Multi-objective road pricing: a cooperative and competitive bilevel optimization approach

Costs associated with traffic externalities such as congestion, air pollution, noise, safety, etcetera are becoming unbearable. The Braess paradox shows that combating congestion by adding infrastructure may not improve traffic conditions, and geographical and/or financial constraints may not allow infrastructure expansion. Road pricing presents an alternative to combat traffic externalities. The traditional way of road pricing, namely congestion charging, may create negative benefits for society. In this effect, we develop a flexible pricing scheme internalizing costs arising from all externalities. Using a game theoretical approach, we extend the single authority road pricing scheme to a pricing scheme with multiple authorities/regions (with likely contradicting objectives)

TN, ROM, ML, PINNs – Four approaches for real-time temperature estimation in electric motors in comparison

ne of the major trends in electrical machines for automotive applications is towards higher power-densities and more integrated components. With that, accurate thermal management of the machine and capable cooling systems are of great significance to the safety and reliability of the traction system. Thermal simulations are an integral part in the design process of electrical drives. However, recently thermal models are also more frequently used in the context of machine control. The latter demanding for fast, yet accurate, temperature estimations

Road pricing mechanisms: a game theoretic and multi-level approach

By 2050, it is expected that more than 9 billion people will be living on Earth. Development will reach to many places on Earth, demand for a better life will rise, car/vehicle ownership will increase, leading to high demand for road capacities and infrastructures, yet supply for these road capacities and infrastructures is not going to increase in the same rate as their demand. Further, this increase in vehicle ownership will escalate the traffic externalities such as congestion, emission, noise and so on. Due to financial, geographical, and political limitations, and the fact that even the expansion of the existing infrastructure may not lead to efficient use of transportation networks, it is envisaged that road pricing seems a viable option for achieving a more efficient use of the existing infrastructure. With all its potentials, road pricing has not gained all the supports it needed, mainly due to how the pricing schemes are developed and perceived by stakeholders and road users.\ud We developed models for road pricing schemes taking into account the (usually) conflicting interests of various stakeholders and the road users, and all traffic externalities. For a just and acceptable road pricing scheme, we developed a novel idea from the concept of Nash equilibrium from game theory in the form of multi-stakeholder and multi-objective problems. We found that even in simple practical cases, that Nash equilibrium may not exist among the actors. This means that point of consensus might not be reached among stakeholders, an indication why talks on the adoption of road pricing have failed in many countries. \ud To tackle this problem once and for all, we developed a mechanism that ensures that the point of consensus is reached among the stakeholders. The mechanism further ensures that the scheme adopted by these stakeholders is optimal for the society.\ud To further address the issues of fairness and equity, and complications resulting from a link or kilometre-based charges, we developed a zone-based pricing scheme called an origin-destination based road pricing scheme. The scheme ensures efficient use of the road infrastructure by charging road users based on their origin and destination

Analysis of Utility-Based Data Dissemination Approaches in VANETs

By disseminating data through Vehicular Ad-hoc Networks (VANETs), vehicles are able to share relevant sensor data to acquire information about accidents, traffic, and even pollution. Data relevance is measured by a utility function which considers the contextual information that vehicles currently have about their environment. To be effective, data dissemination protocols must cope with intermittent connectivity due to the high speeds of vehicles. Problems arise when not all data can be exchanged due to the limited time available. In this paper, we explore and compare two fundamentally distinct approaches to tackling this problem. The first aims to maximize the system efficiency. In contrast, the second trades efficiency by a fair data distribution over vehicles by means of Nash Bargaining as used in game theory. By means of an extensive simulation campaign, an approach relying on fairness is shown to outperform efficiency in terms of delivery ratio, Jain’s fairness index, sum of utility gains, number of hops and number of files downloaded

Analysis of Utility-Based Data Dissemination Approaches in VANETs

By disseminating data through Vehicular Ad-hoc Networks (VANETs), vehicles are able to share relevant sensor data to acquire information about accidents, traffic, and even pollution. Data relevance is measured by a utility function which considers the contextual information that vehicles currently have about their environment. To be effective, data dissemination protocols must cope with intermittent connectivity due to the high speeds of vehicles. Problems arise when not all data can be exchanged due to the limited time available. In this paper, we explore and compare two fundamentally distinct approaches to tackling this problem. The first aims to maximize the system efficiency. In contrast, the second trades efficiency by a fair data distribution over vehicles by means of Nash Bargaining as used in game theory. By means of an extensive simulation campaign, an approach relying on fairness is shown to outperform efficiency in terms of delivery ratio, Jain’s fairness index, sum of utility gains, number of hops and number of files downloaded

Achieving Data Utility Fairness in Periodic Dissemination for VANETs

In addition to safety, Vehicular Ad-hoc Networks (VANETs) enable the development of new information-rich applications that disseminate relevant data to vehicles. One key challenge in such networks is to use the available bandwidth efficiently when there is: (i) a short connectivity time due to the rapidly changing road environment, and (ii) bandwidth congestion due to continuous collection and dissemination of data. Numerous solutions were proposed to alleviate bandwidth congestion by using transmission power and beaconing rate control. However, the reduction of data messages transmitted by using priority-based data selection mechanisms has not been fully explored. In this work, we propose a periodic data dissemination protocol for non-safety applications which distributes data utility fairly among vehicles with conflicting data interests. Furthermore, given a defined maximum network load allowed, only the least relevant data is suppressed. Fairness is achieved using the concept of Nash Bargaining from game theory. Simulation results show that our approach leads to an efficient bandwidth utilization in terms of utility per message received and higher fairness index compared with other approaches

Achieving Data Utility Fairness in Periodic Dissemination for VANETs

In addition to safety, Vehicular Ad-hoc Networks (VANETs) enable the development of new information-rich applications that disseminate relevant data to vehicles. One key challenge in such networks is to use the available bandwidth efficiently when there is: (i) a short connectivity time due to the rapidly changing road environment, and (ii) bandwidth congestion due to continuous collection and dissemination of data. Numerous solutions were proposed to alleviate bandwidth congestion by using transmission power and beaconing rate control. However, the reduction of data messages transmitted by using priority-based data selection mechanisms has not been fully explored. In this work, we propose a periodic data dissemination protocol for non-safety applications which distributes data utility fairly among vehicles with conflicting data interests. Furthermore, given a defined maximum network load allowed, only the least relevant data is suppressed. Fairness is achieved using the concept of Nash Bargaining from game theory. Simulation results show that our approach leads to an efficient bandwidth utilization in terms of utility per message received and higher fairness index compared with other approaches

Multi-Objective Road Pricing: A Game Theoretic and Multi-Level Optimization Approach.

Using a game theoretical approach, we develop a pricing scheme that internalizes multiple traffic externalities. Further, we extend the single authority road pricing scheme to a scheme with multiple actors/stakeholders or regions. Road users’ interests are represented in the upper and the same level as the decision makers, thus, making them active players in the toll setting game. Having shown that pure Nash equilibrium (NE) toll may not exist among the stakeholders (with likely opposing objectives), we design a mechanism that induces NE which coincides with system optimum