Network Knowledge and Route Choice

  • Ramming M
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Abstract

Models of urban traveler route choice are reviewed in the context of Intelligent Transportation Systems, particularly Advanced Traveler Information Systems. Existing models suffer from assumptions of perfect information about travel conditions and infinite information processing capabilities of drivers. We present evidence that a majority of travelers fail to minimize travel time or distance. We also show that travelers with more network knowledge appear to vary their commute route to respond to changing travel conditions. Coefficient estimates of a model of network knowledge, based on the geographical idea of spatial ability, are presented. To better understand habitual route choice behavior, we examine many possible route generation algorithms. A simulation approach is preferred because it allows for heterogeneity in driver perceptions and it has a quick computational time. Alternative route choice model specifications such as Multinomial Logit, CLogit, Path Size Logit, Cross-Nested Logit and Logit Kernel Probit are evaluated. The exponential specification of the Path Size term, using a large parameter value, offers a considerable improvement in fit over MNL, C-Logit and CNL. A hybrid Path Size Logit and Logit Kernel Probit model offers the best overall fit; however, the stability of these estimates requires further examination. The hybrid Path Size Logit and CNL model provides the next best empirical fit. Random coefficient specifications of MNL, PSL and LK Probit models were also examined. Significant random coefficient parameter estimates were only obtained for the MNL model. This result suggests that random coefficients capture variation in route choice models that would be more effectively explained by a Path Size or LK Probit specification. Model fit can be further improved by adding an Implicit Availability/Perception term that includes estimated network knowledge. However, this term provides limited explanatory power, as can be seen by its standard errors and by forecasts that are relatively insensitive to changes in traveler knowledge. These results suggest that continued development of better attitudinal surveys to assess network knowledge and wayfinding strategies would allow estimation of route choice models with better explanatory power.

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Authors

  • Michael Scott Ramming

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