A Multi-Agent Congestion and Pricing Model

A multi-agent model of travelers competing to utilize a roadway in time and space is presented in this paper to illustrate the effect of congestion and pricing on traveler behaviors and network equilibrium. To realize the spillover effect among travelers, N-player games are constructed in which the strategy set include (N+1) strategies. We solve the discrete N-player game (for N less than 8) and find Nash equilibria if they exist. This model is compared to the bottleneck model. The results of numerical simulation show that the two models yield identical results in terms of lowest total costs and marginal costs when a social optimum exists.Agent-based Model, Game Theory, Congestion, Queueing, Traffic Flow, Congestion Pricing, Road Pricing, Value Pricing

Evaluation of Impacts of Adaptive Cruise Control on Mixed Traffic Flow

This paper addresses the impacts of Adaptive (Intelligent) Cruise Control (ACC) laws on traffic flow. Semi-automated vehicles, such as ACC Vehicles, with the capability to automatically follow each other in the same lane, will coexist with manually driven vehicles on the existing roadway system before they become universal. This mixed fleet scenario creates new capacity and safety issues. In this paper, simulation results of various mixed fleet scenarios under different ACC laws are presented. Explicit comparison of two ACC laws, Constant Time Headway (CTH) and Variable Time Headway (VTH), are based on these results. It¹s found that the latter one has better performance in terms of capacity and stability of traffic. Throughput increases with the proportion of CTH vehicles when flow is below capacity conditions. But above capacity, speed variability increases and speed drops with the CTH traffic compared with manual traffic, while the VTH traffic always performs better.Adaptive Cruise Control

Financing and Deploying Automated Freight Systems

New technologies are bringing Automated Freight Systems (AFS), which aim to reduce congestion, mitigate environmental impacts and enhance public safety, to fruition. The financing and deployment issues of AFS differ from other Intelligent Transport System applications. This chapter briefly introduces major concepts of AFS. The financing strategies for these concepts are discussed, in which the government subsidies play an important role through the use of public-private partnership. Economies of scale and externalities of the current and new systems are discussed. In the discussion of the deployment of AFS, it is suggested that deployment schemes are highly correlated with financing strategies.Automated Freight, Pipeline, Trucks, Rail

Vehicle Based Intersection Management with Intelligent Agents

Signal-based intersection management will change when vehicles with intelligent capability are available in the future. Intelligent agents embedded in vehicle software will be responsible for vehicle control and route guidance. Intersection management can be achieved through the collaboration of these agents, without a centralized control infrastructure. This research focuses on the use of distributed multi-agent systems to provide microscopic adaptive control which might reduce traffic delay and chances of collisions at intersections. A hypothesized Mobile Ad-hoc Network provides communication links to connect the agents.Intelligent Agents, Adaptive Intersection Control

Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper.

Verification and control of partially observable probabilistic systems

We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event’s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning