No abstract

Microscopic simulation models have been widely used in both transportation operations and management analyses because simulation is safer, less expensive, and faster than field implementation and testing. While these simulation models can be advantageous to engineers, the models must be calibrated and validated before they can be used to provide meaningful results. However, the transportation profession has not established any formal or consistent guidelines for the development and application of these models. In practice, simulation model–based analyses have often been conducted under default parameter values or bestguessed values. This is mainly due to either difficulties in field data collection or lack of a readily available procedure for simulation model calibration and validation. A procedure was proposed for microscopic simulation model calibration and validation and an example case study is presented with real-world traffic data from Route 50 on Lee Jackson Highway in Fairfax, Virginia. The proposed procedure consisted of nine steps: ( a) measure of effectiveness selection, ( b) data collection, ( c) calibration parameter identification, ( d) experimental design, ( e) run simulation, ( f) surface function development, ( g) candidate parameter set generations, ( h) evaluation, and ( i) validation through new data collection. The case study indicates that the proposed procedure appears to be properly calibrating and validating the VISSIM simulation model for the test-bed network.

Traffic signal optimization programs have been used widely among transportation professionals. However, none of the existing computer programs can optimize all four traffic control parameters (i.e., cycle length, green split, offset, and phase sequence) simultaneously, even for undersaturated conditions. In this paper, a genetic algorithm-based signal optimization program that can handle oversaturated signalized intersections is presented. The program consists of a genetic algorithm (GA) optimizer and a mesoscopic traffic simulator. The GA optimizer is designed to search for a near-optimal traffic signal timing plan on the basis of a fitness value obtained from the mesoscopic simulator. The proposed program is compared with the newly released TRANSYT-7F version 8.1 on the basis of CORSIM simulation program. Three different demand volume levels-low, medium, and high demand-are tested. For the low-demand and high-demand volume cases, the GAbased program produced statistically better signal timing plans than did TRANSYT-7F in terms of queue time. In the case of mediumdemand volume level, the signal timing plan obtained from the GA-based program produced statistically equivalent queue time compared with TRANSYT-7F. Both programs are judged to provide superior capability for oversaturated conditions due to their queue blockage model when compared with previously available signal timing optimization software.Traffic congestion during peak periods is prevalent for most urban areas. A recent study notes urban arterial systems have experienced increasing traffic congestion (1). Thus, there is a need for effectively managing traffic signal control systems during congested or oversaturated periods. Oversaturated conditions are defined as the condition when vehicles are prevented from moving freely, either because of the presence of vehicles in the intersection itself or because of queue backup in any of the exit links of the intersection (2). Even though oversaturated conditions may last only briefly, the aftereffect may take a long time to clear.Traffic signal coordination and optimization are desirable as costeffective means of reducing urban traffic congestion, especially when additional road construction is impossible because of either high construction cost or lack of available land. Therefore, optimal traffic control plans that would maximize the operational efficiency of existing facilities should be developed and implemented. This can be achieved by maximizing the use of green time and preventing formation of queue blocking of output flows. BACKGROUND Signal OptimizationCurrent traffic signal optimization programs fall into two categories: delay-based models and bandwidth-based models. TRANSYT, a representative delay-based model, minimizes a linear combination of network-wide delay and stops by optimizing cycle length, green split, and offset. In contrast, bandwidth-based programs maximize the sum of directional bands for progression by choosing optimal phase sequence, offset, and cycle length.The limitation of exis...

BackgroundViral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs. Many computational methods have been developed to predict PPIs, but most of them are intended for PPIs within a species rather than PPIs across different species such as PPIs between virus and host.ResultsIn this study, we developed a prediction model of virus-host PPIs, which is applicable to new viruses and hosts. We tested the prediction model on independent datasets of virus-host PPIs, which were not used in training the model. Despite a low sequence similarity between proteins in training datasets and target proteins in test datasets, the prediction model showed a high performance comparable to the best performance of other methods for single virus-host PPIs.ConclusionsOur method will be particularly useful to find PPIs between host and new viruses for which little information is available. The program and support data are available at http://bclab.inha.ac.kr/VirusHostPPI.

The operation of traffic signals is currently limited by the data available from traditional point sensors. Point detectors can provide only limited vehicle information at a fixed location. The most advanced adaptive control strategies are often not implemented in the field because of their operational complexity and high-resolution detection requirements. However, a new initiative known as connected vehicles allows the wireless transmission of the positions, headings, and speeds of vehicles for use by the traffic controller. A new traffic control algorithm, the predictive microscopic simulation algorithm, which uses these new, more robust data, was developed. The decentralized, fully adaptive traffic control algorithm uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future. The objective function uses either delay only or a combination of delay, stops, and decelerations. To measure the objective function, the algorithm uses a microscopic simulation driven by present vehicle positions, headings, and speeds. The algorithm is relatively simple, does not require point detectors or signal-to-signal communication, and is completely responsive to immediate vehicle demands. To ensure drivers' privacy, the algorithm does not store individual or aggregate vehicle locations. Results from a simulation showed that the algorithm maintained or improved performance compared with that of a state-of-the-practice coordinated actuated timing plan optimized by Synchro at low and midlevel volumes, but that performance worsened under saturated and oversaturated conditions. Testing also showed that the algorithm had improved performance during periods of unexpected high demand and the ability to respond automatically to year-to-year growth without retiming.

Microscopic traffic simulation models have been playing an important role in the evaluation of transportation engineering and planning practices for the past few decades, particularly in cases in which field implementation is difficult or expensive to conduct. To achieve high fidelity and credibility for a traffic simulation model, model calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice, and they have seldom proposed a systematic procedure or guideline for the calibration and validation of simulation models. This paper proposes a procedure for microscopic simulation model calibration. The validity of the proposed procedure was demonstrated by use of a case study of an actuated signalized intersection by using a widely used microscopic traffic simulation model, Verkehr in Staedten Simulation (VISSIM). The simulation results were compared with multiple days of field data to determine the performance of the calibrated model. It was found that the calibrated parameters obtained by the proposed procedure generated performance measures that were representative of the field conditions, while the simulation results obtained with the default and best-guess parameters were significantly different from the field data.

No abstract

A radial basis function (RBF) neural network has recently been applied to time-series forecasting. The test results of an RBF neural network in forecasting short-term freeway traffic volumes are provided. Real observations of freeway traffic volumes from the San Antonio TransGuide System have been used in these experiments. For comparison of forecasting performances, Taylor series, exponential smoothing method (ESM), double exponential smoothing method, and backpropagation neural network were also designed and tested. The RBF neural network model provided the best performance and required less computational time than BPN. It seems that RBF and ESM can be a viable forecasting routine for advanced traffic management systems. There are some tradeoffs between RBF and ESM. Although the performance of ESM is inferior to RBF, the former does not need a complicated training process or historic database, and vice versa. However, even in the best performance case, 35 percent of the forecast traffic volumes showed 10 percent or more percentage errors. This means that we cannot heavily depend on the forecast traffic volumes as long as we are utilizing the models tested. Further work is needed to provide a more reliable traffic forecasting model.