Understanding evacuation response behavior is critical for public officials in deciding when to issue emergency evacuation orders for an impending hurricane. Such behavior is typically measured by an evacuation response curve that represents the proportion of total evacuation demand over time. This study analyzes evacuation behavior and constructs an evacuation response curve on the basis of traffic data collected during Hurricane Irene in 2011 in Cape May County, New Jersey. The evacuation response curve follows a general S-shape with sharp upward changes in slope after the issuance of mandatory evacuation notices. These changes in slope represent quick response behavior, which may be caused in part by an easily mobilized tourist population, lack of hurricane evacuation experience, or the nature of the location, in this case a rural area with limited evacuation routes. Moreover, the widely used S-curves with different mathematical functions and the state-of-the-art behavior models are calibrated and compared with empirical data. The results show that the calibrated S-curves with logit and Rayleigh functions fit empirical data better. The evacuation behavior analysis and calibrated evacuation response models from this hurricane evacuation event may benefit evacuation planning in similar areas. In addition, traffic data used in this study may also be valuable for the comparative analysis of traffic patterns between the evacuation periods and regular weekdays and weekends.
Documentation was done on the effect of a raised median, signalized and redesigned intersections, curbs, and sidewalks on vehicle speed, pedestrian exposure risk, driver predictability, and vehicle volume along a four-lane suburban roadway in central New Jersey. The analysis used both quantitative tools (speed and volume counts, timing runs) and qualitative methods (pedestrian tracking, video, before-and-after photography). The results are that the 85th-percentile vehicle speed fell by 2 mi/h and pedestrian exposure risk decreased by 28%. Also, the median allows pedestrians to cross one direction of traffic at a time and signals, curbs, median, redesigned intersections, and striping patterns work together to manage driver behavior. In regard to vehicles, it was found that vehicle volumes were not affected and that vehicle speeds acted independently of vehicle volumes. A collision analysis projected a savings of $1.7 million over the next 3 years in direct and indirect costs. The goal of the report was to produce a simple and straightforward analysis tool for similar projects in the area. Some of the benefits of roadway projects such as these can be quantified numerically, whereas others rely on qualitative analyses. For example, before-and-after speeds are easily gathered and compared, whereas before-and-after pedestrian behavior at the raised median requires a more in-depth approach made easier by digital cameras. Together, before-and-after data and before-and-after imaging present a more holistic picture of the benefits and limitations of a project.
Evacuation modeling and analysis are concerned primarily with identifying the types of traffic movements associated with a disaster evacuation, as well as the estimation of evacuation and clearance times. Thus, an efficient evacuation planning model is important in determining evacuation times, identifying critical locations in the transportation network, and assessing traffic operations strategies and evacuation policies. In this paper various scenarios, including a hurricane, a toxic chemical leak, dirty bombs, and a nuclear event, are studied to understand the evacuation and highway network effects of the evacuating population. Unlike corridor studies or bottleneck studies found in the literature, a network model with equilibrium assignment is used. The scenarios are tested with a case study of Northern New Jersey, modeled with the North Jersey Regional Transportation Model–Enhanced, a large-scale travel demand model of the region. The results presented in this paper focus on the effect of several assumptions and input data on the evacuation estimates, giving planners an idea of the necessary considerations for evacuation planning with a modeling context. The experience with this study shows that regional planning models are suitable tools to model evacuation; however, the modeler must be careful in their use. Multiple methodologies can be used, and assumptions, such as time of day, notice or no-notice, passengers per car, and background traffic in the network, have wide-ranging effects.
In 1998, New Jersey Transit (NJ Transit) conducted an onboard survey of passengers on three of its commuter rail lines on their preferences for new shuttle services. During the first half of the 2000s, community-based shuttle service was introduced in several New Jersey communities—many under the federal Congestion Mitigation and Air Quality Improvement Program. Some of these services continue today, but others were discontinued. This study uses data from the onboard survey to identify the rider characteristics and spatial characteristics of communities that influence stated preferences for shuttle service to rail stations. Correlation analysis, factor analysis, and logistic regression are used to identify these characteristics. With the use of results from the stated preference analysis, the communities where shuttle service has continued were compared with communities where service has been discontinued, to identify the factors that may influence the shuttle's success. The analysis of stated preferences and the comparison of communities suggest that concentration of immigrant populations, non-English speakers, and persons with moderate income may be important for the success of shuttles in the study area. Sporadic evidence was found that parking costs and availability of parking at stations may influence people's decision to use shuttles. Similar evidence is found indicating that people with high incomes, people who live close to stations, and people who already use rail transit regularly may be indifferent to new shuttle service.
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