Multimodal Transportation最新文献

{"title":"Effect of COVID-19 pandemic on freight volume, revenue and expenditure of deendayal port in India: An ARIMA forecasting model","authors":"Deepjyoti Das ,&nbsp;Aditya Saxena","doi":"10.1016/j.multra.2025.100201","DOIUrl":"10.1016/j.multra.2025.100201","url":null,"abstract":"<div><div>Shipping sector is vital to Indian economy, making it crucial to understand the economic impact of the COVID-19 pandemic on port operations to develop strategies for future resilience. This study examines the effects of COVID-19 on Deendayal Port, a key Indian port, by analyzing freight volume, revenue, and expenditure data from April 2012 to October 2022. Autoregressive Integrated Moving Average (ARIMA) modeling covers pre-COVID, two COVID-19 waves, and post-COVID scenarios. Ordinary Least Squares (OLS) regression models for revenue and expenditure evaluate economic losses. The results show 6.2% decline in freight volume during the first wave, with a decrease from 123.4 million tons (Mt) to 115.8 Mt, leading to a monthly average loss of 0.6 Mt. The second wave saw recovery, with freight volume increasing from the forecasted 127.6 Mt to 129.6 Mt, resulting in a monthly gain of 0.2 Mt. Revenue losses during wave 1 were 215 crore INR, while wave 2 saw a revenue increase of 57 crore INR. The study highlights the importance of operational efficiency and managing key cost drivers like volume and manpower to maintain financial stability. These findings lay a foundation for future research to strengthen the shipping industry's resilience and sustainability in post-pandemic world.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the adoption of urban air mobility based on technology acceptance and risk perception theories: A case study on flying cars","authors":"Sangen Hu ,&nbsp;Zikang Huang ,&nbsp;Ke Wang ,&nbsp;Haiyuan Lin ,&nbsp;Mingyang Pei","doi":"10.1016/j.multra.2025.100200","DOIUrl":"10.1016/j.multra.2025.100200","url":null,"abstract":"<div><div>Flying cars, a symbol of Urban Air Mobility (UAM), signify a pivotal step in revolutionizing urban transportation and play a pivotal role in shaping future transport systems. To enhance travelers' willingness to accept flying cars and promote the widespread adoption of this novel transportation mode, this study develops a comprehensive model to explore key factors determining the public's acceptance of flying cars by integrating the Technology Acceptance Model, Risk Perception Theory, and Trust Theory. The validity of the model was confirmed through a rigorous structure equation modeling analysis, utilizing 553 sample data collected from a network questionnaire survey across a diverse demographic of the Chinese market. Results revealed significant associations between the intention to use flying cars and various factors, including attitudes towards usage, perceived usefulness, and personal innovativeness. Heterogeneity analysis further uncovered how demographic factors (such as age, gender, education, and possession of a driver's license) impacted perceptions and acceptance. As the study concludes, despite general optimism, public acceptance of flying cars is strongly influenced by factors such as cost, safety, and privacy concerns play crucial roles in public acceptance. The insights from this study provide valuable implications for manufacturers, policymakers, and marketers in strategizing the introduction and promotion of flying cars.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mind the perception gap: Identifying differences in views among stakeholder groups of shared mobility services through bayesian best-worst method","authors":"Ehsan Amirnazmiafshar ,&nbsp;Marco Diana","doi":"10.1016/j.multra.2025.100198","DOIUrl":"10.1016/j.multra.2025.100198","url":null,"abstract":"<div><div>This study investigates perception gaps among stakeholders—policy-makers, operators, users, and non-users—regarding car-sharing, bike-sharing, and scooter-sharing systems in Turin, Italy. Based on 628 surveys collected between November 2021 and February 2022 and analyzed using the Bayesian Best-Worst Method (BWM) multicriteria technique, it highlights key differences in prioritizing factors influencing shared mobility demand.</div><div>Key Findings: For car-sharing, policy-makers overestimate the importance of trip purpose compared to both users and non-users, while undervaluing service availability. Operators undervalue trip-related factors, such as travel time and departure time, while overemphasizing user-friendliness. For bike-sharing, policy-makers overestimate travel time compared to users while undervaluing travel comfort and environmental friendliness compared to both users and non-users. Operators underestimate trip-related factors, including travel distance and trip purpose, while overemphasizing environmental friendliness, particularly compared to non-users. For scooter-sharing, policy-makers underestimate trip-related characteristics, such as travel time and departure time, while overestimating travel cost and user-friendliness compared to non-users. Operators undervalue travel comfort and service availability, while overestimating travel distance, especially compared to users.</div><div>Managerial Insights: For car-sharing, policy-makers should expand service coverage and incentivize vehicle deployment, while operators should use dynamic fleet management and offer flexible booking options. For bike-sharing, policy-makers should subsidize fleet expansion and improve infrastructure, while operators should transition to free-floating models and integrate navigation tools. For scooter-sharing, policy-makers should enforce safety standards and improve accessibility, while operators should invest in high-quality scooters and adopt competitive pricing models.</div><div>Bridging these perception gaps is essential for fostering shared mobility adoption and enhancing user satisfaction.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 2","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143099538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of motorcyclists crash severity using cluster correspondence and hierarchical binary logit models","authors":"Richard Dzinyela ,&nbsp;Bahar Dadashova ,&nbsp;Grant Westfall ,&nbsp;Subasish Das ,&nbsp;Chiara Silvestri-Dobrovolny ,&nbsp;Emmanuel Kofi Adanu ,&nbsp;Dominique Lord","doi":"10.1016/j.multra.2025.100197","DOIUrl":"10.1016/j.multra.2025.100197","url":null,"abstract":"<div><div>Crashes involving motorcyclists account for a significant portion of traffic-related injuries and fatalities. Despite motorcycles making only three percent of all registered vehicles, motorcyclists account for 14 percent of all roadway fatalities. As the number of motorcyclists increase, there is an urgent need to understand the factors that affect the severity of injuries they sustain in crashes. In this paper, we use cluster correspondence analysis (CCA) and hierarchical binary logit model to explore the factors associated with motorcyclists’ crash injury severities in Utah between 2016 and 2020. Cluster correspondence analysis was used to cluster the crash data into seven groups, while hierarchical binary logit model was used to identify the significant factors that contributed to the injury severity of motorcycle crashes. The results of this study indicate that among the crash-contributing factors the motorcyclist age, roadway alignment, roadside safety systems and temporal factors significantly contribute to motorcyclist crash severities. The model results further account for the correlation of variables within the clusters in the crash data. With the deeper understanding of the relationship between crash factors and injury severity in this study, the findings can help decision makers to implement targeted countermeasures to improve motorcyclist safety.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cost function approximation method for dynamic vehicle routing with docking and LIFO constraints","authors":"Markó Horváth,&nbsp;Tamás Kis,&nbsp;Péter Györgyi","doi":"10.1016/j.multra.2025.100194","DOIUrl":"10.1016/j.multra.2025.100194","url":null,"abstract":"<div><div>In this paper, we study a dynamic pickup and delivery problem with docking constraints. There is a homogeneous fleet of vehicles to serve pickup-and-delivery requests at given locations. The vehicles can be loaded up to their capacity, while unloading has to follow the last-in-first-out (LIFO) rule. The locations have a limited number of docking ports for loading and unloading, which may force the vehicles to wait. The problem is dynamic since the transportation requests arrive real-time, over the day. Accordingly, the routes of the vehicles are to be determined dynamically. The goal is to satisfy all the requests such that a combination of tardiness penalties and traveling costs is minimized. We propose a cost function approximation based solution method. In each decision epoch, we solve the respective optimization problem with a perturbed objective function to ensure the solutions remain adaptable to accommodate new requests. We penalize waiting times and idle vehicles. We propose a variable neighborhood search based method for solving the optimization problems, and we apply two existing local search operators, and we also introduce a new one. We evaluate our method using a widely adopted benchmark dataset, and the results demonstrate that our approach significantly surpasses the current state-of-the-art methods.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in crash types and contributing factors after bus rapid transit (BRT) infrastructure installation in Albuquerque, New Mexico","authors":"Nicholas N. Ferenchak,&nbsp;Brady A. Woods","doi":"10.1016/j.multra.2025.100192","DOIUrl":"10.1016/j.multra.2025.100192","url":null,"abstract":"<div><div>Bus rapid transit (BRT) is an increasingly popular form of public transportation that seeks to achieve the speed and reliability of fixed rail with the flexibility and affordability of a bus system. In this paper, we examine safety outcomes before and after the construction of BRT infrastructure, specifically investigating how different crash types and contributing factors changed for all motor vehicle crashes and for pedestrian crashes. New Mexico Department of Transportation (NMDOT) provided crash data for the Central Avenue corridor of the Albuquerque Rapid Transit (ART) system in Albuquerque, NM. The construction of ART correlated with significant reductions in crashes attributed to excessive speed (for all modes) and left turning vehicles (for all modes and pedestrians). Crashes attributed to excessive speed decreased by 19.1 % (<em>p</em> = 0.059) after ART construction while crashes attributed to excessive speed resulting in fatal or serious (KA) injury decreased 100.0 % (<em>p</em> &lt; 0.001). Although the number of KA pedestrian crashes increased 15.2 % (<em>p</em> = 0.272), KA pedestrian crashes involving a left-turning motor vehicle decreased by 80.0 % (<em>p</em> = 0.070). For all modes, crashes involving left-turning vehicles decreased by 34.8 % (<em>p</em> &lt; 0.001) and crashes involving left-turning vehicles resulting in a KA injury decreased by 87.5 % (<em>p</em> = 0.009). This research provides insights into the multimodal traffic safety implications of the burgeoning public transportation mode of BRT.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling passenger satisfaction in rail transit through a consumption values perspective","authors":"Siti Norida Wahab ,&nbsp;Muhammad Iskandar Hamzah ,&nbsp;Norazah Mohd Suki ,&nbsp;Yueh Suan Chong ,&nbsp;Chin Pei Kua","doi":"10.1016/j.multra.2025.100196","DOIUrl":"10.1016/j.multra.2025.100196","url":null,"abstract":"<div><div>This study aims to investigate passenger satisfaction in rail transit systems within emerging economies, utilizing a theory of consumption values. It seeks to understand how various values, such as functional, social, emotional, conditional, and epistemic, influence passenger perceptions and experiences providing insights for enhancing rail transit services. This study employs a self-administered questionnaire distributed to 418 passenger rail transit users in Kuala Lumpur city centre over a three-month period. Smart-PLS software was utilized to examine relationships between consumption values and passenger satisfaction. The study findings reveal strong support for functional and social values in influencing passenger satisfaction within rail transit systems of emerging economies. Similarly, emotional and conditional values also play a significant role. Surprisingly, epistemic value does not exhibit substantial influence, highlighting potential disparities in passenger perceptions and priorities. Rail transit operators and regulators should focus on these facets of consumption values in order to maximize passenger satisfaction in rail transit. Conditional aspects such as safety, punctuality, frequency, and accessibility should also be given priority. What is new to the existing literature is that epistemic value was confirmed as the trivial predictor of passengers' satisfaction in rail transit. Hence, clear signage, informative announcements, or accessible digital resources provided by the transit authority will enhance passengers' knowledge and overall experience. Being among a few studies in measuring rail transit satisfaction using the consumption values approach, particularly in the context of Asia-Pacific emerging economies, the empirical results attained broadened the growing literature pertinent to consumer behaviour, consumption values, and sustainable transportation. The findings offer new insights into enhancing rail transit services, emphasizing the need for clear communication and informative resources to boost passenger satisfaction.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal adaptive traffic signal control: A decentralized multiagent reinforcement learning approach","authors":"Kareem Othman ,&nbsp;Xiaoyu Wang ,&nbsp;Amer Shalaby ,&nbsp;Baher Abdulhai","doi":"10.1016/j.multra.2025.100190","DOIUrl":"10.1016/j.multra.2025.100190","url":null,"abstract":"<div><div>Public transit is considered a compelling alternative to the car, renowned for its affordability and sustainability, given that a single transit vehicle can accommodate a substantially higher number of passengers compared to regular passenger vehicles. In urban areas, a significant portion of the travel time spent by street-running transit vehicles is consumed waiting at traffic signals. Thus, transit signal priority (TSP) strategies have evolved over the years to give preference to transit vehicles at signalized intersections. Traffic signals are usually optimized for the general vehicular traffic flow, with TSP logic subsequently inserted as an add-on to modify the underlying signal timing plans, thereby granting priority to transit vehicles. However, one major issue associated with the implementation of TSP is its negative impact on the surrounding traffic, creating a conflict between prioritizing passenger vehicles versus transit vehicles. This paper proposes a novel decentralized multimodal multiagent reinforcement learning signal controller that simultaneously optimizes the total person delays for both traffic and transit. The controller, named embedding communicated Multi-Agent Reinforcement Learning for Integrated Network-Multi Modal (eMARLIN-MM), consists of two components: the encoder that is responsible for transforming the observations into latent space and the executor that serves as the Q-network making timing decisions. eMARLIN-MM establishes communication between the control agents by sharing information between neighboring intersections. eMARLIN-MM was tested in a simulation model of five intersections in North York, Ontario, Canada. The results show that eMARLIN-MM can substantially reduce the total person delays by 54 % to 66 % compared to pre-timed signals at different levels of bus occupancy, outperforming the independent Deep Q-Networks (DQN) agents. eMARLIN-MM also outperforms eMARLIN which does not incorporate buses and bus passengers in the signal timing optimization process.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination","authors":"Farshad Farahnakian,&nbsp;Paavo Nevalainen,&nbsp;Fahimeh Farahnakian,&nbsp;Tanja Vähämäki,&nbsp;Jukka Heikkonen","doi":"10.1016/j.multra.2025.100191","DOIUrl":"10.1016/j.multra.2025.100191","url":null,"abstract":"<div><div>Ship movement prediction models are crucial for improving safety and situational awareness in complex maritime shipping networks. Current prediction models that utilize Automatic Identification System (AIS) data to forecast ship movements typically rely on a fixed look-back window size. This approach does not effectively consider the necessary amount of data required to train the models properly. This paper presents a framework that dynamically determines the optimal look-back window size for AIS data, tailored to user-defined prediction intervals. Initially, a DBSCAN clustering method, along with various pre-processing techniques, has been employed to efficiently eliminate non-essential data points and address noise in the raw AIS data. Following this, Temporal Convolutional Networks (TCNs) have been trained using the dynamic characteristics of ship movements based on one month of AIS data (April 2023) collected from the Baltic Sea, evaluating various look-back window sizes to identify the optimal size required for predictions. Subsequently, the framework has been tested using an additional AIS dataset in two scenarios: 1-hour and 5-hour predictions. The experimental results indicate that the proposed framework can effectively select the necessary AIS samples for forecasting a ship’s future movements. This framework has the potential to optimize prediction services by identifying the ideal look-back window size, thereby providing maritime agents with high-quality and accurate predictions to enhance their decision-making processes.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous vehicle safety: An advanced bagging classifier technique for crash injury prediction","authors":"Sai Sneha Channamallu,&nbsp;Deema Almaskati,&nbsp;Sharareh Kermanshachi,&nbsp;Apurva Pamidimukkala","doi":"10.1016/j.multra.2025.100189","DOIUrl":"10.1016/j.multra.2025.100189","url":null,"abstract":"<div><div>The increasing utilization of autonomous vehicles (AVs) makes it critical to understand and mitigate their involvement in traffic accidents. This study, therefore, addresses a significant gap in the research on AV safety by focusing on predicting the possibility of injuries in AV-involved crashes. The California Department of Motor Vehicles’ comprehensive dataset of accidents that occurred from 2014 to May 2024 was utilized, and advanced machine learning techniques were applied to develop a model capable of predicting the outcomes of accidents involving AVs. The study found that the bagging classifier model outperforms other models in reliably predicting and identifying severe crashes and minimizing misclassification. Evaluations made through precision-recall, validation, and learning curves confirm the model's robustness, ability to generalize across data subsets, and effectiveness in increasing training data. Key predictors of crash severity include the extent of damage to the AV, vehicle type, manufacturer, and presence of a traffic signal. The study highlights the importance of tailored safety measures, robust safety mechanisms, and advanced traffic management systems to mitigate crash severity. The real-world application of this advanced model promises substantial benefits for vehicle manufacturers, urban planners, policymakers, and end-users, and will contribute to safer roadways.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 1","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}