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Patterns and controls of chlorophyll-a and primary productivity cycles in the Southern California Bight

Factors that influence the magnitude and the depth of the chlorophyll maximum layer in the ocean off Southern California are explored using observations from the long-term California Cooperative Oceanic Fisheries Investigations (CalCOFI) program. The data record is sufficiently long to reveal patterns not always evident from single stations or single cruises. Processes such as coastal wind-driven upwelling, geostrophic circulation, and annual physical and chemical cycles are illustrated to demonstrate their effect on euphotic zone nutrient availability, and subsequent phytoplankton biomass and primary productivity. In this area, where the influence of wind-driven upwelling is spatially restricted and advected waters are generally nutrient depleted, geostrophically induced upwelling and winter convection become important in determining spatial and temporal patterns of phytoplankton.     

Developing freeway lane‐changing support systems using artificial neural networks

Lane‐changing involves many concerns about safety and efficiency which makes it one of the most difficult tasks of driving. It is indeed quite personal since drivers operate vehicles according to their integrated perception of comprehensive circumstances rather than individual rules. A lane‐changing decision support model is developed in this study using artificial neural networks (ANN). The advantages of the ANN approach lie in the learning capability. Due to its nature, an ANN model can consolidate various kinds of information surrounding the vehicle for the drivers and generate reliable results to help control vehicles. It then becomes a useful mechanism to assist drivers in judging current situations and making the right decisions. Several preliminary validations and comparisons are conducted with the field survey data. It is confirmed that the ANN model mimics traffic characteristics more accurately than conventional methods. This product would expedite the implementation of relevant applications in the intelligent transportation systems context. In particular, the ANN model can be adapted to individual driver characteristics. This reveals practical feasibility and significant market potential for customized in‐vehicle equipment.     

Optimization of bus stop locations for improving transit accessibility

A mathematical model is developed in this paper to improve the accessibility of a bus service. To formulate the optimization model, a segment of a bus route is given, on which a number of demand entry points are distributed realistically. The objective total cost function (i.e. the sum of supplier and user costs) is minimized by optimizing the number and locations of stops, subject to non‐additive users' value of time. A numerical example is designed to demonstrate the effectiveness of the method thus developed to optimize the bus stop location problem. The sensitivity of the total cost to various parameters (e.g. value of users' time, access speed, and demand density) and the effect of the parameters on the optimal stop locations are analyzed and discussed.     

Using automatic passenger counter data in bus arrival time prediction

Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.     

Automated Vehicle Merging Maneuver Implementation for AHS

Summary This paper presents a real-time implementation of a general merging algorithm for automated highway systems. A merging control problem is proposed first. A real-time algorithm is then presented, which is used to calculate a smooth reference speed trajectory for the merging vehicle based on the speed of the main lane vehicle. This algorithm can also be applied even when the main lane vehicles change speed. To make the algorithm adapt to different road layouts and to increase safety, a concept of virtual platooning is proposed. It effectively shifts the time of platoon formation forward prior to the start of real merging. Aspects closely related to real-time implementation are discussed, such as the controller adopted, the use of magnetometer based distance measurement and information passing by communication from main lane vehicles. Test results are presented and briefly analyzed.