Multimodal Transport Research Articles

IMPReSS: A comprehensive method to classify MaaS systems

Mobility as a Service (MaaS) has the potential to improve urban mobility by reducing car ownership and promoting multimodal transport through a single mobility app that provides trip planners, booking, ticketing, and payment for different modes and bundles. However, not all existing MaaS (or potential MaaS) systems provide a fully integrated system. Several authors have proposed different methodologies to assess MaaS integration. However, these approaches have some limitations regarding the scalability or characterization of the systems, making it hard to compare between them, as the current approaches are all incremental. In this work, we propose a new method called IMPReSS, a binary coding approach that assesses several dimensions of MaaS: Information, Multimodality, Payment, Reservation, Subscription, and Societal goals. It allows proper classification and comparison of different systems, using the societal goals as a cornerstone for its evaluation. Also, we propose a complementary scoring system based on the IMPReSS topology. This qualitative and quantitative methodology will allow practitioners and researchers to characterize, identify, and adequately assess transportation services and their potential to become a fully integrated MaaS system. Our results show that mature MaaS systems with public authorities involved, and some often excluded from MaaS discussion systems are ranked higher, while some wrongly called MaaS apps are ranked at the bottom. In summary, it assesses systems from a different and flexible perspective.

Investigating urban mobility through multi-source public transportation data: A multiplex network perspective

The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.

Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach

Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns.First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon’s entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman’s method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

SPATIAL PARAMETERS FOR MULTIMODAL INTEGRATION AT METRO STATIONS: A CONDUCIVE CASE STUDY OF METRO STATIONS IN DELHI

Urban public transit contributes to the multisector growth of a developing economy and is an essential component of sustainable urban development. Public transit has seen considerable upsurge in ridership over the past decade. In order to make public transportation more appealing to regular commuters than private automobiles, the current infrastructure in the majority of India's metropolitan cities must be modernized in a sustainable way. The Delhi Metro was designed with an identical objective in consideration, anticipating the increasing concern over traffic in and around the Delhi region, and after being implemented, it significantly reduced the number of individual vehicles on the road. Delhi Metro operates within the framework of the citywide multi-modal integrated transportation system, reinforced by other modes such as buses and IPT. It is the lifeline of Delhi's urban transit system. This research paper aims to study numerous aspects of multimodal integration at metro stations in order to increase the passengers' comfort and convenience. Convenient transitions in a multimodal transportation system are greatly aided by station amenities like the physical infrastructure, which includes the parking space, transfer area, metro ridership data, pedestrian network, cycling facilities, feeder shelter, taxi stands (IPT Stands), etc. Furthermore, it intends to promote walkability by establishing a high-quality public realm that is both welcoming and secure for anyone to use. This research is centered around Delhi Metro stations on the Yellow line. An extensive inventory of the station area and a commuter travel time questionnaire were used to evaluate the current state of affairs. Several standards of design were analyzed to optimize the output of research. The inferences yield specific recommendations and modifications for the present system, in addition to identifying several best practices that could be reiterated across all stations.

Research on optimization of multimodal hub-and-spoke transport network under uncertain demand

In the cargo transportation system, the hub-and-spoke transport network can make full use of the scale effect between logistics hubs and reduce logistics costs. Joint transportation of multiple modes of transportation can give full play to the advantages of different modes of transportation, which not only reduces logistics costs but also improves transportation efficiency. Therefore, this paper combines the advantages of multimodal transportation and hub-and-spoke network, and establishes an optimization model of multimodal hub-and-spoke transport network under demand uncertainty. The model takes into account hub capacity constraints and customer satisfaction with respect to transportation time, and to facilitate the model solution, we utilize the fuzzy expected value method and the fuzzy chance constraints based on credibility to clarify the uncertain variables in the model. We use mixed coding to describe the selection of hubs, assignment of nodes, and choice of transportation modes in this study and use the NSGA-II algorithm with local reinforcement to solve the problem. Finally, numerical experiments are designed to verify the validity of the model and algorithm through sensitivity analysis of relevant parameters, determine the reasonable number of hubs and confidence level, and obtain the influence of the change of hub capacity limit and the ratio of single and double hub transit on the research objectives. The results show that: the NSGA-II algorithm with local reinforcement can significantly improve the convergence speed of the algorithm; There is benefit inversion between economic cost and time cost, and the pursuit of economic cost minimization and time cost minimization, respectively, will lead to different choices of the number of hubs; Increasing the ratio of goods transfer between hubs is beneficial for fully utilizing the scale effect between hubs, achieving the goal of reducing economic costs, but at the same time, it will increase time costs.

복합가치항만 성장을 위한 인천항의 물동량 인센티브 제도 개선방안 연구

Purpose : In order for Incheon Port to grow into a complex value port, this study intends to propose ways to strengthen the feedback function of the incentive system from a long-term perspective, flexibly manipulate strategic area incentives, and support Sea&Air multimodal transportation through the analysis of the current status and effectiveness of Incheon Port's volume incentives. Research design, data and methodology : This thesis adopted a literary focused research method. In order to deal with the contents determined by the scope of the study, a theoretical approach targeting the current status of domestic and foreign port incentives, support systems, and related theses was used. Results : Effect analysis showed that incentive systems influenced the decision of port users regarding which port to use. Each port user was found to have a very positive perception of the financial effects of offsetting future logistics costs through port incentive systems. Conclusions : As an improvement plan, first, it seems necessary to strengthen the feedback function of the incentive system. Second, flexible adjustments of incentives in strategic areas are needed. Third, Sea&Air incentives must be improved. Fourth, there may be need for review of incentive system standardization.

Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne

Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including motorized (i.e. cars and public transport) and non-motorized (i.e. walk and bicycle) modes of transport is a complicated and involved task. This paper presents an open workflow for creating large-scale multi-modal agent-based transport simulation models. The workflow brings together a number of external tools, for example, an activity-based demand generation tool and a road network generation tool, and a set of tools developed for the agent-based model parameter estimation, calibration, and simulation post-processing. We used this workflow to create an activity- and agent-based model for Melbourne and compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behavior of travelers. The calibrated model could be used to test road network change interventions. In addition, a similar workflow can be applied for building simulation models for other cities or could be expanded to include more complicated travel behaviors.

Enhancing Port Security and Predictive Maintenance with IoT: Cadets' Perspectives

This research explores the perspectives of 100 cadets studying multimodal transportation on integrating Internet of Things (IoT) to enhance port security and predictive maintenance. Using qualitative methods, including interviews and document analysis, the study investigates the effectiveness of IoT in real-time monitoring, data security, and predictive maintenance. The findings highlight cadets' recognition of IoT's potential to transform port operations, particularly in improving security measures and maintenance strategies. Cadets also emphasize the importance of professionalism and adherence to standards in IoT integration, valuing compliance with standards, understanding of IoT, training and education, and ethical considerations. The research underscores the need for continuous education and training programmes to prepare future industry professionals for the challenges and opportunities presented by IoT technologies. Overall, this study contributes to the advancement of knowledge in transportation management and education, offering insights for policymakers, industry practitioners, and educators on the integration of IoT in port operations.

АНАЛИЗ ФУНКЦИОНИРОВАНИЯ МУЛЬТИМОДАЛЬНЫХ ГРУЗОПЕРЕВОЗОК НА ПРИМЕРЕ ПЕРИОДА ПАНДЕМИИ

The article examines the state of the Russian transport industry during the period of the massive spread of the COVID-19 pandemic. The paper analyzes the main performance indicators of freight forwarding organizations in the context of a pandemic and examines the concept of multimodal transportation. An assessment of the contribution of each type of transport in the implementation of cargo transportation is given, considering the impact of COVID-19

Enhancing Multimodal Route Optimization in Maritime Transport: Integrating Real-Time Data and Professionalism

This research investigates methods to enhance multimodal route optimization in the maritime sector through the integration of real-time traffic data and professionalism standards. Conducted by researchers and lecturers at the Maritime Institute, the study involves 100 cadets predominantly studying multimodal transportation, logistics, and port management. The research utilises qualitative methods to explore cadets' perspectives on route optimization and professionalism in the industry. Findings reveal a recognition of the significance of real-time traffic data for informed routing decisions, along with an emphasis on compliance with international standards, training, and ethical considerations. Furthermore, cadets perceive machine learning algorithms as effective tools to address the complexities of route optimization. These insights contribute to advancing knowledge in transportation management and education, highlighting the importance of integrating real-time data and professionalism in enhancing route optimization practices.

An Evolutionary Multitasking Ant Colony Optimization Method Based on Population Diversity Control for Multimodal Transport Problems

Multimodal transport is a challenging, NP-hard problem in combinational optimization and has been solved using evolutionary algorithms, which excel at solving large-scale problems. However, few studies have used evolutionary algorithms, particularly swarm intelligence algorithms, to concurrently handle multiple multimodal transport instances. Ant colony optimization (ACO), which is a population intelligence technique that is adept at identifying the optimal paths in graphs, has been primarily used to address tasks separately rather than concurrently. Therefore, in this study, we introduce a multipopulation-based multitask environment where task-specific populations run in parallel, and ACO serves as the optimizer for each task. A variance-based population diversity measure is then calculated to characterize the distribution differences among individuals. If the population diversity of a specific task falls below a predetermined threshold, the valuable routing traits extracted from other tasks are transferred to the stagnant population. Our method is called population diversity-controlled multitask ACO (PDMTACO). We use multiple benchmark traveling salesman problem (TSP) instances at different scales to validate the efficacy of PDMTACO. Subsequently, we extend PDMTACO to address a series of multimodal transport problems. Our experimental results demonstrate that the use of information transferred by our method significantly reduces its logistics costs and carbon emissions in all multimodal transport tasks.

Strategic integration of vertiport planning in multimodal transportation for urban air mobility: A case study in Beijing, China

In pursuit of strategic sustainable development goals, this study seeks to analyze the spatial placement of vertiports for urban air mobility, positioning them as integral components within the multimodal transportation system. We propose an iterative weighted k-means clustering method that incorporates the value of travel time of different transportation modes to identify potential vertiport locations. A multi-objective optimization model that maximizes total air taxi ridership and minimizes total facility costs and travel distances to vertiports is developed considering the constraints of the distance from vertiports to existing mobility hubs. A case study is conducted using the data of Beijing city, incorporating the modes of taxis and metro. We found that 19 vertiports can guarantee a robust performance of urban air mobility service. Users of urban air mobility can benefit from substantial travel time savings of 80% (48 min on average). This study contributes to a deeper understanding of the optimal setting of urban air mobility facilities from the perspective of network synchronization, offering useful insights into policy decision-making for sustainable infrastructure planning.

Modified dung beetle optimizer with multi-strategy for uncertain multi-modal transport path problem

Abstract Uncertain multi-modal transport path optimization (UMTPO) is a combined optimization non-deterministic polynomial-time hard problem. Its goal is to determine a path with the lowest total transportation cost and carbon emissions from the starting point to the destination. To effectively address this issue, this article proposes a modified dung beetle optimizer (DBO) to address it. DBO is a swarm-based metaheuristic optimization algorithm that has the features of a fast convergence rate and high solution accuracy. Despite this, the disadvantages of weak global exploration capability and falling easily into local optima exist. In this article, we propose a modified DBO called MSHDBO for function optimization and to solve the UMTPO problem. However, for the vast majority of metaheuristic algorithms, they are designed for continuous problems and cannot directly solve discrete problems. Therefore, this article employs a priority based encoding and decoding method to solve the UMTPO problem. To verify the performance and effectiveness of the MSHDBO algorithm, we compared it with other improved versions of the DBO algorithm used in the literature. We confirmed the excellent performance of MSHDBO using 41 benchmark test functions from the IEEE CEC 2017 test suite and IEEE CEC 2022 test suite. Additionally, we compared the MSHDBO algorithm with 10 other state-of-the-art metaheuristic optimization algorithms through a practical UMTPO problem. The experimental results indicated that the MSHDBO algorithm achieved very good performance when solving the UMTPO problem.

Reliable truck-drone routing with dynamic synchronization: A high-dimensional network programming approach

Within the context of burgeoning multimodal transportation systems, the emerging integration of drones and trucks is shaping the landscape of last-mile delivery. In this service-centric industry, uncertainty in travel time challenges the stability of delivery systems, highlighting the need for a more reliable and effective methodology. To tackle this challenge, we propose a reliable truck-drone collaborative routing problem enabling dynamic synchronization between trucks and drones in this paper. An integer programming model based on a space–time-state network has been developed to minimize the total travel time as well as their associated variabilities. Furthermore, to overcome the computational challenges for large-scale scenarios, a multi-dimensional-based hierarchical decomposition has been developed to identify a high-quality solution effectively. Numerical experimental results based on an illustrative network and a real-world network demonstrate the effectiveness of the introduced algorithm. Our proposed methodology advances operational planning for truck-drone collaborative delivery by appropriately navigating unavoidable variations of travel time, shedding light on the broad development prospects of the multimodal transportation system.

Modeling a Multimodal Routing Problem with Flexible Time Window in a Multi-Uncertainty Environment

In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them.

OPPORTUNITIES AND RESULTS OF INTEGRATION OF KAZAKHSTAN'S TRANSPORT SYSTEM WITH INTERNATIONAL TRANSPORT SYSTEMS

The article examines the transport and transit industry of Kazakhstan, strategic planning for improving the logistics infrastructure, creating a network of multimodal transport and logistics centers integrated into regional transport and logistics systems, ensuring close integration of the Kazakh transport system as an equal partner with international transport systems. The main components of the logistics infrastructure of multimodal transport and logistics centers, international transport corridors that ensure coordinated interaction of modes of transport and other participants in the transport and logistics process in Kazakhstan are considered as strategic points for the development of the economy of Kazakhstan. Modern international transport corridors are becoming the main core of the effective development of interstate economic relations in the era of globalization of the world economy. This industry is considered as an important factor of socio-economic prosperity and the Republic of Kazakhstan, which is heading towards an innovative path of development. Because international transport corridors are one of the most important industries that contributes to the prosperity of the global economy, facilitating trade and exchange of goods between countries of the world.

Methodology for Researching the Synthesis of Transport and Logistics Systems of the Kyrgyz Republic

The article discusses the research methodology for the synthesis of transport and logistics systems, adapted to the specific conditions of the Kyrgyz Republic. The relevance of the study is due to the need to increase the competitiveness of the national economy and realize the country’s transit potential through the creation of an effective logistics infrastructure. The proposed methodology is based on the integrated application of modern theories and concepts from the field of transport logistics, modeling, geographic information systems, intelligent transport systems, as well as the principles of green logistics and sustainable development. Particular attention is paid to taking into account the features of the mountainous terrain of Kyrgyzstan and the need to integrate various modes of transport into multimodal transportation. The methodology provides for the creation of an optimal network of logistics centers and transport hubs that concentrate cargo flows and ensure their efficient processing. The implementation of information technologies and systems is considered as a key factor in the integration of supply chain participants, monitoring of transport flows, modeling and optimization of logistics processes. Successful implementation will create a competitive transport and logistics system, integrated into global supply chains and meeting modern requirements for efficiency and sustainable development.

Pursuing Optimization Using Multimodal Transportation System: A Strategic Approach to Minimizing Costs and CO2 Emissions

The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO2 emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO2 emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability.

Empirical Insights into Salvage Wood Logistics

Climate crises and natural disasters increasingly challenge wood supply chains by the critical need to transport high volumes of salvage wood quickly out of the forests to limit wood value loss and to prevent inducing forest calamities. Salvage wood logistics covering unimodal, multimodal and multi-echelon unimodal transport as well as storage plays an essential role in tackling existing problems and future risks by linking diverse actors along wood supply chains. Since little is known about specific challenges in salvage wood logistics, an extensive empirical study was conducted to evaluate logistics concepts and coping strategies involving stakeholders representing the entire wood value chain in Austria. The knowledge and experience of 161 forest owners, transport operators and wood-based industry actors were surveyed based on questionnaires with 108 matrix, rating, Likert scale and open questions. Results show a significant loss in salvage wood transport capacity due to long waiting times at receiving mills leading to more than one-fourth lost transport capacity in the unimodal salvage wood supply chain. Sustainable and resilient wood supply chain management can be achieved by increasing train and semitrailer truck wood transport as well as wood storage capacity and by improving both working conditions for self-loading log-truck drivers and cooperation between stakeholders.