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

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Train Dwell Time Evaluation at High Passenger Volume Stations

Train dwell time is complicated and depends on many factors, one of the dominant ones being passenger volume. High passenger volume on a platform always causes trains to stop longer and consequently delays the service. This research used London Underground’s actual train movement data to evaluate train dwell times on the Victoria line, which is one of the most crowded lines in the London Underground system. In the morning peak of the northbound service of the Victoria line, Victoria station becomes a critical station that determines the line capacity due to the extended dwell time at the platform. This research introduces the Data Envelopment Analysis to benchmark dwell times at each station on the line in relation to passenger volume at that station, and it suggests that stations be classified based on their demand profile. Stations with the same demand profile as Victoria station (defined as high-passenger-volume stations in this research) would be looked into further. The dwell times at these high-passenger-volume stations are highly variable and dependent on passenger movements. Many studies have found that dwell times at high-passenger-volume stations are difficult to predict accurately using dwell time models. The current method for calculating a dwell time in a train schedule uses the value calculated from the prediction model or the calculation of historical data. Considering the perspective of the uncertainty inherent in dwell time evaluation, this research proposes a new dwell time evaluation approach to evaluate the likelihood and consequences of dwell time delays at different passenger volume levels. The research contributed to the evaluation framework to evaluate the risk of dwell time delays and found that the best-case scenario with the lowest risk of dwell time delays occurs when the dwell time margin is 20 seconds, and the train is loaded to 70 percent of its maximum capacity prior to arriving at the critical station

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Hazards threatening underground transport systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11069-020-03860-wMetro systems perform a significant function for millions of ridership worldwide as urban passengers rely on a secure, reliable, and accessible underground transportation way for their regular conveyance. However, hazards can restrict normal metro service and plans to develop or improve metro systems set aside some way to cope with these hazards. This paper presents a summary of the potential hazards to underground transportation systems worldwide, identifying a knowledge gap on the understanding of water-related impacts on metro networks. This is due to the frequency and scope of geotechnical and air quality hazards, which exceed in extreme magnitude the extreme precipitation events that can influence underground transportation systems. Thus, we emphasize the importance of studying the water-related hazards in metro systems to fill the gaps in this topic.Peer ReviewedPostprint (author's final draft

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

Development and application of dynamic models for predicting transit arrival times

Stochastic variations in traffic conditions and ridership often have a negative impact in transit operations resulting in the deterioration of schedule/headway adherence and lengthening of passenger wait times. Providing accurate information on transit vehicle arrival times is critical to reduce the negative impacts on transit users. In this study, models for dynamically predicting transit arrival times in urban settings are developed, including a basic model, a Kalman filtering model, link-based and stop-based artificial neural networks (ANNs) and Neural/Dynamic (ND) models. The reliability of these models is assessed by enhancing the microscopic simulation program CORSIM which can calculate bus dwell and passenger wait times based on time-dependent passenger demands and vehicle inter-departure times (headways) at stops. The proposed prediction models are integrated with the enhanced CORSIM individually to predict bus arrival times while simulating the operations of a bus transit route in New Jersey. The reliability analysis of prediction results demonstrates that ANNs are superior to the basic and Kalman filtering models. The stop-based ANN generally predicts more accurately than the link-based ANN. By integrating an ANN (either link-based or stop-based) with the Kalman filtering algorithm, two ND models (NDL and NDS) are developed to decrease prediction error. The results show that the performance of the ND models is fairly close. The NDS model performs better than the NDL model when stop-spacing is relatively long and the number of intersections between a pair of stops is relatively large. In the study, an application of the proposed prediction models to a real-time headway control model is also explored and experimented through simulating a high frequency light rail transit route. The results show that with the accurate prediction of vehicle arrival information from the proposed models, the regularity of headways between any pair of consecutive operating vehicles is improved, while the average passenger wait times at stops are reduced significantly

A Land-Use Approach for Capturing Future Trip Generating Poles

Changes in the usage of a particular urban or regional area have immediate effects on transportation, such as the development of a new multimodal terminal within a city, or the creation of a business park in its outskirts. Thus far, this correlation has been underresearched at a national level in Greece. As a result, its effects on trip generation and passenger flows has been underestimated at the planning level, leading to the implementation of projects that are neither viable nor sustainable. This paper proposes that land use changes ought to be considered in tandem with transport-related changes at the planning stage. To this effect, we present a three-step methodology for an integrated approach to capturing future trip generation: the identification of future trip-generating poles within the study area; the development of scenarios related to the probability of these changes occurring and their potential magnitude; an estimation of future trends in passenger flows. The methodology is applied to the Metropolitan area of Thessaloniki, Greece. Using data obtained from development plans, national statistical services and research projects’ and studies’ findings, we estimate future trip-generation subsequent to land use change. Data is processed and evaluated by a local experts’ group, representing various key-disciplines of the area’s planning stakeholders