We propose in this paper a decentralized traffic signal control policy for urban road networks. Our policy is an adaptation of a so-called BackPressure scheme which has been widely recognized in data network as an optimal throughput control policy. We have formally proved that our proposed BackPressure scheme, with fixed cycle time and cyclic phases, stabilizes the network for any feasible traffic demands. Simulation has been conducted to compare our BackPressure policy against other existing distributed control policies in various traffic and network scenarios. Numerical results suggest that the proposed policy can surpass other policies both in terms of network throughput and congestion.

An approach to freeway travel time prediction based on recurrent neural networks is presented. Travel time prediction requires a modeling approach that is capable of dealing with complex nonlinear spatio-temporal relationships among flows, speeds, and densities. Based on the literature, feedforward neural networks are a class of mathematical models well suited for solving this problem. A drawback of the feed-forward approach is that the size and composition of the input time series are inherently design choices and thus fixed for all input. This may lead to unnecessarily large models. Moreover, for different traffic conditions, different sizes and compositions of input time series may be required, a requirement not satisfied by any feedforward data-driven method. The recurrent neural network topology presented is capable of dealing with the spatiotemporal relationships implicitly. The topology of this neural net is derived from a state-space formulation of the travel time prediction problem, which is in line with traffic flow theory. The performance of several versions of this state-space neural network was tested on synthetic data from a densely used highway stretch in the Netherlands. The neural network models were capable of accurately predicting travel times experienced, producing about zero mean normally distributed residuals, rarely outside 10% of the real expected travel times. Moreover, analyses of the internal states and weight configurations revealed that the neural networks could develop an internal model linked to the underlying traffic processes.

Microscopic simulation models predict different forms of selforganization in pedestrian flows, such as the dynamic formation of lanes in bi-directional pedestrian flows. The experimental research presented in this paper provides more insight into these dynamic phenomena as well as exposing other forms of self-organization, i.e. in case of over-saturated bottlenecks or crossing pedestrian flows. The resulting structures resemble states occurring in granular matter and solids, including their imperfections (so-called vacancies). Groups of pedestrians that are homogeneous in terms of desired walking speeds and direction appear to form structures consisting of overlapping layers. This basic pattern forms the basis of other more complex patterns emerging in multi-directional pedestrian flow: in a bi-directional pedestrian flow, dynamic lanes are formed which can be described by the layer structure. Diagonal patterns can be identified in crossing pedestrian flows. This paper both describes these structures and the conditions under which they emerge, as well as the implications for theory and modeling of pedestrian flows.

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In order to get a better understanding of how driving with Advanced Driver Assistance (ADA) systems effects traffic flow in terms of safety, throughput and environment in practice a Field Operational Test (FOT), called "The Assisted Driver" was conducted by the Dutch Road Authority Rijkswaterstaat in The Netherlands. The main component of this project was the so-called Full Traffic FOT in which 20 cars, equipped with Adaptive Cruise Control (ACC) and Lane Departure Warning, were driven in mixed traffic for five months. During this period a vast amount of data was collected by installed data-loggers in order to perform an objective impact assessment. The results are quite promising. Driving with ACC and LDW improves traffic safety with approximately 8% and fuel consumption decreases with 3%. Associated emissions can decrease up to 10% and there seems to be no direct negative effect for throughput.