521 research outputs found
Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
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PASSENGER DETECTION THOUGH VIDEO PROCESSING AND SIGNAL SENSORS IN THE BOSTON SUBWAY TO ADDRESS LEFT-BEHIND PASSENGERS
Crowding is one the most common problems for public transportation systems worldwide. It has been proven to cause anxiety to commuters and create reliability problems when commuters are not able to board on the first train or bus that arrives. These commuters are referred as left-behind passengers, and their number is directly related to various basic performance measures of public transportation systems that represent the user’s experience. Among these measures the most significant are ridership, service quality and, more importantly, travel time. Identifying left behind passengers is a tool to address crowding in stations and respond appropriately, by applying various operational strategies such as decreasing headways.
The methodology proposed in this study has been applied to two stations with high probability of left behind passengers, Sullivan Square and North Station on the MBTA Orange Line in Boston, Massachusetts. Two types of technologies were used to detect passengers being left behind in the platform. The first one was an object detection software, namely You Only Look Once (YOLO), using surveillance cameras. The second type was a Bluetooth and Wi-Fi sensor mounted on the two selected stations. Moreover, manual counts of left behind passengers were collected in the two stations. Both technologies will be individually compared with the manual counts to test accuracy and precision. Finally, the two technologies are compared with the manual counts to determine a best way to detect left behind passengers
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Optimization and Technology-Based Strategies to Improve Public Transit Performance Accounting for Demand Distribution
Public transit is important to societies worldwide. The operation of public transit systems is generally associated with great benefits for the users, but there are also cases in which these systems demonstrate inefficient performance. Quantifying transit performance is an important area of research over the last decades. This dissertation presents models to improve transit system performance through optimization techniques and new technologies, recognizing the effects of non-uniform distribution of demand over space and time. The contributions span fixed route transit services and on-demand transit, as well as models for flexible transit operations that lie in between.
Regarding fixed route systems, a methodology is proposed to estimate the number of passengers being left-behind subway train vehicles due to overcrowding. Methods to identify appropriate time periods and locations for studying this phenomenon are presented. The effects of overcrowding on passenger waiting times are also investigated. The challenging case of transit networks where passengers tap-in only upon entrance is analyzed, adding a new methodology to a very short list of similar studies and enhancing previous work in this field.
For demand responsive systems, this dissertation focuses on optimizing the operation of paratransit services through coordination with alternative providers in order to decrease high operating costs of such a service. The analysis includes a heuristic-based method. The proposed model is more detailed than existing aggregated methods and is able to perform well in high demand levels, unlike existing exact approaches. This part of the dissertation also assists in making transportation network companies a complementary part of public transit, rather than a competitor.
Finally, flexible transit systems are studied to identify the operational and demand related characteristics of a service area that could serve as indicators of such systems\u27 efficient performance. The focus here is on route deviation flexible services. Continuous approximation is used to model this flexible system. A new optimized hybrid transit system with elements of both fixed route and flexible services is proposed. Finally, it is highlighted that the current COVID-19 pandemic has proven the need for public transit systems that could be adjusted to accommodate changes in transit demand
Anomaly Detection Based on Multiple Streams Clustering for Train Real-Time Ethernet
With the increasing traffic of train communication network (TCN), real-time Ethernet becomes the development trend. However, Train Control and Management System (TCMS) is inevitably faced with more security threats than before because of the openness of Ethernet communication protocol. It is necessary to introduce effective security mechanism into TCN. Therefore, we propose a train real-time Ethernet anomaly detection system (TREADS). TREADS introduces a multiple streams clustering algorithm to realize anomaly detection, which considers the correlation between the data dimensions and adopts the decay window to pay more attention to the recent data. In the experiment, the reliability of TREADS is tested based on the TRDP data set collected from the real network environment, and the models of anomaly detection algorithms are established for evaluation. Experimental results show that TREADS can provide a high reliability guarantee, besides, the algorithm can detect and analyze network anomalies more efficiently and accurately
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