This paper presents a functional description of PLANiTS (Planning and Analysis Integration for Intelligent Transportation Systems). The methodology consists of a process-based computer system that supports a series of mutually interdependent steps which progress toward developing and programming transportation improvement projects. PLANiTS can translate problems and goals to performance measures, examine possible competing and complementary actions that can address the problems, systematically evaluate the impacts of actions by using appropriate knowledge-based and model-based tools, and support human communication and interaction between planning group members.

The objective of this study is to understand how people deal with congestion and how they might respond to a multimodal Advanced Traveler Information System (ATIS). Travelers' route, departure time and mode selection decisions in response to incident and recurring congestion were investigated through a survey of Bay Area automobile commuters. This document summarizes the survey methodology, assesses representativeness of the sample and discusses the initial insights obtained from uni-variate and bi-variate analysis.

This study explores the development and availability of Advanced Public Transportation Systems (APTS) technologies. The study refines a taxonomy of transit technologies and uses it to explore the availability of new technologies and their impacts in transit agencies. THe taxonomy is based on defining the features, functions and performance characteristics of transit technologies. Based on the taxonomy, three surveys of technology suppliers were conducted. Questions were related to technology features, functions, performance, implementation, and impacts.0

This paper develops a data synthesis methodology in PLANiTS (Planning and Analysis Integration for Intelligent Transportation systems) using case- based reasoning. The reasoner contains mechanisms for matching, ranking and analyzing past cases in relation to current cases. The current cases consist of transportation improvement actions, performance measures and environments defined in terms of spatial, temporal and user dimensions. PLANiTS users can apply increasing levels of stringency to match cases. The authors also discuss issues related to computer implementation and the limitations of case-based reasoning.

The objective of this study is to evaluate the potential benefits of a multimodal Advanced Traveler Information System (ATIS). The propensity of auto commuters to switch to public transportation in response to unexpected traffic congestion is investigated using a detailed survey undertaken in the Golden Gate Bridge corridor in 1993. A stated preference analysis tied to the situation for which the commuting behavior was reported is used to estimate the propensity of travelers to divert to transit under a multimodal ATIS.

Urban sprawl makes conventional transit less competitive and points to the need for more innovative and flexible demand-responsive transit systems in the future. To increase their efficiency, such systems can take advantage of the emerging advanced public transportation systems technologies, e.g., vehicle location and information systems. However, little is known about how consumers might respond to such systems and what they desire. This paper explores the demand for a consumer-oriented Personalized Demand Responsive Transit (PDRT) service in the San Francisco Bay Area. Such a system could provide services to the traveling public for journeys to work and to non-work destinations. Results from six focus group meetings and a computer-assisted telephone survey of commuters and non-commuters are reported. While about 60% of those surveyed were willing to consider PDRT as an option, about 12% reported that they were "very likely" to use PDRT (N=1000). Many were willing to pay for the service and valued highly the flexibility in scheduling the service. Spatial analysis of the survey responses suggests localities where a PDRT may be field-tested.

TravInfo is a Field Operational Test (FOT) sponsored by the Federal Highway Administration (FHWA) and California Department of Transportation. It aimed to develop a multi-modal traveler information system for the San Francisco Bay Area, combining public and private sector talents. The Broad Area Study is part of the TravInfo FOT evaluation. This paper addresses issues on the willingness to pay for traveler information. Two waves of telephone surveys of Bay Area households were conducted, one prior to and one after the Field Operational Test. The initial survey was conducted in November 1995, eight months prior to the TravInfo FOT began and the final survey was conducted in November 1998, three months after the FOT was ended. The paper presents the findings of the second wave Broad Area survey regarding the willingness to pay for traveler information. The rationale is that information can help travelers make better decisions that ultimately reduce traffic congestion and pollution. However, the personal benefits of certain types of high quality travel information may motivate individuals to pay for information. This study analyzes the preferences of automobile and transit travelers' willingness to pay for a high quality advanced traveler information service. The most desirable information content options were constant updates, alternate route information, in-car computer information, expected delay and comparing route times. The results also indicate that a significant population of "information seeking" travelers is willing to pay for specific information content. As expected, there are individual differences in preferences for information content. Future commercialization efforts may focus on experimenting with various types of information content and conducting demonstration projects that charge for information. Keywords: Traveler behavior, Advanced Traveler Information Systems, survey research, modeling, California

This paper presents a simple prototype to demonstrate the key functions of PLANiTS (Planning and Analysis for Integration for Intelligent Transportation Systems). The prototype has a planning vector that allows users to enter and edit transportation actions, performance measures and environmental descriptors. This guide demonstrates how users specify each of these elements in terms of temporal, spatial and user dimensions and analyze the planning vector with models and case-based reasoning. During the process of planning vector specification and analysis, users at different locations can communicate by sending and receiving messages and sharing the planning vector.

Advanced Traveler Information Systems are becoming an integral part of urban transportation systems. This paper discusses issues related to how people access, acquire and use travel information. The perceived benefits and willingness to pay for dynamic information are discussed. Empirical evidence from a major field operational test is examined to answer questions about why, how and who uses travel information. Empirical evidence suggests that information helps travelers to switch routes and departure time. The potential for information benefits is perhaps higher in cases of unexpected incidents. However, only one-third of the Target survey respondents changed their travel decisions in response to the incident. While this is a significant number in terms of demand reduction due to information, perhaps the full benefits of dynamic information are not realized because the quality of information available in just such situations is relatively low. New information media can focus on variables that are sensitive to travel-time uncertainty in order to improve the quality of information in high-uncertainty situations.

This report presents a study whose objective is to explore how people deal with unexpected congestion during the pre-trip stage and how they might respond to Advanced Traveler Information Systems (ATIS). Travelers' route, departure time and mode selection decisions were investigated through a survey of San Francisco Bay Area automobile commuters. The effects of various factors are examined, such as sources of congestion information, trip characteristics, and route attributes. The pre-trip response to future ATIS technologies is explored through stated preferences.