Agent-based modelling of air transport demand

Constraints such as opening hours or passenger capacities influence travel options that can be offered by an airport and by the connecting airlines. If infrastructure, policy or technological measures modify transport options, then the benefits do not only depend on the technology, but also on possibly heterogeneous user preferences such as desired arrival times or on the availability of alternative travel modes. This paper proposes an agent-based, iterative assignment procedure to model European air traffic and German passenger demand on a microscopic level, capturing individual passenger preferences. Air transport technology is simulated microscopically, i.e. each aircraft is represented as single unit with attached attributes such as departure time, flight duration or seat availability. Trip-chaining and delay propagation can be added. Microsimulation is used to verify and assess passengers’ choices of travel alternatives, where those choices improve over iterations until an agent-based stochastic user equilibrium is reached. This requires fast simulation models, thus, similar to other approaches in air traffic modelling a queue model is used. In contrast to those approaches, the queue model in this work is solved algorithmically. Overall, the approach is suited to analyze, forecast and evaluate the consequences of mid-distance transport measures

A Lagrangian discretization multiagent approach for large-scale multimodal dynamic assignment

This paper develops a Lagrangian discretization multiagent model for large-scale multimodal simulation and assignment. For road traffic flow modeling, we describe the dynamics of vehicle packets based on a macroscopic model on the basis of a Lagrangian discretization. The metro/tram/train systems are modeled on constant speed on scheduled timetable/frequency over lines of operations. Congestion is modeled as waiting time at stations plus induced discomfort when the capacity of vehicle is achieved. For the bus system, it is modeled similar to cars with different speed settings, either competing for road capacity resources with other vehicles or moving on separated bus lines on the road network. For solving the large-scale multimodal dynamic traffic assignment problem, an effective-path-based cross entropy is proposed to approximate the dynamic user equilibrium. Some numerical simulations have been conducted to demonstrate its ability to describe traffic dynamics on road network.multimodal transportation systems; Lagrangian discretization; traffic assignment; multiagent systems

An adaptive route choice model for integrated fixed and flexible transit systems

Over the past decade, there has been a surge of interest in the transport community in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modeling decision in the development of an agent-based simulation model for the evaluation of flexible transit is how one chooses to represent the mode- and route-choices of travelers. The real-time adaptive behavior of travelers is intuitively important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a closer to real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behavior and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. Model properties are first explored in a choice between fixed- and flexible-transit modes for a toy network. The framework is then applied to illustrate level-of-service trade-offs and analyze traveler mode choices within a mixed fixed- and flexible transit system in a case study based on a real-life branched transit service in Stockholm, Sweden.Comment: 33 pages, 9 figures, preprin

a cross-entropy based multiagent approach for multiclass activity chain modeling and simulation

This paper attempts to model complex destination-chain, departure time and route choices based on activity plan implementation and proposes an arc-based cross entropy method for solving approximately the dynamic user equilibrium in multiagent-based multiclass network context. A multiagent-based dynamic activity chain model is developed, combining travelers' day-to-day learning process in the presence of both traffic flow and activity supply dynamics. The learning process towards user equilibrium in multiagent systems is based on the framework of Bellman's principle of optimality, and iteratively solved by the cross entropy method. A numerical example is implemented to illustrate the performance of the proposed method on a multiclass queuing network.dynamic traffic assignment, cross entropy method, activity chain, multiagent, Bellman equation

Modelling mixed autonomy traffic networks with pricing and routing control

Connected and automated vehicles (CAVs) are expected to change the way people travel in cities. Before human-driven vehicles (HVs) are completely phased out, the urban traffic flow will be heterogeneous of HVs, CAVs, and public transport vehicles commonly known as mixed autonomy. Mixed autonomy networks are likely to be made up of different route choice behaviours compared with conventional networks with HVs only. While HVs are expected to continue taking individually and selfishly selected shortest paths following user equilibrium (UE), a set of centrally controlled AVs could potentially follow the system optimal (SO) routing behaviour to reduce the selfish and inefficient behaviour of UE-seeking HVs. In this dissertation, a mixed equilibrium simulation-based dynamic traffic assignment (SBDTA) model is developed in which two classes of vehicles with different routing behaviours (UE-seeking HVs and SO-seeking AVs) are present in the network. The dissertation proposes a joint routing and incentive-based congestion pricing scheme in which SO-seeking CAVs are exempt from the toll while UE-seeking HVs have their usual shortest-path routing decisions are subject to a spatially differentiated congestion charge. This control strategy could potentially boost market penetration rate of CAVs while encouraging them to adopt SO routing behaviour and discouraging UE-seeking users from entering congested areas. The dissertation also proposes a distance-based time-dependent optimal ratio control scheme (TORCS) in which an optimal ratio of CAVs is identified and selected to seek SO routing. The objective of the control scheme is to achieve a reasonable compromise between the system efficiency (i.e., total travel time savings) and the control cost that is proportional to the total distance travelled by SO-seeking AVs. The proposed modelling frameworks are then extended to bi-modal networks considering three competing modes (bus, SO-seeking CAVs, and UE-seeking HVs). A nested logit-based mode choice model is applied to capture travellers’ preferences toward three available modes and elasticity in travel demand. A dynamic transit assignment model is also deployed and integrated into the mixed equilibrium SBDTA model to generate equilibrium traffic flow under different scenarios. The applicability and performance of the proposed models are demonstrated on a real large-scale network of Melbourne, Australia. The research outcomes are expected to improve the performance of mixed autonomy traffic networks with optimal pricing and routing control

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System

With the growing problem of urban traffic congestion, departure time choice is becoming a more important factor to commuters. By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. To start with, we verified the model by comparison with traditional analytical methods. Then the formation process of departure time equilibrium is investigated additionally. Seeing the validity of the model, some initial assumptions were relaxed and two groups of experiments were carried out considering commuters’ heterogeneity and memory limitations. The results showed that heterogeneous commuters’ departure time distribution is broader and has a lower peak at equilibrium and different people behave in different pattern. When each commuter has a limited memory, some fluctuations exist in the evolutionary dynamics of the system, and hence an ideal equilibrium can hardly be reached. This research is helpful in acquiring a better understanding of commuter’s departure time choice and commuting equilibrium of the peak period; the approach also provides an effective way to explore the formation and evolution of complicated traffic phenomena

Large-Scale Multi-Agent Simulations for Transportation Applications

In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with ten million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially “plug-and-play.” Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed