Constraints on Initial AHS Deployment and the Concept Definition of a Shuttle Service for AHS Debut

Highway automation and its evolution involve a multitude of systems issues. Particularly important and difficult in defining a deployment sequence is the very first step, i.e. the first user service involving fully automated freeway driving. However, this importance and the difficulty imply that many factors may severely constrain the initial deployment. After discussing the paramount importance of initial AHS deployment, this paper points out major high-level issues and constraints. Any realistic deployment strategy must take into consideration gradual technology maturation, introduction of new driver role and diminishing conventional driver role for automated driving, high cost of early-generation automation-equipped vehicles, gradual infrastructure modification, gradual commitment of automakers to manufacture and service automation-equipped vehicles, gradual commitment of insurance industry to carry liability, and gradual acceptance by the interest groups and the general public. This paper then proposes a freeway shuttle van service for AHS debut. This user service could be a good candidate for the 1997 AHS demonstration required by ISTEA and has a good chance of leading to a successful long-term AHS deployment supported by the general public

Formal evaluation of the ADVANCE targeted deployment

The Advanced Driver and Vehicle Advisory Navigation Concept (ADVANCE) advanced traveler information system (ATIS) demonstration project in northeastern Illinois was re-scoped in late 1994 from its originally-planned deployment of 3,000--5,000 in-vehicle navigation units to a so-called ``targeted`` deployment in which up to 75 vehicles were equipped with devices enabling them to receive real-time traffic information. These devices included (1) global positioning system (GPS) transmitters/receivers that enabled the vehicles while in the ADVANCE study area to serve as dynamic traffic probes as well as recipients of location data; and (2) navigation units that employed a comprehensive map data base and average (static) link travel times by time of day, stored on CD-ROM, which together computed efficient (least duration) routes between any origin and destination in the northwest portion of the Chicago metropolitan area. Experiments were designed to dispatch these equipped vehicles along links at headways or frequencies comparable to what would have been observed had full deployment actually occurred. Thus, within the limitations of this controlled environment, valuative experiments were conducted to assess the quality of several of the key sub-systems of ADVANCE in the context of structured performance hypotheses. Focused on-road tests began on June 1 and continued through December 14, 1995, followed by a period of data evaluation, documentation of results, and development of conclusions about the findings and usefulness of the project. This paper describes the tests, discusses development of the overall evaluation plan and the evaluation management concept which guided them, and reports on issuses and results of data analysis known at time of writing

Activity patterns in space and time: calculating representative Hagerstrand trajectories

Activity trajectories, Alignment analysis, Optimal matching, Travel patterns,

Generalized Reverse Discrete Choice Models

Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM assumption, in its simplest form, posits that a consumer with a finite set of brands to choose from chooses the brand that gives her the maximum amount of utility. An alternative approach would be to assume that consumers choose the alternative that offers them the least disutility. Our paper proposes and tests a broad class of generalized extreme value models based on this hypothesis. We model the decision process of the consumer the assumption random disutility minimization (RDM) and derive a new class of discrete choice models based on this assumption. Our findings reveal that there are significant theoretical and econometric differences between the discrete choice models derived from a RUM framework and the RDM framework proposed in this paper. On the theoretical front we find that the class of discrete choice models based on the assumption of disutility minimization is structurally different from the models in the literature. Further, the models in this class are available in closed form and exhibit the same flexibility as the GEV models proposed by McFadden (1978). In fact, the number of parameters are identical to and have the same interpretation as those obtained via RUM based GEV models. In addition to the theoretical differences we also uncover significant empirical insights. With the computing effort and time for both models being roughly the same this new set of models offers marketing academics and researchers a viable new tool with which to investigate discrete choice behavior. Copyright Springer Science + Business Media, Inc. 2005discrete choice models, brand choice models, utility maximization, disutility minimization, logit, generalized extreme value, scanner data,