Multimodal Transportation Network Research Articles

Large-scale multimodal transportation network models and algorithms-Part II: Network capacity and network design problem

Transportation network capacity enhancement is essential in urban transportation planning. In this paper, a general multimodal network capacity problem (MNCP) is proposed, which can depict the transfers, mode overlap, and common line problem and congestion effect of transit. The problem is established as a bi-level model with combined mode choice and traffic assignment as the lower-level programming. Based on the MNCP, a tri-level multimodal network design problem (MNDP-MNCP) is developed to maximize the network capacity. The models are solved with an efficient Kriging-surrogate-based optimization algorithm in real-scale urban networks. Numerical results demonstrate the performances of the proposed framework.

Influence of Socio-Demography and Operating Streetscape on Last-Mile Mode Choice

This study investigated how personal and operational factors (travel distance and streetscape) influence traveler mode choice decisions for the last-mile home-bound trip stage from rail transit stations. Personal factors include the socio-demography of travelers, and attributes of the streetscape include the built environment (degree of areal development), prevalence of cycling, availability of short-range transport modes, and walking/cycling infrastructure. Interviewers randomly intercepted pedestrians to administer a mode choice survey at five rail transit station exits and engaged all available cyclists at bicycle parking areas in the vicinity of stations in Singapore. A multimodal logit regression model revealed a significant relationship between the last-mile home-bound trip maker’s mode choice with factors of age, gender, travel distance between transit station and destination, number of cyclists along adjacent links surrounding transit stations, number of feeder bus services to destination, availability of private vehicle, and household income. The calibrated model was applied to compute the probability of walking, cycling, and taking a feeder bus for the last-mile home-bound trip maker from a transit station. This study provides useful information for improving the efficiency and connectivity of first/last-mile mobility in a multimodal transport network.

Modeling Network Capacity for Urban Multimodal Transportation Applications

Since the diversity of urban transport modes and the growth of public transport demands recently, it is essential to consider the multiple mode options in the network capacity problem. This paper derives a comprehensive network capacity model from a single-mode transportation network with only route choice to a multimodal transportation network with both mode choice and route choice. To avoid biases in the evaluation of the multimodal network capacity, two characteristics of the multimodal transportation system are considered in modeling and formulating the problem: (1) the mode interaction between cars and buses is explicitly reflected when they share the same link; (2) the correlation of travel alternatives (modes or routes) is measured by developing a combined modal split and traffic assignment (CMSTA) problem, in which the nested logit (NL) model is employed to account for mode similarity in mode split, while the path-size logit model (PSL) is employed to account for route overlapping in traffic assignment. Numerical experiments demonstrate the characteristics of the new model. It also shows how planning schemes or management strategies affect the multimodal transportation network capacity via a real network case.

Modelling Approach to Develop a Multimodal Transportation Network

Multimodal transportation (MMT) refers to a system that utilizes various means of transportation to transfer people & goods from one place to another. Transit operations are measured as the maximum number of passengers that can be carried past a fixed route, in a given period. Investing in and using multiple modes of transportation does not only bring efficiencies but also goes a long way to reduce time & delay, as well as the overall cost of transportation. This paper presents the efficient mode of transportation system at selected routes in the Bengaluru city, the route stretch selected is 12.2 km in length and it has 8 major intersections. The type of pavement surface consists of both bituminous and Reinforced cement concrete (R.C.C). The complete study stretch is a 2-way 4-lane road. This study provides walkability-index, traffic volume studies in terms of passenger car unit (PCU), time and delay study, and Cost & time analysis. Four modes of transportation are used in this study to find the effective multimodal-transportation system namely, 2-wheeler, 4-wheeler, para-transit [15], and buses/metro. The outcome of this paper is to arrive at the shortest distance for reducing delay & time, and cost-effective travel using multi-mode transport. While focusing on the present condition of the para-transit transport facilities. The time, delay & cost during MMT and to provide a safe, cost-effective & convenient way to travel or transport. The result shows the reduction in time, cost & delay of transportation by using the MMT model. This MMT system could be adopted as an efficient mode of transportation on this route. Similar rural-urban junction routes around the world could use this approach for effective transit by making a modal study.

Feeder Bus Reformation for an Urban Rail Project: The Case of Khon Kaen City, Thailand

The ability to use public transportation should be available throughout the whole service area and the public transportation network should be well connected. This research compared the potential coverage of a feeder bus network in support of urban rail transportation, as well as the impact of future transit network plans on public transportation accessibility in the city of Khoan Kaen, Thailand. The performance of the public transportation system was predicted based on multimodal transport and the completed urban rail public transportation plan, as projected in the year 2036, in order to fill gaps in the existing feeder bus network. The feasibility and characteristics of the route reformation policy concept should provide an effective feeder network for the urban rail system. A comparative study was conducted on stakeholder impact for a three-fold scenario: 1) separate individual lines for bus routes; 2) both forms of feeder bus networks (conventional and reformed); and 3) access to three designated utility areas from the entire feeder bus network. In this scenario, the most effective urban mobility support was provided by public facilities combined with a major roadway directly connecting to the designated positions. The time used on the extended bus route network increased by around 11% on average for the entire trip, while accessibility increased by approximately 67.75%, 47.9%, and 43.68% for the entire multimodal transport network. These analytical results make a significant contribution to future knowledge on urban transformation through urban mass transit projects. The contribution of land acquisition was significant. Also, the demand-responsive connection approach used in this study can be adopted to determine feeder bus reformation options, particularly in emerging economies.

Routing and congestion in multi-modal transportation networks

With the increasing complexity and demand for transportation networks, effective routing planning has attracted more and more attention. Different modes of transportation, such as the airplane, railway and so on, work together, forming a multi-modal transportation network. Therefore, this paper studies the routing and congestion problems in the multi-modal transportation network, and shows how to increase the network capacity as much as possible while saving time and economic costs, so as to avoid congestion and realize the effective use of different modes of transportation. This paper simulates the influence of two main factors on the network capacity, the parameter which shows the importance between time costs and economic costs, and the difference between different transportation modes. The results show the change of the network capacity when the time cost and economic cost are of different importance. There exists a critical point that can balance time costs and economic costs, so as to maximize network capacity. Then this paper further finds out the method of estimating the condition when the network takes the maximum capacity through theoretical analysis.

Accessibility-based vulnerability analysis of multi-modal transportation networks with weibit choice models

This study proposes utility-based accessibility measures and a weibit-based analysis method for analyzing multi-modal transportation network vulnerability. To account for the multi-dimensional travel choice behavior in multi-modal transportation networks, an advanced weibit-based combined modal split and traffic assignment model is adopted to derive the accessibility measures based on the weibit-expected travel disutility from both route choice and mode choice dimensions. The accessibility at the modal split level is derived from the nested weibit model, while that at the route choice level is obtained based on the path-size weibit model. The proposed weibit-based vulnerability measures are inherently suitable for assessing the relative degradation in network performance and can measure the vulnerability of networks with heterogeneous scales without overestimating the importance of modes or routes sharing similar features. Using a simplified multi-modal transportation network of Hong Kong, numerical experiments are conducted to demonstrate the properties of the proposed multi-modal vulnerability analysis. The results indicate that the proposed analysis can effectively address similarity and heterogeneity issues in the analysis of choice behavior and network vulnerability, which are often ignored by the traditional logit-based measures.

Developing an integrated restoration model of multimodal transportation network

This paper presents a new optimisation model for restoring a multimodal transportation network disrupted by a catastrophic disaster. The model provides an integrated decision of restoration activities by considering the interdependencies of the road network and multimodal terminals for sending relief goods. A new particle swarm optimisation is proposed to determine the number of repair teams for the road network and restoration activities at the terminal. The problem of road restoration is addressed by presenting a new variant of dynamic programming and a greedy heuristic. The model was applied to a multimodal transportation network that was struck by the earthquake and tsunami. The optimal solution considering multimodal restoration decisions has been found to reduce the unsatisfied demand. The proposed particle swarm optimisation and greedy heuristic offer stability in searching for optimal solutions, whereas the proposed dynamic programming can provide an optimal solution with lower computational time.

A novel reliable path planning approach for multimodal networks based on a two-factor bound convergence algorithm

Due to the influence of diverse factors, travel time is highly uncertain. Travelers are eager to find the most reliable path in multimodal networks to reduce the penalty caused by late arrival. However, the research considering the traveler preferences in multimodal transportation networks to solve the reliable path problem with given budgets is limited. Thus, we propose two multimodal reliable path models to find personalized and reliable paths. First, we build a multimodal network based on smart card data to incorporate the multimodal transfers between public and private transportation and solve corresponding parking issues effectively. Next, we build a multimodal time-reliable path model to find time-reliable paths. Further, considering traveler preferences, we design a multimodal utility-reliable path model to find personalized and reliable paths. A novel two-factor reliability bound convergence algorithm is developed to solve the proposed models and proved for its theoretical feasibility. Finally, a real-world case study is used to verify the effectiveness and efficiency of the proposed models and algorithm.

A review on passenger emergency evacuation from multimodal transportation hubs

Multimodal transportation hubs serve as transfer points with the function of collecting, transferring, and distributing passengers and play a significant role in the entire multimodal transportation network. In an emergency, the development of an effective and efficient emergency evacuation strategy to evacuate passengers from multimodal transportation hubs is very important. This paper aims to conduct a comprehensive review on passenger emergency evacuations from multimodal transportation hubs, summarize the achievements, identify gaps in existing literature, thus to lay a solid foundation for future studies. This paper first reviewed studies on evacuee behavior characteristics in emergency evacuations, including pedestrian characteristics, pedestrian behavior characteristics, and pedestrian flow characteristics. Then evacuation models, including pedestrian evacuation models and multimodal transportation evacuation models, were investigated. In addition, a retrospective analysis was carried out for evacuation simulation. It is found that while many studies have been conducted on passenger emergency evacuations from multimodal transportation hubs, most of them focus more on evacuating pedestrians from inside to outside of hub buildings; the studies on evacuation at multimodal transfer points are limited. It is also found that most existing studies tried to establish a general model to handle all types of emergency evacuations; in fact, a model would be more efficient if it is just established for a specific emergency and for a specific type of hubs. Also, existing data collection methods may collect biased data; new data collection methods need to be explored to increase the accuracy of data collection. Future study directions include the evacuation at multimodal transfer points, interactive effects between individual pedestrian behavior and pedestrian group behavior, more specific types of emergency evacuations, more accurate data collection methods, and tradeoffs between cost and benefit in emergency evacuations. • Reviewed studies on passenger emergency evacuation from multimodal transportation hubs. • Summarized findings on evacuee behavior, models, and simulations of emergency evacuation studies and analyzed the gaps. • Proposed further studies directions for passenger emergency evacuation from multimodal transportation hubs.

Large-scale multimodal transportation network models and algorithms-Part I: The combined mode split and traffic assignment problem

Modelling the combined mode split and traffic assignment (CMSTA) problem is essential to capture the travelers’ behaviors and predict the flow distribution in multimodal transportation networks. In this paper, a general fixed-point model that combines the CNL-based mode split and the VI-based traffic assignment is developed to formulate the CMSTA problem in a more realistic multimodal network. The transit common line problem and asymmetric travel cost are considered. A new solution algorithm that integrated with several effective methods and strategies is proposed to solve the fixed-point model in large-scale networks. Numerical results show the effectiveness of the proposed model and algorithm.

Resilience Assessment of Multimodal Urban Transport Networks

With the development of urbanization and the evolution of urban network systems, multimodal urban transport network (MUTN) systems play a vital role in improving network effects and operational efficiency. However, urban transport networks are easily affected by natural disasters and traffic incidents, which can lead to significant human and economic losses. Accordingly, it is vital to be able to assess the resilience of transport networks in the face of various disruptions. This study, therefore, utilizes complex network theory to analyze the resilience of multimodal urban transport networks, with the resilience accessed based on topological indices. The MUTN in Beijing is selected as a case study for simulation analysis. Based on the road network and subway network, a model MUTN is established, and the Monte Carlo method is used to simulate random attacks. The results show that the MUTN in Beijing has good resilience against disruptions. This study guides the evaluation of the overall resilience of multimodal urban transport networks and will be useful for transportation planners and decision-makers in dealing with emergencies and natural disasters in the future.

ML-MMAS: Self-learning ant colony optimization for multi-criteria journey planning

Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major shortcomings: 1) pheromone trails are instance-specific; hence they need to be generated for each new problem instance, 2) solution is constructed based on static pheromone trails, which ignores the impact of the evolving decisions on the final solution. In this paper, we study the personalized journey route planning problem on multimodal public transport networks (MMPTN) that considers multiple travel criteria. The problem is addressed with a weighted sum method, which provides a journey route that best matches passenger’s preference vector consisting of multiple travel criteria. We propose a Machine Learning (ML) based Max–Min Ant System (called ML-MMAS) to solve optimization problems by incorporating ML techniques into Ant Colony Optimization algorithms. ML-MMAS learns a pheromone function to directly produce prominent pheromone trails in order to construct solutions for any new instance, without the need to initialize and update the pheromone trails from scratch. We propose a self-learning framework to train the ML-MMAS using incremental solutions generated by MMAS, hence avoiding the need for pre-computed optimal solutions. Specifically, we develop a deep learning-based pheromone prediction model. We design several groups of features to train the model to characterize the evolving states of the search space during solution construction. Finally, we propose a solution component embedding (SCE) model to learn representations of solution components (transit services), which takes into account the transferability among transit services and passenger transfer preferences. The SCE model enables the extraction of high-quality features for the solution components. It can also be directly applied to solve other optimization problems with solutions that can be modeled as sequences of solution components. We evaluate the proposed ML-MMAS by comparing with exact algorithms and the underlying MMAS, using the MMPTN and passenger demands of Singapore. Results show that ML-MMAS is significantly faster than both the exact algorithm and the original MMAS, while achieving near-optimal solutions.

Assessing robustness in multimodal transportation systems: a case study in Lisbon

IntroductionWorldwide public transport systems are exposed to disruptions caused by malfunctions, accidents, maintenance, reduced fleet, and disasters, compromising mobility. Transport networks’ multimodal planning and management can be explored to increase their robustness against these events. In this context, this research paper proposes and empirically compares methods to assess the robustness of a multimodal transport network, looking at aspects regarding the single-mode and multimodal network topology.Materials and MethodsWe hypothesize that the appropriate multilayered and traffic sensitive modeling of a multimodal transport network can help characterize robustness and further unravel vulnerabilities related to the integration of different transport modes. Using metric-based targeting, we evaluate how the network decreases performance when simulating failures on stations and pathways using different scenarios. The following six extraction strategies for nodes and edges were used in the simulation: Random removal; Initial Degree removal; Initial Betweenness removal; Recalculate Degree removal; Recalculate Betweenness removal; and Multimodal Hubs removal. Lisbon’s public transport is used as a case study and is modeled as a multiplex network integrating eight different modes of transport. Proposing a novel normalized version of assessing the impact of failures, we were able to compare side by side the robustness of each modality layer, regardless of their size. Lastly, we simulate cascading events such as the breakdown of an entire transportation line.ConclusionsUsing different ways to induce failures in the network, we observe that to leave all nodes completely disconnected, we would need to remove about half the network nodes, highlighting the robustness of the Lisbon public transport network. Comparing different failure scenarios, methods that rely on recalculating network metrics yield a higher impact on the network robustness assessment. The impact of different events is quantified, showing that failures in stations are generally more dangerous than in pathways and offering views on the consequences of deactivating particular network modules. Overall, the results of this study allow decision-makers to gain further understanding of the topological vulnerabilities of a transportation network.

Incentive design and profit sharing in multi-modal transportation networks

We consider the situation where multiple transportation service providers cooperate to offer an integrated multi-modal platform to enhance the convenience to the passengers through ease in multi-modal journey planning, payment, and first and last mile connectivity. This market structure allows the multi-modal platform to coordinate profits across modes and also provide incentives to the passengers. Accordingly, in this paper, we use cooperative game theory coupled with the hyperpath-based stochastic user equilibrium framework to study such a market. We assume that the platform sets incentives (price discount or excess charge on passengers) along every edge in the transportation network. We derive the continuity and monotonicity properties of the equilibrium flow with respect to the incentives along every edge. The optimal incentives that maximize the profit of the platform are obtained through a two time-scale stochastic approximation algorithm. We use the asymmetric Nash bargaining solution to design a fair profit sharing scheme among the service providers. We show that the profit for each service provider increases after cooperation on such a platform. Finally, we complement the theoretical results through two numerical simulations.

Risk-Based Formulation of the Transit Priority Network Design

Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks.

Capacitated Air/Rail Hub Location Problem With Uncertainty: A Model, Efficient Solution Algorithm, and Case Study

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{4})$ </tex-math></inline-formula> variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Optimization of Joint Decision of Transport Mode and Path in Multi-Mode Freight Transportation Network.

This paper mainly studies the joint decision of transportation mode and path in the multi-mode transportation network to provide the optimal plan for freights. This paper constructs a multi-mode transportation network system by setting virtual connections between networks with different transportation modes. The Dijkstra and multi-objective optimization algorithms are used to select the path in the network. After determining the optimal path, the paths’ time, cost, and risk functions are established. The multi-objective function is converted into a single objective function by setting constraint conditions through the analytic hierarchy process. Then, the function is optimized by using the gradient descent method. Finally, the transportation plan for the case of chemical freights is formulated by using the above algorithms. The results show that the proposed algorithm can successfully find the solution for the joint decision of transportation mode and path in the complex network. After a quantitative analysis of the planned effect, the optimization actions of changing the initial transportation time and adjusting the upper limit of resources are proposed. The study findings provide a theoretical basis for improving the efficiency of the comprehensive transportation network.

Integration of shared transport at a public transport stop: mode choice intentions of different user segments at a mobility hub

To create an integrated transport system that can compete with and reduce private car usage, we need a better understanding of the transport and user characteristics that relate to people's intentions to use shared and public transport at a mobility hub. For this purpose, this paper describes the results of a survey surrounding the case study of Leyenburg, The Hague in which a scenario of integrating shared mobility at an existing public transport stop is proposed. This study investigates the intention to use shared modes and public transport in a multimodal transport network and the factors and user characteristics that affect this intention. As digital technologies become important in the integration of modalities by offering digital planning and payment options, concerns regarding digital exclusion in transport services are growing. In this paper we developed a digital skills measure to reflects one's ability to perform tasks that are inherent to the digital services seen in the transport sector. Using an ordinal logistics regression analysis, the study has found that the intention to use shared transport is higher for people who are younger, have a high level of education and a high level of digital skills. In addition, having prior experience with shared transport in the past year and currently using multiple means of transportation during the trip are positively affecting the intention to use shared transport. The intention to combine shared transport with the bus or tram during a trip is similar to the intention to use shared transport and is related to similar characteristics, except for education. The intention to use the bus or tram is found to be mainly related to current transport usage and trip-specific factors and not to other user characteristics. For transport providers, the results provide evidence that offering shared motor scooters and bicycles would be an attractive option for young and highly-educated users who intend to combine the use of shared and public transport.

A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows

As a traditional way for transporting containerized cargoes, international liner shipping has long played a dominant role in the China-Europe freight transportation market. In recent years, China Railway Express has offered a new and competitive freight transportation option, contributing to a significant increase in the trade volume by rail between China and Europe. With the goal of providing a holistic computational tool that can predict and analyze the spatial distribution of containerized freight flows and suggest strategic transportation infrastructure and service expansion options, this paper develops and implements a multimodal multicommodity freight transportation network equilibrium model, based on a newly constructed supernetwork integrating international liner shipping lines, international freight rail services, and international and domestic highway networks in relevant countries. The combined transportation mode-route choice behavior of individual shippers is characterized by the widely used multinomial logit model, where the values of time of different commodity categories are included in its utility functions. The congestion effects arising from bottleneck facilities along liner shipping and freight rail lines (i.e., seaports, water channels, and break-of-gauge stations) are first captured by queueing models and then approximated by polynomial delay functions, while the accommodation capacity of individual service lines is explicitly embedded into the model as hard constraints. This supernetwork equilibrium model is tackled by a subgradient algorithm in the Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm. Computational experiments are conducted with a multi-layer, hetero-structure set of supply and demand data collected from multiple data repositories, government agencies, freight carriers, and research papers and reports. The preliminary results exhibit the computational performance of the solution procedure and reveal the variation of congestion levels of bottleneck facilities, capacity usage profiles of service lines, and competitiveness conditions between liner shipping and freight rail services under different monthly demand patterns.