Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Simulation based evaluation of dynamic congestion pricing algorithms and strategies

Congestion pricing is defined as charging motorists during peak hours to encourage them to either switch their travel times or to use alternative routes. The theory behind road pricing suggests that, in order to reach social optimum conditions, a toll needs to be charged which is equal to the difference between social marginal costs and private average costs of users. In recent years, with the help of technological developments such as electronic toll collection system, pricing can be done dynamically, that is, tolls can be set in a real-time fashion according to the on-line measured traffic conditions. Dynamic pricing is only being used in High Occupancy Toll (HOT) lanes. However time-dependent pricing idea can be used in a network setting where drivers have to make route choices that are relatively more complex than the choices they make in the case of HOT lanes. This thesis proposes a simulation-based evaluation of dynamic congestion pricing on the crossings of New York City where many of the limited number of crossings to the island of Manhattan are tolled and function as parallel alternatives. One of the key aspects of this study is the estimation of realistic values of time (VOT) for different classes of users, namely, commuters and commercial vehicles. New York region-specific VOT for commercial vehicles is estimated using a logit model of stated preference data. Two different simulation studies are conducted. First simulation study is performed using the software TransModeler by considering the Manhattan network with a simple step-wise dynamic tolling algorithm and modeling the driver behavior by taking VOT into consideration. In the second simulation study, a tolling algorithm which is applicable to two tolled alternative crossings is developed. The algorithm includes real time toll rate calculation depending on travel times on crossings and models the driver behavior in response to toll rates and real-time measured travel time information on alternative routes. The algorithm is tested in traffic simulation software Paramics on a network including the two tunnels between New Jersey and New York City with a microscopic simulation of the traffic entering Manhattan.M.S.Includes bibliographical referencesby Ender Faruk Morgu

Understanding & Modeling Bus Transit Driver Availability

Bus transit agencies are required to hire extraboard (i.e. back-up) operators to account for unexpected absences. Incorrect sizing of extra driver workforce is problematic for a number of reasons. Overestimating the appropriate number of extraboard operators has financial implications while underestimating can lead to service disruption. It is therefore important that transit agencies properly manage extraboard operator staffing. A review of relevant literature showed that current models for extraboard management are generally agency-specific and that, in practice, extra driver assignments are usually based on the experience of the decision makers rather than the utilization of a mathematically sound modeling process. In this study, two mathematical programming models with probabilistic constraints were developed to determine daily optimal extraboard size for bus transit (driver availability and deployment) while incorporating reliability and risk measures in the decision making process. Two distinct solution approaches were proposed. The first approach used pLEP’s as the solution methodology and the second approach used second order stochastic dominance constraints. The models were tested using long-term data obtained from three Tri-County Metropolitan Transportation District of Oregon (TriMet) garage. The individual performance of both models under different cost assumptions was evaluated and then the actual historical assignments were compared with the optimal solutions obtained from these models. The results revealed possible improvements of extra driver size for one of the three garages studied. These models can be easily used in a computerized environment to assist agencies in efficient decision-making, which is also illustrated using a simulation procedure developed for comparison with observed driver assignment data

Modeling of Bus Transit Driver Availability for Effective Emergency Evacuation in Disaster Relief

Potential evacuees without access to personal automobiles are expected to use transit, especially buses, to reach safer regions. For a transit agency, operation problems to be considered include establishing bus launch areas, positioning the minimum number of required buses, and coordinating transit operators, especially determining whether the number of drivers will be sufficient to cover the number of vehicles (i.e., buses) to be used during the evacuation. It is also highly probable that during an emergency, absenteeism rates for bus drivers might increase. In this study, the authors developed two stochastic models to determine the need for extra drivers during an emergency evacuation and to provide optimal solutions using well-established concepts in mathematical programming. First, the authors reviewed the literature to develop an effective methodology for the development of optimal extraboard management strategies. The authors found that although several recent reports clearly mentioned the problem of not having enough bus drivers during emergency evacuation operations, no analytical study incorporated the optimal extraboard size problem into emergency evacuation operations. Second, two mathematical models are presented in this paper. The aim of the developed models is to fill the gap in the literature for determining optimal extraboard size for transit operations during emergency evacuations. The models are specifically designed to capture risk-averse behavior of decision makers. Finally, these models were tested with hypothetical examples from real-world data from New Jersey. Results show that both models give reasonable extraboard size estimates, and under different conditions, these models are responsive to the changes in cost and quality of service preferences. The results are encouraging in terms of the models' usefulness for real-world applications