Ehsan Jafari

Increasing Evacuation Communication Through ICTs: An Agent-based Model Demonstrating Evacuation Practices and the Resulting Traffic Congestion in the Rush to the Road

Abstract Understanding evacuation practices and outcomes helps crisis and disaster personnel plan, manage, and rebuild during disasters. Yet the recent expansion in the number of information and communication technologies (ICTs) available to individuals and organizations has changed the speed and reach of evacuation-related messages. This study explores ICTs’ influences on evacuation decision-making and traffic congestion. Drawing from both social science and transportation science, we develop a model representative of individual decision making outcomes based on the amount of ICT use, evacuation sources, and the degree of evacuation urgency. We compare the evacuation responses when individuals receive both advance notice of evacuation (ANE) and urgent evacuation (UE) messages under conditions of no ICTs and prolific ICT use. Our findings from the scenarios when there is widespread ICT use reveal a shift in the evacuation time-scale, resulting in traffic congestion early in the evacuation cycle. The effects of this congestion in urgent situations are significantly worse than traffic congestion in the advance notice condition. Even under conditions where face-to-face communication is the only option, evacuations still occur, but at a slower rate, and there are virtually no traffic congestion issues. Our discussion elaborates on the theoretical contributions and focuses on how ICTs have changed evacuation behavior. Future research is needed to explore how to compensate for the rush to the road.

Investigation of Centroid Connector Placement for Advanced Traffic Assignment Models with Added Network Detail

Advanced traffic assignment models, such as simulation-based dynamic traffic assignment, typically incorporate more detailed network representations than do traditional planning models. In this context, the placement of centroid connectors may have a significant effect on model performance, and attention must be paid to their number and location to avoid unrealistic congestion or low utilization of minor roadways by local traffic. Given that the manual inspection of centroid connector placement may be too time-consuming in large regional networks, this paper proposes two simple automatic centroid connector placement strategies for dynamic traffic assignment applications. The first approach radially distributes the connectors to the nearest nodes and is intended to exemplify some limitations of the most common techniques in practice. The second strategy involves dividing the centroid and subsequent demand into two parts, distributing the demand across one sub-centroid linked to nearby nodes and one linked to the periphery, and thus effectively establishing a bilevel distribution. A modification of this strategy involves eliminating nodes at signalized intersections as viable candidates for connection. As part of the evaluation of the methods, a new metric, the locality factor, has been introduced to describe the use of minor streets by local traffic. The numerical experiments, conducted on two real-world networks, exemplify the effects of the incorporation of local streets and the placement of centroid connectors on model results. Sensitivity testing and limited field data comparisons suggest that the bilevel centroid connector placement strategy achieves more realistic results.

Online Charging and Routing of Electric Vehicles in Stochastic Time-Varying Networks

This paper formulates the problem of online charging and routing of a single electric vehicle in a network with stochastic and time-varying travel times. Public charging stations, with nonidentical electricity prices and charging rates, exist through the network. Upon arrival at each node, the traveler learns the travel time on all downstream arcs and the waiting time at the charging station, if one is available. The traveler aims to minimize the expected generalized cost—formulated as a weighted sum of travel time and charging cost—by considering the current state of the vehicle and availability of information in the future. The paper also discusses an offline algorithm by which all routing and charging decisions are made a priori. The numerical results demonstrate that cost savings of the online policy, compared with that for the offline algorithm, is more significant in larger networks and that the number of charging stations and vehicle efficiency rate have a significant impact on those savings.