The transport sector accounts for approximately a quarter of the EU’s total greenhouse gas emissions, with freight transport alone accounting for about one-third of the overall transport-related emissions. Mitigating the sector’s environmental impact is crucial for tackling climate change and achieving sustainable development goals. Modal shift is one of the main solutions to address this challenge; however, many companies have yet to realize its full potential. This paper presents a survey conducted in the Flanders region of Belgium, aiming to identify the challenges and barriers faced by industry players in this key geographical area and to explore the reasons behind the limited implementation of synchromodal transport among them. The survey evaluates the current state of synchromodal transport adoption and offers valuable insights for policymakers and industry stakeholders aiming to enhance sustainability in the logistics sector. The findings emphasize that to overcome the identified challenges, both policy support and the companies’ commitment are required. Policy support includes establishing consistent regulations and promoting greener transport modes through providing incentives and technological advancements. This research contributes to the field by examining barriers to the adoption of synchromodality and exploring its application within the context of Flanders. By focusing on this strategic logistics hub, the study provides insights and recommendations tailored to the specific challenges of the region’s logistics sector. The challenges faced by industry players in Flanders offer a deeper understanding of modal shift dynamics, facilitating informed decision-making for policymakers and industry stakeholders. Implementing these strategies paves the way for more environmentally friendly, efficient, and integrated transport, benefiting both the industry and the planet.

Despite the presence of numerous navigable rivers in Brazil, they remain underutilized for Inland Waterway Transport (IWT). Given the recent changes in transport systems which aim to reduce reliance on highways due to their high cost and lack of sustainability, it becomes crucial to explore new transport models in Brazil, shifting part of the transport from roads to railways and mainly to waterways. To fill this gap in Brazil’s transport system, it was decided to check why waterways are little used and what challenges cause them to be underutilized and, additionally, what opportunities could leverage their use in the country. In this sense, in this study, we identify the biggest challenges and opportunities faced by IWT in Brazil. To achieve this objective, interviews were conducted using content analysis with managers who work in public and private IWT organizations active in Brazil. Results show that IWT has seen a recovery in interest in recent years due to the need for cheaper and greener logistics. It was also found that the main challenges that IWT faces are a lack of public policies followed by precarious infrastructure of waterways, ports and locks, as well as modest integration with other transport modes. Conversely, the most significant opportunity lies in the potential reduction in transport costs, coupled with the enhanced sustainability of transport activities via the utilization of IWT, thereby fostering greener transport practices. These results can contribute to a better understanding of practical and theoretical approaches related to IWT in Brazil, and they can serve as a reflection for new research focusing on the development of IWT especially in other emerging countries facing similar issues.

Appropriate public transport systems are crucial in modern cities. Given the high costs that they represent and the impact they have on people's lives, effective tools are required to support their design. The Transit Network Design and Frequency Setting problem (TNDFSP) has been extensively studied in operations research. This problem consists of designing a set of public transport lines and a service frequency to each line. The main contribution of this work is to consider, for some links of the underlying network, a maximum ‘combined’ frequency among all the lines using that link and addressing the crowding issues that can result from that. These additional constraints intend to limit, for instance, the number of buses circulating in certain areas, in line with current urban design policies. A bi-objective memetic algorithm is proposed to solve the problem. The algorithm generates a set of non-dominated solutions that comply with the link-capacity constraints in 1 h of computing time. Additionally, alternative solutions are generated by designing the line plan without considering the link-capacity constraints and adapting the frequencies afterwards, to compare the two approaches. The algorithm is tested on an instance representing the bus network in the city of Utrecht, The Netherlands. The algorithm that takes into account the link-capacity constraints during the optimization process, generates better results. Moreover, the algorithm generates transit networks with less lines traversing the city centre, allowing higher individual frequencies for those lines. The algorithm could become an important tool for policy makers and transit operators, allowing the design of efficient transit systems that adjust better to contemporary urban requirements.

Appropriate public transport systems are crucial in modern cities. Given the high costs that they represent and the impact they have on people’s lives, effective tools are required to support their design. With this in mind, the Transit Network Design problem (TNDP) and the Transit Network Design and Frequency Setting problem (TNDFSP) have been extensively studied in the domain of Operations Research. However, due to the complexity of these problems, multiple simplifications are typically made when modelling and designing solution algorithms. Therefore, still no optimization techniques are available to address these problems in practice. Moreover, different studies address different versions of the problem, with varying assumptions and constraints, complicating the comparison of results or solution approaches. This paper presents an extensive survey of studies addressing the TNDP and the TNDFSP. It discusses the different assumptions, constraints, objectives, solution approaches and testing instances that have been considered in the literature. Furthermore, a detailed analysis is done regarding the case studies considered for the TNDFSP. Moreover, the variants of the passenger assignment subproblem that have been applied within the TNDP and the TNDFSP are discussed. The analysis shows that extensive research has been done regarding these problems. However, it also identified the significant gap that still exists between theory and practice, even in the studies addressing case studies.

This paper presents an approach to design better line plans for realistic cases. The planning problem is modelled as a realistic instance of the Transit Network Design and Frequency Setting Problem (TNDFSP). It incorporates additional assumptions taken from practice such as discrete frequencies, a subset of allowed terminal nodes and circular lines. A bi-objective memetic algorithm minimizes the average travel time (ATT) of the passengers and the fleet size. The method to generate the TNDFSP instance from real data is described in detail. Moreover, a new metric is proposed to compare different transit networks. In order to illustrate the approach, it is applied to the area of Utrecht, The Netherlands. The results show that the current network is modelled accurately and that the algorithm successfully generates alternative bus line plans within reasonable CPU time. It returns a subset of non-dominated solutions from which a compromise solution can be selected for practice. There are solutions with the same fleet size as the current solution, but a 6% lower ATT, or solutions with the same ATT, but a fleet size which is 19% smaller. Compared to the current network, the algorithm finds that it is convenient to substantially reduce the number of lines and to leave a small portion of demand unsatisfied. This paper also presents extensive experiments to test the impact of different assumptions about demand, bus capacity and minimum frequency.

Urban transportation contributes significantly to CO₂ emissions. Public transport systems are a good strategy to reduce these, but the emissions generated by public transport vehicles should not be neglected during the design of the service. The Transit Network Design and Frequency Setting Problem (TNDFSP) has usually been addressed considering only the passengers’ and the operator's point of view. However, we show it is worthwhile to consider also the emissions already during this planning phase. This paper proposes a memetic algorithm to address the bi-objective TNDFSP where both the total travel time and the CO₂ emissions are minimized. The analysis considers a heterogeneous fleet, meaning that buses of different sizes and technologies can be assigned under a budget constraint. The results on benchmark instances show that the proposed memetic algorithm performs as well as state-of-the-art algorithms where CO₂ emissions are not considered. In addition, several experiments are carried out to observe the effect of incorporating emissions and heterogeneous fleet into the model. The heterogeneous fleet allows reducing travel times and emissions at the same time, compared to solutions without a heterogeneous fleet. Moreover, the explicit minimization of CO₂ emissions within a bi-objective framework allows illustrating the trade-off between both objectives. Reductions of about 30% in the emissions can be achieved by increasing the travel time only 1%, while the costs for the operator remain the same. This clearly demonstrates the benefits of considering both the CO₂ emissions and a heterogeneous fleet during the design stage of public transport systems.

In this paper, a matheuristic iterative approach (MHIA) is proposed to solve the line planning problem, also called network design problem, and frequency setting on the Chinese high-speed railway network. Our optimization model integrates the cost-oriented and passenger-oriented objectives into a profit-oriented objective. Therefore, the passenger travel time is incorporated in the ticket price using a travel time value. As a result, transfers and detours will result in lower ticket prices and thus lower revenues for the operator. When evaluating the performance of a given line plan, the way in which passengers will travel through the network needs to be modelled. This passenger assignment is typically a time-consuming calculation. The proposed line planning approach iteratively improves the line plan using easy-to-determine indicators. During the process, a mixed integer linear programming model addresses the passenger assignment and optimizes the frequency setting in order to maximise the operational profit. Extensive computational experiments are executed to show the effectiveness of the proposed approach to deal with the real-world railway network line planning problem. Through extensive computational experiments on the small example network and real-world-based instances, the results show that the proposed model can improve the profits by 22.4% on average comparing to their initial solutions. When comparing to an alternative iterative approach, our proposed method has advantage of obtaining high quality of solutions by improving the profit 10.8% on average. For small, medium, and large size networks, the obtained results are close to the optimal solutions, when available.

A critical step in the design of urban transport networks is the determination of the routes and the frequencies of buses. This situation entails a highly combinatorial optimization problem with a complex computational solution, even for small instances. Several studies have addressed such a situation, minimizing travel times as the main objective. However, the growing trend toward the development of sustainable transport operations requires that the design of the network also considers the emissions of toxic gases that result from combustion, which leads to a new variant of this type of problem, called the pollution transit network design problem. In this paper, the problem is formulated as a biobjective mathematical programming model. Complex problem instances are proposed for this problem, and by using a multi-objective genetic algorithm, we approach the unimodal and bimodal version of the problem by taking into account the elastic demand between buses and cars. By using the proposed mathematical programming model and the genetic algorithm for small and large problem instances, respectively, we show that the generated pollutant emissions are drastically reduced without increasing travel times or costs.