The research team, including co-principal investigators Kaan Ozbay, PhD, and Harry Capers, Jr., PE, conducted additional field tests and detailed finite element analysis for more accurate condition evaluation of the bridges. To operate the bridges safely and cost-effectively for their remaining lives, the team made recommendations for appropriate maintenance. Based on the study of the selected railway bridges, they also provided general guidelines for bridge inspection and maintenance.
“We conducted a benefits analysis for raising the weight restriction on the New Jersey Transit HX Drawbridge to 286,000 pounds using available data,” said Dr. Nassif. “Currently, the bridge supports only weights of 263,000 pounds per rail car, and raising this restriction is expected to allow increased weight transported by freight rail cars using this line. The economic results show a potential benefit of up to $7.49 million over 25 years.”
Selection included a bridge variety
Based on field inspections, a number of critical bridges on New Jersey’s rail lines were selected and load-rated based on current American Railway Engineering and Maintenance-of-Way Association (AREMA) specifications as well as the analytical studies. Sufficient sample bridges were selected so they could represent bridges with various structural systems and material types.
The research team adopted finite element modeling for more accurate bridge assessment and to develop a methodology for evaluating and load-rating railroad bridges. Based on the field inspection results, the team selected critical bridges, which were instrumented and tested under live loads.
Research applies to other states’ bridges
Finally, they made recommendations for bridge load rating, maintenance, repair, and rehabilitation for safe operation on various New Jersey lines. The recommendations are applicable for other railroad bridges that support railcars with the increased standard weight.
Besides Dr. Nassif, the project team included, and student researchers Peng Lou, Dan Su, Shri Iyer, Megan Valeo, and Etkin Kara.
Peng Lou presents paper on behalf of Dr. Hani Nassif at mini-symposium.
Hani Nassif, PhD, organized a Mini-Symposium 10 (MS-10) with three South Korean colleagues, Soobong Shin, PhD, from Inha University, Ho-Kyung Kim, PhD, from Seoul National University, and Nam-Sik Kim, PhD, from Pusan National University. The symposium was held during the 7th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2014) in Shanghai, China on July 7-11, 2014.
IABMAS has developed into a significant association that covers all aspects related to bridge maintenance, safety, and management worldwide. The conference focused on those topics while promoting international cooperation. The objective of MS-10 was to present a lifetime perspective for design and maintenance of short, medium, and long-span bridges. Two sessions, 16 papers, and 12 participants were prepared and presented.
On behalf of Dr. Nassif, Peng Lou, a PhD candidate in RIME Group, presented two transit-related papers entitled “Evaluation of Existing Railway Bridges Using Structural Health Monitoring and Finite Element Modeling,” and “Modeling of Train-Bridge Dynamic Interaction System for Bridges with Stepped-Beam Cross-Sections.”
Deva Deka, PhD, Assistant Director for Research, Voorhees Transportation Center
Studies analyzed paratransit use to predict future needs.
Two recently published articles on ADA paratransit offer insight into paratransit users and the factors that cause trip delays. The authors say that accuracy in predicting future demand depends on understanding paratransit trip generators and current demand patterns. Both studies analyzed data from roughly two million trips made over a 24-month period by clients of the New Jersey's no-cost ADA paratransit service, Access Link.
Pickup and drop-off locations share similarities
The Generators of Paratransit Trips by Persons with Disabilities, by Deva Deka, PhD, and E.J. Gonzales, PhD, was part of a larger effort to forecast future paratransit trip demand in different parts of New Jersey. Since no data was available on the purpose of each trip, the research team analyzed data on pickups and drop-offs, cross-referencing it with census block group data on population and employment. By identifying the characteristics of the block groups where the paratransit clients lived and those of the block groups they visited, they were able to identify trip generators at a zonal level.
Statistical models revealed that the population size, proportion of elderly people, proportion of African Americans, and proportion of dwellings with a very large number of units per block group all positively associate with the number of clients and at-home pickups.
Health care, services, attract paratransit riders
The models for non-home drop-offs indicated that health care and social assistance jobs, retail jobs, administrative and support jobs, and accommodation and food services jobs are more likely to attract paratransit trips than other characteristics of those block groups. Population characteristics of block groups had very little effect for non-home drop-offs.
By cross-referencing the precise drop-off locations with geocoded business locations from Dun and Bradstreet, the study confirmed that the establishments located within 75 feet of the non-home drop-offs were more likely to be health care establishments, social service establishments, business service establishments, membership organizations (primarily religious establishments), and educational establishments than other types of establishments.
Several factors contribute to trip delays
The second article, An Exploration of the Environmental and Rider Characteristics Associated with Disability Paratransit Trip Delay by Dr. Deka, explored the characteristics of places, trips, and trip makers (passengers) associated with paratransit trip delay. The study first estimated trip delay by subtracting the time trips should have taken in free-flow conditions from the actual time taken. Statistical models revealed that the density of population, jobs, and intersections at the pickup and drop-off locations are positively correlated. Household income of the locations, however, is negatively associated with trip delay.
Other factors typically associated with vehicle traffic delay, such as time of day and day of the week, were also found to be associated with paratransit trip delay. The analysis showed that vehicle type (van/bus versus sedan), the use of wheelchairs, the presence of personal care attendants, and characteristics such as the age and disability type of the trip makers significantly impact trip delay.
The study offers suggestions to minimize paratransit trip delay.