An intermodal freight transport system for optimal supply chain logistics

Complexity in transport networks evokes the need for instant response to the changing dynamics and uncertainties in the upstream operations, where multiple modes of transport are often available, but rarely used in conjunction. This paper proposes a model for strategic transport planning involving a network wide intermodal transport system. The system determines the spatio-temporal states of road based freight networks (unimodal) and future traffic flow in definite time intervals. This information is processed to devise efficient scheduling plans by coordinating and connecting existing rail transport schedules to road based freight systems (intermodal). The traffic flow estimation is performed by kernel based support vector mechanisms while mixed integer programming (MIP) is used to optimize schedules for intermodal transport network by considering various costs and additional capacity constraints. The model has been successfully applied to an existing Fast Moving Consumer Goods (FMCG) distribution network in India with encouraging results.     

Estimating the CO2 intensity of intermodal freight transportation

This paper looks at the environmental effects of shifting from road to rail freight transportation. Little data is available to shippers to calculate the potential CO₂ savings of an intermodal shift. In this paper we analyze a data set of more than 400,000 intermodal shipments to calculate the CO₂ intensity of intermodal transportation as a distinct mode. Our results indicate an average intensity of 67 g of CO₂ per ton-mile, but can vary between 29 and 220 g of CO₂ per ton-mile depending on the specific origin–destination lane. We apply the market area concept to explain the variance between individual lane intensities and demonstrate the complexity in predicting the potential carbon savings in a switch from truckload to intermodal.     

Achieving transport modal split targets at intermodal freight hubs using a model predictive approach

The increase of international freight commerce is creating pressure on the existing transport network. Cooperation between the different transport parties (e.g., terminal managers, forwarders and transport providers) is required to increase the network throughput using the same infrastructure. The intermodal hubs are locations where cargo is stored and can switch transport modality while approaching the final destination. Decisions regarding cargo assignment are based on cargo properties. Cargo properties can be fixed (e.g., destination, volume, weight) or time varying (remaining time until due time or goods expiration date). The intermodal hub manager, with access to certain cargo information, can promote cooperation with and among different transport providers that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs is modeled based on a mass balance, taking into account hub cargo inflows and outflows, plus an update of the remaining time until cargo due time. Using this model, written in a state-space representation, we propose a model predictive approach to address the Modal Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo assignment to the available transport capacity such that the final destination can be reached on time while taking into consideration the transport modality used. The model predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance, following a push of cargo towards the final destination approach. Through the addition of a modal split constraint it is possible to guide the daily cargo assignment to achieve a transport modal split target over a defined period of time. Numerical experiments illustrate the validity of these statements.     

Intermodal transport in freight distribution: a literature review

There has been significant growth in research on intermodal transport in freight distribution since the 1990s. Differentiating itself from previously published literature reviews, this paper evaluates the current state of this research using Systematic Literature Review methodology. The complementary aims are: (a) to identify the research lines developed and to propose a criterion for classifying the literature, and (b) to discuss the empirical evidence that identifies existing interrelationships. The analysis has enabled three main lines of research to be identified. The first research line, basic principles of intermodal transport, groups together works related to the definition of intermodal transport and the results obtained using this transportation system. The second, improvements to the way that intermodal transport systems work, frames elements and variables that impact intermodal transport systems’ logistics efficiency, such as quality of service, information and communication systems, and freight planning and linkages among system operators to provide an adequate service. Finally, the third line, intermodal transport system modelling, identifies the main variables used to optimise these transport systems, the different focuses and approaches used in modelling, and the advantages and disadvantages of each focus. These research lines take in more specific sublines that incorporate articles that develop related research questions. Lastly, the discussion of the content of each of these research sublines enables us to identify gaps in the literature and comment on directions for future research.     

Mapping and evaluating the complexity of information flows in freight transport chains

ABSTRACT

The role of information in the efficient management of freight transport systems is well acknowledged. Administrative functions, such as negotiation or payments, involve intensive communication, while the production of the transport service relies heavily on the sharing of information (e.g. track and trace). Yet, specific literature on information flows is relatively scarce. This paper sets out to contribute to filling in this gap. Firstly, it elaborates a set of maps of the information flows for four conceptualisations – direct link, corridor, hub and spoke, and connected hubs – of maritime freight transport, initially proposed by Woxenius (2007, “Generic Framework for Transport Network Designs: Applications and Treatment in Intermodal Freight Transport Literature.” Transport Reviews 27 (6): 733–749. doi:10.1080/01441640701358796). The results reveal a high degree of similarity between import and export services. Secondly, it assesses the complexity of each conceptualisation’s flow network using the concept of entropy. Flows are characterised along four variables: involved agents, timing, and contents and message type. Additionally, a new taxonomy is proposed to categorise the messages. The results evidence the heterogeneous contributions of each category to the complexity of the flows. Finally, the results also reveal that the information flows for the tasks in the pre-transport stage are not specific to the conceptualisation in question and are of comparable complexity to the flows occurring during the transport stage.     

Integrated operations planning and revenue management for rail freight transportation

In the rail industry, profit maximization relies heavily on the integration of logistics activities with an improved management of revenues. The operational policies chosen by the carrier have an important impact on the network yield and thus on global profitability. This paper bridges the gap between railroad operations planning and revenue management. We propose a new bilevel mathematical formulation which encompasses pricing decisions and network planning policies such as car blocking and routing as well as train make-up and scheduling. An exact solution approach based on a mixed integer formulation adapted to the problem structure is presented, and computational results are reported on randomly generated instances.     

An integrated risk assessment model: A case of sustainable freight transportation systems

The major challenge in the development of sustainable freight transportation systems (SFTSs) is due to the involvement of numerous dynamic uncertainties and intrinsic sustainability risks. Sustainability risks are potential threats that can have undesirable impacts on the sustainability of a system. The main objective of this study is to identify and evaluate the sustainability risks associated with freight transportation systems (FTSs). Accordingly, a risk analysis approach is developed by innovatively integrating the intuitionistic fuzzy set theory and D-number theory to quantitatively model the sustainability risks. Intuitionistic fuzzy numbers can examine both the membership and non-membership degrees of an element while the D-number theory increases the objectivity of assessments by fusing multiple expert judgments. The proposed risk assessment model facilitates the managers in the development of SFTSs by ensuring visibility, predictability and measurability in freight operations. Unlike the conventional perception, the findings indicate that most of the high priority sustainability risks in FTSs are socially induced rather than financially driven and consciousness in people’s conduct is must to attain the positive results. The analysis alerts the freight managers toward the high priority sustainability risks and guides in pro-active strategy formulation and optimum allocation of mitigation resources to minimize disruptions in SFTSs.     

An agent-based multimodal simulation model for capacity planning of a cross-border transit facility

Cross-border transit facilities constitute major public investment, and thus must serve the long-term needs of the communities, such as providing access to schools and businesses, contributing to a shared regional culture and lifestyle, fostering international trade, and supporting jobs for the region’s residents. Numerous studies have been conducted to evaluate the economic implications of vehicular flow delays at border crossings, however none of the studies focused on assessing cross-border flow of bus passengers and pedestrians. Since pedestrians are considered to be autonomous, intelligent, and perceptive, it is a challenging task to predict pedestrian movement and behavior in comparison to vehicular flows which follow a specific set of traffic rules. This paper presents a multiagent based multimodal simulation model to evaluate the capacity and performance of a cross-border transit facility. The significance of this research is the use of dynamic mode choice functionality in the model, which allows an individual person to make instantaneous choices between available modes of transportation. The scope of interest of the paper is limited to simulating access interface, circulation areas, ancillary and processing facilities. The developed model was calibrated to ensure realistic performance, and validated against specific performance criteria such as throughput per processing facility. In order to demonstrate the applicability of the developed simulation model, capacity and operational planning of a pedestrian transit facility was performed. The relative performance of alternative design or configuration was evaluated using the level of service criteria. Lastly, the effectiveness of each proposed capacity or operational improvement strategy was compared to the “do-nothing” scenario.     

An assessment of the performance of the European long intermodal freight trains (LIFTS)

Intermodal rail/road freight transport has always been considered as a competitive alternative to its road freight counterpart in the European medium- to long-distance corridors (markets). Such consideration has been based on the increasing competitiveness of some innovative rail services and the existing and prospective performance of both modes in terms of the full social – internal or operational and external – costs. The most recent innovation of rail technologies and related services launched by some European railway companies, still at the conceptual level, is the Long Intermodal Freight Train (LIFT). This is supposed to be a block train operating in long-distance corridors (markets) with a substantial and regular freight demand.This paper develops analytical models for assessing the performance of the LIFTs, the already-operating Conventional Intermodal Freight Trains (CIFTs), and their road counterpart as well. The performance consists of the full – internal (private) and external – costs of the door-to-door delivery of loading units – containers, swap-bodies, and semi-trailers. The internal costs embrace the operational costs of the transport (rail and road) and intermodal terminal operators. The external costs include the costs of the impacts of door-to-door delivery of loading units on society and the environment. These negative externalities include noise, air pollution, traffic accidents, and congestion.The models are applied to a simplified version of intermodal and road transport system using inputs from the European freight transport sector. The aims are to compare the full costs of particular modalities in order to investigate the potential of the LIFTs as compared with the CIFTs in improving the internal efficiency of the rail freight sector and its competitiveness with respect to its road counterpart. In addition, the paper attempts to assess some effects on the potential modal shift of EU (European Union) transport policies on internalizing transport externalities.     

Calculation of external costs for freight transport

Abstract

The negative impacts of transport are in general associated with costs. These costs are usually denoted as ‘external costs’ or ‘externalities’. This paper presents a tool for calculating external costs for freight transport together with its application to a number of case studies. The categories considered include: air pollution, greenhouse gases, noise, accidents and congestion. Results are presented for a number of different transport alternatives as total costs and divided into categories. The uncertainties in the results are discussed. The assessment of these costs is essential for predicting future transport costs.     

GHG-emission models for assessing the eco-friendliness of road and rail freight transports

Intermodal rail/road transportation is an instrument of green logistics, which may help reducing transport related greenhouse gas (GHG) emissions. In order to assess the environmental impact of road and rail transports, researchers have formulated very detailed microscopic models, which determine vehicle emissions precisely based on a vast number of parameters. They also developed macroscopic models, which estimate emissions more roughly from few parameters that are considered most influential. One of the goals of this paper is to develop mesoscopic models that combine the preciseness of micro-models while requiring only little more information than macro-models. We propose emission models designed for transport planning purposes which are simple to calibrate by transport managers. Despite their compactness, our models are able to reflect the influence of various traffic conditions on a transport’s total emissions. Furthermore, contrasting most papers considering either the road or the rail mode, we provide models on a common basis for both modes of transportation. We validate our models using popular micro- and macroscopic models and we apply them to artificial and real world transport scenarios to identify under which circumstances intermodal transports actually effect lower emissions. We find that travel speed and country-specific energy emission factors influence the eco-friendliness of intermodal transports most severely. Hence, the particular route chosen for a transnational intermodal transport is an important but so far neglected option for eco-friendly transportation.     

A framework to evaluate policy options for supporting electric vehicles in urban freight transport

Electric vehicles (EVs) are considered as a feasible alternative to traditional vehicles. Few studies have addressed the impacts of policies supporting EVs in urban freight transport. To cast light on this topic, we established a framework combining an optimization model with economic analysis to determine the optimal behavior of an individual delivery service provider company and social impacts (e.g., externalities and welfare) in response to policies designed to support EVs, such as purchase subsidy, limited access (zone fee) to congestion/low-emission zones with exemptions for EVs, and vehicle taxes with exemptions for EVs. Numerical experiments showed that the zone fee can increase the company’s total logistics costs but improve the social welfare. It greatly reduced the external cost inside the congestion/low-emission zone with a high population, dense pollution, and heavy traffic. The vehicle taxes and subsidy were found to have the same influence on the company and society, although they have different effects with low tax/subsidy rates because their different effects on vehicle routing plans. Finally, we performed a sensitivity analysis. Local factors at the company and city levels (e.g., types of vehicle and transport network) are also important to designing efficient policies for urban logistics that support EVs.     

Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation

This paper presents a framework to evaluate the logistics performance of intermodal freight transportation. Fuzzy set techniques are applied to assess the logistics performance within the decision process of freight operators. Using a fuzzy‐based approach, fuzzy‐AHP is applied to assess the criteria by different judgment procedures. Consequently, fuzzy‐MCDM is used to assess operators' perception of the logistics performance via proper assignment of numerical scores. The subjective judgments for hierarchical criteria are transformed into fuzzy degrees of score. The methodology provides an alternative approach to facilitate the importance of a set of performance criteria. It can also entail use of improved corresponding parameters to develop a better freight transport system.     

Flexible strategic planning of transport systems

Abstract

This paper presents a decision support methodology for long-range planning of transport systems that exhibits strategic flexibility and stochastic system parameters. Unlike one-off strategic decisions, flexible decisions should be dynamically reformulated with time. The proposed methodology is based on the construction of a tree structure of multiple interlinked tactical planning problems, each associated with a scenario in the tree, where problems under scenarios at intermediate dates incorporate in their formulation the solution of the corresponding problems associated with past (future) connected scenarios. The resulting tree structure of interconnected planning decisions becomes a strategic-tactical decision support system that allows managers to formulate suitable flexible strategic decisions that mitigate the consequences associated with downside scenarios while taking advantage of the upside opportunities. The methodology is applied to the planning of a fleet deployment through charter contracts where contract prices depend on both market behavior and the duration of the contract itself.     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

Challenges raised by freight for the operations planning of a shared-use rail network. A French perspective

Operating rail infrastructures that are shared among different uses is complex. In Western Europe, the predominance of passenger traffic over freight has traditionally led to thorough scheduling of capacity use, with an increasing tendency to anticipate through the design of regular-interval timetables. The paper discusses the specific challenges posed by fitting freight into the timetabling process for a mixed-use rail network, based on current French experience. The analysis is carried out from the perspective of the infrastructure manager. It is mainly supported by the results of a series of about 30 interviews, carried out in 2012 and 2013 with the parties involved in the timetabling process. The paper provides a comprehensive understanding of the process in terms of organization, rules and practices, with an emphasis on the characteristics of freight traffic compared with passenger traffic. The author highlights three key management issues for the French infrastructure manager when dealing with freight: (1) the uncertainty surrounding the mid-long term development of the rail freight market at the national level; (2) the heterogeneity resulting from the diversity of commodities, convoys and profiles and behaviors of the capacity applicants; (3) the volatility of some freight traffic resulting in a great amount of activity in the later stages of the timetabling process. If uncertainty about the future appears to be a highly sensitive issue in the French context, heterogeneity and volatility of freight traffic can be perceived as management challenges that may be experienced, to a greater or lesser degree, on other rail networks.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

An eigenmodel for iterative line planning,timetabling and vehicle scheduling in public transportation

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

Development of efficient stop planning optimization process for high‐speed rail systems

The Taiwan High Speed Rail (THSR) has recently added three additional stations to its original network. Although the three additional stations can improve accessibility to the system, these new stations can present difficulties in the transportation planning process, particularly for planning of train stops. The additional stations may benefit some passengers, but may also lengthen the travel time for the other passengers. Therefore, the main challenge faced by THSR is finding an efficient way to design appropriate stopping patterns. Past studies on stop planning usually adopted meta‐heuristics or decomposition methods to solve this complex problem. Although these solution techniques can improve solution efficiency, none of them can guarantee the optimality of the solution and capture the transfer movement of different stopping patterns. In this research, we proposed an innovative network structure to address complex stop planning problems for high‐speed rail systems. Given its special network structure, two binary integer programming models were developed to simultaneously form and determine the optimal stopping patterns for real‐world THSR stop planning problems. An optimization process was also developed to accurately estimate the station transfer time corresponding to the variation in stopping patterns and passenger flow. Results of the case studies suggest that the proposed binary integer programming models exhibit superior solution quality and efficiency over existing exact optimization models. Consequently, using this stop planning optimization process can help high‐speed rail system planners in designing optimal stopping patterns that correspond to passenger demand. Copyright © 2017 John Wiley & Sons, Ltd.