The Stability of downtown parking and traffic congestion

In classical traffic flow theory, there are two velocities associated with a given level of traffic flow. Following Vickrey, economists have termed travel at the higher speed congested travel and at the lower speed hypercongested travel. Since the publication of Walters' classic paper (1961, Econometrica 29, 676-699), there has been an on-going debate concerning whether a steady-state hypercongested equilibrium can be stable. For a particular structural model of downtown traffic flow and parking, this paper demonstrates that a steady-state hypercongested equilibrium can be stable. Some other sensible models of traffic congestion conclude that steady-state hypercongested travel cannot be stable, and that queues develop to ration the demand in steady states. Thus, we interpret our result to imply that, when steady-state demand is so high that it cannot be rationed through congested travel, the trip price increase necessary to ration the demand may be generated either through the formation of steady-state queues or through hypercongested travel, and that which mechanism occurs depends on details of the traffic system

q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization

The q-Gaussian distribution results from maximizing certain generalizations of Shannon entropy under some constraints. The importance of q-Gaussian distributions stems from the fact that they exhibit power-law behavior, and also generalize Gaussian distributions. In this paper, we propose a Smoothed Functional (SF) scheme for gradient estimation using q-Gaussian distribution, and also propose an algorithm for optimization based on the above scheme. Convergence results of the algorithm are presented. Performance of the proposed algorithm is shown by simulation results on a queuing model.Comment: 5 pages, 1 figur

Pair correlation function of short-ranged square-well fluids

We have performed extensive Monte Carlo simulations in the canonical (NVT) ensemble of the pair correlation function for square-well fluids with well widths $\lambda-1$ ranging from 0.1 to 1.0, in units of the diameter $\sigma$ of the particles. For each one of these widths, several densities $\rho$ and temperatures $T$ in the ranges $0.1\leq\rho\sigma^3\leq 0.8$ and $T_c(\lambda)\lesssim T\lesssim 3T_c(\lambda)$, where $T_c(\lambda)$ is the critical temperature, have been considered. The simulation data are used to examine the performance of two analytical theories in predicting the structure of these fluids: the perturbation theory proposed by Tang and Lu [Y. Tang and B. C.-Y. Lu, J. Chem. Phys. {\bf 100}, 3079, 6665 (1994)] and the non-perturbative model proposed by two of us [S. B. Yuste and A. Santos, J. Chem. Phys. {\bf 101}, 2355 (1994)]. It is observed that both theories complement each other, as the latter theory works well for short ranges and/or moderate densities, while the former theory does for long ranges and high densities.Comment: 10 pages, 10 figure

Adaptive traffic signal control using approximate dynamic programming

This thesis presents a study on an adaptive traffic signal controller for real-time operation. An approximate dynamic programming (ADP) algorithm is developed for controlling traffic signals at isolated intersection and in distributed traffic networks. This approach is derived from the premise that classic dynamic programming is computationally difficult to solve, and approximation is the second-best option for establishing sequential decision-making for complex process. The proposed ADP algorithm substantially reduces computational burden by using a linear approximation function to replace the exact value function of dynamic programming solution. Machine-learning techniques are used to improve the approximation progressively. Not knowing the ideal response for the approximation to learn from, we use the paradigm of unsupervised learning, and reinforcement learning in particular. Temporal-difference learning and perturbation learning are investigated as appropriate candidates in the family of unsupervised learning. We find in computer simulation that the proposed method achieves substantial reduction in vehicle delays in comparison with optimised fixed-time plans, and is competitive against other adaptive methods in computational efficiency and effectiveness in managing varying traffic. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised. The proposed ADP algorithm is in compliance with a range of discrete systems of resolution from 0.5 to 5 seconds per temporal step. This study demonstrates the readiness of the proposed approach for real-time operations at isolated intersections and the potentials for distributed network control

The stability of downtown parking and traffic congestion

Consider a transport facility in steady state that is operating at maximum throughput. How does it respond to a once-and-for-all increase in demand? The trip price must increase to ration the increased demand, but how? These questions have been the subject of a debate in transport economic theory dating back to Walters’ classic paper (1961). The current wisdom is that the facility continues to operate at full capacity, with travel at reduced velocity and/or increased queuing serving to increase the trip price. This paper analyzes the transient dynamics and stability of steady states for a spatially uniform road network with on-street parking, and finds in this context that the increase in demand may cause operation at reduced throughput

The Stability of Downtown Parking and Traffic Congestion

In classical traffic flow theory, there are two velocities associated with a given level of traffic flow. Following Vickrey, economists have termed travel at the higher speed congested travel and at the lower speed hypercongested travel. Since the publication of Walters. classic paper, there has been an on-going debate concerning whether a steady-state hypercongested equilibrium can be stable. For a particular structural model of downtown traffic flow and parking, this paper demonstrates that a steady-state hypercongested equilibrium can be stable. Some other sensible models of traffic congestion conclude that steady-state hypercongested travel cannot be stable, and that queues develop to ration the demand in steady states. Thus, we interpret our result to imply that, when steady-state demand is so high that it cannot be rationed through congested travel, the trip price increase necessary to ration the demand may be generated either through the formation of steady-state queues or through hypercongested travel, and that which mechanism occurs depends on details of the traffic system.traffic congestion, cruising for parking, on-street parking, hypercongestion