Usage of computerized traffic case management systems. Volume I. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Traffic mitigation and demand management: summary of national experience and potential applications in New York. Final report.

New York - New Jersey Port Authority, New York

Highway traffic safety management orientation module development--administrative evaluation. Technical report. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Police traffic services, management information system, phase I - synopsis.

National Highway Traffic Safety Administration, Washington, D.C.

Police traffic services, management information system, (phase I). Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Management and evaluation handbook for demonstration projects in traffic safety. 1976.

National Highway Traffic Safety Administration, Washington, D.C.

Traffic mitigation and demand management : summary of national experience and potential applications in New York /

"DOT-I-88-02"--Cover.

Traffic lights management using belief propagation

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Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

En Route Speed Reduction Concept for Absorbing Air Traffic Flow Management Delays

This paper proposes an en route speed reduction to complement current ground delay practices in air traffic flow management. Given a nominal cruise speed, there exists a bounded range of speeds that allows aircraft to fly slower with the same or lower fuel consumption than the nominal flight. Therefore, flight times are increased and delay can be partially performed in the air, at no extra fuel cost for the operator. This concept has been analyzed in an initial feasibility study, computing the maximum amount of delay that can be performed in the air in some representative flights. The impact on fuel consumption has been analyzed, and two scenarios are proposed: the flight fuel remains the same as in the nominal flight, and some extra fuel allowance is permitted in order to face uncertainties. Results show significant values of airborne delay that may be useful in many situations, with the exception of short hauls where airborne delay may be too short. If cruise altitude is changed, the amount of airborne delay increases, since changes in cruise speed modify the optimal flight altitudes. From the analyzed flights, a linear dependency is found relating the airborne delay with the amount of extra fuel allowance.

TRAFFIC IMPACT ANALYSIS OF SEVERAL DYNAMIC LANE MANAGEMENT STRATEGIES FOR CONGESTION MITIGATION BASED ON DTA MODEL

Persistent daily congestion has been increasing in recent years, particularly along major corridors during selected periods in the mornings and evenings. On certain segments, these roadways are often at or near capacity. However, a conventional Predefined control strategy did not fit the demands that changed over time, making it necessary to implement the various dynamical lane management strategies discussed in this thesis. Those strategies include hard shoulder running, reversible HOV lanes, dynamic tolls and variable speed limit. A mesoscopic agent-based DTA model is used to simulate different strategies and scenarios. From the analyses, all strategies aim to mitigate congestion in terms of the average speed and average density. The largest improvement can be found in hard shoulder running and reversible HOV lanes while the other two provide more stable traffic. In terms of average speed and travel time, hard shoulder running is the most congested strategy for I-270 to help relieve the traffic pressure.