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Drivers’ Response to In-Vehicle Route Guidance Information Systems: An Experiemnt with a Mock-Up Guidance System.

The paper reports an exploratory study, using an unusual technique to investigate drivers' response to in-vehicle route guidance information systems. Eighteen drivers were recruited, and asked to make a series of three trips in an unfamiliar area. Each driver was given turning advice, via a speech synthesiser, on one of these trips. This advice was based on average traffic conditions for the time of day. Unbeknown to the drivers, the advice was in fact triggered by the experimenter, who was riding as a back-seat passenger. Details were kept of times and routes taken with and without guidance, and with different levels of network familiarity. Records were also kept (using questionnaires and video and audio recording) of planning and route-following strategies. As expected, both receipt of guidance and even very rudimentary network familiarity resulted in reduced journey times, and routes closer to the guidance recommendations. The study indicated that factors including the directness of possible routes, their perceived complexity, and familiarity all affect route choice, but to different extents for different individuals and under different circumstances. Error was shown to be important in determining the route actually followed when guidance was withheld. The study showed that giving in-vehicle guidance using the mock-up technique described is practicable, and does influence drivers' route-choice and route-following behaviour. A possible future study is outlined, aimed at identifying the determinants of the drivers' level of compliance with advice when they believe that advice is based on real-time traffic information

Monotonicity and error bounds for networks of Erlang loss queues

Networks of Erlang loss queues naturally arise when modelling finite communication systems without delays, among which, most notably\ud (i) classical circuit switch telephone networks (loss networks) and\ud (ii) present-day wireless mobile networks.\ud \ud Performance measures of interest such as loss probabilities or throughputs can be obtained from the steady state distribution. However, while this steady state distribution has a closed product form expression in the first case (loss networks), it has not in the second case due to blocked (and lost) handovers. Product form approximations are therefore suggested. These approximations are obtained by a combined modification of both the state space (by a hyper cubic expansion) and the transition rates (by extra redial rates). It will be shown that these product form approximations lead to\ud \ud - secure upper bounds for loss probabilities and\ud - analytic error bounds for the accuracy of the approximation for various performance measures.\ud \ud The proofs of these results rely upon both monotonicity results and an analytic error bound method as based on Markov reward theory. This combination and its technicalities are of interest by themselves. The technical conditions are worked out and verified for two specific applications:\ud \ud - pure loss networks as under (i)\ud - GSM-networks with fixed channel allocation as under (ii).\ud \ud The results are of practical interest for computational simplifications and, particularly, to guarantee blocking probabilities not to exceed a given threshold such as for network dimensioning.\u

Fast temporal adaptation of on-off units in the first optic chiasm of the blowfly

We recorded from spiking units in the first optic chiasm between lamina and medulla in the brain of the blowfly (Calliphora vicina). Both previously characterized neuron types, on-off units and sustaining units, were encountered. On-off units had a temporal frequency response with a lower cut-off frequency than blowfly photoreceptors. This low cut-off frequency is related to a fast temporal adaptation of the on-off units to trains of short light pulses. Temporal adaptation occurred independently for short on- and off-pulses. On-off units only responded to stimuli of relatively large contrast. Contrasts of less than 10% gave little or no response.