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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
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Publication Number: FHWA-RD-96-143
Date: April 1997
Development of Human Factors Guidelines for Advanced Traveler Information Systems and Commercial Vehicle Operations: Definition and Prioritization of Research Studies
The goal was to investigate human factors issues specific to user acceptance of Advanced Traveler Information Systems (ATIS) and Commercial Vehicle Operations (CVO) systems. This was accomplished both analytically and empirically.
Chapter 1 defines the problem area by reviewing available prior research. Automated Teller Machines (ATM's) are described as a case history in acceptance of new technology. Although ATM's are ubiquitous and have been available to consumers for almost two decades, less than half the population uses ATM's. Even those who do use ATM's seldom take advantage of all the features the technology provides. Finally, older consumers (50+ age group) tend not to use ATM's at all. Consumers do not accept new technology merely because it exists.
Simulation studies of ATIS devices indicate high acceptance (70 to 95 percent) by drivers. However, part of this acceptance may be due to demand characteristics in the laboratory and to problems in matching real–world motivation. For example, cash rewards during simulated trips may not have the same motivational effects as being stuck in traffic for long periods of time. Studies that surveyed the behavior of urban commuters indicated much lower rates of diversion (50 percent or less) from usual routes.
A useful starting point for explaining consumer acceptance of new technology is the model created by Mackie and Wylie (1988) to address the procurement of large, expensive military systems. This model was modified to create a consumer–oriented Technology Acceptance Model (TAM) better suited for Intelligent Transportation Systems (ITS) technology. This model is quite complex with many variables and parameters.
Chapter 2 presents two experiments based upon questionnaire methodology. In experiment 1, 109 drivers were shown two videotapes about TravTek. In experiment 1B these same drivers plus 20 commercial vehicle operators were given a demonstration of CityGuide route selection software. Experiment 1 used a questionnaire with 155 subjective rating dependent variables and experiment 1B used 93 variables. Results from these experiments were analyzed in three phases. First, descriptive statistics were calculated directly. This analysis for experiment 1 permits easy comparisons with TravTek data collected in Orlando because 21 items in the questionnaire were taken directly from the TravTek survey. Second, a repeated–measures analysis of variance (ANOVA) was used to examine relationships between subject variables and knowledge of system capabilities as tested in the questionnaire. Third, a model based upon constructs of fidelity, attention, system trust, self–confidence, capabilities understanding, and driver characteristics was used to explain feature–pattern desirability as calculated from a Principal Factor Analysis (PFA) with a Varimax rotation. This model was very successful in reducing the large set of subjective rating variables to a very small number of underlying factors in both experiment 1 (six factors) and 1B (four factors). These experiments revealed patterns of features that drivers find desirable and related them to constructs that account for acceptance of ATIS technology. Thus, the present model–based approach should be continued because it provides important information that is hidden when only descriptive statistics are presented.
Chapter 3 presents an experiment using the Battelle Route Guidance Simulator (RGS). This simulator uses real–time video presentation of actual on–the–road driving scenes, thus avoiding some of the key limitations of earlier simulator research that has been based upon artificial hypothetical road networks or static presentation of traffic scenes. The driver sees two computer displays. The first shows actual traffic scenes in Seattle in real–time. The second is a touch screen with a map of Seattle; the driver uses this to select his or her route and to purchase traffic information. Before starting the simulation, the 48 drivers indicated their preferred route from downtown Seattle to a landmark shopping mall in Bellevue. This destination forces drivers to cross Lake Washington. Since there are only two bridges across the lake, the experimenters have considerable control over traffic conditions encountered by the drivers regardless of the route selected. Results showed that drivers accepted ATIS information almost always: only 7.8 percent of the simulated journeys were on the indicated preferred route.
An important independent variable in this experiment was information reliability, which could be either 100 percent or 77 percent accurate. Will inaccurate information cause drivers to ignore ATIS advice? Results showed that drivers continued to purchase traffic information, even when it was only 77 percent accurate. While trust in the system decreased after it gave erroneous information that caused delays, harmless inaccurate information did not decrease driver trust. Furthermore, subsequent accurate information caused trust to increase again.
Chapter 4 presents an experiment that addressed CVO function acceptance issues. In this study, paper and pencil questionnaires were administered to both local and long–haul drivers. The questionnaires were coupled with verbal explanations and examples of function applications. A direct magnitude estimation task, a psychophysical forced–choice analysis and a relatively new link–weighted network analysis (Schvaneveldt, 1990) were used to understand user acceptance issues of potential CVO functions.
Sixteen potential ATIS functions were rated for their value as job performance aids by two groups of commercial vehicle drivers (local drivers and long–haul drivers). A network analysis was used to identify preliminary groupings of the functions which differed in driver–assigned value. For both local drivers and long–haul drivers, ATIS functions that improved driver safety were judged most valued. Ancillary information services were judged to be of little or no value and perhaps even of negative value because of potential interference with the driver's primary task of vehicle operation. Other ATIS functions such as routing aids, fleet management, and dispatch control were rated differently by local and long–haul drivers. Local drivers attached some job–related value to these functions; whereas long–haul drivers appeared to be neutral, at best.
From these as yet unsupported findings, tentative recommendations can be made for strategies to introduce ATIS systems into commercial vehicles. Since both groups of drivers placed considerable value on ATIS functions that increase driver safety, functions such as hazard warning, road condition information, and automatic emergency aid requests should be included in any initial commercial vehicle ATIS systems. For long–haul drivers, the introductory suite of functions may also include vehicle and cargo monitoring as well as enhanced voice and message communications functions. The incentives for drivers to use this type of safety configuration is obvious. It is clearly intended to increase their personal safety. As long as the components of the initial system operate within acceptable limits of information accuracy and system reliability, commercial driver acceptance should be facilitated. Moreover, acceptance by fleet operators should also be facilitated on the premise that increasing driver safety will improve accident avoidance. There may be two benefits for fleet operators in this approach to ATIS introduction. First, reducing accidents directly reduces overall operating costs. Second, emphasizing driver safety may have the added effect of increasing driver loyalty and hence reducing turnover rates. Thus, the advantages, and the incentives, appear to be present for both drivers and management to adopt an ATIS system that is configured for driver safety.
As the driver–safety ATIS is deployed, there will be opportunities to highlight the value of additional functions. For example, including a vehicle locating function would allow an automatic aid request to include the vehicle's current location, thereby speeding the response of emergency vehicles. The fact that a vehicle locating function is also at the core of many vehicle and fleet management functions introduces a strong negative for some drivers; but appropriate early education to emphasize the safety value may overcome some of the resistance. The addition of a vehicle locating function may be best introduced for local drivers. In our study, local drivers rated routing and re–rerouting functions relatively highly as job performance aids. Without the vehicle locating function, the routing functions require considerable driver set–up; however, with vehicle locating, the routing functions can automatically provide better guidance to the driver. As they navigate around congestion and into unfamiliar areas, local drivers may find that a vehicle locating function has value beyond its connection to driver safety.
The incremental introduction of ATIS functions into a base system configured for driver safety appears to be a strategy that will initially meet some of the needs of both drivers and fleet operators. The base system does not include those functions that may be most valuable to fleet operators because those same functions are likely to be the most resisted by drivers. As the base system is deployed and accepted, there should be a foundation for introducing more ATIS functions. These added functions must be accompanied by appropriate education of both drivers and fleet operators and by proper management for change. The incremental introduction of ATIS/CVO systems suggested here is one piece of the total program for acceptance.
Chapter 5 presents conclusions about driver acceptance of ITS technology. A tentative model for driver acceptance of ATIS devices, based in part on the obtained experimental results, was formulated. However, additional research will be required to validate this model. Recommendations for the design of equipment, educational techniques, and incentives that could be used to promote ITS acceptance and use in CVO are provided.