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Xin W.,KLD Engineering | Prassas E.S.,New York University
21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World | Year: 2014

Adaptive traffic signal control dynamically adjusts traffic signals based on prevailing traffic conditions. The acquisition and processing of real-time traffic data play a crucial role. The emerging trend of "big data", characterized as "three Vs'", i.e., Volume, Velocity and Variety, potentially enables novel signal control concepts and more effective adaptive signal control implementations. However, there has been a lack of relevant real-time big-data management architecture - an architecture that recognizes the disadvantages of existing general-purpose big-data technologies such as Hadoop/MapReduce or NoSQL, an architecture that is specifically targeted and optimized for adaptive signal control, capable of managing big-data that is very large (volume), very fast (velocity), and diverse (variety), and an architecture that allows real-time predictive analysis and performs regional adaptive traffic control that calls for parallel executions of relevant signal optimization algorithms for different sub-areas of complex traffic networks. This paper first examines the historical evolvement of adaptive traffic signal control, and points out the challenges and opportunities in nowadays data-rich environment. The relevance of the generalpurpose big-data technologies (MapReduce/Hadoop and NoSQL) is discussed from the signal control perspective. A new real-time big-data management architecture is proposed, considering massive realtime traffic data available nowadays and new types of data emerging in future. These data are generally collected at high frequency, in large amount, and supplied from different sources in realtime. The proposed architecture is specifically targeted for adaptive signal control applications. It features a hybrid design with both centralized and distributed elements, taking into account the efficient data archival and retrieval at physical disk sectors and memory levels, real-time traffic data fusion and synthetizing, in-memory caching and indexing, and a set of customized analytics supporting the novel concept of Signal Optimization Repository in adaptive traffic control. This architecture has been implemented as the core technology of the ACDSS system, which is a multiregime, variable-objective adaptive traffic control system developed by KLD. A case study is presented showing the real-life application of the proposed architecture in ACDSS operations with hundreds of signalized intersections of New York City arterials and grid networks.

Xin W.,KLD Engineering | Levinson D.,University of Minnesota
Mathematical Population Studies | Year: 2015

In a stochastic roadway congestion and pricing model, one scheme (omniscient pricing) relies on the full knowledge of each individual journey cost and of early and late penalties of the traveler. A second scheme (observable pricing) is based on observed queuing delays only. Travelers are characterized by late-acceptance levels. The effects of various late-acceptance levels on congestion patterns with and without pricing are compared through simulations. The omniscient pricing scheme is most effective in suppressing the congestion at peak hours and in distributing travel demands over a longer time horizon. Heterogeneity of travelers reduces congestion when pricing is imposed, and congestion pricing becomes more effective when cost structures are diversified rather than identical. Omniscient pricing better reduces the expected total social cost; however, more travelers improve welfare individually with observable pricing. The benefits of a pricing scheme depend on travelers’ cost structures and on the proportion of late-tolerant, late-averse, and late-neutral travelers in the population. © 2015, Copyright © KLD Associates, Inc.

Kld Engineering | Date: 2014-03-03


Xin W.,KLD Engineering | Chang J.,KLD Engineering | Muthuswamy S.,KLD Engineering | Talas M.,Long Island City | Prassas E.,New York University
Transportation Research Record | Year: 2013

A hierarchical adaptive signal control was developed and implemented in New York City to manage congestion in a complex urban roadway environment. Control strategies, including strategically regulating traffic demand and balancing the queue-storage ratio at critical intersections, work in concert to systematically alleviate congestion and improve mobility. The high usage of electronic toll collection tags in this area allows large amounts of per trip travel time data to be collected (nearly 1 million per trip travel time records daily) and used in real time for effective control. Congestion levels are mapped to different control regimes. Various demand-regulating strategies are applied at the peripheral roadways of the target control zone. These strategies proactively employ signal offsets and splits to exert a tapering and rebalancing effect on the traffic. Demand regulation results in a better use of available network storage spaces while preserving the capacity of the target control zone. Inside the target control area, a dynamic queue-balancing strategy is implemented at selected critical intersections to prevent propagation of spillovers with stabilized or diminished queues. The initial implementation covered 110 intersections in the highly congested central business district of midtown Manhattan New York City. Results to date are summarized.

Liu T.,New York University | Jiang Z.-P.,New York University | Xin W.,KLD Engineering | McShane W.R.,KLD Engineering
Proceedings of the American Control Conference | Year: 2013

In this paper, a modified dynamic traffic assignment model is developed to explicitly formulate the impact of inaccuracy of cost measurement/estimation and the time-varying travel demand. The modified model is analyzed by using Lyapunov methods and robust stability results in non-linear control theory. A robust convergence property of the model is derived, and interestingly, is closely related to Sontag's input-to-state stability (ISS) property. Simulation results are employed to validate the main result. © 2013 AACC American Automatic Control Council.

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