1,084 research outputs found
System optimal traffic assignment with departure time choice
No full textThis thesis investigates analytical dynamic system optimal assignment with departure time
choice in a rigorous and original way. Dynamic system optimal assignment is formulated here
as a state-dependent optimal control problem. A fixed volume of traffic is assigned to
departure times and routes such that the total system travel cost is minimized. Although the
system optimal assignment is not a realistic representation of traffic, it provides a bound on
performance and shows how the transport planner or engineer can make the best use of the
road system, and as such it is a useful benchmark for evaluating various transport policy
measures. The analysis shows that to operate the transport system optimally, each traveller in
the system should consider the dynamic externality that he or she imposes on the system from
the time of his or her entry. To capture this dynamic externality, we develop a novel
sensitivity analysis of travel cost. Solution algorithms are developed to calculate the dynamic
externality and traffic assignments based on the analyses. We also investigate alternative
solution strategies and the effect of time discretization on the quality of calculated
assignments. Numerical examples are given and the characteristics of the results are discussed.
Calculating dynamic system optimal assignment and the associated optimal toll could be too
difficult for practical implementation. We therefore consider some practical tolling strategies
for dynamic management of network traffic. The tolling strategies considered in this thesis
include both uniform and congestion-based tolling strategies, which are compared with the
dynamic system optimal toll so that their performance can be evaluated. In deriving the
tolling strategies, it is assumed that we have an exact model for the underlying traffic
behaviour. In reality, we do not have such information so that the robustness of a toll
calculation method is an important issue to be investigated in practice. It is found that the
tolls calculated by using divided linear traffic models can perform well over a wide range of
scenarios. The divided linear travel time models thus should receive more attention in the
future research on robust dynamic traffic control strategies design. In conclusion, this thesis
contributes to the literature on dynamic traffic modelling and management, and to support
further analysis and model development in this area
Objectives, stimulus and feedback in signal control of road traffic
No full textThis article identifies the prospective role of a range of intelligent transport systems technologies for the signal control of road traffic. We discuss signal control within the context of traffic management and control in urban road networks and then present a control-theoretic formulation for it that distinguishes the various roles of detector data, objectives of optimization, and control feedback. By reference to this, we discuss the importance of different kinds of variability in traffic flows and review the state of knowledge in respect of control in the presence of different combinations of them. In light of this formulation and review, we identify a range of important possibilities for contributions to traffic management and control through traffic measurement and detection technology, and contemporary flexible optimization techniques that use various kinds of automated learning
Equilibrium analysis of trip chains in congested networks
No full textIn this paper, we develop a model of travel in a chain of trips joining several locations througha congested network. We develop a microscopic analysis of individual benefits obtained byspending time at each of the locations and costs incurred through travel between them. This iscombined with a macroscopic equilibrium model of travel during congested peak periods toshow how individuals? travel choices are influenced by the congestion that result fromcorresponding choices made by others. We show how different travellers can achieveidentical net utilities by making different combinations of choices within the equilibrium. Theresulting model can be used to investigate the effect on travel behaviour and individual utilityof various transport interventions, and we illustrate this by considering the effect of a peakperiodcharge that eliminates congestion
A continuous network design model in stochastic user equilibrium based on sensitivity analysis
No full textThe continuous network design problem (CNDP) is known to be difficult to solve due to the intrinsic properties of non-convexity and nonlinearity. Such kinds of CNDP can be formulated as a bi-level programme, in which the upper level represents the designer's decisions and the lower level the travellers' responses. Formulations of this kind can be classified as either Stackelberg approaches or Nash ones according to the relationship between the upper level and the lower level parts. This paper formulates the CNDP for road expansion based on Stackelberg game where leader and follower exist, and allows for variety of travellers' behaviour in choosing their routes. In order to solve the problem by the Stackelberg approach, we need a relation between link flows and design parameters. For this purpose, we use a logit route choice model, which provides this in an explicit closed-form function. This model is applied to two example road networks to test and briefly compare the results between the Stackelberg and Nash approaches to explore the differences between them
Managing traffic at motorway junctions: a ramp metering development using intelligent vehicles
No full textMotorways provide an important transport facility for people and goods with social, environmental and economic consequences. The demand for their use continues to increase, leading to more extensive and severe congestion; therefore, finding ways to reduce it is a priority, and Intelligent Transport Systems (ITS) have been identified as a contributor. Intelligent vehicles equipped with in-car communication systems are capable of receiving messages from the infrastructure and communicating with other vehicles. This communication enables the cooperation among them and offers many opportunities for developing a new generation of ITS that is referred to as Cooperative Intelligent Transport Systems. This research presents an innovative control algorithm for managing motorway merges using intelligent vehicles, exploiting the cooperation made possible by communication. This innovative system, called Cooperative Ramp Metering (CoopRM), requires the cooperation of equipped vehicles on the main carriageway in order to create gaps for facilitating the merging of on-ramp vehicles, aiming to reduce congestion at motorway junctions. First, similar management systems are reviewed, and the algorithms are classified based on their characteristics, then similarities, dissimilarities, trends and research gaps are described. Established a state-of-the-art in this research field, the Cooperative Ramp Metering algorithm is defined analytically. Macroscopic traffic flow theory is used in combination with microscopic theory to determine the equations governing the CoopRM control strategy. The accuracy of this formulation is then validated by comparing theoretical against simulation results. Finally, the traffic performance of the CoopRM is evaluated using a stochastic microscopic simulation model, calculating and comparing indexes representative of congestion and disruptions at traffic flow for different scenarios. Results show a substantial reduction in congestion, a decrease of perturbations and a more efficient merging procedure. This study demonstrates how this innovative Cooperative ITS is able to improve the current motorway infrastructure through the use of emerging communication technology
Network models of route choice
No full textNetwork models are used in transport studies to explore the effects of individual travellers’ route choice. These model the likely consequences of changes in the demand for travel, facilities provided, and ways in which demand is assigned. This enables planners to anticipate the response of travellers to changes and developments when investigating their effects on network performance. The outputs calculated from these models include estimates of various costs and flows
Measuring traffic flow using real-time data
No full textThe theory of traffic flow based upon speed, flow and density that vary only slowly in space and time is well established. However, matching field observations up to this theory and extracting estimates of quantities of interest is not always straightforward. Spatial density of traffic is not measured readily, and
inductive loops are often used instead to measure the proportion of a sampling period for which a vehicle is present, which is known as occupancy: the relationship between occupancy and density k is k = w / L , where L is the mean effective length of a vehicle at the detector. According to this, correct
interpretation of occupancy depends on the composition of the traffic that is measured, which will affect the value of L. Estimates of the capacity of a road are most useful when they are expressed in units that are independent of traffic composition. In this paper, we show how the value of L can be estimated from the 1-minute point observations of a kind that are available from the Highways Agency MIDAS data that are collected on the UK motorway network. The value of this quantity was found to vary substantially over time during the day, between lanes on the road, and according to the control status of the road thus
reflecting variations in traffic composition, and variations in lane usage. The consequences of this are discussed for interpretation and use of traffic data of this kind in estimating the speed-density relationship, capacity and related properties of a road section
Towards distributed adaptive control for road traffic junction signals using learning classifier systems
No full textThis chapter considers an approach to distributed traffic responsive signal controlusing Learning Classifier Systems (Holland, 1976). The intention is to accommodaterealistic kinds of detector data and wide ranges of candidate performance criteria fortraffic management in a fully flexible manner. The approach to achieving this is to useevolutionary computing (eg Holland, 1975) and reinforcement learning (eg Sutton andBarto, 1998) with performance fed back from microscopic traffic simulations: thisapproach has the advantage that it is not specific to any particular objective or form ofprimary data. The purpose of this work is to develop an approach to distributedoptimisation that can achieve good traffic performance flexibly according to any on arange of possible criteria using data from existing traffic detectors. Here each junctionin a road network is controlled by a Learning Classifier System using only locallyavailable input and performance data; a multi-agent approach is proposed.Learning Classifier Systems (LCS) can be used for optimisation in a way thatoffers substantial promise for application in traffic-responsive signal control systemswhere the way in which the control responds to variations in traffic flows can beadapted according to measured conditions. This is important in order to achieve trafficcontrol that is sufficiently flexible to respond rapidly when traffic conditions change ina fundamental way, as occurs at the start of a peak period, without being undulysensitive to short-term variations in flow. The expectation is that this will be possibleby their use of both reinforcement learning and evolutionary computing techniques.Furthermore, they offer the automated rule development of neural networks togetherwith the transparency of production system rules.The importance of this approach for traffic control is that it offers a means bywhich signal control strategies can be developed directly according to theirperformance, evaluated using detailed microscopic simulation as opposed to thatestimated from formulae that have been adopted on grounds of analytical convenience.This closed-loop approach to development of control strategies offers severaladvantages over the use of traditional explicit optimisation formulations. Theseinclude flexibility in respect of objectives so that multiple and varying needs can beaccommodated, ability to use various different kinds of detector data according to theiravailability, and freedom from dependence on a single explicit evaluation formula thatis intended to embody the whole of a traffic model. This final point has been found tobe especially important in recent research work where certain fine details of themodels used have been found to have an unexpectedly strong influence onperformance
(∈,∈∨q)-Intuitionistic Fuzzy Ideals of BG-algebra
No full textAbstractIn this paper, we introduce the concept of (∈,∈∨q)-intuitionistic fuzzy ideals of BG-algebra and investigate some of their basic properties
Estimating probability distributions of dynamic queues
No full textQueues are often associated with uncertainty or unreliability, which can arise from chance or climatic events, phase changes in system behaviour, or inherent randomness. Knowing the probability distribution of the number of customers in a queue is important for estimating the risk of stress or disruption to routine services and upstream blocking, potentially leading to exceeding critical limits, gridlock or incidents. The present paper focuses on time-varying queues produced by transient oversaturation during demand peaks where there is randomness in arrivals and service. The objective is to present practical methods for estimating a probability distribution from knowledge of the mean, variance and utilisation (degree of saturation) of a queue available from computationally efficient, if approximate, time-dependent calculation. This is made possible by a novel expression for time-dependent queue variance. The queue processes considered are those commonly used to represent isolated priority (M/M/1) and signal-like (M/D/1) systems, plus some statistical variations within the common Pollaczek-Khinchin framework. Results are verified by comparison with Markov simulation based on recurrence relations
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