Abstract

Many possible emergency conditions, including evacuations, negatively affect the urban transportation system by substantially increasing the travel demand and/or reducing the supplied capacity. A transportation model can be used to quantify and understand the impact of the underlying disasters and corresponding management strategies. To this end, we develop an efficient methodology suitable for simulating multimodal transportation systems affected by emergencies, based on the novel integration of an activity-based choice model with both pre-trip and en-route choices, and a macroscopic or mesoscopic dynamic network loading model. The model structure first estimates the daily equilibrium and then uses that result as a starting point to simulate the emergency situation without further iterations. Unlike previous efforts, our methodology satisfies all requirements identified from literature regarding transportation modeling for emergencies, and is sufficiently general to investigate a wide range of emergency situations and management strategies. An evacuation case study for Delft shows the feasibility of applying the methodology. Furthermore, it yields practical insights for urban evacuation planning that stem from complex system dynamics, such as important interactions among travel directions and among modes. This supports the need for a comprehensive modeling methodology such as the one we present in this paper.

Abstract

Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent that process. This research aimed to identify the main factors influencing drivers' choice to resume manual control. A mixed logit model that predicted the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The data set was collected in an on-road experiment in which 23 participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich, Germany, during peak hours. The results reveal that drivers were more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers were more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system had been activated and when the vehicle decelerated. Everything else being equal, some drivers had higher probabilities to resume manual control. This study concludes that a novel conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics, and environmental factors is needed to model driver behavior in control transitions between ACC and manual driving.