ABSTRACT-Intelligent Transportation Systems present a well-known innovation opportunity to address urban congestion and allow greater access to transportation networks. New sources of travel information are emerging rapidly and they are likely to significantly impact traveler decisions and transportation network performance. To assess the value and impact of these new sources, this paper develops a comprehensive conceptual model based on information processing and traveler response. Specifically, the model accounts for the effect of information source, content and quality on information access and travel behavior. The paper presents empirical evidence from several behavioral surveys conducted in the San Francisco Bay Area between 1995-1999. The surveys used innovative methods to study the response of the whole population, response of people more inclined to use information technology (early adopters), and traveler decision-making in high-benefit incident situations. The conceptual model helps us integrate and interpret empirical findings from the surveys. We discuss the issues of access to new and conventional technologies and services, their current market penetration levels, switching behavior regarding new information sources/information service providers, desired information content and willingness to pay for dynamic information. The opportunities and limitations of new technologies and the implications for future technology implementations are discussed. 3 INTRODUCTIONContinuing transportation problems such as congestion and pollution and recent developments in advanced traveler information systems (ATIS) raise interesting questions about the effect these innovations will have on travelers. In particular, we wish to know if the new technologies will help people plan for their travel and allow them to travel faster and at lower cost.There are two sets of developments that can offer partial answers to such questions. One is the development of models that characterize how people make their travel decisions and use dynamic travel information. The other is the growing body of empirical evidence regarding traveler decisions and the impacts of new and improved information systems acquired through federally sponsored field operational tests.One such test is TravInfo, which is a regional traveler information system in the San Francisco Bay Area. Its goal is to broadly disseminate accurate, comprehensive, timely, and reliable information about traffic and multi-modal travel options to the public. The TravInfo Field Test officially began operation in September 1996 and ended in September 1998, when it started a transitional phase to full deployment as an integral part of the Bay Area transportation infrastructure (1). To evaluate TravInfo effectiveness, significant resources were devoted to designing surveys and to collecting behavioral data. The surveys were based on a contemporary understanding of traveler behavior and the factors that might influence it, including dynamic information. In the several years over whic...

Accurate estimates of bicycle and pedestrian volume inform safety studies, trend monitoring, and infrastructure improvements. The Federal Highway Administration’s Traffic Monitoring Guide advises current practice for estimation of nonmotorized traffic. While methodologies have been developed to minimize error in estimation of annual average daily nonmotorized traffic (AADNT), challenges persist. This study provides new guidance for monitoring and volume estimation of nonmotorized traffic. Using continuous count data from 102 sites across six cities, the findings confirm that mean absolute percent error (MAPE) in estimated AADNT is minimized when seven-day short duration counts are collected in June through September and for 24-h counts, when data are collected Tuesdays through Thursdays (except for pedestrian-only counts). MAPE across all days (except holidays) and seasons was 34% for 24-h and 20–22% for seven-day short duration counts. The magnitude of bicycle and pedestrian volumes did not significantly affect estimation errors. For factor groups larger than one counter, the length of short duration samples may influence accuracy of AADNT estimates more than the number of counters per group, all else equal. To maximize precision of estimates of AADNT, four or more counters per factor group for bicycle and five or more for pedestrian travel monitoring are recommended. These findings provide guidance for practitioners seeking to establish or improve nonmotorized traffic monitoring programs.

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