In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.

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An increasing number of cities have severe traffic problems. We identify three main challenges for managing these problems. The first one is to achieve a proper amount of monitoring. Secondly, predictions of the effects of network wide management measures require knowledge of the underlying travel behaviour. Finally, measures should be in line with needs and expectations of travellers to be effective. In this paper we focus on these challenges. We use loop detectors near traffic lights in the Dutch city of Enschede to monitor the traffic situation in its network. We developed a method to estimate delays from these measurements. We also use a simple forecasting algorithm to predict flows and travel times for different time horizons. Regarding travel behaviour, we used a license plate survey to study route choice. We discuss how the results from these studies may be used to improve urban traffic management.