Urban road dust can be highly enriched with trace metals such as copper, lead, zinc, and platinum group elements (PGEs) that are released primarily from vehicles. Concentrations of these metals are up to about 100 times higher than background levels. Lead concentrations in sediment core have declined dramatically due to phase out of leaded gasoline. Recent ban on the use of lead wheel balancing weight in Europe and in some US states will accelerate the decline of lead in the environment. Concentrations of copper, zinc, and PGEs in sediments in many urban areas have increased continuously due to increase of urban sprawl and increased numbers and travel distance of vehicle. Wear of brake pads and tyres are the primary sources of copper and zinc in urban road dust. PGEs have been released from catalytic converters since the mid-1970s. Contaminated urban road dust is picked up by stormwater runoff and delivered into local receiving waterbodies (e.g. streams, rivers, lakes, and estuaries). Concentrations of these metals in waterbodies and sediments receiving stormwater runoff from densely populated urban areas are high enough to impair the health of aquatic organisms and frequently exceed water quality criteria and sediment quality guidelines. In order to improve the quality of urban waterbodies, legal regulations are necessary to reduce release of toxic metals from vehicle use as demonstrated in the case of lead in gasoline. Recent regulations and agreement to reduce copper content in brake pads to 0.5% by 2026 in the USA should result in significant reduction of copper entering urban watersheds. A less optimal option is removal of suspended sediments from stormwater runoff before they enter receiving waterbodies using best management practices such as retention ponds, detention basins, and grass swales.

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.

With the advent of route guidance systems (RGS), the prediction of short-term link travel times has become increasingly important. For RGS to be successful, the calculated routes should be based on not only historical and real-time link travel time information but also anticipatory link travel time information. An examination is conducted on how realtime information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods (of 5 minutes’ duration). The methodology developed consists of two steps. First, the historical link travel times are classified based on an unsupervised clustering technique. Second, an individual or modular artificial neural network (ANN) is calibrated for each class, and each modular ANN is then used to predict link travel times. Actual link travel times from Houston, Texas, collected as part of the automatic vehicle identification system of the Houston Transtar system were used as a test bed. It was found that the modular ANN outperformed a conventional singular ANN. The results of the best modular ANN were compared with existing link travel time techniques, including a Kalman filtering model, an exponential smoothing model, a historical profile, and a real-time profile, and it was found that the modular ANN gave the best overall results.

The objective of this research is to develop a dynamic model to forecast multi-interval path travel times between bus stops of origin and destination. The research also intends to test the proposed model using real-world data. This research was brought about by the shortcomings of the existing real-time based short-term-prediction models, which have been widely utilised for single interval predictions. The developed model is based on the Nearest Neighbour Non-Parametric Regression using historical and current data collected by the Automatic Vehicle Location technology. In a test with real-world bus data in Seoul, Korea, the proposed multi-interval-prediction model performed effectively in terms of both prediction accuracy and computing time.