Pay for Intersection Priority: A Free Market Mechanism for Connected Vehicles

The rapid development and deployment of vehicle technologies offer opportunities to re-think the way traffic is managed. This paper capitalizes on vehicle connectivity and proposes an economic instrument and corresponding cooperative framework for allocating priority at intersections. The framework is compatible with a variety of existing intersection control approaches. Similar to free markets, our framework allows vehicles to trade their time based on their (disclosed) value of time. We design the framework based on transferable utility games, where winners (time buyers) pay losers (time sellers) in each game. We conduct simulation experiments of both isolated intersections and an arterial setting. The results show that the proposed approach benefits the majority of users when compared to other mechanisms both ones that employ an economic instrument and ones that do not. We also show that it drives travelers to estimate their value of time correctly, and it naturally dissuades travelers from attempting to cheat

A bi-level model of dynamic traffic signal control with continuum approximation

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure

Smart traffic control for the era of autonomous driving

This thesis aims to take on the challenges to address some of the key issues in traffic control and management, including intersection protocol design, congestion measurement, selfish routing and road infrastructure automation, under the assumption that all vehicles on the road are connected and self-driving. To design and test traffic control mechanisms for AVs, we introduced a formal model to represent road networks and traffic. Based on this model, we developed a simulation system on top of an existing open-source platform (AIM4) and used it to examine a number of traffic management protocols specifically designed for traffic with fully autonomous vehicles. Simulation outcomes show that traffic management protocols for AVs can be more subtle, sensitive and variable with traffic volumes/flow rate, vehicle safe distance and road configuration. In addition, by analyzing the real-world traffic data and simulation data, we found that measuring congestion with exponential functions has considerable advantages against the traditional BPR function in certain aspects. The deployment of autonomous vehicles provides traffic management with an opportunity of choosing either centralised control or decentralised control. The price of anarchy (PoA) of autonomous decision-making for routing gives an applicable quantitative criterion for selection between them. We extended the existing research on PoA with the ˙class of exponential functions as cost functions. We found an expression for the tight upper bound of the PoA for selfish routing games with exponential cost functions. Unlike existing studies, this upper bound depends on traffic demands, with which we can get a more accurate estimation of the PoA. Furthermore, by comparing the upper-bounds of PoA between the BPR function and the exponential function, we found that the exponential functions yield a smaller upper bound than the BPR functions in relatively low traffic flows. To specify traffic management systems with autonomous roadside facilities, we propose a hybrid model of traffic assignment. This model aims to describe traffic management systems in which both vehicles and roadside controllers make autonomous decisions, therefore, are autonomous agents. We formulated a non-linear optimization problem to optimize traffic control from a macroscopic view of the road network. To avoid the complex calculations required for non-linear optimization, we proposed an approximation algorithm to calculate equilibrium routing and traffic control strategies. The simulation results show that this algorithm eventually converges to a steady state. The traffic control scheme in this steady state is an approximately optimal solution

The Price of Anarchy in Transportation Networks: Efficiency and Optimality Control

Uncoordinated individuals in human society pursuing their personally optimal strategies do not always achieve the social optimum, the most beneficial state to the society as a whole. Instead, strategies form Nash equilibria which are often socially suboptimal. Society, therefore, has to pay a price of anarchy for the lack of coordination among its members. Here we assess this price of anarchy by analyzing the travel times in road networks of several major cities. Our simulation shows that uncoordinated drivers possibly waste a considerable amount of their travel time. Counterintuitively,simply blocking certain streets can partially improve the traffic conditions. We analyze various complex networks and discuss the possibility of similar paradoxes in physics.Comment: major revisions with multicommodity; Phys. Rev. Lett., accepte