Arif Mohaimin Sadri

How to evacuate: model for understanding the routing strategies during hurricane evacuation

This paper explains a modeling approach that offers better understanding of the routing strategies taken by evacuees to reach a safe destination during hurricane evacuation. Route choice during evacuation is a complex process because evacuees may prefer to take the usual or familiar route on the way to the destination, or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted, i.e., the routing behavior is random. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. Estimation findings indicate that the choices of evacuation routing strategy involve a complex interaction of variables related to household location, evacuation characteristics, and socioeconomic characteristics. The findings of this study are useful to determine the manner in which different factions of people select a type of route for a given sociodemographic profile during an evacuation. Language: en

Use of Social Media Data to Explore Crisis Informatics

Microblogs posted to Twitter after the tornado in Moore, Oklahoma, on May 20, 2013, were analyzed in this study. The potential of social media data was explored for the extraction of relevant and useful information during natural disasters and as an additional data source for better understanding of individual behavior during a crisis. Data records were attributed to user groups, and the most frequently used words were ranked to track the variation of common interests for each user group. In addition, the data were classified into different content categories, and the temporal variation patterns were analyzed. A sentiment analysis, which revealed variations in public mood and perception over time, was conducted to quantify the sentiment in the data. The techniques presented can be applied to the analysis of similar major social crises and natural disasters (e.g., hurricanes and earthquakes) to provide valuable complementary information in crisis awareness and response to users, first responders, and emergency preparedness agencies. Different stakeholders can determine the needs and activities of people during disasters by using the proposed method with the help of social media data.

The Role of Social Networks and Information Sources on Hurricane Evacuation Decision Making

AbstractHurricanes often threaten to have catastrophic impacts on the lives of residents in coastal areas of the U.S. Timely evacuation limits this impact, but people may choose to evacuate or not ...

Modeling Social Network Influence on Joint Trip Frequency for Regular Activity Travel Decisions

Interest in activity-based travel demand modeling has recently increased significantly because of the level of accuracy offered by this type of modeling and its applicability in travel behavior research. Understanding the linkage between social influence and travel behavior enables efficient characterization of discretionary activities that account for a major fraction of total urban trips. In particular, some recent studies looked at the social network structure of individuals and measured the influence that social network members have on the performance of social activities. However, it is expected that the process of taking joint trips (trips that individuals take with their network members) has an intrinsic social context for different activities in general (not only social activities). In this regard, this study used an egocentric (i.e., personal) network approach to explore empirically the influence of personal network characteristics on the frequency of weekly trips that individuals (egos) took part in with their personal network members (alters). With the help of zero-inflated Poisson models and egocentric social network data, the results of this study present a framework for predicting the number of joint trips for six types of activities: work, eating out, shopping, recreation, study, and extra curricular activities. Estimation findings suggest that personal network measures such as network density, homophily, heterogeneity, and ego–alter tie attributes have a significant impact on the joint trip-making process, that is, the number of weekly shared trips in which an individual participates. The findings of this study should help practitioners implement targeted policies such as car sharing for various user groups.

Hurricane Evacuation Route Choice of Major Bridges in Miami Beach, Florida

Evacuation is a typical recourse to prevent loss of life if a high storm surge occurs, especially in hurricane-prone regions. Bridges are the key locations of bottlenecks. Because of the specific geographic shape and roadway network of Miami Beach, Florida, residents need to evacuate over one of the six major bridges or causeways: MacArthur Causeway, Venetian Causeway, Julia Tuttle Causeway, John F. Kennedy Causeway, Broad Causeway, and Haulover Bridge. A mixed logit model is presented to identify the determining factors for evacuees from Miami Beach in selecting one of these bridges during a major hurricane. The model was developed by using data obtained from a survey that included a hypothetical Category 4 (major) hurricane scenario to reveal the most likely plans for evacuees from this area. The estimation findings suggest that the preference over a given bridge involves a complex interaction of variables, such as distance to reach the evacuation destination, evacuation-specific characteristics (evacuation day, time, mode, and destination), and evacuee-specific characteristics (gender, race, evacuation experience, and living experience). The normally distributed random parameters in the model account for the existence of unobserved heterogeneity across different observations. The findings of this study will help emergency officials and policy makers to develop efficient operational measures and better evacuation plans for a major hurricane by determining different fractions of people taking each of the six bridges.

Best practices for maximizing driver attention to work zone warning signs.

10 mph. • Changeable message signs (CMS) are ideal for short-term and mobile work zones due to their portability. For CMS displaying text messages, innovative use of fonts and content may achieve greater speed reduction. Graphic-aided CMS are found to be more legible and recognizable compared with text-message CMS. The location of CMS has significant impact on the effectiveness of this technology; placing in advance of a work zone is recommended. • The presence of police cars may reduce car speeds by 4.4 mph and truck speeds by 5 mph (in a 45 mph speed zone). High-speed drivers are found to be more affected by police enforcement. Despite the effectiveness of CMS, “halo” effects and high costs are known to be the two main drawbacks of the technology. • Implementations of variable speed limit (VSL) resulted in reduced speed variance, increased speed compliance, and improved traffic throughput and Joint transportation research program