with medium-to-high flexibility demonstrate 3 times of the general level of responsiveness. The approach initiates the data-driven, causal modelling of human behaviour dynamics to support policy developments and managerial interventions.
{"title":"Data-driven causal behaviour modelling from trajectory data: A case for fare incentives in public transport","authors":"Yuanyuan Wu , Alex Markham , Leizhen Wang , Liam Solus , Zhenliang Ma","doi":"10.1016/j.jpubtr.2024.100114","DOIUrl":"10.1016/j.jpubtr.2024.100114","url":null,"abstract":"<div><div>Behaviour modelling has been widely explored using both statistical and machine learning techniques, primarily relying on analyzing correlations to understand passenger responses under different conditions and scenarios. However, correlation alone does not imply causation. This paper introduces a data-driven causal behaviour modelling approach, comprising two phases: causal discovery and causal inference. Causal discovery phase uses Peter-Clark (PC) algorithm to learn a directed acyclic graph that captures the causal relationships among variables. Causal inference phase estimates the corresponding model parameters and infers (conditional) causal effects of interventions designed to influence user behaviour. The method is validated by comparing the results with those from conventional modelling approaches (logistic regression and expert knowledge) using smart card data from a real-world use case on a pre-peak fare discount incentive program in the Hong Kong Mass Transit Railway system. The results highlight that the purely data-driven causal discovery method can produce reasonable causal graph. The method can also quantify the behavioural impacts of the incentive, identify key influencing factors, and estimate the corresponding causal effects. The overall causal effect of the incentive is approximately 0.7 %, with about 3 % of the population changing behaviour from previous statistical analysis. Interestingly, passengers with the highest flexibility exhibit a negative response, while those with medium-to-high flexibility demonstrate 3 times of the general level of responsiveness. The approach initiates the data-driven, causal modelling of human behaviour dynamics to support policy developments and managerial interventions.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100114"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100124
Malek Sarhani , Abtin Nourmohammadzadeh , Stefan Voß , Mohammed EL Amrani
The utilization of public transport data has evolved rapidly in recent decades. Ferries, with their unique characteristics and sensitivity to weather conditions, pose significant challenges for delay prediction. Given their pivotal role in the transportation systems of numerous cities, accurately predicting ferry delays is crucial for synchronizing transit services.
This paper demonstrates the value of open data for improving ferry delay predictions through machine learning, focusing on two case studies. Our approach leverages General Transit Feed Specification (GTFS) data, ridership and vessel information, and hourly weather data, combined with SHAP explainable artificial intelligence analysis to assess key delay determinants. While support vector regression and deep neural networks showed high accuracy in individual case studies, gradient boosting consistently offered the best balance between prediction accuracy and computational efficiency. Moreover, SHAP analysis reveals that operational and temporal features – such as stop sequence, trip start time, headway, and vehicle label – are the dominant drivers of delays, with weather-related factors exerting only a modest influence.
{"title":"Predicting and analyzing ferry transit delays using open data and machine learning","authors":"Malek Sarhani , Abtin Nourmohammadzadeh , Stefan Voß , Mohammed EL Amrani","doi":"10.1016/j.jpubtr.2025.100124","DOIUrl":"10.1016/j.jpubtr.2025.100124","url":null,"abstract":"<div><div>The utilization of public transport data has evolved rapidly in recent decades. Ferries, with their unique characteristics and sensitivity to weather conditions, pose significant challenges for delay prediction. Given their pivotal role in the transportation systems of numerous cities, accurately predicting ferry delays is crucial for synchronizing transit services.</div><div>This paper demonstrates the value of open data for improving ferry delay predictions through machine learning, focusing on two case studies. Our approach leverages General Transit Feed Specification (GTFS) data, ridership and vessel information, and hourly weather data, combined with SHAP explainable artificial intelligence analysis to assess key delay determinants. While support vector regression and deep neural networks showed high accuracy in individual case studies, gradient boosting consistently offered the best balance between prediction accuracy and computational efficiency. Moreover, SHAP analysis reveals that operational and temporal features – such as stop sequence, trip start time, headway, and vehicle label – are the dominant drivers of delays, with weather-related factors exerting only a modest influence.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100124"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100125
Bumsoo Lee , Lindsay M. Braun , Jesus M. Barajas , Amanda Merck , Kyuhyun Lee
Public transit systems in smaller urban areas in the U.S. have encountered pronounced declines in ridership since the mid-2010s, compounded by distinct challenges inherent to their scale and context. Despite this trend, understanding of the unique characteristics and needs of riders within such systems remains limited, as prevailing transit research predominantly focuses on larger metropolitan areas. Addressing this gap, our study examines the characteristics and needs of bus riders across four small urban areas in the U.S. Midwest through an on-board survey and subsequent rider typology analysis. We find that bus riders in these smaller communities are markedly economically disadvantaged compared to those in larger cities; 60 % live in households earning incomes at or below the poverty threshold, and over half lack access to a private vehicle. The low socioeconomic status of small-city transit riders results in a heavy reliance on public transportation, with nearly 90 % of respondents depending exclusively on buses or a mix of buses and active modes for daily travel. Such dependency brings to light the challenges that these individuals face, with more than 40 % experiencing severe disruptions like job loss or restricted access to essential services due to inadequate transportation options. Yet, the pressing need for improved service frequency and speed to increase the efficiency of public transit underscores a critical tension in transit network planning: that between providing frequent service and ensuring extensive coverage.
{"title":"Who rides the bus in small cities in the U.S. Midwest?","authors":"Bumsoo Lee , Lindsay M. Braun , Jesus M. Barajas , Amanda Merck , Kyuhyun Lee","doi":"10.1016/j.jpubtr.2025.100125","DOIUrl":"10.1016/j.jpubtr.2025.100125","url":null,"abstract":"<div><div>Public transit systems in smaller urban areas in the U.S. have encountered pronounced declines in ridership since the mid-2010s, compounded by distinct challenges inherent to their scale and context. Despite this trend, understanding of the unique characteristics and needs of riders within such systems remains limited, as prevailing transit research predominantly focuses on larger metropolitan areas. Addressing this gap, our study examines the characteristics and needs of bus riders across four small urban areas in the U.S. Midwest through an on-board survey and subsequent rider typology analysis. We find that bus riders in these smaller communities are markedly economically disadvantaged compared to those in larger cities; 60 % live in households earning incomes at or below the poverty threshold, and over half lack access to a private vehicle. The low socioeconomic status of small-city transit riders results in a heavy reliance on public transportation, with nearly 90 % of respondents depending exclusively on buses or a mix of buses and active modes for daily travel. Such dependency brings to light the challenges that these individuals face, with more than 40 % experiencing severe disruptions like job loss or restricted access to essential services due to inadequate transportation options. Yet, the pressing need for improved service frequency and speed to increase the efficiency of public transit underscores a critical tension in transit network planning: that between providing frequent service and ensuring extensive coverage.</div></div>","PeriodicalId":47173,"journal":{"name":"Journal of Public Transportation","volume":"27 ","pages":"Article 100125"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.jpubtr.2025.100138
Thiago Carvalho, Ahmed El-Geneidy
As older adults cease driving, public transit can support them maintain their independence and remain connected to their communities. This population is particularly sensitive to travel times. While previous research has explored the impact of ideal and perceived travel times on satisfaction, the role of tolerable travel times—representing the maximum acceptable time threshold before satisfaction declines—has been underexplored. Understanding this relationship can provide valuable insight for improving transit experiences and meeting the mobility needs of older adults. To explore this gap, we examine how subjective (perceived and tolerable) and objective (actual) measures of travel time influence trip satisfaction among older adults. To do so, we use data from the 2023 Aging in Place Survey, a Canadian bilingual online survey, focusing on respondents who used transit at least once in the past year from Toronto, Montréal, and Vancouver (N = 731). We asses the impact of these measures of travel time on trip satisfaction through multi-level ordered probit models, accounting for both individual and regional factors. Our findings suggest that older adults are more likely to be satisfied with their trip when perceived travel time aligns with what they consider as tolerable, rather than the actual, objective trip duration. They also reinforce the strong role of previous transit experiences and perceptions on shaping future trip satisfaction. Given the link between satisfaction and continuous transit use, these findings are relevant for practitioners and policymakers seeking to improve public transit experiences for older adults and support their healthy aging.
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