The Merit of Agents in Freight Transport

While intermodal freight transport has the potential to introduce efficiency to the transport network,this transport method also suffers from uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to when exactly their container will be released from the ship, from the stack, or from customs. This leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. In this chapter, the authors examine agent-based solutions as a mechanism to handle job arrival uncertainty in the context of a drayage case at the Port of Rotterdam. They compare their agent-based solution approach to a wellknown on-line optimization approach and study the comparative performance of both systems across four scenarios of varying job arrival uncertainty. The chapter concludes that when less than 50% of all jobs are known at the start of the day then an agent-based approach performs competitively with an on-line optimization approach.

"Social Potential" Models for Modeling Traffic and Transportation

The “Social Potential”, which the authors will refer to as the SP, is the name given to a technique of implementing multi-agent movement in simulations by representing behaviors, goals, and motivations as artificial social forces. These forces then determine the movement of the individual agents. Several SP models, including the Flocking, Helbing-Molnar–Farkas-Visek (HMFV), and Lakoba-Kaup-Finkelstein (LKF) models, are commonly used to describe pedestrian movement. A systematic procedure is described here, whereby one can construct and use these and other SP models. The theories behind these models are discussed along with the application of the procedure. Through the use of these techniques, it has been possible to represent schools of fish swimming, flocks of birds flying, crowds exiting rooms, crowds walking through hallways, and individuals wandering in open fields. Once one has an understanding of these models, more complex and specific scenarios could be constructed by applying additional constraints and parameters. The models along with the procedure give a guideline for understanding and implementing simulations using SP techniques.

Applying Situated Agents to Microscopic Traffic Modelling

Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors bring this discussion forward and propose a multi-agent model of the traffic domain where integration is ascribed to the way the environment is represented and agents interoperate. While most approaches still deal with drivers and vehicles indistinguishably, in the proposed framework vehicles are merely moveable objects whereas the driving role is played by agents fully endowed with cognitive abilities and situated in the environment. The authors discuss on the role of the environment dynamics in supporting a truly emergent behaviour of the system and present an extension to the traditional car-following and lane-change models based on the concept of situated agents. A physical communication model is proposed to base different interactions and some performance issues are also identified, which allows for more realistic representation of drivers’ behaviour in microscopic models.

A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns

Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the environment, habits, and activity-travel patterns. Agents learn through their experiences with the transport systems, changes in the environments and from their social network. Conceptually, agents are assumed to have an aspiration level associated with choice sets that in combination with evaluation results determine whether the agent will start exploring or persist in habitual behavior; an activation level of each (location) alternative that determines whether or not the alternative is included in the choice set in the next time step, and an expected (utility) function to evaluate each (location) alternative given current beliefs. Each of these elements is dynamic. Based on principles of reinforcement learning, Bayesian learning, and social comparison theories, the framework specifies functions for experience-based learning, extended and integrated with social learning.

A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing collaboration between the controllers and the users of the airspace could lessen the impact of the resulting air traffic flow problems. The authors summarize a new concept that has been proposed for collaborative air traffic flow management, the problems it is meant to address, and our approach to evaluating the concept. The authors present their initial simulation design and experimental results, using several simple route selection strategies and traffic flow management approaches. Though their model is still in an early stage of development, these results have revealed interesting properties of the proposed concept that will guide their continued development, refinement of the model, and possibly influence other studies of traffic management elsewhere. Finally, they conclude with the challenges of validating the proposed concept through simulation and future work.

Multiagent Learning on Traffic Lights Control

In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in the system. In a distributed urban traffic control, where each junction has an independent controller, agents that learn can benefit from exchanging information, but this exchange of information may not always be useful. In this chapter the authors analyze how agents can benefit from sharing information in an urban traffic control scenario and the consequences of this cooperation in the performance of the traffic system.

An Unmanaged Intersection Protocol and Improved Intersection Safety for Autonomous Vehicles

Fully autonomous vehicles promise enormous gains in safety, efficiency, and economy for transportation. In previous work, the authors of this chapter have introduced a system for managing autonomous vehicles at intersections that is capable of handling more vehicles and causing fewer delays than modern- day mechanisms such as traffic lights and stop signs [Dresner & Stone 2005]. This system makes two assumptions about the problem domain: that special infrastructure is present at each intersection, and that vehicles do not experience catastrophic physical malfunctions. In this chapter, they explore two separate extensions to their original work, each of which relaxes one of these assumptions. They demonstrate that for certain types of intersections—namely those with moderate to low amounts of traffic—a completely decentralized, peer-to-peer intersection management system can reap many of the benefits of a centralized system without the need for special infrastructure at the intersection. In the second half of the chapter, they show that their previously proposed intersection control mechanism can dramatically mitigate the effects of catastrophic physical malfunctions in vehicles such that in addition to being more efficient, autonomous intersections will be far safer than traditional intersections are today.

Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems

This study discusses adaptation effects and congestion in a multi-agent system (MAS) to analyse real transport and traffic problems. Both methodological discussion and an empirical case study are presented in this chapter. The main focus is on the comparison of an analysis of a MAS simulation analysis and an analysis that solely uses discrete choice modelling. This study explains and discusses some important concepts in design empirical MAS in traffic and transportation, including validation Minority Game and adaptation effects. This study develops an empirical MAS simulation model based on real stated-preference data to analyse the effect of a real road-user charge policy and a complimentary park and ride scheme at the Upper Derwent Valley in the Peak District National Park, England. The simulation model integrates a transport mode choice model, Markov queue model, and Minority Game to overcome the disadvantages of a conventional approach. The results of the simulation model show that the conventional analysis overestimates the effect of the transportation and environment policy due to the lack of adaptation affects of agents and congestion. The MAS comprehensively analysed the mode choices, congestion levels, and the user utility of visitors while including the adaptability of agents. The MAS also called as agent-based simulation successfully integrates models from different disciplinary backgrounds, and shows interesting effects of adaptation and congestion at the level of an individual agent.

New Approach to Smooth Traffic Flow with Route Information Sharing

With maturation of ubiquitous computing technology, it has become feasible to design new systems to improve our urban life. In this chapter, the authors introduce a new application for car navigation in a city. Every car navigation system in operation today has the current position of the vehicle, the destination, and the currently chosen route to the destination. If vehicles in a city could share this information, they could use traffic information to improve traffic efficiency for each vehicle and the whole system. Therefore, this chapter proposes a cooperative car navigation system with route information sharing (RIS). In the RIS system, each vehicle transmits route information (current position, destination, and route to the destination) to a route information server, which estimates future traffic congestion using current congestion information and this information and feeds its estimate back to each vehicle. Each vehicle uses the estimation to re-plan their route. This cycle is then repeated. The authors’ purpose in this chapter is to confirm the effectiveness of the proposed cooperative car navigation system with multiagent simulation. To evaluate the effect of the RIS system, we introduce two indexes; individual incentive and social acceptability. In theor traffic simulation with three types of road networks, the authors observe that the average travel time of the drivers using the RIS system is substantially shorter than the time of other drivers. Moreover, as the number of the RIS drivers increases, the average travel time of all drivers decreases. As a result of simulation, this chapter confirms that a cooperative car navigation system with the RIS system generally satisfied individual incentive and social acceptability, and had a effect for the improvement of traffic efficiency.

Towards Simulating Cognitive Agents in Public Transport Systems

In this chapter, the authors present a methodology for simulating human navigation within the context of public, multi-modal transport. They show that cognitive agents, that is, agents that can reason about the navigation process and learn from and navigate through the (simulated physical) environment, require the provision of a rich spatial environment. From a cognitive standpoint, human navigation and wayfinding rely on a combination of spatial models (“knowledge in the head”), (default) reasoning processes, and knowledge in the world. Spatial models have been studied extensively, whereas the reasoning processes and especially the role of the “knowledge in the world” have been neglected. The authors first present an overview of research in wayfinding and then envision a model that integrates existing concepts and models for multi-modal public transport illustrated by a case study.