Towards truly agent-based traffic and mobility simulations

Traveling is necessary and desirable; yet, it imposes external costs on other people. Quantitative methods help finding a balance. Multi-agent simulations seem an obvious possibility here. A real world traffic simulation consists of many modules, all requiring different expertise. The paper discusses how such modules can be coupled to a complete simulation system, how such a system can be made fast enough to deal with real-world sizes (several millions of travelers), and how agent memory can be introduced. A real-world case study is presented, which says that multi-agent methods for traffic are mature enough to be used alongside existing methods. Finally, some outlook into the near future is given

A review of traffic simulation software

Computer simulation of tra c is a widely used method in research of tra c modelling, planning and development of tra c networks and systems. Vehicular tra c systems are of growing concern and interest globally and modelling arbitrarily complex tra c systems is a hard problem. In this article we review some of the tra c simulation software applications, their features and characteristics as well as the issues these applications face. Additionally, we introduce some algorithmic ideas, underpinning data structural approaches and quanti able metrics that can be applied to simulated model systems

Building Transportation Foundation Model via Generative Graph Transformer

Efficient traffic management is crucial for maintaining urban mobility, especially in densely populated areas where congestion, accidents, and delays can lead to frustrating and expensive commutes. However, existing prediction methods face challenges in terms of optimizing a single objective and understanding the complex composition of the transportation system. Moreover, they lack the ability to understand the macroscopic system and cannot efficiently utilize big data. In this paper, we propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data. The proposed approach shows promising results in accurately predicting traffic outcomes in an urban transportation setting

Agent architecture for simulating pedestrians in the built environment

The paper discusses an agent architecture for investigating visualized simulated pedestrian activity and behavior affecting pedestrian flows within the built environment. The approach will lead to a system that may serve as a decision support tool in the design process for predicting the likely impact of design parameters on pedestrian flows. UML diagrams are used to communicate about the interpretation of the agent architecture

OTSS: Oulu traffic simulation system

Abstract. This thesis presents the design and the implementation of Oulu Traffic Simulation System (OTSS), a traffic simulation system for the City of Oulu, Finland. Following agent-based approach, the simulation generates artificial agents that represent the population synthesis of the City of Oulu. Data from several sources, including official statistics, government-organized open data and crowdsourced information were collected and used as input for the simulation. Two traffic demand models are presented in this thesis: (1) the random model which generates traffic trips as random, discrete events; and (2) the activity-based model which defines traffic trips as sequential events in the agents’ day plan. The software development of the system follows the spiral model of software development and enhancement. During the implementation, several development cycles were conducted before the UML software design. The system was executed on two computation systems to test its real-time performance. To evaluate the traffic models, data extracted from the simulation was compared with aggregated survey data from Finnish Transport Agency and traffic count stations around the city. The results showed that a typical server is capable of running the simulation, and even though there were differences in the duration and distance of individual trips, the simulation reflects real-life traffic count significantly well

Applicability of the Future State Maximization Paradigm to Agent-Based Modeling: A Case Study on the Emergence of Socially Sub-Optimal Mobility Behavior

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general

Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations

Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly applied in evacuation planning and operation areas. The large size in TAZ and aggregated information of macroscopic simulation underestimate the real evacuation performance. To take advantage of the high resolution demographic data LandScan USA (the zone size is much smaller than TAZ) and agent-based microscopic traffic simulation models, many new problems appeared and novel solutions are needed. A series of studies are conducted using LandScan USA Population Cells (LPC) data for evacuation assignments with different network configurations, travel demand models, and travelers compliance behavior. First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP) problem is defined for generating Origin Destination matrix in evacuation assignments when using LandScan dataset. Second, a new agent-based traffic assignment framework using LandScan and TRANSIMS modules is proposed for evacuation planning and operation study. Impact analysis on traffic analysis area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime), and departure time choice models (normal S shape model vs location based model) are studied. Third, based on the proposed framework, multi-scale network configurations (two levels of road networks and two scales of zone sizes) and three routing schemes (shortest network distance, highway biased, and shortest straight-line distance routes) are implemented for the evacuation performance comparison studies. Fourth, to study the impact of human behavior under evacuation operations, travelers compliance behavior with compliance levels from total complied to total non-complied are analyzed.Comment: PhD dissertation. UT Knoxville. 130 pages, 37 figures, 8 tables. University of Tennessee, 2013. http://trace.tennessee.edu/utk_graddiss/259

Boids On Wheels A Proof of Concept Study of the Boid as a Vehicle

The following project is a proof of concept study exploring the feasibility of simulating traffic as a multi-agent system, with the individual vehicle being implemented as a boid as defined by C. Reynolds in 1987. Furthermore, the simulator is to serve as a tool for urban planning. This thesis first explores the growth in using computers to simulate traffic since the 1940s, along with a brief review of the work done on Reynolds’ boids. This is followed by a discussion of tool selection and the reasons behind it. Secondly the thesis discusses the different steering behaviours developed for a boid vehicle,as well as their implementation. This is followed by a description of the preliminary evaluations carried out and the problems encountered. Finally the thesis concludes with some ideas for further research and the conclusion that within the scope of this project, implementing the boid as a vehicle was found to be feasible, and ripe for further wor

Boids On Wheels A Proof of Concept Study of the Boid as a Vehicle

The following project is a proof of concept study exploring the feasibility of simulating traffic as a multi-agent system, with the individual vehicle being implemented as a boid as defined by C. Reynolds in 1987. Furthermore, the simulator is to serve as a tool for urban planning. This thesis first explores the growth in using computers to simulate traffic since the 1940s, along with a brief review of the work done on Reynolds’ boids. This is followed by a discussion of tool selection and the reasons behind it. Secondly the thesis discusses the different steering behaviours developed for a boid vehicle,as well as their implementation. This is followed by a description of the preliminary evaluations carried out and the problems encountered. Finally the thesis concludes with some ideas for further research and the conclusion that within the scope of this project, implementing the boid as a vehicle was found to be feasible, and ripe for further wor