Ender Faruk Morgul

Modeling Evacuation Behavior Under Hurricane Conditions

The understanding of evacuation behavior is critical to establishing policies, procedures, and organizational structure for an effective response to emergencies. This study specifically investigated the evacuation behavioral responses under hurricane conditions. The study aimed to explore the association between contributing factors and the evacuation decision choices as well as evacuation destination choices. Unlike previous studies that modeled each response behavior separately, this study proposed to use the structural equation modeling approach to examine the interrelationship between response behaviors. A case study was performed with the data set from a survey conducted in New Jersey. With Bayesian estimation approaches, the proposed structural equation models were estimated, and the effect of each predictive variable was captured. An important finding is that individuals’ preference to evacuate did not significantly affect their choices of evacuation destinations. In addition, other socioeconomic and demographic characteristics that affected evacuation behavior were identified.

Assessing the impact of urban off-hour delivery program using city scale simulation models

This paper describes two different types of models to assess the traffic impacts of an off-hour delivery program for the New York City (NYC) borough of Manhattan. Traffic impacts are measured in New York City metropolitan region using both a regional travel demand model and a mesoscopic simulation model. Analysis is conducted to determine the effectiveness and impacts of the scenarios modeled; focusing on the changes predicted by the traffic models. The results from both models are compared and analyzed, and a discussion on the usage of these models is presented. While macroscopic models can be used to measure traffic effects in a large urban region, mesoscopic models similar to the one used in this paper have their advantages in terms of better quantifying traffic impacts of system-wide benefits. However, simulation time makes it impractical to use mesoscopic simulation for large urban regions. In this work, both the macroscopic regional travel demand model and a mesoscopic sub-simulation network show a measurable impact to congestion and network conditions. However, even when the results show an increasing benefit in terms of travel time savings and increasing speeds, cost–benefit analysis show that when compared with the costs (in this case implementation costs by providing incentives), only small receiver participation justifies the costs of the off-hour deliveries (OHD) program. As incentive amounts increase, receiver participation increases greatly, though the monetized traffic benefits do not necessarily increase at the same rate. Additional analysis was also performed with a targeted program where large traffic generators and large businesses were the recipients of the incentive. The benefits of the targeted program are estimated to be roughly equivalent to the cheapest scenario run for the broad-based program (

Development of Online Scalable Approach for Identifying Secondary Crashes

5,000 tax incentive assumption) at a fraction of the cost.

Virtual sensors: Web-based real-time data collection methodology for transportation operation performance analysis

Secondary crashes are some of the most critical incidents occurring on highways. Such crashes can induce extra traffic delays and affect highway safety performance. Transportation agencies are interested in understanding the mechanism of the occurrence of secondary crashes and implementing appropriate countermeasures. However, no well-established procedure identifies secondary crashes; this deficiency in turn impedes the possibility of investigating the underlying mechanism of their occurrence. The intent of this study was to develop an online scalable approach for helping to identify secondary crashes for the large number of highways with insufficient traffic surveillance units to collect the continuous traffic data required to classify such crashes accurately. The developed approach consisted of two major components: (a) acquisition of open source traffic data and (b) identification of secondary crashes through the use of these data. Unlike existing approaches based on static thresholds, queuing models, or infrastructure-based sensor data, the developed approach took advantage of various open-source data to identify traffic conditions in the presence of incidents. This study proposed to develop virtual sensors collecting traffic data from private traffic information providers such as Bing Maps, Google Maps, and MapQuest. The availability of such data greatly expands the ability of transportation agencies to cover more highways without installing infrastructure sensors. The virtual-sensor output provides the basic input to run the developed automatic identification algorithm for identifying secondary crashes. The algorithm is described step by step to provide a readily deployable approach for transportation agencies interested in identifying secondary crashes on their highway networks.

Extended Implementation Method for Virtual Sensors: Web-Based Real-Time Transportation Data Collection and Analysis for Incident Management

Recent advances in mobile networks and an increase in the number of GPS-equipped vehicles have led to exponential growth in real-time data generation. In the past decade, several online mapping and vehicle tracking services have made their data available to third-party users. This paper explores opportunities for use of real-time traffic data provided by online services and introduces a virtual sensor methodology for collecting, storing, and processing large volumes of network-level data. To assess the validity of the collected data with the proposed methodology, this paper compares these data with data from physical loop detectors and electronic toll tag readers. Statistical analyses show a strong correlation between the travel time measurements from infrastructure-based sensors and virtual sensors. A travel time reliability analysis is then conducted with the virtual sensor data methodology. The results are promising for future research and implementation.

Modeling Safety Impacts of Off-Hour Delivery Programs in Urban Areas

Open data sources and social media data are gaining increasing attention as important information providers in transportation and incident management. In this paper, practical evidence for the emerging potential of online and open data sources is presented. The authors’ previous research on virtual sensors is combined and extended by integrating real-time incident information and social media network engagement. The fundamental contribution of this paper is the development of an extended virtual sensor framework to provide an automated travel time data collection method as incidents occur. In addition, social media data can be useful for more effective real-time incident response. The proposed framework can easily be modified and used to evaluate travel time effects of incidents on roadways and clearance times and to make use of social media data in obtaining time-critical incident-related information.

Comparison of Mode Cost by Time of Day for Nondriving Airport Trips to and from New York City's Pennsylvania Station

Truck travel on urban road networks during daytime can be a major contributor to traffic congestion. A possible approach to relieve traffic congestion in urban areas is shifting a portion of trucks from regular daytime hours to nighttime off-hours. Benefits of this off-hour delivery strategy can be noticeable, but safety impacts need to be investigated. Manhattan, the most densely populated borough of New York City, with a large demand for truck deliveries, was the study area. Truck crashes, traffic volumes, and geometric design features of 256 road segments in Manhattan were collected to develop safety evaluation models. For quantification of the safety impacts of off-hour deliveries, an improved modeling approach was proposed; it involved the use of the multivariate Poisson-lognormal model integrated with measurement errors in truck volumes. The proposed model could address the inherent correlation of specific truck crash types and correct the estimation bias for safety effects of daytime and nighttime truck volumes. A Bayesian approach was employed to estimate the parameters of the proposed model. From the Bayesian posterior distributions, daytime and nighttime truck volumes did not have significantly different effects on either minor or serious crashes. In addition, truck crash counts were estimated with the proposed model under scenarios with different proportions of truck traffic shifted to nighttime. Results showed that off-hour delivery programs were not expected to increase the overall risk of truck-involved crashes significantly. Study findings can give transportation planners and policy makers insight on safety implications and decision making on off-hour deliveries.

Value of Schedule Delays by Time of Day: Evidence from Usage Data from High-Occupancy Toll Lanes on State Road 167

A novel methodology used taxi global position system data and high-resolution transit schedule information to compare travel times and travel fares of the two main nondriving travel modes for airport ground access: taxi and transit. Five origin–destination pairs between Pennsylvania Station in New York City and three airports in the New York region were used as an example to demonstrate these methods. An analysis of total trip cost considered both travel time and expenditures on fare. A binary logit model was used to model the mode choice of travelers. The results indicate that transit is the more likely choice during most of the day except the midnight period when transit service has longer headways. A sensitivity analysis shows the relationship between the value of time and total trip cost per passenger for different numbers of passengers traveling together and at different times of day. The higher the value of time and the number of passengers in a group, the more likely it is that a taxi is chosen for airport trips. The attractiveness of one mode relative to the other varies spatially and temporally according to the travel time and price. This paper focuses on understanding temporal variation of total cost of each mode and the effect that this variation is likely to have on mode share.

Application of Bayesian Stochastic Learning Automata for Modeling Lane Choice Behavior on High-Occupancy Toll Lanes on State Road 167

This study estimated the value of schedule delays (VSD) along the high-occupancy toll (HOT) lanes on State Road 167 in the state of Washington on the basis of revealed preference data from toll-paying users. More than 5 months of tolling records that included more than 1 million lane choices were used in the analysis. A mixed logit model was estimated to account for heterogeneity at the individual level. Drivers were assumed to evaluate reliability relative to their desired or expected arrival times (i.e., reference points). Three empirically determined reference point specifications were employed to calculate schedule delays, and the sensitivity of the results with respect to reference points was reported accordingly. The model estimations showed significant variations in the estimated VSDs for different times of day and also in different travel directions. The differences in VSD estimations could be as high as

Evaluating the Usability of Geo-located Twitter as a Tool for Human Activity and Mobility Patterns: A Case Study for New York City

17/h in a single time period depending on the reference point assumption. Reference point assumption was also shown to have a significant effect on the VSD when scheduling delays were used for measuring travel time reliability. Empirical findings of this study provide useful insights into the variability of the value of travel time of users traveling in different time periods. In particular, these results can help in the development of policies for more effective allocation of traffic capacity to HOT lanes, especially during peak congestion periods.