Multimodal Transportation Network Research Articles

Chabahar Port: A Geopolitical Need Or, Economic Compulsion for India

In the year 2024, in the case of Chabahar port in Iran, two important events took place; first, the visit of Mr. S Jaishankar, India’s External Affairs Minister to Tehran in January 2024 and second, one is the terror attack on Chabahar port by Jaish-al-Ahad on 3 April 2024. Both these developments are pole apart as far as the development of Chabahar port and the realisation of The International North-South Transportation Corridor (INSTC) is concerned. The first one talks about development, hope, connectivity, and the future of trade through a new corridor and the second one is about the existing security challenges in the region which has put this project on a slow grind since the year 2002. As we are aware the International North-South Transportation Corridor (INSTC) is a multi-modal transportation network connecting India, Iran, Russia, Central Asia, and Europe. It aims to facilitate the movement of goods and reduce transportation costs by providing an alternative route to traditional sea routes. The Chabahar Port, located in southeastern Iran, plays a crucial role in the INSTC as a key maritime gateway.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports.

System capacity model and algorithm for urban multimodal transport network with transfer

ABSTRACT This paper proposes a method for calculating transport networks capacity in dealing with multimodal transfers. Multimodal networks are represented by a modified ‘supernetwork’, while the passenger’s travels are defined as ‘superpaths’. Within this framework, the relation between the travel demand from O-D matrices and the resulting link flows in the supernetwork is modelled as a relationship matrix to describe urban mobility by using a logit-based stochastic user equilibrium. Based on this relationship matrix, an approximate iteration algorithm (AIA) is developed. Our numerical results show that the AIA performs better than the sensitivity analysis-based algorithm (SAB) and genetic algorithm (GA) regarding the execution-time, and that the capacity of multimodal transport networks can be underestimated if the combined travels are neglected.

Multiobjective route finding in a multimode transportation network by NSGA-II

Route finding is an everyday challenge for urban residents. While many route planner applications exist, they cannot find suitable routes based on user preferences. According to user preferences, routing in a multimode urban transportation network can be considered a multiobjective optimization problem. Different objectives and modes for transportation, along with many routes as decision elements, give rise to the complexity of the problem. This study uses an elitism multiobjective evolutionary algorithm and the Pareto front concept to solve the problem. The data of a simulated multimode network consisting of 150 vertexes and 2600 edges are used to test and evaluate the proposed method. Four transport modes are considered: the metro, bus, taxi, and walking. Also, three minimization objective functions are considered: expense, discomfort, and time. The results show the competence of the algorithm in solving such a complex problem in a short run time. The optimal setting for the algorithm parameters is found by considering the algorithm run time, diversity of solutions, and convergence trend by running sensitivity analyses. A repeatability test is applied using the optimal setting of the algorithm, which shows a high level of repeatability. While NSGA-II (Non-dominated Sorting Genetic Algorithm II) may be a well-established algorithm in the literature, its application in multiobjective route finding in multimode transport networks is unique and novel. The outcomes of the proposed method are compared with existing methods in the literature, proving the better performance of the NSGA-II algorithm.

Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

AbstractMultimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning‐powered travel time estimation in multimodal freight transportation networks (DLTTE‐MFTN) technique. The goal of the DLTTE‐MFTN technique is to estimate the travel time using a hyperparameter‐tuned ensemble learning approach. To achieve this, the DLTTE‐MFTN method initially undergoes data pre‐processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE‐MFTN method estimates travel time using an ensemble of three DL approaches including one‐dimensional convolutional neural network (1D‐CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE‐MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE‐MFTN technique attains superior performance over other ML and DL models.

Evaluation Framework for Multi-Modal Public Transport Systems Based on Connectivity and Transfers at Stop Level

Multi-modal public transport (PT) networks within metropolitan areas are often characterized by complexity resulting mainly from their infrastructure, design, operations, and demand. This complexity leads to a significant amount of effort on behalf of the transit agencies to properly evaluate their performance at certain locations and proceed with improvements. This study proposes a methodology based on clustering techniques that facilitates the evaluation of PT networks. The evaluation framework refers to the comparison between the levels of supply and demand at a certain stop. Service supply is quantified through an existing connectivity index, whereas demand is considered through the number of transfers that are performed at each stop. Transfers are critical within multi-modal mobility and often serve as a hindrance for choosing PT. The case study here is the Helsinki PT network in Finland. General Transit Feed Specification (GTFS) data are used for quantifying connectivity and a dataset deriving from smartphone ticketing application for quantifying transfers. Results include the evaluation for each PT mode and for the overall multi-modal PT network. Focusing on the evaluation of the overall multi-modal PT network, connectivity and transfers levels for 75.60% of stops are found to be well aligned. Therefore, these stops could be eliminated from the list of candidate stops for performing improvements. Of the remaining stops, 19.73% belongs to the case of higher connectivity than transfers and 4.67% to the case of lower connectivity than transfers. Stops included in these two cases require further attention and prioritization during planning processes.

Door to door space-time path planning of intercity multimodal transport network using improved ripple-spreading algorithm

The main objective of this study is to solve the problem of recommending door-to-door space–time paths in an intercity multimodal transport network. The available modes include railway, metro, bus, rapid-bus, and walking. In terms of mathematical modeling, the reconstructed space–time path planning model considers multiple constraints, such as acyclic constraint, flow balance constraint and transfer constraints, to ensure the viability of the path, which is significantly different from the path in the traditional graph theory and road network. To attain the K shortest paths in two situations, the improved ripple-spreading algorithm (hereafter referred to as IRSA) is introduced, which is inspired by the ripple-spreading phenomenon and can find global optimal paths through only one iteration. A method for reducing the search space is applied to speed up the computing process. We demonstrate the feasibility and effectiveness of the proposed model and algorithm by comparing it with Gurobi Solver and Dijkstra algorithm using a small-scale network. The experiment results are evaluated by solving the door-to-door itinerary problem from Taiyuan to Beijing. Results indicate that the proposed approach can find constrained K shortest paths within an acceptable computing time. A set of experiments are conducted to test the performance of different methods, and results show that the speed-up method provides a significant improvement in problem-solving efficiency, while attempting to guarantee path optimality, which could reduce the CPU time by 51% to 92% for the tested OD pairs.

Topology-based Resilience Assessment of Port Multimodal Transport Networks: A Case Study of Shanghai Port

As transit nodes in the field of international trade, ports are of critical importance for establishing and maintaining the effectiveness of the trade supply chain. However, the system performance of ports is vulnerable to disruptions caused by unexpected events such as natural disasters, accidental catastrophes, and public health emergency. These disruptive events will have serious negative impacts on the normal operation performance of ports and international trade among countries. This paper is to propose a comprehensive assessment framework of the system resilience of the port multimodal transport networks before and after the occurrence of disruption events, which can effectively improve its ability to deal with emergencies, minimize losses, and enhance the system performance levels. This paper also establishes several simulation experiments focusing on the full lifecycle of resilience based on topology of the transport networks, and simulates a variety of scenarios regarding different damage and recovery modes, such as deliberate attack, random attack, planned recovery, and random recovery, in the assessment process. Then the actual transport situation of Shanghai port is taken as a case study, and three multimodal transport networks including the sea-rail network, the sea-road network, and the sea-road-rail network under different disruption scenarios are compared，and evaluate the evolutions of the network resilience of the multimodal transport networks of Shanghai port stage by stage. In addition, managerial suggestions are also proposed based on the resilience assessments of different disruption scenarios to maximize the resilience of multimodal transport networks of Shanghai port.

A Material Allocation Model for Public Health Emergency under a Multimodal Transportation Network by Considering the Demand Priority and Psychological Pain

In a public health emergency, residents urgently require a large number of rescue materials for treatment or protection. These rescue materials are usually located far from the emergency area. The government must organize rescue materials transportation by selecting suitable transport modes. Thus, we propose a material allocation model for public health emergencies under a multimodal transportation network to determine the best rescue material supply route. In this model, we set the demand priorities according to the emergency degrees to decide the transportation sequence. Meanwhile, we introduce the psychological pain cost brought by the rescue material shortage into the proposed model to trade off the priority and fairness of demand. Having compared it to the research literature, this is the first study that considers multiple categories of materials, absolute pain costs, relative pain costs and demand priority under multimodal transportation. The research problem is formulated into an integer programming model, and we develop a modified genetic algorithm to solve it. A set of numerical examples are conducted to test the performance of the proposed algorithm, and to investigate features and applications of the proposed model. The results indicate that the modified genetic algorithm performs better in the calculation examples at different scales. For small-scale instances, the algorithm produces consistent results with Gurobi. As the instance size increases, Gurobi fails to find the optimal solution within 1800 s, while this algorithm is able to find the optimal solution within an acceptable time frame. Additionally, when dealing with large-scale instances, the algorithm exhibits a significant advantage in terms of runtime. Sensitivity analysis of key factors indicate that (1) Adjusting the relative pain cost coefficient can make the best trade-off between fairness, economy and timeliness; (2) Compared with a single mode of transport, multimodal transport can reduce the psychological pain cost and the logistics cost; (3) Improving the loading and unloading capacity of nodes can reduce the delivery time of materials and the psychological pain cost of residents, but the influence of other factors and cost-effectiveness need to be considered.

A Recommender for Personalized Travel Planning Using Stacked Autoencoder in a Multimodal Transportation Network

A Recommender for Personalized Travel Planning Using Stacked Autoencoder in a Multimodal Transportation Network

Assessing Betweenness Centrality for Multimodal Transport Network – A Case Study of Delhi Metropolitan Area

This study introduces an approach for analysis of network centrality, with a focus on betweenness centrality in complex urban transport systems. Central to this research is the development of a Path Evaluation Function (PEF), a tool that enriches traditional centrality assessments for transport networks by factoring in critical network attributes like link capacity and travel time. This approach is particularly adept at unraveling the intricacies of betweenness centrality in multimodal transportation networks. Using this approach, the study delves into the dynamics of betweenness centrality within the Delhi Metropolitan transport network, an evolving urban landscape characterized by diverse transit modes. The analysis reveals a marked concentration of high betweenness centrality along key transport corridors, highlighting their role in facilitating urban mobility. This refined understanding of betweenness centrality, obtained through the PEF, offers deeper insights into how crucial nodes and links function within the network, thereby enabling more informed decision-making in urban planning and policy. Furthermore, the versatility of the PEF extends beyond betweenness centrality, showing promise as a tool for evaluating other centrality measures within varied network contexts.

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method

In the last decade, there has been an abundance of research on identifying critical nodes in single-modal transportation network, but less for multi-modal transportation network (MTN). In addition, none of them consider the effect of interactions between nodes in a transportation corridor on node importance. For this purpose, a modified weighted k-shell (MWKS) model is proposed for identifying the critical nodes of the MTN, which combines the network topology and traffic demand. It is modified by the differential contribution of the multi-order neighbor nodes embodied by the passenger volume and the transportation corridor idea. Then the effectiveness of MWKS is verified with an example of a real transportation system using susceptible-infected (SI) model and cascading failure model. The analysis proves that MWKS is suitable for identifying critical nodes in the MTN. Furthermore, the proposed contribution attenuation coefficient based on the idea of the transportation corridor outperforms the topology-based and constant. It demonstrates the necessity of capturing the difference in the contribution of each neighbor node based on traffic demand when calculating the node importance. The results can provide a research basis for traffic planning to improve network reliability.

A Novel Approach for Route Prediction in Multimodal Transport Networks: A Monte Carlo Simulation and Long Short-Term-based Model

A Novel Approach for Route Prediction in Multimodal Transport Networks: A Monte Carlo Simulation and Long Short-Term-based Model

Exploring Representations for Optimizing Connected Autonomous Vehicle Routes in Multi-Modal Transport Networks Using Evolutionary Algorithms

Exploring Representations for Optimizing Connected Autonomous Vehicle Routes in Multi-Modal Transport Networks Using Evolutionary Algorithms

Development Strategy of Czech State Railway Infrastructure

The article deals on development strategy of Czech state railway infrastructure of both conventional and high-speed lines. Based on priorities of both the EC and Czech government, the strategy defines long-term goals until 2050 like high-speed rail traffic will triple, rail freight traffic will double, scheduled collective travel of under 500 km should be carbon neutral within the EU and finally the multimodal Trans-European Transport Network (TEN-T) equipped for sustainable and smart transport with high speed connectivity will be operational for the comprehensive network. Správa železnic, state organization has introduced its strategic management since 2018. Development strategy of the organization, faced with increasing of intermodal competition between both railway and road transport, is a basis for qualified setting of measurable and it means checkable goals of which the organization shall reach and together with it setting of provisions leading to match those goals. Matching of strategic goals is assessed in quarter-year cycles by means of individual provisions in Action plan. The Czech Republic invests increasingly in the modernization of conventional railways and the preparation of high-speed lines.

Routing in Multimodal Transportation Networks with Non-scheduled Lines

Over the last decades, new mobility offers have emerged to enlarge the coverage and the accessibility of public transportation systems. In many areas, public transit now incorporates on-demand transport lines, that can be activated at user need. In this paper, we propose to integrate lines without predefined schedules but with predefined stop sequences into a state-of-the-art trip planning algorithm for public transit, the Trip-Based Public Transit Routing algorithm [ 33 ]. We extend this algorithm to non-scheduled lines and explain how to model other modes of transportation, such as bike sharing, with this approach. The resulting algorithm is exact and optimizes two criteria: the earliest arrival time and the minimal number of transfers. Experiments on two large datasets show the interest of the proposed method over a baseline modelling.

Measuring route diversity in spatial and spatial-temporal public transport networks

Route diversity is a pivotal metric for evaluating the redundancy of transportation networks, a fundamental aspect of system resilience. This study provides a comprehensive exploration of route diversity in multimodal public transport systems, specifically considering the unique characteristics of bus networks. Our research introduces a two-pronged approach to measuring route diversity within spatial public transport networks, delving into service-based and infrastructure-based perspectives. The former assesses redundancy against transit service disruptions by factoring in transit line overlaps, while the latter evaluates redundancy against infrastructure-related interruptions by considering overlapping road segments on which transit lines rely. In addition, the study advances by modelling public transport systems as multilayer spatial-temporal networks, integrating time-variable service frequency and travel time uncertainty, which enables us to derive route diversity for origin-destination (O-D) pairs across different time periods.We present a case study of the multimodal public transport network in Jiading District, Shanghai, revealing a power-law distribution of the route diversity. The results unveil O-D pairs with lower route diversity, underscoring their lack of resilience against various disturbances. Lower service-based route diversity suggests the necessity of enhancing service frequency and bolstering cross-line combined transit scheduling, while lower infrastructure-based route diversity could warrant the design of road-diversified transit lines, especially within bus corridors. Besides, relative route diversity between the two indices highlights specific O-D pairs with notable disparities in service and infrastructure redundancy. This difference is correlated with the distance between O-D pairs and the road network's configuration. This analysis underscores the method's potential to offer more relevant insights to public transport planners and operators for adjusting transit lines and schedules through a resilience-focused lens.

Traveling itinerary problem in a scheduled multimodal transportation network for a fixed sequence of cities.

Developing an efficient and economical journey plan in multimodal transportation networks is of significant and fast-growing importance, but it is still an annoying experience for a traveler. This paper aims to find the journey plan at a combined cross-border and inter-regional level when visiting a sequence of cities while utilizing several transport modes to reduce travel costs and planning time. We study a traveling itinerary problem in a scheduled multimodal transportation network with constraints on both arcs and nodes as a new extension of the shortest path problem. We formulate a 0-1 integer linear programming model for the traveling itinerary problem and develop an exact algorithm that finds a combined cross-border and inter-regional low costs journey plan. We present case studies based on real-world transport data to illustrate the usefulness and computational efficiency of the proposed approaches. We compare the results with the previously proposed approach to demonstrate the benefits of multimodal journeys. Finally, we compare the results with the solution obtained by the general-purpose 0-1 integer linear programming solver to evaluate the computational time.

Identification of critical transportation cities in the multimodal transportation network of China

Identification of critical transportation cities in the multimodal transportation network of China