Multimodal Transportation Network Research Articles

Optimization of Multimodal Discrete Network Design Problems Based on Super Networks

In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.

REINFORCE: rapid augmentation of large-scale multi-modal transport networks for resilience enhancement

With the recent and continuous growth of large metropolis, the development, management and improvement of their urban multi-modal transport networks become a compelling need. Although the creation of a new transport mode often appears as a solution, it is usually impossible to construct at once a full networked public transport. Therefore, there is a need for efficient solutions aimed at prioritizing the order of construction of the multiple lines or modes that a transport operator might want to construct to increase its offer. For this purpose, we propose in this paper a simple and quick-to-compute methodology, called REINFORCE, to prioritize the order of construction of the lines of a newly designed transport mode by maximizing the transport network performances and enhancing the transport network resilience, as described by complex networks metrics. REINFORCE could also be helpful to support the rapid and quick response to disruptions by setting up or reinforcing an adapted emergency transport line (e.g., bus service) over a set of predefined itineraries.

Interrelations between sea hub ports and inland hinterlands: Perspectives of multimodal freight transport organization and low carbon emissions

In 2020, a “dual carbon” goal was proposed by China's government. As a low-pollution and low-consumption transportation organization mode, multimodal freight transport has been highly valued. Strengthening the organization and management of multimodal freight transport between seaports and inland areas is conducive to reducing carbon emission levels and logistics costs in the transportation industry. In the multimodal freight transport network, a reasonable hinterland for the main hub port can effectively avoid homogeneous competition, realize the optimal allocation of transport resources, and promote the integrated and coordinated development of the region. In constructing a multimodal freight transport network and arranging a reasonable organization mode, it is beneficial to divide the hinterland of main hub ports and promote energy conservation and emission reduction in the transportation industry. From the perspective of hinterland division and coordination of transportation organization methods, we construct a 0–1 planning model to minimize total cost, transportation time, and carbon emissions. By combining the nondominated ranking method and ideal point method, the algorithm sifts and reorganizes each transportation mode and path in different cities, thereby further overcoming the subjectivity of the weighting method and obtaining the nondominated Pareto optimal frontier solution set. Finally, the evaluation function is used to determine the optimal scheme. Taking the main hub port in the Yangtze River Delta region as an example to verify the validity of the model and algorithm, this research can provide decision support for departments when they formulate policies related to optimizing the regional transportation structure, can promote regional integration and coordinated development of coastal and inland areas, and has practical significance for the “dual carbon” goal.

Passenger’s routes planning in stochastic common-lines’ multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation

In the urban transportation network, most passengers choose public transportation to travel. However, bad weather, accidents, traffic jams and other factors lead to uncertainty in transportation network. Besides, transport vehicles running on the same segments of routes and belonging to different modes or routes make the transportation network more complicated. In order to improve the efficiency of passenger’s travel, this paper aim to introducing an approach for optimizing passenger travel routes. This approach takes the travel cost and the number of transfers as constraints to finding the shortest total travel duration of passenger in urban transporta-tion network. The running duration and dwell duration of the vehicles in the network are uncertain, and the vehicles are running according to the timetables. As transportation modes, bus, rail transit and walk are consid-ered. In terms of methodological contribution, this paper combines Genetic Algorithm (GA) and Monte Carlo simulation to deal with optimization problem under stochastic conditions. This paper uses Monte Carlo simula-tion to simulate the running duration and dwell time of vehicles in different scenarios to deal with the uncertainty of the network. The shortest path of passenger’s travel is solved by GA. Two kinds of population management strategies including single population management strategy and multiple population management strategy are designed to guide the solution population evolving process. The two kinds of population management strategies of GA are tested in numerical example. The satisfactory convergence performance and efficiency of the model and algorithm is verified by the numerical example. The numerical example also demonstrated that the multiple population management strategy of GA can get better results in a shorter CPU time. At the same time, the influ-ences of some significant variables on solution are performed. This paper is able to provide a scientific quantita-tive support to the path scheme selection under the influence of common-lines and timetables of different modes of transportation in stochastic urban multimodal transportation network.

Military Transportation in State Freight and Defense Community Plans

The US Department of Defense (DoD) makes extensive use of the US multimodal transportation network to move material for operational support, keep facilities supplied, and move personnel. Federal agencies, especially the US Department of Transportation, work closely with the DoD. States and localities also play a critical role in defense infrastructure. This research examines state freight and defense community plans to identify how the transportation needs of the DoD are being addressed in state planning. The findings are 72% of the 50 state freight plans mention defense transportation, while 34% considered defense transportation needs in-depth. About 6 of 19 identified state defense community plans mention transportation, but only 3 have specific defense transportation goals. Nationally, the DoD and federal agencies work closely together to maintain the nation’s defense transportation network, but at the local and state level closer cooperation on transportation issues should be developed. Military readiness remains high, but efficiencies in the system can be improved.

Connectivity-based time centrality in time-varying graphs

Abstract Time-varying graphs (TVGs) enable the study and understanding of the dynamics of many real-world networked systems. The notion of centrality in TVG scenarios generally refers to metrics that assess the relative importance of nodes over time in the evolution of a complex dynamic network. In contrast, the notion of Time Centrality, which evaluates the relative importance of time instants in dynamic complex networks, is still little explored. Indeed, the few works on Time Centrality base their findings on the study of diffusion processes, for example, identifying the best time instant to start the dissemination of a message envisaging a more efficient diffusion given the expected network dynamics. In contrast, in this work, we study Time Centrality from a network connectivity perspective. In this context, we propose two connectivity-based metrics to identify the most important time instants in the network. The first metric is the Time Degree Centrality that, analogously to node degree centrality, computes the number of connections that each time instant has. The second metric, Time PageRank Centrality, searches for the time instants that receive the largest number of accumulated connections since they are considered weights at each time instant. To validate the metrics, we model a public multimodal transport network considering time as an aspect of each node. We then apply the proposed metrics and analyse the results from a connectivity-based time centrality perspective. Our results show that the two metrics can identify the most important time instants in different scenarios of complex network dynamics.

Estimation of urban network capacity with second-best constraints for multimodal transport systems

Transport network capacity enhancement is a significant aspect of urban transport planning and demand management, and a suitable measurement of the network capacity is of considerable importance. In this paper, the network capacity with second-best constraints (NCSC) is investigated to meet some specific development requirements of urban transport networks. Herein, the network capacity is restricted to an inferior “second-best solution”, due to various concerns/constraints regarding the public transport mode share, serviceability, and emissions, etc. For the sake of presentation, these constraints are termed as second-best constraints, and the NCSC problem can also be referred as second-best network capacity (SBNC) problem. A bi-level model is formulated to analyse the NCSC problem. The upper-level model maximizes the total origin-destination (OD) demand, which incorporates the second-best constraints into consideration. The lower-level model is a transport network equilibrium model, which measures the network performance under a given OD demand pattern. To better investigate some important second-best constraints (e.g., public transport mode share) and also the demand elasticity, the modelling framework is extended to a multimodal transport network. An exact solution method is developed for the NCSC problem; wherein, a modified improved gradient projection (MIGP) algorithm is designed for the lower-level multimodal flow equilibrium problem, and a tailored sensitivity analysis-based (SAB) method is employed for solving the NCSC problem. The proposed models and solution methods are verified by numerical examples, demonstrating that NCSC can be an efficient tool for transport planning and management.

A method to improve the resilience of multimodal transport network: Location selection strategy of emergency rescue facilities

This paper studies the location selection strategy of emergency rescue facilities in the multimodal transport network to improve the resilience of the network. Based on the overall perspective, we propose a cooperative coverage model. On one hand, it helps network operators improve the recovery speed of the entire multimodal transport network in the event of a disaster. On the other hand, it maximizes the effect of the rescue cost of government managers. But we must note that in most transport networks, failure data is difficult to be collected or is insufficient. Therefore, we propose the premise that the network failure may occur at any point. The rescue demand is quantified by the links/nodes importance, which is calculated by the multi-attribute decision-making method based on hesitant intuitionistic fuzzy set-technique for order preference by similarity to an ideal solution (TOPSIS). Then, a multi-objective non-linear programming model is established, and fast non-dominated sorting genetic algorithm-cellular automata (NSGA-II-CA) is designed to solve the model. Taking the multimodal transport network in the Sichuan-Tibet region of China as an example, calculation and analysis are performed to verify the effectiveness and advancement of the proposed model and algorithm. The innovation of this paper is to propose a method of emergency rescue facility location from the perspective of global optimization.

Random Forest–Based Covariate Shift in Addressing Nonstationarity of Railway Track Data

AbstractThe multimodal transportation network in which freight rail plays an essential role continues to enhance the United States’ contributions in the global market. For years, track geometry def...

Evaluating the Capacity Coordination in the Urban Multimodal Transport Network

The urban multimodal transport network is composed of multiple layers of networks; thus, coordinating the capacity equilibrium among different sub-transport networks plays a crucial role to keep the entire network running efficiently. To quantify and evaluate the passenger flow distribution in an urban multimodal transport network, this research proposes a method to evaluate the capacity coordination in an urban multimodal transport network on the basis of assignment results calculated by the Stochastic User Equilibrium (SUE) model considering the link and path impedance of different sub-transport networks. It suggests evaluation functions for the indicator level of service (LOS) of the multimodal transport network, Gini coefficient of transport network, and mode share of transport modes, and it shows how the functions were estimated. Then, it reports on results with the evaluation scheme collected in a multimodal example application for roadway network, transit networks (bus transit network and urban rail transit network), and connection network. The evaluation results under different assumed origin–destination (OD) demand show the coordination degree and can be used to recognize shortcomings of the network. Moreover, the OD demand interval of real network with good coordination can be deduced, which can also help transport planners to find the optimal strategy.

Challenges To The Multimodal Transport Networks Based On Sustainable Growth In The Volume Of Containerized Cargoes

Globalization is a significant factor for optimization of the existing transport chains. It is a major power for sustainable development of containerized transport, the relevant transport infrastructure and terminal equipment. The development of seaports and inland waterway connections became important part in global supply chain. The transportation models which are using multimodal transport service consist from maritime, port and hinterland parts. Choosing the appropriate strategy allows the shipping companies to reduce the transport cost and helps them to be more competitive on the cargo market. This article aims to identify the challenges and prospects for development of multimodal transport operations with containerized cargoes using of system approach to the each subsystems of the multimodal transport chain. To achieve the scope of the paper, research has been done on the global effects that big shipping companies contribute to the final transport cost; to the organization in container terminals and further influence on the local terminal operators; to the improvement of port infrastructure, including the number of offered services and implementation of innovations for increasing terminal efficiency. As a result, the main parameters for evaluation of the importance of one container terminal in the supply chain are defined.

A mixed-equilibrium model of individual and household activity–travel choices in multimodal transportation networks

This paper develops a novel household-oriented activity-based mixed-equilibrium model for estimating individual and household activity–travel choices in multimodal transportation networks with interactions between private car and public transit modes. In the novel model, household members with heterogeneous errors of perception on the time-dependent utility of different activity types make daily joint/solo activity–travel choices in a mixed-equilibrium manner, which maximizes either perceived household utility or perceived individual utility. A logit-based stochastic choice model is developed to capture the mixed equilibrium with heterogeneous errors of perception and used to predict the choices of alternative joint activity–travel paths (JATPs) on a supernetwork platform. Based on this stochastic JATP choice model, the mixed-equilibrium model is formulated as an equivalent variational inequality (VI) problem and solved using a modified diagonalization method. This converts the time-dependent activity–travel scheduling problem into an equivalent static traffic assignment problem on JATPs. The conditions required for the existence and uniqueness of a solution to the equivalent VI problem in terms of a JATP flow pattern are also identified. Numerical examples are provided to illustrate the model’s merits and its applications for examining the effect of the coronavirus disease 2019 (COVID-19) pandemic.

Vulnerability assessment of China–Europe Railway Express multimodal transport network under cascading failures

Transshipment and transnational long-haul transport make the multimodal transport network (MTN) vulnerable when subjected to unexpected disturbances, which may cause cascading failures. Therefore, this paper presents a dynamic layered road–rail MTN to measure the vulnerability of China–Europe Railway Express (CR Express) MTN. The statistical analysis of the topological characteristics and the final simulation results show that the layered road–rail MTN with priority connection mechanism exhibits power-law distribution characteristics. The final simulation results reveal that the road–rail MTN shows a better ability to suppress the risk cascading propagation when the transshipment coupling enhances the dynamic flexibility of the network. Although the optimal inter-layer transshipment coupling changes positively with the external disturbance and negatively with the intra-layer coupling in special cases, the moderate inter-layer coupling is still the most effective in improving the anti-cascading ability of general MTN. These findings provide recommendations for the CR Express MTN to suppress the cascading failures.

Multi-modal network evolution in polycentric regions

The development of public transport networks in polycentric regions involves making complex trade-offs between extending network coverage, densifying local connections, offering improved connections between urban centers and increasing capacity on existing links. Moreover, different modalities might be suitable for each of these development decisions depending on their speed, capacity and cost function structure. The objective of this study is to identify the influence of polycentric configurations and their respective travel demand distributions on the emerging topology of the corresponding multi-modal public transport network using an iterative growth model. Since polycentricity comes in many forms, our experimental design considers four polycentric configurations inspired by the cases of London, Tokyo, the Flemish Diamond and the Rhine-Ruhr area.The results are analysed in terms of both the properties of the obtained network structure as well as the evolutionary path and the sensitivity of both to key model settings and design variables. We find that more uneven population distributions result with the construction of fewer links and consequently the less connected and shorter the network becomes. The network evolutionary path is marked by distinctive intra- and inter- agglomeration expansion, densification and bulking phases. While the costs associated with each investment are the same regardless of the moment at which the investment is made, the benefits are not. This path dependency means that the evolutionary path is characterized by the need to attain a critical mass to justify further developments.

Investigation of Performance Indicator for Public Transit Connectivity in Multi-Modal Transportation Network of NCT Delhi

Centrality plays a crucial role as agencies at the federal and state level focus on expanding the public transit system to meet the demands of a multimodal transportation system. Transit agencies have a need to explore mechanisms to improve connectivity by improving transit service. This requires a systemic approach to develop measures that can prioritize the allocation of funding to locations that provide greater connectivity, or in some cases direct funding towards underperforming areas. The concept of centrality is well documented in social network literature and to some extent, transportation engineering literature. However, centrality measures have limited capability to analyze multi-modal public transportation systems which are much more complex in nature than highway networks. In my study area, we propose measures to determine Network centrality from a QGIS SOFTWARE which is based on graph theoretic approach for all levels of transit service coverage integrating routes, schedules, socioeconomic, demographic and spatial activity patterns. The objective of using Network centrality as an indicator is to quantify and evaluate transit service in terms of prioritizing transit locations for funding; providing service delivery strategies, especially for areas with large multi-jurisdictional, multi-modal transit networks; providing an indicator of multi-level transit capacity for planning purposes; assessing the effectiveness and efficiency for node/stop prioritization; and making a user friendly tool to determine locations with highest connectivity while choosing transit as a mode of travel. The proposed analysis offers reliable indicators that can be used as tools for determining the transit connectivity of a multimodal transportation network.

Low-Carbon Multimodal Transportation Path Optimization under Dual Uncertainty of Demand and Time

The research on the optimization of a low-carbon multimodal transportation path under uncertainty can have an important theoretical and practical significance in the high-quality development situation. This paper investigates the low-carbon path optimization problem under dual uncertainty. A hybrid robust stochastic optimization (HRSO) model is established considering the transportation cost, time cost and carbon emission cost. In order to solve this problem, a catastrophic adaptive genetic algorithm (CA-GA) based on Monte Carlo sampling is designed and tested for validity. The multimodal transportation schemes and costs under different modes are compared, and the impacts of uncertain parameters are analyzed by a 15-node multimodal transportation network numerical example. The results show that: (1) the uncertain mode will affect the decision-making of multimodal transportation, including the route and mode; (2) robust optimization with uncertain demand will increase the total cost of low-carbon multimodal transportation due to the pursuit of stability; (3) the influence of time uncertainty on the total cost is significant and fuzzy, showing the trend of an irregular wave-shaped change, like the ups and downs of the mountains. The model and algorithm we proposed can provide a theoretical basis for the administrative department and logistic services providers to optimize the transportation scheme under uncertainty.

The route problem of multimodal transportation with timetable: stochastic multi-objective optimization model and data-driven simheuristic approach

PurposeDistribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.Design/methodology/approachMonte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.FindingsNumerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.Originality/valueThe impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

Route Selection of Multimodal Transport Based on China Railway Transportation

The advantage of multimodal transport is that it can deliver the goods to their destination in a reasonable combination of transport modes while ensuring security and punctuality. Multimodal transportation can effectively reduce logistics costs, improve logistics efficiency, and reduce environmental pollution. In the process of multimodal transportation, due to the interference of natural factors (weather, terrain, etc.) and some special human factors, it may have different degrees of impact on the transportation time and transportation safety of different transportation modes. Therefore, when choosing a transportation method, it is necessary to consider the transportation time and transportation safety under the interference. However, the current research on multimodal transport has not considered the impact of external interference on transportation time and transportation safety. Compared with other modes of transportation, external interference has a relatively small impact on railway transportation. Railways can safely deliver goods to their destinations on time. Under the background of China’s huge railway network and advanced heavy-duty technology, this paper establishes a multimodal transport route selection model for considering railway as the core, introduces time penalty cost and damage compensation cost, and takes the lowest comprehensive transportation cost as the model objective under the premise of considering transportation reliability and transportation safety. Finally, taking a multimodal transport network in China as an example, an improved ant colony algorithm is used to solve the model and the results verify the rationality of the model.

Roman Transport Network Connectivity and Economic Integration

AbstractWe show that the creation of the first integrated multi-modal pan-European transport network during Roman times influences economic integration over two millennia. Drawing on spatially highly disaggregated data on excavated Roman ceramics, we document that contemporary interregional trade was influenced by connectivity within the network. Today, these connectivity differentials continue to influence integration as approximated by cross-regional firm investment behaviour. Continuity is partly explained by selective infrastructure routing and cultural integration due to bilateral convergence in preferences and values. We show that our results are Roman-connectivity specific and do not reflect pre-existing patterns of exchange using pre-Roman trade data.

Creation of a Multimodal Urban Transportation Network through Spatial Data Integration from Authoritative and Crowdsourced Data

One of the most significant challenges in cities concerns urban mobility. Urban mobility involves the use of different modes of transport, which can be individual or collective, and different organizations can produce their respective datasets that, usually, are used isolated from each other. The lack of an integrated view of the entire multimodal urban transportation network (MUTN) brings difficulties to citizens and urban planning. However, obtaining reliable and up-to-date spatial data is not an easy task. To address this problem, we propose a framework for creating a multimodal urban transportation network by integrating spatial data from heterogeneous sources. The framework standardizes the representation of different datasets through a common conceptual model for spatial data (schema matching), uses topological, geometric, and semantic information to find matches among objects from different datasets (data matching), and consolidated them into a single representation using data fusion techniques in a complementary, redundant and cooperative way. Spatial data integration makes it possible to use reliable data from official sources (possibly outdated and expensive to produce) and crowdsourced data (continuously updated and low cost to use). To evaluate the framework, a MUTN for the Brazilian city of Belo Horizonte was built integrating authoritative and crowdsourced data (OpenStreetMap, Foursquare, Facebook Places, Google Places, and Yelp), and then it was used to compute routes among eighty locations using four transportation possibilities: walk, drive, transit, and drive–walk. The time and distance of each route were compared against their equivalent from Google Maps, and the results point to a great potential for using the framework in urban computing applications that require an integrated view of the entire multimodal urban transportation network.