A method has been developed to estimate the relative accident risk posed by different patterns of driving over a multi-day period. The procedure explicitly considers whether a driver is on-duty of off-duty for each half hour of each day during the period of analysis.

From a data set of over 1000 drivers, 9 distinct driving patterns are identified. Membership in the patterns is determined exclusively by the pattern of duty hours for seven consecutive days; for some drivers and accident occurred on the eighth day while others had no accident. Therefore each pattern can be associated with a relative accident risk. Additional statistical modeling allowed consideration, in addition to driving pattern, of driver age, experience with the firm, hours off-duty prior to the last trip and hours driving on the last trip (either until the accident or successful completion of the trip).

The findings of the modeling are that driving patterns over the previous seven days significantly affect accident risk on the eighth day. In general, driving during afternoon and evening hours (e.g. noon to midnight) has the highest accident risk while driving during night and morning hours (e.g. midnight to noon) has lower risk. Consecutive hours driven also has a significant effect on accident risk: the first hour of driving and the ninth and tenth hour of driving have the highest risk. Hours 2 through 8 follow a flattened "u" shape. Driver age and hours off-duty immediately prior to a trip do not appear to affect accident risk significantly.

These findings quantitatively assess the relative accident risk of multi-day driving patterns using data from actual truck operations. Further research is recommended in the areas of refining model structures, adding explanatory variables (such as highway type) and testing more complex models.

This research is aimed at the identification of all types of accidents that have occurred on freeways which may affect the vehicle movement in the assumed automated left lane. The emphasis was on six-lane or more urban freeways. The accident data was obtained from the California TASAS ( Traffic Accidents Surveillance and Analysis System) data base and were used to identify all the accidents that originated outside the potential automated area and which affected movement of traffic in the leftmost lane.

This study focused on determining whether advanced transportation services and information systems could enhance the mobility of disabled and elderly travelers in their use of transit, paratransit, or advanced paratransit services. The advanced proposed systems included regular paratransit and two advanced paratransit systems (real-time paratransit and real-time ridesharing) and five transportation information systems: computer, television, operator-assisted telephone, automated telephone, and personal information systems.

Interstate motor carriers are subject to limitations on the hours that their drivers may be on-duty and driving. The regulations include a requirement that a driver be off-duty for a minimum of 8 hours after driving for 10 hours or being on-duty for 15 hours. There are also cumulative restrictions for on-duty time over several days: 70 hours on duty in 8 days for carriers operating 7 days a week and 60 hours in 7 days or those operating 5 days a week. These limitations, referred to as the hours of service regulations, were initiated in the 1930's. Since then the U.S. highway system has changed dramatically as has the nature of the trucking business and the technology of the vehicles. Despite these changes, there have been rather limited attempts to assess the safety implications of the hours of service for contemporary conditions.

This paper describes the experimental analysis techniques and modelling efforts applied to sequential pre-trip route choice behavior data under the influence of an Advanced Traveler Information System (ATIS). This effort is the first step in a process to develop a basic understanding of the factors which influence route choice and how ATIS will affect drivers' route choice behavior over time.

A model of drivers' route choice behavior under advanced traveler information systems (ATIS) is developed based on data collected from learning experiments using interactive computer simulation. A neural network model is used as a convenient modeling technique in the analysis. Results indicated that most subjects made route choices based mainly on their recent experiences. Results also demonstrated that route choice behavior is related to the personal characteristics as well as the characteristics of the respective routes. Travel experiences had less effect on route choice and the results indicate that the prediction accuracy of the model, the acceptance rate of advice, and the quality of advice are closely related.

This report documents the results of a user needs assessment conducted to define the concept of real-time ridesharing, and identify the problems that are to be addressed by the proposed Real-Time Rideshare Matching System. The user needs are then utilized to develop functional requirements for the real-time rideshare matching system. The functional requirements identify services that should be provided by the system, and the interfaces to be used to access the system.

This paper presents a statistical analysis of commuters' route choice behavior and the influence of traffic information. The analysis is based on a 1992 computer-aided telephone interview survey of Los Angeles area morning commuters. The results underscore the important relationship between the use of traffic information and the propensity to change routes. In addition, important relationships are uncovered relating the influence that commuters' socioeconomic characteristics and the level of traffic congestion they face have on traffic information use and route- change frequency. The results' important implications for advanced traveler information systems (ATIS) development are discussed.

This report reviews the recent studies adopted in order to understand drivers' behavior, and in particular, behavior when influenced by an Advanced Traveler Information System (ATIS). Different approaches were used in these studies: field experiments, route choice surveys, interactive computer simulation games, route choice simulation and/or modeling, and stated preference. These studies are classified according to the main approach used, and the main objective, method, and findings are presented. Results indicate the need to understand how drivers choose or change routes in the absence of information systems in order to gain an understanding of route choice behavior in the presence of information.

This study examined the potential of advanced information systems to increase the mobility of disabled travelers using public transportation. A stated preference survey inquired as to whether subjects would make more trips by transit, paratransit, and/or real-time paratransit if kiosk, on-board, in-home, and/or personal information systems were available. The results show that these systems do have the potential to increase the use of public transportation by disabled travelers, as they allow for more trip flexibility that what is currently available. Regression and log-linear models show that transit users and those without cars are more likely to use transit if advanced systems were available. Wheelchair users are less likely than non-wheelchair users to use transit with advanced systems. KEY WORDS transit, paratransit, real-time paratransit, disabled travelers, ITS, transit information systems, mobility