Satish V. Ukkusuri

Understanding urban human activity and mobility patterns using large-scale location-based data from online social media

Location-based check-in services enable individuals to share their activity-related choices providing a new source of human activity data for researchers. In this paper urban human mobility and activity patterns are analyzed using location-based data collected from social media applications (e.g. Foursquare and Twitter). We first characterize aggregate activity patterns by finding the distributions of different activity categories over a city geography and thus determine the purpose-specific activity distribution maps. We then characterize individual activity patterns by finding the timing distribution of visiting different places depending on activity category. We also explore the frequency of visiting a place with respect to the rank of the place in individuals visitation records and show interesting match with the results from other studies based on mobile phone data.

Location Routing Approach for the Humanitarian Prepositioning Problem

Events such as Hurricanes Katrina and Rita in 2005 have highlighted the need to understand network disruptions, in particular in the humanitarian supply chain. Humanitarian supply logistics cannot be improvised at the time of the emergency since little can be done after a disruption occurs. Therefore, special attention should be given to preparedness efforts and the design of the supply chain network. Inventory prepositioning is one of the strategies in the preparedness stage that enhances postdisaster response because it reduces the lead time needed to reach the affected destinations once a disaster has occurred. In this paper, the prepositioning of supplies for disasters is modeled as a facility location problem that accounts for the routing of vehicles and possible disruptions in the transportation network. The approach uses a combination of the most reliable path and an integer programming model to find the optimal location of supplies. The problem is solved on an example transportation network. The problem exploits a variant of the shortest-path problem and can be solved efficiently even for medium and large-scale transportation networks.

Behavioral Model to Understand Household-Level Hurricane Evacuation Decision Making

Hurricanes are one of the most costly natural disasters in the United States and have increased in frequency in the last few years. The critical role of evacuation, particularly for the vulnerable communities, has been realized from some disastrous evacuation experiences in recent hurricanes (for example, Hurricanes Katrina and Rita in 2005). Therefore, a thorough understanding of the determinants of evacuation behavior is needed to protect the loss of lives, especially in the vulnerable communities. However, a household’s decision-making process under a hurricane risk is a very complex process influenced by many factors. This paper presents a model of household hurricane evacuation behavior accounting for households’ heterogeneous behavior in decision making by using original data from Hurricane Ivan. It develops a mixed logit (also known as random-parameters logit) model of hurricane evacuation decision, where random parameters reflect the heterogeneous responses of households caused by a hurricane. We report several factors consistent with some of the previous findings, which are important for understanding household-level evacuation decision making. We also explain the varied influences of some of the determining variables on the hurricane evacuation decision.

Emergency Logistics Issues Affecting the Response to Katrina: A Synthesis and Preliminary Suggestions for Improvement

Extreme events pose serious logistical challenges to emergency and aid organizations active in preparation, response, and recovery operations, because the disturbances they produce have the potential to turn normal conditions suddenly into chaos. Under these conditions, delivering critical supplies (e.g., food, water, medical supplies) becomes an extremely difficult task because of the severe damage to the physical and virtual infrastructure and the limited or nonexistent transportation capacity. In this context, the recovery process is made more difficult by the prevailing lack of knowledge about the nature and challenges of emergency supply chains. As a result, the design of reliable emergency logistics systems is hampered by lack of knowledge about how formal and informal (emergent) supply chains operate and interact; methods to analyze and coordinate the flows of priority and nonpriority goods; and, in general, scientific methods to analyze logistics systems under extreme conditions. This paper describes the key logistical issues that plagued the response to Hurricane Katrina. The logistical failures following Katrina, which in August 2005 devastated the U.S. Gulf Coast, provide an example of the need to improve the efficiency of supply chains to the site of an extreme event. The paper is based on public accounts of the event and interviews conducted during a number of field visits to the Katrina-affected area in the aftermath of the event, as part of a research project funded by the National Science Foundation.

Overall impacts of off-hour delivery programs in New York city metropolitan area

This paper examines the chief findings of research conducted on policies to foster off-hour deliveries (OHDs) in the New York City metropolitan area. The goal was to estimate the overall impacts of eventual full implementation of an OHD program. As part of the research, a system of incentives was designed for the receivers of deliveries the system combined Global Positioning System (GPS) remote sensing monitoring with GPS-enabled smart phones to induce a shift of deliveries to the off-hours from 7:00 p.m. to 6:00 a.m. The concept was pilot tested in Manhattan by 33 companies that switched delivery operations to the off-hours for a period of 1 month. At the in-depth interviews conducted after the test, the participants reported being very satisfied with the experience. As an alternative to road pricing schemes that target freight carriers, this was the first real-life trial of the use of financial incentives to delivery receivers. The analyses indicate that the economic benefits of a full implementation of the OHD program are in the range of

Exploring the determinants of pedestrian-vehicle crash severity in New York City

147 to

Random parameter model used to explain effects of built-environment characteristics on pedestrian crash frequency

193 million per year, corresponding to savings on travel time and environmental pollution for regular-hour traffic as well as productivity increases for the freight industry. The pilot test also highlighted the great potential of unassisted OHD—that is, OHD made without personnel from the receiving establishment present—because almost all participants who used this modality decided to continue receiving OHD even after the financial incentive ended.

A robust transportation signal control problem accounting for traffic dynamics

Pedestrian-vehicle crashes remain a major concern in New York City due to high percentage of fatalities. This study develops random parameter logit models for explaining pedestrian injury severity levels of New York City accounting for unobserved heterogeneity in the population and across the boroughs. A log-likelihood ratio test for joint model suitability suggests that separate models for each of the boroughs should be estimated. Among many variables, road characteristics (e.g., number of lanes, grade, light condition, road surface, etc.), traffic attributes (e.g., presence of signal control, type of vehicle, etc.), and land use (e.g., parking facilities, commercial and industrial land use, etc.) are found to be statistically significant in the estimated model. The study also suggests that the set of counter measures should be different for different boroughs in the New York City and the priority ranks of countermeasures should be different as well.

A novel transit rider satisfaction metric: Rider sentiments measured from online social media data

Pedestrian safety has been a major concern for megacities such as New York City. Although pedestrian fatalities show a downward trend, these fatalities constitute a high percentage of overall traffic fatalities in the city. Data from New York City were used to study the factors that influence the frequency of pedestrian crashes. Specifically, a random parameter, negative binomial model was developed for predicting pedestrian crash frequencies at the census tract level. This approach allows the incorporation of unobserved heterogeneity across the spatial zones in the modeling process. The influences of a comprehensive set of variables describing the sociodemographic and built-environment characteristics on pedestrian crashes are reported. Several parameters in the model were found to be random, which indicates their heterogeneous influence on the numbers of pedestrian crashes. Overall, these findings can help frame better policies to improve pedestrian safety.

Integration of Environmental Objectives in a System Optimal Dynamic Traffic Assignment Model

Transportation system analysis must rely on predictions of the future that, by their very nature, contain substantial uncertainty. Future demand, demographics, and network capacities are only a few of the parameters that must be accounted for in both the planning and every day operations of transportation networks. While many repercussions of uncertainty exist, a primary concern in traffic operations is to develop efficient traffic signal designs that satisfy certain measures of short term future system performance while accounting for the different possible realizations of traffic state. As a result,uncertainty has to be incorporated in the design of traffic signal systems. Current dynamic traffic equilibrium models accounting for signal design, however, are not suitable for quantifying network performance over the range of possible scenarios and in analyzing the robust performance of the system. The purpose of this paper is to propose a new approach-robust system optimal signal control model; a supply-side within day operational transportation model where future transportation demand is assumed to be uncertain. A robust dynamic system optimal model with an embedded cell transmission model is formulated. Numerical analysis are performed on a test network to illustrate the benefits of accounting for uncertainty and robustness.