Unexpected versus expected network disruption: Effects on travel behavior

Abstract

This paper discusses the observed evolution of traffic in the Minneapolis-St Paul (Twin Cities) region road network following the unexpected collapse of the I-35W Bridge over the Mississippi River. The observations presented within this paper reveal that traffic dynamics are potentially different when a prolonged and unexpected network disruption occurs rather than a preplanned closure. Following the disruption from the I-35W Bridge's unexpected collapse, we witnessed a unique trend: an avoidance phenomenon after the disruption. More specifically, drivers are observed to drastically avoid areas near the disruption site, but gradually return after a period of time following the collapse. This trend is not observed in preplanned closures studied to date. To model avoidance, it is proposed that the tragedy generated a perceived travel cost that discouraged commuters from using these sections. These perceived costs are estimated for the Twin Cities network and found to be best described as an exponential decay cost curve with respect to time. After reinstituting this calibrated cost curve into a mesoscopic simulator, the simulated traffic into the discouraged areas are found to be within acceptable limits of the observed traffic on a weekby-week basis. The proposed model is applicable to both practitioners and researchers in many traffic-related fields by providing an understanding of how traffic dynamics will evolve after a long-term, unexpected network disruption.

Travel Pattern Analysis on Switching Behavior in Response to the COVID-19 Pandemic

“…This section details our analysis of the switching behavior of respondents. Switching behavior refers to the tendency of respondents to change their transportation mode after a disruption (Di et al, 2017;Danczyk et al, 2017), such as the stay-at-home order in New York City in this study. Since respondents were allowed to record multiple modes of transportation, a respondent was labeled as a switcher if they changed one of their transportation modes from before the stay-athome order to after the stay-at-home order.…”

Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction

“…Recently, studies have examined the optimization of traffic signal timings as an active management tool for nonrecurring congestion (3)(4)(5)(6). Traffic assignment models, equipped with behavior models that characterize travelers' behavior changes after incidents, and predictioncorrection models are often built to simulate the timedependent diverted traffic flow under pre-defined incident scenarios (7)(8)(9)(10)(11)(12)(13). The signal timings, optimized for a given incident scenario, then favor specified directional movements to minimize the induced congestion (14).…”

The disruption transport model: computing user delays resulting from infrastructure failures for multi-modal passenger & freight traffic

“…However, evidence shows that some passengers tend to avoid the location of a failed infrastructure element completely even though there may still be capacity available (e.g. partially closed due to a slope failure) due to the perception of a safety risk [5]. Besides switching to another route, changing departure time is also often reported.…”

“…The behavioural responses mentioned in the previous sections are likely to result in a new equilibrium of traffic flows. However, evidence shows that this may take several weeks [5] due to habitual patterns and routines. Evidence has also shown that disruptions tend to lead to overreactions in passenger behaviour [4], leading to oscillating traffic among several routes (e.g.…”