Transport & Mobility Leuven

Objective: The fisheries sector is essential to the economies of developing countries, but it is a contributor to greenhouse gas emissions. Although emissions can be substantially reduced through energy efficiency measures, compliance with the Paris Agreement of 2015 requires further action through national frameworks for the decarbonization of fishing vessels. The objective of this paper is to explain the impact in greenhouse gas emissions from fishing vessels, discuss the possible regulatory indexes that could be made applicable to fishing vessels and how these ships can transition to alternative and low carbon fuels, identifying the main challenges in view of accident analysis and inspections. Methods: It is recognized that mandatory indexes developed at the International Maritime Organisation are not feasible to apply, so new indexes are needed and possibly connected to fish captures. Most of these zero or near zero greenhouse gas emission fuels require technical and operational measures for accident prevention but, due to high rates of accidents related to fire, explosion and inhalation of gases, their use can lead to increase the rates of fatalities in an already dangerous profession. The main problem for their use is ventilation, enclosure of machinery spaces leading to preventive and design measures for the use of batteries and electricity and suitable training. Conclusion: To avoid accidents, additional extra measures would be needed for fishing vessels. Therefore, to ease the transition towards new fuels those which are more similar to fossil marine diesels should be used; notwithstanding the use of other sources of energy such as solar and wind power.

Traffic flow data is essential for urban planning, logistics, transport management, and similar applications. However, achieving full sensor coverage across a road network is often infeasible due to high installation and maintenance costs. Simulation data from traffic models can help in filling this gap. However, calibrating and validating these traffic models is time-consuming. This paper presents a framework that combines real-time traffic flow predictions from sensor-equipped road segments with 24-hour static simulation data across an entire network. By applying a method based on the Breadth-First Search algorithm, this framework updates network-wide traffic flow by utilizing the data-driven predictions at sensor-equipped road segments and simulation data. Evaluation on a network with over 27000 road segments shows that this approach improves prediction accuracy over static simulation and is viable for real-time deployment.

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

Driving simulator data can be sampled in function of distance (equally spaced) or time (with constant frequency). Consequently, the sampling data might have problems in the envisaged type of analysis (i.e. point location based analysis vs. zonal-based analysis). These issues are illustrated by means of five driving simulator datasets. The nearest sampled parameter value in the direct vicinity of the specific point is a very good proxy for the driving parameter value at the point of interest along the road. The analysis of driving parameters in zones requires a different approach. In summary, the interpolation technique is preferred over using raw sampled data to calculate mean parameter values. We introduce an equivalent time integral formula to compute the mean value of a driving parameter with respect to distance. Based on this paper, we demonstrate that it is very important to mention the data processing approach in driving simulator methodology.

As cities grow larger, they often struggle in finding sustainable and liveable mobility solutions to accommodate this growth. Many alternative modes of transport—such as public transport, carsharing systems, bikesharing systems—exist next to private car travel. The effects of expanding those alternatives are often challenging to model. This is in particular the case for small and medium sized cities, which often use straightforward and easy-to-use four-step traffic models. The alternative modes of transport could be modelled using extensive agent-based traffic models. However, these are expensive to make and require a lot of data and expertise. In the context of the EU H2020 project “MOMENTUM”, we developed an intermediate modelling approach that aims to reconcile the user-friendliness of four-step traffic models with the predictive power of agent-based models to investigate the effects of alternative modes of transport. In this paper, we demonstrate the modelling of a policy plan—away from private transport towards durable modes of transport such as shared mobility and public transport—in the city of Leuven, Belgium. We focus particularly on the developed disaggregate car-ownership model, induced demand model, and link-level emission model. It was found that an improved carsharing supply can significantly reduce the car ownership of a city’s households. The largest reduction is seen in households that own several cars and decide they can do with one fewer. These households can use the carsharing system for the occasional trip they would make with the additional car. Moreover, policy measures for the promotion of alternative modes of transport—which might increase the travel times to reach the city for privately owned cars—were found to be able to reduce the city’s mobility-related emissions. In conclusion, we demonstrated that the developed intermediate modelling approach is versatile and applicable to the cities like Leuven, such that they can also account for new modes of transport. The developed models and concepts can help other small- and medium-sized cities to shape their mobility plans.

Evaluating ridesharing potential is a trend in current research efforts because ridesharing provides additional mobility alternatives without extra putting vehicles on the road. Nevertheless, in most studied scenarios, the demand revealed by surveys and demographic information does not include multi-day characteristics of a trip such as frequencies on weekdays. Yet this is important for estimating the supply of rides, as the recurrence or regularity of a trip may affect the likelihood of a driver making the effort of registering the trip as being available for sharing. Likewise, if automated apps are used to recognize patterns in one’s trips and pro-actively offer them for sharing, the successful anticipation of such apps may again depend on the regularity of the trip. However, since multi-day data are complex to produce, in this paper, a data fusion procedure is proposed to generate an enriched synthetic demand for more realistic assessments. This can be achieved by combining standard single-day data sets with travel behavior patterns, which can be extracted from lifelogging data collected by most existing mobile apps. The resulting data sets after transferring information from the travel patterns to a recipient data set via statistical matching, will constrain matching trips by multi-day characteristics allowing complex scenarios. This approach enhances the evaluation of ridesharing and other shared-mobility systems and thus their ability to plan better strategies.

Air pollution is a global challenge, and especially urban areas are particularly affected by acute episodes. Traditional approaches used to mitigate air pollution primarily consider the technical aspects of the problem but not the role of citizen behaviour and day-to-day practices. ClairCity, a Horizon 2020 funded project, created an impact assessment framework considering the role of citizen behaviour to create future scenarios, aiming to improve urban environments and the wellbeing and health of its inhabitants. This framework was applied to six pilot cases: Bristol, Amsterdam, Ljubljana, Sosnowiec, Aveiro Region and Liguria Region, considering three-time horizons: 2025, 2035 and 2050. The scenarios approach includes the Business As Usual (BAU) scenario and a Final Unified Policy Scenarios (FUPS) established by citizens, decision-makers, local planners and stakeholders based on data collected through a citizen and stakeholder co-creation process. Therefore, this paper aims to present the ClairCity outcomes, analysing the quantified impacts of selected measures in terms of emissions, air quality, population exposure, and health. Each case study has established a particular set of measures with different levels of ambition, therefore different levels of success were achieved towards the control and mitigation of their specific air pollution problems. The transport sector was the most addressed by the measures showing substantial improvements for NO2, already with the BAU scenarios, and overall, even better results when applying the citizen-led FUPS scenarios. In some cases, due to a lack of ambition for the residential and commercial sector, the results were not sufficient to fulfil the WHO guidelines. Overall, it was found in all cities that the co-created scenarios would lead to environmental improvements in terms of air quality and citizens’ health compared to the baseline year of 2015. However, in some cases, the health impacts were lower than air quality due to the implementation of the measures not affecting the most densely populated areas. Benefits from the FUPS comparing to the BAU scenario were found to be highest in Amsterdam and Bristol, with further NO2 and PM10 emission reductions around 10%–16% by 2025 and 19%–28% by 2050, compared to BAU.

On-demand mobility services are changing the way we move in cities, fostered by digitalisation, vehicle automation and Mobility-as-a-Service (MaaS) platforms. Transport planning tools and techniques are expected to keep up to date to support decision-makers on the successful integration of these new mobility options in the urban transport mix. However, the evolution of the emerging mobility solutions is highly uncertain, as it is a function of complex factors that go beyond the influence of transport stakeholders. This poses a challenge for the definition of the requirements that data analysis techniques and modelling tools will face. This paper presents a series of explorative scenarios that provide different contexts for the evolution of urban mobility in Europe, in order to grasp the plausible pathways that shared mobility services will follow in the upcoming decades. The scenarios are adapted from those developed by the climate change research community and are used in a Delphi poll to gather expert visions on how shared mobility services will evolve. The availability of a range of possible futures for shared mobility facilitates the identification of the capabilities that transport decision-makers will demand from data analysis techniques and modelling tools.

ClairCity, a project funded by the EU Horizon 2020 research and innovation programme, developed an innovative quantification framework aiming to assess environmental, health and economic impacts. The quantification framework consists of (i) an integrated urban module based on the household and dwelling characteristics, (ii) emission rates linked with on-road transport, (iii) emission data linked with the industrial, residential, commercial and institutional sectors, (iv) daily and hourly consumption profiles based on the energy and power generation data, (v) air quality patterns and related population exposure, (vi) health-related impacts and costs, and (vii) carbon footprint estimates. This framework was applied for the baseline situation of 6 pilot cities. In particular, the second-generation Gaussian model URBAIR was setup and ran to simulate NO2 and particulate matter concentrations for distinct computational domains covering the urban area of each case study for the full baseline year of 2015. The ClairCity impact assessment framework is applied to evaluate the impact of scenarios for 2025, 2035 and 2050, namely the Business As Usual (BAU) scenario and 3 additional scenarios translating the expectations of citizens and local experts based on data collected through engagement process. The outcomes of the assessment of impacts were used to inform the Policy Workshops for each case study to help decision-makers and local planners to define the final integrated Unified Scenario.

This paper uses a combination of a regional computable general equilibrium model(EDIP) and a household micro-simulation model (EUROMOD) to assess the welfare effects of a transport tax reform. The transport tax reform replaces current car fuel and ownership taxes by a road charge differentiated by time and place. This is combined with five realistic tax recycling scenarios that can be ordered by their degree of progressivity. The net revenues are used to reduce taxes on labour. The tax reform leads to modest increases in real wage, disposable income and GDP, while reducing external costs of transport. Using the combination of general equilibrium modelling with micro-simulation we can go into more detail on the distributive impact of the road tax reform. Where other authors have found progressive or mildly regressive impacts of road charging, we find that within each income group there is a wide divergence of positively and negatively affected households. As such, the support or opposition for a road charging policy may depend more on the profile of the car user than on the relative ranking of the user in the income distribution.

Technological approaches to carbon emission and air pollution data modelling consider where the issues are located and what is creating emissions. This paper argues that more focus should be paid to people—the drivers of vehicles or households burning fossil fuels (‘Who’) and the reasons for doing so at those times (‘Why’). We applied insights from social psychology (social identity theory and social cognitive theory) to better understand and communicate how people’s everyday activities are a cause of climate change and air pollution. A new method for citizen-focused source apportionment modelling and communication was developed in the ClairCity project and applied to travel data from Bristol, U.K. This approach enables understanding of the human dimension of vehicle use to improve policymaking, accounting for demographics (gender or age groups), socio-economic factors (income/car ownership) and motives for specific behaviours (e.g., commuting to work, leisure, shopping, etc.). Tailored communications for segmented in-groups were trialled, aiming to connect with group lived experiences and day-to-day behaviours. This citizen-centred approach aims to make groups more aware that ‘people like me’ create emissions, and equally, ‘people like me’ can take action to reduce emissions.

This paper studies the integration of the Vehicle Routing Problem with Cross-Docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are used in order to explore a large neighborhood space. The second phase focuses on solving the set partitioning model to determine the final solution. The proposed matheuristic is tested on the available benchmark VRPCD instances and compared with the state-of-the-art algorithms. Experimental results show the competitiveness of the proposed matheuristic as it is able to improve the best known solutions for 80 instances and to obtain the same results for the remaining 10 instances, with an average improvement of 12.6%. On new and larger instances, our proposed matheuristic maintains its solution quality within acceptable CPU times and outperforms a pure ALNS algorithm. We also explicitly analyze the performance of the matheuristic considering the solution quality and CPU time.

Cities should try to combine the various conflicting interests in the field of urban mobility aiming to extract the maximum benefits of the different mobility options. Although this task concerns mainly the current situation, cities should also prioritize future mobility measures. The purpose of this paper is to explore how emerging mobility concepts would evolve under future mobility scenarios related to carsharing services, micromobility services, Demand Responsive Transport (DRT) services, Connected Autonomous Vehicles (CAVs), Urban Air Mobility and Mobility-as-a-Service. Two diverging or complementary scenarios have been associated to each concept expected to transform future urban mobility. A Delphi poll has identified the future scenarios related to data sources and transport modelling and assessed the effects that each mobility innovation may have on urban transportation. The impacts of these emerging mobility solutions on transport planning tools and techniques are also investigated and prioritized. Finally, data sharing between operators and policy-makers and the lack of skills among transport planners are rated by the Delphi poll as the most important gaps in terms of transport data sources and barriers that hinder the modelling of the new urban mobility options respectively.

The ownership form of Air Navigation Service Providers varies across countries, ranging from state agencies belonging to the Department of Transport, to government-owned corporations, to semi-private firms with for-profit or not-for-profit mandates. This research focusses on the link between the performance of ANSPs and their ownership form. Economic theory suggests that effort to achieve cost efficiency will be higher in the case of public companies with a board of stakeholders composed of airspace users and in the case of private companies with-stakeholders that are also shareholders. A stochastic frontier analysis estimation of the production and cost functions of 37 European air navigation service providers over nine years suggests that the public-private ownership form with stakeholder involvement achieves statistically significantly higher productive and cost efficient en-route levels compared to either a government corporation or a state agency. We also find substantial levels of inefficiency across the European air traffic control market.

The world’s population has been growing continuously, with most people inhabiting urban settlements. Furthermore, air pollution has become a growing concern, mainly in densely populated cities, where human health is threatened by acute air pollution episodes. The H2020 ClairCity project aims to substantially improve future air quality and carbon policies in European cities by initiating new modes of engaging citizens, stakeholders and policy makers. ClairCity applies an innovative quantification framework developed to assess environmental, health and economic impacts. In this work, the quantification framework was applied and calibrated for the baseline situation in Bristol, the ClairCity pilot city. The second-generation Gaussian model URBAIR was set up to simulate NO 2 and particulate matter (PM) concentrations for the entire year of 2015. An analysis of source contribution was performed providing information on the contributions of different source sectors (e.g. road transport, industrial, residential and commercial) to NO 2 and PM concentrations. The results point to a predominant contribution of road transport sector of 53% to NO 2 concentrations in Bristol, while the residential sector is the main contributor (with a contribution of 82%) to particulate matter concentrations, mainly linked with a high use of solid biomass combustion in this sector. These results can be powerful to support the design of air quality management plans and strategies and to forecast potential benefits of reducing emissions from a particular source category.

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.