EW
Erwin Walraven
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3 records found
1
Journal article
(2026)
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Tygo Nijsten, Jan Pieter Dorsman, Michel Mandjes, Erwin Walraven, Maaike Snelder
Cars remain the most widely used mode of transport today. However, in many urban areas, high car usage leads to negative externalities such as congestion, pollution, and inefficient land use. Optimising parking policies in cities is a promising approach to reduce these externalities, though it often involves trade-offs; for example, reducing parking space can increase the time drivers spend searching for a spot. We present a model to optimise parking capacities in urban areas using a multi-objective framework that simultaneously minimises (1) travel time, (2) distance travelled by car, and (3) the number of parking spaces. We address this problem using a bi-level programming framework as parking capacity decisions (upper level) influence driver route and parking choices (lower level), which in turn affect the objective values. Our main methodological contribution lies in enhancing the upper level optimisation through a novel mutation operator, which helps achieve lower objective values. We apply our model to the city of Delft, the Netherlands, demonstrating that a diverse set of solutions with low objective values can be obtained. Moreover, we show through an example within this case study that our model can help policy-makers assess trade-offs in the conflicting objectives.
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Cars remain the most widely used mode of transport today. However, in many urban areas, high car usage leads to negative externalities such as congestion, pollution, and inefficient land use. Optimising parking policies in cities is a promising approach to reduce these externalities, though it often involves trade-offs; for example, reducing parking space can increase the time drivers spend searching for a spot. We present a model to optimise parking capacities in urban areas using a multi-objective framework that simultaneously minimises (1) travel time, (2) distance travelled by car, and (3) the number of parking spaces. We address this problem using a bi-level programming framework as parking capacity decisions (upper level) influence driver route and parking choices (lower level), which in turn affect the objective values. Our main methodological contribution lies in enhancing the upper level optimisation through a novel mutation operator, which helps achieve lower objective values. We apply our model to the city of Delft, the Netherlands, demonstrating that a diverse set of solutions with low objective values can be obtained. Moreover, we show through an example within this case study that our model can help policy-makers assess trade-offs in the conflicting objectives.
Gathering information about the environment state is the main goal in several planning tasks for autonomous agents, such as surveillance, inspection and tracking of objects. Such planning tasks are typically modeled using a Partially Observable Markov Decision Process (POMDP), and in the literature several approaches have emerged to consider information gathering during planning and execution. Similar developments can be seen in the field of active inference, which focuses on active information collection in order to be able to reach a goal. Both fields use POMDPs to model the environment, but the underlying principles for action selection are different. In this paper we create a bridge between both research fields by discussing how they relate to each other and how they can be used for information gathering. Our contribution is a tailored approach to model information gathering tasks directly in the active inference framework. A series of experiments demonstrates that our approach enables agents to gather information about the environment state. As a result, active inference becomes an alternative to common POMDP approaches for information gathering, which opens the door towards more cross cutting research at the intersection of both fields. This is advantageous, because recent advancements in POMDP solvers may be used to accelerate active inference, and the principled active inference framework may be used to model POMDP agents that operate in a neurobiologically plausible fashion.
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Gathering information about the environment state is the main goal in several planning tasks for autonomous agents, such as surveillance, inspection and tracking of objects. Such planning tasks are typically modeled using a Partially Observable Markov Decision Process (POMDP), and in the literature several approaches have emerged to consider information gathering during planning and execution. Similar developments can be seen in the field of active inference, which focuses on active information collection in order to be able to reach a goal. Both fields use POMDPs to model the environment, but the underlying principles for action selection are different. In this paper we create a bridge between both research fields by discussing how they relate to each other and how they can be used for information gathering. Our contribution is a tailored approach to model information gathering tasks directly in the active inference framework. A series of experiments demonstrates that our approach enables agents to gather information about the environment state. As a result, active inference becomes an alternative to common POMDP approaches for information gathering, which opens the door towards more cross cutting research at the intersection of both fields. This is advantageous, because recent advancements in POMDP solvers may be used to accelerate active inference, and the principled active inference framework may be used to model POMDP agents that operate in a neurobiologically plausible fashion.
The field of Artificial Intelligence (AI) seems promising for traffic and transport. All kinds of possibilities and applications are suggested, but are these suggestions feasible and when will they become available? To address this question for traffic management, a picture of the field and its latest, state-of-the-art innovations is painted and opportunities for the future are investigated. Applications that have already been implemented or tested as pilots are described, as well as those applications that domain experts expect to be developed within one to five years, with a focus on applications that generate the greatest improvements in terms of traffic flow, safety, and sustainability. Also, the study looks at what the possible pitfalls and challenges could be during development and implementation.
The research method consisted of several elements. Interviews were conducted with experts in the field of AI and traffic management and the interviewees were asked about possible opportunities and obstacles. In addition to the interviews, relevant and current sources describing applications of AI in traffic management were studied. The focus was on the added value of applications that have already been implemented. Based on the information gathered, a selection of the most promising future applications was made and these applications were discussed in a workshop.
The current applications of AI in traffic management show that the focus is now on performing one specific task, using a limited number of data sources. It also shows there is great future potential for AI-based applications that combine multiple data sources or address multiple complex tasks in a combined fashion. This could, for example, lead to new insights about traffic being derived from data; insights that are not readily apparent with existing methods and a single data source.
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The field of Artificial Intelligence (AI) seems promising for traffic and transport. All kinds of possibilities and applications are suggested, but are these suggestions feasible and when will they become available? To address this question for traffic management, a picture of the field and its latest, state-of-the-art innovations is painted and opportunities for the future are investigated. Applications that have already been implemented or tested as pilots are described, as well as those applications that domain experts expect to be developed within one to five years, with a focus on applications that generate the greatest improvements in terms of traffic flow, safety, and sustainability. Also, the study looks at what the possible pitfalls and challenges could be during development and implementation.
The research method consisted of several elements. Interviews were conducted with experts in the field of AI and traffic management and the interviewees were asked about possible opportunities and obstacles. In addition to the interviews, relevant and current sources describing applications of AI in traffic management were studied. The focus was on the added value of applications that have already been implemented. Based on the information gathered, a selection of the most promising future applications was made and these applications were discussed in a workshop.
The current applications of AI in traffic management show that the focus is now on performing one specific task, using a limited number of data sources. It also shows there is great future potential for AI-based applications that combine multiple data sources or address multiple complex tasks in a combined fashion. This could, for example, lead to new insights about traffic being derived from data; insights that are not readily apparent with existing methods and a single data source.