Scanning the Issue

Petros Ioannou
2019 IEEE transactions on intelligent transportation systems (Print)  
This paper presents a novel methodology to estimate the vessel times of arrival in port areas. The presented approach is based on a data-driven path-finding algorithm that exploits historical ship reporting systems data. In particular, both historical Automatic Identification System and Long Range Identification and Tracking maritime traffic data over a desired area of interest have been used. The methodology has been applied to a real scenario with real data sets and has been compared with
more » ... r two strategies: the first one is based on the well-known Haversine formula to approximate the great circle distance between two geographical positions; the other one connects the source to the destination by avoiding the land. The experimental results show that making use of the datadriven algorithm allows the system to achieve higher accuracy in terms of time of arrival estimation error. Efficient Freeway MPC by Parameterization of ALINEA and a Speed-Limited Area G. S. van de Weg, A. Hegyi, S. P. Hoogendoorn, and B. De Schutter Freeway congestion can reduce the freeway throughput due to the capacity drop or due to blocking caused by spillback to upstream ramps. Research has shown that congestion can be reduced by the application of ramp metering and variable speed limits. However, it is difficult to determine the ramp metering rates and variable speed limit settings over time, which optimizes the throughput by reducing congestion. This paper proposes an approach to optimize the control signals in a time efficient way. As a side effect of the optimization approach, the optimized control signal is more efficient than standard model predictive control optimization approaches. It is shown using a simulation study that parameterization realizes improved throughput when compared with a nonparameterized strategy when using the same amount of computation time. environmental benefits can be gained by communicating the signal phase and timing information of the upcoming traffic signals with fixed time control to the driver. However, similar applications to actuated signals pose a significant challenge due to their randomness to some extent caused by vehicle actuation. Based on the framework previously developed by the authors, a real-world testing has been conducted along the El Camino Real corridor in Palo Alto, CA, USA, to evaluate the system performance in terms of energy savings and emissions reduction. Strategies and algorithms are designed to be adaptive to the dynamic uncertainty for actuated signal and real-world traffic. It turns out that the proposed EAD system can save 6% energy for the trip segments when activated within dedicated short-range communication ranges and 2% energy for all trips. The proposed system can also reduce 7% of CO, 18% of HC, and 13% of NOx for all trips. Those results are compatible with the simulation results and validate the previously developed EAD framework. A path planning strategy named as "selecting trajectory point on a preview cross section", as well as, a speed planning strategy based on the curvature of the target path, is proposed. Following the strategies, objective functions are established to describe the behavior of "path-speed" selection for drivers with different driving styles. Constraints are designed on a compromise of roadway geometry, pavement condition, car performance, and ride comfort. And a rolling-horizon algorithm for "path-speed" simultaneously solving is also developed. Using the proposed models, target path-speed for a driving style of a passenger car driver can be planned for minor traffic roadways with complex geometric features, such as circuits and mountain roads. Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework
doi:10.1109/tits.2018.2885837 fatcat:crcgiff6czez3k5j4xn6mdcsj4