KLD Engineering

NY, United States

KLD Engineering

NY, United States
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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.


Chang J.,KLD Engineering | Talas M.,Systems Engineering | Muthuswamy S.,KLD Engineering
Transportation Research Record | Year: 2013

A simple methodology estimates queue length on an approach to a signalized intersection. This method has a minimal set of data requirements - flow, occupancy, cycle length, and detector setback - in contrast to prevailing methods that use detailed data defined on a per signal cycle basis to estimate vehicle trajectories. The key element of the algorithm is the estimation of two baseline occupancies that correspond to the relative position of the queue with respect to the detector location. The results of the algorithm were evaluated through traffic simulation and were also compared with field observations. Comparison of the queue estimates suggests that the detector location would be ideal for estimating queues if under prevailing conditions the tail of the queue is routinely longer than the detector setback. For detectors with appropriate setback, queue estimates match well in both comparisons. This algorithm was developed as part of the Midtown in Motion project and is operational in New York City as one of the elements of the active traffic management system.


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.


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.


Marsico M.,NYCDOT | Muthuswamy S.,KLD Engineering | Jaber J.,NYCDOT
21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World | Year: 2014

All construction permits include stipulations (stips) that define the lane closures and plans for handling traffic, referred to as Maintenance and Protection of Traffic (MPT/MOT) plans. These are developed to address safety, and to balance the duration of construction (project cost $) with the impacts during construction (delay $). Traffic models provide an excellent platform to complete what-if analyses to identify the best plans, but are data driven. This paper presents the NYCDOT experience and lessons learnt with the use of models as part of developing and refining MPT plans. Specifically, the paper addresses the use of data from multiple sources including the data from ITS technologies, and probe data as part of this process.


Weinisch K.,KLD Engineering | Brueckner P.,KLD Engineering
Journal of Emergency Management | Year: 2015

A shadow evacuation is the voluntary evacuation of people from areas outside a declared evacuation area. Shadow evacuees can congest roadways and inhibit the egress of those evacuating from an area at risk. Federal regulations stipulate that nuclear power plant (NPP) licensees in the United States must conduct an Evacuation Time Estimate (ETE) study after each decennial census. The US Nuclear Regulatory Commission (NRC) published federal guidance for conducting ETE studies in November 2011. This guidance document recommends the consideration of a Shadow Region which extends 5 miles radially beyond the existing 10-mile Emergency Planning Zone (EPZ) for NPPs. The federal guidance also suggests the consideration of the evacuation of 20 percent of the permanent resident population in the Shadow Region in addition to 100 percent of the declared evacuation region within the EPZ when conducting ETE studies. The 20 percent recommendation was questioned in a March 2013 report prepared by the US Government Accountability Office. This article discusses the effects on ETE of increasing the shadow evacuation from 20 to 6O percent for 48 NPPs in the United States. Only five (10 percent) of the 48 sites show a significant increase (30 minutes or greater) in 90th percentile ETE (time to evacuate 90 percent of the population in the EPZ), while seven (15 percent) of the 48 sites show a significant increase in 100th percentile ETE (time to evacuate all population in the EPZ). Study areas that are prone to a significant increase in ETE due to shadow evacuation are classified as one of four types; case studies are presented for one plant of each type to explain why the shadow evacuation significantly affects ETE. A matrix of the four case types can be used by emergency management personnel to predict during planning stages whether the evacuated area is prone to a significant increase in ETE due to shadow evacuation. Potential mitigation tactics that reduce demand (public information) or increase capacity (contraflow, traffic control points, specialized intersection treatments) to offset the impact of shadow evacuation are discussed.


Cohen R.,KLD Engineering | Weinisch K.,KLD Engineering
Journal of Emergency Management | Year: 2015

United States regulations require nuclear power plants (NPPS) to estimate the time needed to evacuate the emergency planning zone (EPZ, a circle with an approximate 10-mile radius centered at the NPP). These evacuation time estimate (ETE) studies are to be used by emergency personnel in the event of a radiological emergency. ETE studies are typically done using traffic simulation and evacuation models, based on traffic engineering algorithms that reflect congestion and delay. ETE studies are typically conducted assuming all evacuation routes are traversable. As witnessed in the Great East Japan Earthquake in March 2011, an earthquake and the ensuing tsunami can cause an incident at a NPP that requires an evacuation of the public. The earthquake and tsunami can also damage many of the available bridges and roadways and, therefore, impede evacuation and put people at risk of radiation exposure. This article presents a procedure, using traffic simulation and evacuation models, to estimate the impact on ETE due to bridge and roadway damage caused by a major earthquake, or similar hazardous event. The results of this analysis are used by emergency personnel to make protective action decisions that will minimize the exposure of radiation to the public. Additionally, the results allow emergency planners to ensure proper equipment and personnel are available for these types of events. Emergency plans are revised to ensure prompt response and recovery action during critical times.


Trademark
Kld Engineering | Date: 2014-03-03

COMPUTER SOFTWARE FOR USE IN TRANSPORTATION PLANNING, TRAFFIC ENGINEERING, EMERGENCY EVACUATION SIMULATION AND ANALYSIS.


Trademark
Kld Engineering | Date: 2014-03-03

COMPUTER SOFTWARE FOR USE IN TRAFFIC MANAGEMENT, TRAFFIC SIGNAL OPTIMIZATION AND REAL-TIME ADAPTIVE TRAFFIC SIGNAL CONTROL.


Trademark
Kld Engineering | Date: 2014-03-03

COMPUTER SOFTWARE FOR USE IN TRAFFIC MANAGEMENT, TRAFFIC SIGNAL OPTIMIZATION AND REAL-TIME ADAPTIVE TRAFFIC SIGNAL CONTROL.

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