Youths in the Kathmandu Valley of Nepal have abundant opportunities to experience private motorization through shared transportation without owning vehicles. Ride-sharing is a new mode of transportation in Nepal where young people are an important market segment for vehicle-sharing organisations. In this context, this study investigated youths’ behavioral intentions toward adopting ride-sharing practices. Following the explanatory research methodology, 396 responses were collected from youths in Kathmandu Valley, Nepal. The findings reveal that environmental concerns significantly impact attitudes, subjective norms, and perceived behavioral control. Similarly, subjective norms have a substantial impact on behavioural intentions towards the adoption of ride-sharing that align with the theory of planned behaviour.
{"title":"Youth and shared mobility: Unveiling intentions in Kathmandu's gig economy","authors":"Merina Maharjan , Niranjan Devkota , Ghanashyam Khanal , Prashiddha Basnet , Devid Kumar Basyal , Udaya Raj Paudel , Ramhari Poudyal , Mijala Kayestha , Ranjana Kumari Dnuwar","doi":"10.1016/j.multra.2025.100237","DOIUrl":"10.1016/j.multra.2025.100237","url":null,"abstract":"<div><div>Youths in the Kathmandu Valley of Nepal have abundant opportunities to experience private motorization through shared transportation without owning vehicles. Ride-sharing is a new mode of transportation in Nepal where young people are an important market segment for vehicle-sharing organisations. In this context, this study investigated youths’ behavioral intentions toward adopting ride-sharing practices. Following the explanatory research methodology, 396 responses were collected from youths in Kathmandu Valley, Nepal. The findings reveal that environmental concerns significantly impact attitudes, subjective norms, and perceived behavioral control. Similarly, subjective norms have a substantial impact on behavioural intentions towards the adoption of ride-sharing that align with the theory of planned behaviour.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145398605","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}
Managing urban traffic is challenging because traffic patterns change unpredictably. Although fuzzy logic-based traffic signal control (TSC) systems like Mamdani and Sugeno work well, they struggle to adjust effectively to real-time traffic changes. This study introduces the Optimization Similarity Fuzzy Inference (OSFI) method, which improves traffic signal control at isolated intersections by continuously adjusting fuzzy rules based on the similarity between actual and desired outcomes. Unlike traditional models, OSFI uses truth tables to dynamically adjust signal timing and phase sequencing based on real-time factors such as vehicle arrival rates and queue lengths. Simulation results show that OSFI reduces average vehicle delays by 1.11–5.73% compared to Mamdani controllers and 0.69–4.84% compared to Sugeno controllers, with traffic throughput improvements of up to 18.75% during heavy traffic. These findings demonstrate OSFI’s ability to consistently improve traffic flow. Future research will focus on expanding OSFI to control networks of intersections and testing its real-world performance to address current challenges related to scalability and efficiency.
{"title":"An optimization similarity fuzzy inference method for traffic signal control at an isolated intersection","authors":"Mahin Esmaeili , Ali Anjomshoae , Nasser Shahsavari-Pour , Punyaanek Srisurin , Ruth Banomyong","doi":"10.1016/j.multra.2025.100234","DOIUrl":"10.1016/j.multra.2025.100234","url":null,"abstract":"<div><div>Managing urban traffic is challenging because traffic patterns change unpredictably. Although fuzzy logic-based traffic signal control (TSC) systems like Mamdani and Sugeno work well, they struggle to adjust effectively to real-time traffic changes. This study introduces the Optimization Similarity Fuzzy Inference (OSFI) method, which improves traffic signal control at isolated intersections by continuously adjusting fuzzy rules based on the similarity between actual and desired outcomes. Unlike traditional models, OSFI uses truth tables to dynamically adjust signal timing and phase sequencing based on real-time factors such as vehicle arrival rates and queue lengths. Simulation results show that OSFI reduces average vehicle delays by 1.11–5.73% compared to Mamdani controllers and 0.69–4.84% compared to Sugeno controllers, with traffic throughput improvements of up to 18.75% during heavy traffic. These findings demonstrate OSFI’s ability to consistently improve traffic flow. Future research will focus on expanding OSFI to control networks of intersections and testing its real-world performance to address current challenges related to scalability and efficiency.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 4","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.multra.2025.100239
Zhuonan Jiang , Tianqi Gu , Zhuo Chen , Hua Liang , Jiao Jiao , Han Li
This study explores the spatiotemporal characteristics of ride-hailing services at intercity transfer hubs in Suzhou, China, examining ride-hailing trip patterns across different land use types, focusing on holidays and regular days. An algorithm is proposed to identify the ride-hailing trips to or from the intercity transfer hubs. During holidays, ride-hailing trips cover longer distances and incur higher fares, with a significant increase in trips related to transportation, tourist areas, and land use for higher education. The spatiotemporal distribution of ride-hailing trips from intercity transfer hubs shows spatial concentration and temporal dispersion during holidays, in contrast to the spatial dispersion and temporal concentration observed on regular days. Specifically, holiday trips are more evenly distributed throughout the day, while regular days exhibit significant morning and evening peaks. It is also found that ride-hailing trips are predominantly concentrated in central business districts, residential areas, and commercial zones, with a noticeable increase in trips to tourist attractions and land use for education during holidays, peaking at the start and end of the holiday period. In contrast, regular days exhibit concentrated travel demand during peak hours, mainly driven by commuting needs. Furthermore, ride-hailing services play a crucial role in areas with limited metro coverage and inconvenient bus transfers, serving as a vital supplement to the urban public transportation system, especially during holidays when passengers often carry large luggage. It highlights a high demand for ride-hailing transfers between different intercity transfer hubs, especially between two local intercity transfer hubs, indicating the need for improved rapid transit connections.
{"title":"Holiday ride-hailing at intercity transfer hubs: Where space squeezes and time stretches","authors":"Zhuonan Jiang , Tianqi Gu , Zhuo Chen , Hua Liang , Jiao Jiao , Han Li","doi":"10.1016/j.multra.2025.100239","DOIUrl":"10.1016/j.multra.2025.100239","url":null,"abstract":"<div><div>This study explores the spatiotemporal characteristics of ride-hailing services at intercity transfer hubs in Suzhou, China, examining ride-hailing trip patterns across different land use types, focusing on holidays and regular days. An algorithm is proposed to identify the ride-hailing trips to or from the intercity transfer hubs. During holidays, ride-hailing trips cover longer distances and incur higher fares, with a significant increase in trips related to transportation, tourist areas, and land use for higher education. The spatiotemporal distribution of ride-hailing trips from intercity transfer hubs shows spatial concentration and temporal dispersion during holidays, in contrast to the spatial dispersion and temporal concentration observed on regular days. Specifically, holiday trips are more evenly distributed throughout the day, while regular days exhibit significant morning and evening peaks. It is also found that ride-hailing trips are predominantly concentrated in central business districts, residential areas, and commercial zones, with a noticeable increase in trips to tourist attractions and land use for education during holidays, peaking at the start and end of the holiday period. In contrast, regular days exhibit concentrated travel demand during peak hours, mainly driven by commuting needs. Furthermore, ride-hailing services play a crucial role in areas with limited metro coverage and inconvenient bus transfers, serving as a vital supplement to the urban public transportation system, especially during holidays when passengers often carry large luggage. It highlights a high demand for ride-hailing transfers between different intercity transfer hubs, especially between two local intercity transfer hubs, indicating the need for improved rapid transit connections.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 4","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.multra.2025.100232
Jiaming Wang , Jiqian Dong , Haotian Shi , Shreyas Sundaram , Samuel Labi , Sikai Chen
The rapid growth of electric vehicles (EVs) exacerbates the existing challenges of vehicle range anxiety and charging infrastructure inadequacy. To address these concerns, promising solutions have emerged, including vehicle-to-vehicle dynamic wireless charging (V2V-DWC) technologies, particularly Mobile Energy Disseminators (MEDs). A MED is a large vehicle bearing non-contact technology equipment that charges participating EVs in its proximity. This paper establishes a platform for reliable simulation of the energy transfer between the MED and the EV, develops models that describe EV battery consumption and V2V charging dynamics, and develops an AI-based framework for MED dispatching. The first component of the developed platform is a realistic reinforcement learning (RL) based highway environment termed ``ChargingEnv.'' This component incorporates reliable charging simulation that accounts for two common practical issues in wireless charging deployment: charging panel misalignment and realistic EV battery consumption patterns. The second component involves multiple deep RL benchmark models trained in ``ChargingEnv'' to maximize the MED deployment charging service quality within charging resource constraints. Findings from numerical experiments demonstrate that the model trained in the framework substantially alleviates battery depletion concerns. The framework can serve as a tool for road agencies or private-sector investors to efficiently plan their MED deployments.
{"title":"Reinforcement learning based mobile energy disseminator dispatching for on-road electric vehicle charging","authors":"Jiaming Wang , Jiqian Dong , Haotian Shi , Shreyas Sundaram , Samuel Labi , Sikai Chen","doi":"10.1016/j.multra.2025.100232","DOIUrl":"10.1016/j.multra.2025.100232","url":null,"abstract":"<div><div>The rapid growth of electric vehicles (EVs) exacerbates the existing challenges of vehicle range anxiety and charging infrastructure inadequacy. To address these concerns, promising solutions have emerged, including vehicle-to-vehicle dynamic wireless charging (V2V-DWC) technologies, particularly Mobile Energy Disseminators (MEDs). A MED is a large vehicle bearing non-contact technology equipment that charges participating EVs in its proximity. This paper establishes a platform for reliable simulation of the energy transfer between the MED and the EV, develops models that describe EV battery consumption and V2V charging dynamics, and develops an AI-based framework for MED dispatching. The first component of the developed platform is a realistic reinforcement learning (RL) based highway environment termed ``ChargingEnv.'' This component incorporates reliable charging simulation that accounts for two common practical issues in wireless charging deployment: charging panel misalignment and realistic EV battery consumption patterns. The second component involves multiple deep RL benchmark models trained in ``ChargingEnv'' to maximize the MED deployment charging service quality within charging resource constraints. Findings from numerical experiments demonstrate that the model trained in the framework substantially alleviates battery depletion concerns. The framework can serve as a tool for road agencies or private-sector investors to efficiently plan their MED deployments.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.multra.2025.100235
Jason Wang , Sisi Jian , Edward N. Robson , Vinayak V. Dixit , Taha H. Rashidi
Previous studies have analysed the impacts of the introduction of autonomous vehicles on transport networks and estimated the safety, congestion, freight, parking and vehicle ownership impacts to social welfare and the economy. However, there appears to be a gap in the literature on the economic impacts of individuals allocating travel time in an autonomous vehicle to labour activities. This additional labour time could then have resulting impacts to productivity and the broader economy. This paper addresses this gap through the development of a novel microeconomic model incorporating time use in autonomous vehicles. The model captures an individual’s consumption behaviour, demand for leisure and supply of labour while accounting for the allocation of travel time to labour and leisure. It is an extension of existing microeconomic models of time use for two features: (1) travel utility, and (2) labour while travelling. This model is then implemented in an integrated computable general equilibrium and transport model for Sydney, Australia, and is tested to understand the order of magnitude of impacts. From this model, the increase in autonomous vehicle penetration rate and the resultant increases to household budget from travelling wages will result in a total welfare increase but with a decreasing rate, which are mainly due to congestion effects. Interestingly, the congestion effects also result in the production and value of time first increase, followed by a decrease, which are counter intuitive.
{"title":"Assessing the economic impacts of labour time in autonomous vehicles","authors":"Jason Wang , Sisi Jian , Edward N. Robson , Vinayak V. Dixit , Taha H. Rashidi","doi":"10.1016/j.multra.2025.100235","DOIUrl":"10.1016/j.multra.2025.100235","url":null,"abstract":"<div><div>Previous studies have analysed the impacts of the introduction of autonomous vehicles on transport networks and estimated the safety, congestion, freight, parking and vehicle ownership impacts to social welfare and the economy. However, there appears to be a gap in the literature on the economic impacts of individuals allocating travel time in an autonomous vehicle to labour activities. This additional labour time could then have resulting impacts to productivity and the broader economy. This paper addresses this gap through the development of a novel microeconomic model incorporating time use in autonomous vehicles. The model captures an individual’s consumption behaviour, demand for leisure and supply of labour while accounting for the allocation of travel time to labour and leisure. It is an extension of existing microeconomic models of time use for two features: (1) travel utility, and (2) labour while travelling. This model is then implemented in an integrated computable general equilibrium and transport model for Sydney, Australia, and is tested to understand the order of magnitude of impacts. From this model, the increase in autonomous vehicle penetration rate and the resultant increases to household budget from travelling wages will result in a total welfare increase but with a decreasing rate, which are mainly due to congestion effects. Interestingly, the congestion effects also result in the production and value of time first increase, followed by a decrease, which are counter intuitive.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.multra.2025.100230
Gengze Li , Changjian Liu , Rufeng Liao , Zhouhao Wu , Huapu Lu
E-commerce has the capacity to redirect certain individuals from physical transportation to online services, gradually altering human travel behaviours. This paper establishes a theoretical model to analyse the impact of e-commerce on the Land Use and Transport Interaction (LUTI) model. To achieve this goal, we employ the Metropolitan Activity Relocation Simulator (MARS) - attractiveness by car model to assess the sensitivity to e-commerce. The results demonstrate that e-commerce significantly affects the curve of attractiveness by car, and there is a positive correlation between them. The revised model offers insight into and explanation of how e-commerce impacts the LUTI model, providing a valuable tool for policymakers and planners to adapt to the current situation and effectively address issues.
{"title":"Analysis of the impact of E-commerce activities on residents' commute trips","authors":"Gengze Li , Changjian Liu , Rufeng Liao , Zhouhao Wu , Huapu Lu","doi":"10.1016/j.multra.2025.100230","DOIUrl":"10.1016/j.multra.2025.100230","url":null,"abstract":"<div><div>E-commerce has the capacity to redirect certain individuals from physical transportation to online services, gradually altering human travel behaviours. This paper establishes a theoretical model to analyse the impact of e-commerce on the Land Use and Transport Interaction (LUTI) model. To achieve this goal, we employ the Metropolitan Activity Relocation Simulator (MARS) - attractiveness by car model to assess the sensitivity to e-commerce. The results demonstrate that e-commerce significantly affects the curve of attractiveness by car, and there is a positive correlation between them. The revised model offers insight into and explanation of how e-commerce impacts the LUTI model, providing a valuable tool for policymakers and planners to adapt to the current situation and effectively address issues.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100230"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145398608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.multra.2025.100233
Rouzbeh Purshamsian, S.M.J. Mirzapour Al-e-Hashem, Akbar Esfahanipour, Pouria Tajasob
In case of disruption to the liner-shipping schedule, vessels cannot arrive at port calls on schedule, which creates a loss in service quality and credibility with a simultaneous increase in operating costs. Liner-shipping companies usually adopt two significant strategies: speeding up and skipping port calls to reduce such issues. This paper presents a mixed-integer nonlinear programming model for the simultaneous use of speed-up and port-skipping strategies for schedule recovery. The model, by taking into consideration lost revenue and the costs of transshipment for skipped ports, departs from the traditional cost-minimization approach by prioritizing profit maximization. It incorporates strategies for speed-up and port-skipping with loading and unloading variables in the process of optimally determining port calls as per demand and profitability. Decision variables on alternative port selections allow multiport alternatives that consider delays and transshipment costs. The nonlinear model is linearized using exact and approximate techniques and solved using CPLEX software. For large-scale cases, a self-learning particle swarm optimization algorithm is employed. Several test problems demonstrate the effectiveness of the metaheuristic. A real-world case is used to validate the model, showing its capability to derive optimal routes and schedules through the concurrent use strategies. The results show that the proposed model minimizes time deviations and operational losses. Moreover, sensitivity analysis highlights that while speed-up is adequate for shorter delays and less viable with high fuel costs, skipping strategies complement speed-up under significant delays, reinforcing the importance of integrated recovery strategies to address disruptions in the maritime transportation industry.
{"title":"Optimizing maritime transport schedule recovery strategies: a novel approach for speeding up and port-skipping in case of disruption","authors":"Rouzbeh Purshamsian, S.M.J. Mirzapour Al-e-Hashem, Akbar Esfahanipour, Pouria Tajasob","doi":"10.1016/j.multra.2025.100233","DOIUrl":"10.1016/j.multra.2025.100233","url":null,"abstract":"<div><div>In case of disruption to the liner-shipping schedule, vessels cannot arrive at port calls on schedule, which creates a loss in service quality and credibility with a simultaneous increase in operating costs. Liner-shipping companies usually adopt two significant strategies: speeding up and skipping port calls to reduce such issues. This paper presents a mixed-integer nonlinear programming model for the simultaneous use of speed-up and port-skipping strategies for schedule recovery. The model, by taking into consideration lost revenue and the costs of transshipment for skipped ports, departs from the traditional cost-minimization approach by prioritizing profit maximization. It incorporates strategies for speed-up and port-skipping with loading and unloading variables in the process of optimally determining port calls as per demand and profitability. Decision variables on alternative port selections allow multiport alternatives that consider delays and transshipment costs. The nonlinear model is linearized using exact and approximate techniques and solved using CPLEX software. For large-scale cases, a self-learning particle swarm optimization algorithm is employed. Several test problems demonstrate the effectiveness of the metaheuristic. A real-world case is used to validate the model, showing its capability to derive optimal routes and schedules through the concurrent use strategies. The results show that the proposed model minimizes time deviations and operational losses. Moreover, sensitivity analysis highlights that while speed-up is adequate for shorter delays and less viable with high fuel costs, skipping strategies complement speed-up under significant delays, reinforcing the importance of integrated recovery strategies to address disruptions in the maritime transportation industry.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420456","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}
The widespread deployment of information and communication technology (ICT) has allowed ride hailing services to grow exponentially over the last few years. This study aims to investigate ride hailing use frequency among university students in Banda Aceh, Indonesia. The city offers unique characteristics since it has limited public transport facilities, a growing number of vehicle ownership, and traffic dominated by motorcycles. Ride hailing services in this city are predominantly motorcycle-based, hence they could potentially have substitution effects on private vehicle use and ownership. This study focused on university students because their travel behaviour often differs from general public, and they form the majority users of ride hailing services. Ordinal logistic regression was employed, drawing from 219 valid responses from a survey disseminated in 2023. The variables explored include individual and household characteristics, travel attributes, and perceptions on ride hailing services. It was found that the most significant factors that encourage students to frequently use ride hailing services include working part-time, having high allowance, perceiving good level of convenience and service. On the other hand, attributes that have the biggest effects in discouraging ride hailing use include low travel cost, sitting in 5th semester or later, and living in a private house. This study offers insights into ride hailing behaviour in the context of a city with limited public transport facilities and severe motorcycle composition, which can help regulators and operators to improve policies around the platforms.
{"title":"Investigating factors that influence the frequency of ride hailing use among university students in Banda Aceh, Indonesia","authors":"Fadhlullah Apriandy, Lulusi Lulusi, Ruhdi Faisal, Juliana Fisaini, Luthfi Chaliqi Taufiq, Aqlima Putri","doi":"10.1016/j.multra.2025.100231","DOIUrl":"10.1016/j.multra.2025.100231","url":null,"abstract":"<div><div>The widespread deployment of information and communication technology (ICT) has allowed ride hailing services to grow exponentially over the last few years. This study aims to investigate ride hailing use frequency among university students in Banda Aceh, Indonesia. The city offers unique characteristics since it has limited public transport facilities, a growing number of vehicle ownership, and traffic dominated by motorcycles. Ride hailing services in this city are predominantly motorcycle-based, hence they could potentially have substitution effects on private vehicle use and ownership. This study focused on university students because their travel behaviour often differs from general public, and they form the majority users of ride hailing services. Ordinal logistic regression was employed, drawing from 219 valid responses from a survey disseminated in 2023. The variables explored include individual and household characteristics, travel attributes, and perceptions on ride hailing services. It was found that the most significant factors that encourage students to frequently use ride hailing services include working part-time, having high allowance, perceiving good level of convenience and service. On the other hand, attributes that have the biggest effects in discouraging ride hailing use include low travel cost, sitting in 5th semester or later, and living in a private house. This study offers insights into ride hailing behaviour in the context of a city with limited public transport facilities and severe motorcycle composition, which can help regulators and operators to improve policies around the platforms.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.multra.2025.100236
Paul (Young Joun) Ha , Monika Filipovska , Runjia Du , Samuel Labi , Sikai Chen
With growing concerns over urban congestion and its associated economic, environmental, and societal costs, innovative solutions are urgently needed. Autonomous vehicles (AVs) offer a promising opportunity to improve urban transportation efficiency, safety, and sustainability. This study introduces a novel framework, DRL-CEDAR, which combines deep reinforcement learning (DRL) for optimizing AV acceleration and a dynamic routing algorithm, Constrained Entropy-based Dynamic AV Routing (CEDAR), to strategically guide AVs through urban traffic networks. The DRL module trains AVs to reduce emissions and fuel consumption while increasing vehicle speeds, while the CEDAR module enables AVs to interact with human-driven vehicles (HDVs) to mitigate network-wide congestion. Simulation results on a midtown Manhattan network demonstrate that even with a 10% AV adoption rate, DRL-CEDAR achieves reductions of 34% in CO emissions, 11% in CO2 emissions, and 78% in vehicle waiting times compared to conventional routing methods. These findings underscore the potential of DRL-CEDAR to significantly improve urban traffic conditions and support sustainable mobility goals.
{"title":"Autonomous vehicle fleet operations with constrained entropy-based dynamic autonomous vehicle routing: Insights for environmentally conscious shared mobility","authors":"Paul (Young Joun) Ha , Monika Filipovska , Runjia Du , Samuel Labi , Sikai Chen","doi":"10.1016/j.multra.2025.100236","DOIUrl":"10.1016/j.multra.2025.100236","url":null,"abstract":"<div><div>With growing concerns over urban congestion and its associated economic, environmental, and societal costs, innovative solutions are urgently needed. Autonomous vehicles (AVs) offer a promising opportunity to improve urban transportation efficiency, safety, and sustainability. This study introduces a novel framework, DRL-CEDAR, which combines deep reinforcement learning (DRL) for optimizing AV acceleration and a dynamic routing algorithm, Constrained Entropy-based Dynamic AV Routing (CEDAR), to strategically guide AVs through urban traffic networks. The DRL module trains AVs to reduce emissions and fuel consumption while increasing vehicle speeds, while the CEDAR module enables AVs to interact with human-driven vehicles (HDVs) to mitigate network-wide congestion. Simulation results on a midtown Manhattan network demonstrate that even with a 10% AV adoption rate, DRL-CEDAR achieves reductions of 34% in CO emissions, 11% in CO<sub>2</sub> emissions, and 78% in vehicle waiting times compared to conventional routing methods. These findings underscore the potential of DRL-CEDAR to significantly improve urban traffic conditions and support sustainable mobility goals.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 1","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-23DOI: 10.1016/j.multra.2025.100238
Wenjun Jia , Ke Zhang , Xiaolei Ma , Meng Li
Multimodal transportation systems significantly enhance travel convenience but also introduce vulnerabilities. Disruptions in one segment can cascade across the network, compromising overall network performance. These disruptions, often stemming from diverse attack scenarios, highlight the critical need to study and enhance resilience in transportation networks. This paper introduces a resilience assessment model that considers the characteristics of abrupt events in passenger networks. A series of attack scenarios are set up, categorized by the extent of node capability degradation, the number and types of nodes subjected to attack, and the duration of the attack. Focusing on Beijing's bus-metro multimodal network, the results show that in certain scenarios, recovery performance is worse when passengers transfer to both nearby bus and subway stations after a subway station attack, compared to transferring only to bus stations. This is due to longer walking transfer times and higher passenger volumes at subway stations, which increase flow delays and risk cascading failures. Furthermore, off-peak node failures also worsen network performance due to reduced scheduling frequency. Consequently, the entire transportation system requires an extended recovery period. These insights are critical for informing targeted emergency recovery strategies in the aftermath of public transportation disruptions.
{"title":"Resilience assessment for bus-metro multimodal networks considering various attacking scenarios","authors":"Wenjun Jia , Ke Zhang , Xiaolei Ma , Meng Li","doi":"10.1016/j.multra.2025.100238","DOIUrl":"10.1016/j.multra.2025.100238","url":null,"abstract":"<div><div>Multimodal transportation systems significantly enhance travel convenience but also introduce vulnerabilities. Disruptions in one segment can cascade across the network, compromising overall network performance. These disruptions, often stemming from diverse attack scenarios, highlight the critical need to study and enhance resilience in transportation networks. This paper introduces a resilience assessment model that considers the characteristics of abrupt events in passenger networks. A series of attack scenarios are set up, categorized by the extent of node capability degradation, the number and types of nodes subjected to attack, and the duration of the attack. Focusing on Beijing's bus-metro multimodal network, the results show that in certain scenarios, recovery performance is worse when passengers transfer to both nearby bus and subway stations after a subway station attack, compared to transferring only to bus stations. This is due to longer walking transfer times and higher passenger volumes at subway stations, which increase flow delays and risk cascading failures. Furthermore, off-peak node failures also worsen network performance due to reduced scheduling frequency. Consequently, the entire transportation system requires an extended recovery period. These insights are critical for informing targeted emergency recovery strategies in the aftermath of public transportation disruptions.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 4","pages":"Article 100238"},"PeriodicalIF":0.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549015","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}
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