When confronted with congestion and delay, drivers often divert their route. Depending on factors like purpose, urgency, destination, route alternatives, type of disruption, and mode options, common diversionary options can range from changing departure time, route, and mode to canceling a trip altogether. One of the foundational building blocks of travel forecasting is traffic assignment, which is used to distribute trips over a network using complex algorithms to aggregately represent the desire of drivers to minimize travel time.
While these assignment models are backed by decades of research and observational experience, they are also limited by the fact that they do not account for the infinite number of factors and conditions that influence the routing of individual drivers. As the ability of transportation agencies to detect incidents and inform drivers of conditions has improved, there is increasing interest in knowing if drivers will divert and why.
This thesis describes results of research using marketing-based survey techniques to evaluate driver diversionary behavior during roadway incidents. The survey was used to identify and assess diversionary choice-making based on a) travel behaviors and habits, b) under routine and adverse conditions, c) for different incidents and route options; and d) under varied guidance information available to them.
Among the broad findings of the study that were consistent with prior research was the general preference of drivers to seek alternate routes around congestion and the importance they place on travel time as their primary motivator. Interestingly, younger males showed the lowest level of influence from guidance information, and route familiarity had a lower influence on diversionary routing among all groups, suggesting a higher level of trust and reliance in real-time mobile routing guidance than was originally anticipated in the beginning of the study. Another interesting finding, not seen in prior research, was the high importance placed on route safety and the time of day, particularly for female drivers. While the study was not able to address why, fundamentally such relationships exist, these findings can be used to improve the predictive accuracy of trip routing and assignment forecasts, particularly under disrupted network conditions
