Drivers' spatial knowledge and en route response to unexpected delay information are examined. Compared are behavioral responses to information across two metropolitan areas in the United States: Chicago and the San Francisco Bay Area. Comparable behavioral data were collected through handout-mailback questionnaires targeting peak-period automobile commuters. The Chicago respondents perceived higher traffic congestion levels, and more of them knew one or more alternate routes. Among the Chicago respondents, fewer received information about unexpected delays from the radio (as opposed to self-observation of congestion) and more automobile commuters diverted to their alternate routes. To understand the factors that influence knowledge of alternate routes and en route diversion in response to unexpected congestion, a full information maximum likelihood nested logit model is estimated. Results of the model indicate that longer duration of residence, higher propensity for discovering new routes, and locational characteristics tend to increase drivers' spatial knowledge. Propensity for en route diversion increases with higher than usual route travel time plus delay and shorter alternate route travel times. Drivers with higher propensity for taking risks to avoid unexpected delays are more likely to divert. The model indicates that route knowledge and en route diversion propensity is higher in Chicago than San Francisco. This implies that information-sensitive behavioral models may be context dependent. Importantly, delay information received through radio traffic reports, as opposed to other sources such as self-observation of congestion, increases en route diversion propensity in unexpected delay situations. This implies a more dynamic readjustment of commuters' route selection decisions. At the same time, the potential benefits from Advanced Traveler Information Systems (ATIS) must compete with the benefits already accruing from radio traffic information.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering