3,122 research outputs found
An Open-Source Microscopic Traffic Simulator
We present the interactive Java-based open-source traffic simulator available
at www.traffic-simulation.de. In contrast to most closed-source commercial
simulators, the focus is on investigating fundamental issues of traffic
dynamics rather than simulating specific road networks. This includes testing
theories for the spatiotemporal evolution of traffic jams, comparing and
testing different microscopic traffic models, modeling the effects of driving
styles and traffic rules on the efficiency and stability of traffic flow, and
investigating novel ITS technologies such as adaptive cruise control,
inter-vehicle and vehicle-infrastructure communication
Reconstructing the Traffic State by Fusion of Heterogeneous Data
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.Comment: For more information see http://www.mtreiber.de or
http://www.akesting.d
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
Stable Infrastructure-based Routing for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) have been instrumental
in reshaping transportation towards safer roads, seamless
logistics, and digital business-oriented services under the umbrella of
smart city platforms. Undoubtedly, ITS applications will demand
stable routing protocols that not only focus on Inter-Vehicle Communications
but also on providing a fast, reliable and secure interface to
the infrastructure. In this paper, we propose a novel stable infrastructure-
based routing protocol for urban VANETs. It enables vehicles
proactively to maintain fresh routes towards Road-Side Units
(RSUs) while reactively discovering routes to nearby vehicles. It
builds routes from highly stable connected intersections using a selection
policy which uses a new intersection stability metric. Simulation
experiments performed with accurate mobility and propagation
models have confirmed the efficiency of the new protocol and its
adaptability to continuously changing network status in the urban
environment
Improving Traffic Efficiency in a Road Network by Adopting Decentralised Multi-Agent Reinforcement Learning and Smart Navigation
In the future, mixed traffic flow will consist of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Effective traffic management is a global challenge, especially in urban areas with many intersections. Much research has focused on solving this problem to increase intersection network performance. Reinforcement learning (RL) is a new approach to optimising traffic signal lights that overcomes the disadvantages of traditional methods. In this paper, we propose an integrated approach that combines the multi-agent advantage actor-critic (MA-A2C) and smart navigation (SN) to solve the congestion problem in a road network under mixed traffic conditions. The A2C algorithm combines the advantages of value-based and policy-based methods to stabilise the training by reducing the variance. It also overcomes the limitations of centralised and independent MARL. In addition, the SN technique reroutes traffic load to alternate paths to avoid congestion at intersections. To evaluate the robustness of our approach, we compare our model against independent-A2C (I-A2C) and max pressure (MP). These results show that our proposed approach performs more efficiently than others regarding average waiting time, speed and queue length. In addition, the simulation results also suggest that the model is effective as the CAV penetration rate is greater than 20%
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