Driver information systems — A North American perspective

Driver information systems (DIS) are considered in North America as a major category of Intelligent Vehicle-Highway Systems (IVHS), which offers to improve the efficiency and safety of driving by means of an amalgamation of information technology with vehicle and highway technologies. Traditionally North America has basic strengths in a number of information technologies that are relevant to DIS. Due to a hiatus in federal funding in the early 1980s, DIS development in North America during the past decade has emphasized autonomous systems on the vehicles until recently. The current acceleration of IVHS development in the United States has resulted from a number of converging forces and has provided the impetus for developing advanced driver information systems (ADIS) that integrates the vehicle and the highway. Large-scale demonstration and implementation of ADIS will hinge on the major legislations in the near future, and on the successful development of public-private sector partnerships in IVHS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43528/1/11116_2005_Article_BF02127038.pd

Impact of Driver Information Systems on Traffic Safety

Zur Verbesserung der Verkehrssicherheit werden seit einigen Jahren Systeme entwickelt und untersucht, die den Fahrer über die aktuell zulässige Höchstgeschwindigkeit informieren und bei ihrer Überschreitung warnen. Diese Systeme sind unter dem Namen Intelligent Speed Adaptation (ISA) bekannt. Zur Wirkung dieser Systeme liegen Veröffentlichungen aus dem Europäischen und Außereuropäischen Ausland vor, eine entsprechende Analyse mit deutschen Fahrern auf deutschen Straßen lag bislang jedoch nicht vor. Die vorliegende Arbeit holt dies nach, in dem zunächst die „Speed Limit Warning“ (SLW) entwickelt und die Implementierung in ein Versuchsfahrzeug integriert wurde. Zur Vermeidung von Unfällen, die an Unfallhäufungsstellen geschehen, wurde zweites, „Hot Spot Warning“ genanntes System entwickelt, das den Fahrer fahrsituationsabhängig an entsprechenden Stellen im Straßennetz warnt. Die Analyse der Wirkungen erfolgte durch Testfahrten in einem Mit-Ohne-Vergleich mit 64 Testpersonen, die eine festgelegte Teststrecke auf öffentlichen Straßen im normalen Straßenverkehr befuhren. Die untersuchten Systeme führten bezogen auf die gesamt Testfahrt zu einer signifikanten mittleren Reduktion der Geschwindigkeit von 3,07 km/h, sowie zu einer signifkant geringeren Anzahl und kürzeren Dauer bußgeldrelevanter Geschwindigkeitsüberschreitungen. Signifikante negative Einflüsse der SLW auf die kognitive Arbeitsbelastung der Probanden oder die visuelle Ablenkung konnten nicht festgestellt werden. Bezogen auf die einzelnen Streckenabschnitte wurden mittlere Geschwindigkeitsreduktionen zwischen 1,93 km/h und 5,14 km/h ermittelt. Bezogen auf das einzelne Fahrzeug können so zwischen 6% und 16% aller polizeilich registrierten Unfälle vermieden werden, bezogen auf tödliche Unfälle sogar zwischen 12% und 29% Unfälle. Reduziert sich die mittlere Geschwindigkeit des Fahrzeugkollektivs um die ermittelten Werte, können zwischen 9% und 29% der Unfälle mit Personenschaden vermieden werden.Driver support systems are currently developed and analysed to inform drivers in their vehicles about the speed limit and to warn them in case they are exceeding the speed limits. These systems are known as "Intelligent Speed Adaptation - ISA". Studies investigating the impact of those systems have been conducted in most motorised countries in Europa, Australia and America, but such studies have never been undertaken in one of the countries with the strongest vehicle industry in Europe - Germany. Therefore, up to now, it was unclear how German drivers would react to ISA like systems, since they are used to unrestricted speed limits on Autobahnen and a rather high speed limit on rural roads. The study at hand describes the development and implementation of an ISA like system, its integration into a test vehicle and the experiments on German public roads and their results. A second application, the "Hot Spot Warning", provides warnings to drivers at accident prone locations. To investigate the impacts of both systems on driver behavior and traffic safety, a between subjects design with 64 subjects has been selected to ensure statistically sound results. Overall, the applications led to a significant reduction of mean speed between experimental and control group of about 3,07 km/h, as well as to a significant reduction of quantity and duration of severe speed limit violations. Compared to other studies, usefulness and satisfaction, as recorded by the van der Laan Scale, was very high, too. No significant impacts on workload and visual distraction of the drivers have been found. With respect to road characteristics, differences in mean speed between 1,93 km/h and 5,14 km/h have been recorded. These reductions can be translated into potential changes of accidents. Considering a single vehicle, between 9% and 16% of all police recorded accidents can be avoided. Taking only accidents with fatalities into account even 9% to 29% of those accidents can be prevented

Visual and task demands of driver information systems

Society of Automotive Engineers, Warrendale, Pa.http://deepblue.lib.umich.edu/bitstream/2027.42/1269/2/92250.0001.001.pd

Mobile geographic range prediction for electric vehicles

Electric Vehicle (EV) requires new driver information systems because drivers need more information. The spread of mobile devices and communications cost reductions gives new business opportunities. Our work proposal is taking into account the range anxiety problem of drivers of EV and using GIS systems, we represent in a map the charging state range based on a predicting driving distance based on driving style, temperature, and charge level. All this information transferred and represented in a mobile device with a information integration of EV and public transportation.Fundação para a Ciência e a Tecnologia (FCT

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Congestion on risky routes with risk adverse drivers

We study the impact of information on risk adverse drivers who maximize their von Neumann and Morgerstern expected utility (rather than minimizing expected travel time). The preferences of the users are described by their utility functions. Beside the (potentially inconsistent) mean variance model used so far in transportation, we consider three other standard utility functions: the mean standard deviation model, and the CARA and CRRA utility functions. We show that maximization of expected utility provides a more general formulation than minimization of expected travel time (the latter case corresponds to the standard Wardrop principle). We illustrate the proposed approach with a simple network which consists in one origin/one destination and two routes in parallel. Total demand is inelastic. Capacity on one route is constant and on the other route it is stochastic, and depends on the states of nature, with two possible values. We assume that all users have the same value of time but that they differ in their risk aversion parameter. Equilibrium travel time then depends on the distribution of risk aversion. We consider two polar information regimes: no information and full information. We study the differential impacts of information according to the level of risk aversion, and compute the social value of information. We introduce a formula to compute the value of information that is the individual willingness to pay for information (or in economic terms, the compensating variation). Moreover, we find that optimal route choice may depend on global factors (and not only on local traffic conditions). This has serious implications on the design of driver information systems. Finally, we study road pricing when users are risk neutral (and minimize the expected travel time) and when users are risk adverse. We compare the level of tolls, as well as the benefits of road pricing with and without taking into account risk aversion.

User equilibrium traffic network assignment with stochastic travel times and late arrival penalty

The classical Wardrop user equilibrium (UE) assignment model assumes traveller choices are based on fixed, known travel times, yet these times are known to be rather variable between trips, both within and between days; typically, then, only mean travel times are represented. Classical stochastic user equilibrium (SUE) methods allow the mean travel times to be differentially perceived across the population, yet in a conventional application neither the UE or SUE approach recognises the travel times to be inherently variable. That is to say, there is no recognition that drivers risk arriving late at their destinations, and that this risk may vary across different paths of the network and according to the arrival time flexibility of the traveller. Recent work on incorporating risky elements into the choice process is seen either to neglect the link to the arrival constraints of the traveller, or to apply only to restricted problems with parallel alternatives and inflexible travel time distributions. In the paper, an alternative approach is described based on the ‘schedule delay’ paradigm, penalising late arrival under fixed departure times. The approach allows flexible travel time densities, which can be fitted to actual surveillance data, to be incorporated. A generalised formulation of UE is proposed, termed a Late Arrival Penalised UE (LAPUE). Conditions for the existence and uniqueness of LAPUE solutions are considered, as well as methods for their computation. Two specific travel time models are then considered, one based on multivariate Normal arc travel times, and an extended model to represent arc incidents, based on mixture distributions of multivariate Normals. Several illustrative examples are used to examine the sensitivity of LAPUE solutions to various input parameters, and in particular its comparison with UE predictions. Finally, paths for further research are discussed, including the extension of the model to include elements such as distributed arrival time constraints and penalties