2,110 research outputs found

    Accounting for Travel Time Reliability, Trip Purpose and Departure Time Choice in an Agent-Based Dynamic Feedback-Control Toll Pricing Approach

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    The primary goal of this study is to modify the original strategy developed by Cheng and Ishak (2013) into an agent-based dynamic feedback-control toll pricing strategy that accounts for the trip purpose, travel time reliability, departure time choice and level of income such that the toll revenue is maximized while maintaining a minimum desired level of service on the managed lanes. An external module was developed to execute the modified strategy. An agent-based modeling was applied to simulate drivers’ learning process based on their previous commuting experience. The study also analyzed how driver’s heterogeneity in value of time, value of reliability for each trip purpose will influence route decisions and thus affect the optimal toll rates. A numerical example was given to explain the modified strategy. The simulation results confirmed that under high traffic demand, drivers with urgent trip purpose have the highest probability of choosing managed lanes, and that the travel time on the managed lanes is more reliable than that on the general purpose lanes. A comparative evaluation is given between the modified strategy, the strategy currently deployed on Interstate 95 express lanes, and the original strategy. Compared to the current strategy, the increase in toll rate is steadier and the toll revenue is significantly higher for the modified strategy, while keeping the speed higher than 45 mph. On the other hand, compared to the original strategy, the modified one offers a more realistic approach that accounts for travel time reliability and delay in route choice and departure time choice, as well as generates higher toll revenue under heavy traffic demand

    Traffic Congestion Pricing Methods and Technologies

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    This paper reviews the methods and technologies for congestion pricing of roads. Congestion tolls can be implemented at scales ranging from individual lanes on single links to national road networks. Tolls can be differentiated by time of day, road type and vehicle characteristics, and even set in real time according to current traffic conditions. Conventional toll booths have largely given way to electronic toll collection technologies. The main technology categories are roadside-only systems employing digital photography, tag and beacon systems that use short-range microwave technology, and in vehicle-only systems based on either satellite or cellular network communications. The best technology choice depends on the application. The rate at which congestion pricing is implemented, and its ultimate scope, will depend on what technology is used and on what other functions and services it can perform. Since congestion pricing calls for the greatest overall degree of toll differentiation, congestion pricing is likely to drive the technology choice.Road pricing; Congestion pricing; Electronic Toll Collection technology

    Study of a Dynamic Cooperative Trading Queue Routing Control Scheme for Freeways and Facilities with Parallel Queues

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    This article explores the coalitional stability of a new cooperative control policy for freeways and parallel queuing facilities with multiple servers. Based on predicted future delays per queue or lane, a VOT-heterogeneous population of agents can agree to switch lanes or queues and transfer payments to each other in order to minimize the total cost of the incoming platoon. The strategic interaction is captured by an n-level Stackelberg model with coalitions, while the cooperative structure is formulated as a partition function game (PFG). The stability concept explored is the strong-core for PFGs which we found appropiate given the nature of the problem. This concept ensures that the efficient allocation is individually rational and coalitionally stable. We analyze this control mechanism for two settings: a static vertical queue and a dynamic horizontal queue. For the former, we first characterize the properties of the underlying cooperative game. Our simulation results suggest that the setting is always strong-core stable. For the latter, we propose a new relaxation program for the strong-core concept. Our simulation results on a freeway bottleneck with constant outflow using Newell's car-following model show the imputations to be generally strong-core stable and the coalitional instabilities to remain small with regard to users' costs.Comment: 3 figures. Presented at Annual Meeting Transportation Research Board 2018, Washington DC. Proof of conjecture 1 pendin

    Equitable Dynamic Pricing for Express Lanes

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    Express lanes mitigate traffic congestion by providing a time-reliable alternative and exploiting travelers\u27 willingness to pay to generate revenue for infrastructure projects. Over the last decade, equity and fairness issues for express lanes have been considered; however, there is a lack of guidance on the design of equitable discounts. In this article, we present a modeling framework for the analysis of equity issues with express lanes for tolls optimized for different objectives. Through simulation-based optimization of tolls using reinforcement learning, we show that the choice of dynamic tolls impacts the delay differentials across different groups. We find that higher toll values and higher demand worsen the delay differentials across travel groups. We also prove that discounts proportional to travelers\u27 value of time address delay differentials across the travel groups, where the optimal discounts may be a function of current toll and travel time savings. The analysis of tradeoffs between equity, revenue, and delay reveals that equitable discounts may result in up to 34% loss of revenue and up to 9% increase in delay. Research findings suggest (a) designing discounts proportional to VOT which can be correlated with income groups and (b) balancing the tradeoffs by carefully identifying the agency\u27s priorities

    A Comparative Evaluation Of Fdsa,ga, And Sa Non-linear Programming Algorithms And Development Of System-optimal Methodology For Dynamic Pricing On I-95 Express

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    As urban population across the globe increases, the demand for adequate transportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities. High –Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today’s highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for indepth study of demand patterns and development of an appropriate pricing scheme which reduces congestion. This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution. We perform a comparative evaluation of FDSA, GA and SA non-linear programming algorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric iv forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 201

    Complex Traffic Network Modeling & Area-wide Hierarchical Control

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    This thesis presents a novel methodology to divide a traffic region into subregions such that in each subregion a Macroscopic Fundamental Diagram (MFD) can be used to determine the state of that subregion. The region division is based on the theory of complex networks. We exploit the inherent network characteristics through PageRank centrality algorithm to identify the most significant nodes in the traffic network. We use these significant nodes as the seeds for a Voronoi diagram based partitioning mechanism of the network. A network wide hierarchical control framework is then presented which controls these sub regions individually and the network as a whole. At the subregion level a feedback controller is designed based on MFD concept. At the network level we develop a dynamic toll pricing algorithm to control the inflows into the network. This dynamic toll pricing is coupled with the subregion controller and thus forming a network wide hierarchical control. We use optimal control theory to design the dynamic toll pricing. A cost function is designed and then Hamilton-Jacobi-Bellman equation is used to derive an optimal control law that uses real-time information. The objective of the dynamic toll algorithm is to strike a balance between the toll price and optimal traffic conditions in each of the subregions. A case study is performed for the Manhattan area in New York city and results are provided through simulations
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