Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation System

The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation

The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time

Modelling Traffic Congestion as a Spreading Phenomenon

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. This dissertation presents frameworks to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. Specifically, we model the spread of congestion in urban networks by adapting a classical epidemic model to include a propagation and dissipation mechanism dependent on time-varying travel demand and consistent with the fundamentals of network traffic flow theory. We describe the dynamics of congestion spread using two macroscopic new parameters (propagation rate β and recovery rate μ) embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. For simplicity, we initially assumed the topological distribution of road networks to be homogeneous. The proposed contagion-based dynamics are verified through empirical multi-city analysis. In addition to the simplistic homogeneous SIR approach, we also explored the significance of the degree of heterogeneity in urban street networks using both empirical and simulation-based traffic data. However, this approach inherently assumes an undirected network with uniform recovery rate compared to a road network, which usually displays directed flow and topological reliance on congestion recovery. Keeping in view of these constraints, we proposed a modification to the heterogeneous mean-field model to describe the spreading process of congestion in urban street networks. A practical application of the proposed model is also tested in this dissertation in the context of signal optimisation using cycle length as a controlling function. The experiments helped quantify the characteristic differences between two widely used traffic assignment models, i.e. Dynamic User Equilibrium (DUE) and Stochastic Route Choice (SRC). Comparison has been made at two levels: link-level flows and network-level congestion patterns. Furthermore, we explored an alternative approach for congestion dynamic modelling, namely the "Reaction-Diffusion (RD) model", with a similar concept as our proposed frameworks but at link-level. This model, with its complexity, requires higher computation time with detailed link-level information of congestion dynamics

The resilience of road transport networks redundancy, vulnerability and mobility characteristics

This thesis is concerned with the development of a composite resilience index for road transport networks. The index employs three characteristics, namely redundancy, vulnerability and mobility, measuring resilience at network junction, link and origin-destination levels, respectively. Various techniques have been adopted to quantify each characteristic and the composite resilience index as summarised below. The redundancy indicator for road transport network junctions is based on the entropy concept, due to its ability to measure the system configuration in addition to being able to model the inherent uncertainty in road transport network conditions. Various system parameters based on different combinations of link flow, relative link spare capacity and relative link speed were examined. The developed redundancy indicator covers the static aspect of redundancy, i.e. alternative paths, and the dynamic feature of redundancy reflected by the availability of spare capacity under different network loading and service level. The vulnerability indicator for road transport network links is developed by combining vulnerability attributes (e.g. link capacity, flow, length, free flow and traffic congestion density) with different weights using a new methodology based on fuzzy logic and exhaustive search optimisation techniques. Furthermore, the network vulnerability indicators are calculated using two different aggregations: an aggregated vulnerability indicator based on physical characteristics and the other based on operational characteristics. The mobility indicator for road transport networks is formulated from two mobility attributes reflecting the physical connectivity and level of service. The combination of the two mobility attributes into a single mobility indicator is achieved by a fuzzy logic approach. Finally, the interdependence of the proposed characteristics is explored and the composite resilience index is estimated from the aggregation of the three characteristics indicators using two different approaches, namely equal weighting and principal component analysis methods. Moreover, the impact of real-time travel information on the proposed resilience characteristics and the composite resilience index has been investigated. The application of the proposed methodology on a synthetic road transport network of Delft city (Netherlands) and other real life case studies shows that the developed indicators for the three characteristics and the composite resilience index responded well to traffic load change and supply variations. The developed composite resilience index will be of use in various ways; first, helping decision makers in understanding the dynamic nature of resilience under different disruptive events, highlighting weaknesses in the network and future planning to mitigate the impact of disruptive events. Furthermore, each developed indicator for the three characteristics considered can be used as a tool to assess the effectiveness of different management policies or technologies to improve the overall network performance or the daily operation of road transport networks

On the Potential of Generic Modeling for VANET Data Aggregation Protocols

In-network data aggregation is a promising communication mechanism to reduce bandwidth requirements of applications in vehicular ad-hoc networks (VANETs). Many aggregation schemes have been proposed, often with varying features. Most aggregation schemes are tailored to specific application scenarios and for specific aggregation operations. Comparative evaluation of different aggregation schemes is therefore difficult. An application centric view of aggregation does also not tap into the potential of cross application aggregation. Generic modeling may help to unlock this potential. We outline a generic modeling approach to enable improved comparability of aggregation schemes and facilitate joint optimization for different applications of aggregation schemes for VANETs. This work outlines the requirements and general concept of a generic modeling approach and identifies open challenges

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

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing