When Taxi Meets Bus: Night Bus Stop Planning over Large-Scale Traffic Data

Intelligent transportation system (ITS) sensor networks, such as road weather information and traffic sensor networks, typically generate enormous amounts of data. As a result, archiving, retrieval, and exchange of ITS sensor data for planning and performance analysis are becoming increasingly difficult. An efficient ITS archiving system that is compact and exchangeable and allows efficient and fast retrieval of large amounts of data is essential. A proposal is made for a system that can meet the present and future archiving needs of large-scale ITS data. This system is referred to as common data format (CDF) and was developed by the National Space Science Data Center for archiving, exchange, and management of large-scale scientific array data. CDF is an open system that is free and portable and includes self-describing data abstraction. Archiving traffic data by using CDF is demonstrated, and its archival and retrieval performance is presented for the Minnesota Department of Transportation–s 30-s traffic data collected from about 4,000 loop detectors around Twin Cities freeways. For comparison of the archiving performance, the same data were archived by using a commercially available relational database, which was evaluated for its archival and retrieval performance. This result is presented, along with reasons that CDF is a good fit for large-scale ITS data archiving, retrieval, and exchange of data.

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Automatic incident classification for large-scale traffic data by adaptive boosting SVM

Information Sciences ◽

10.1016/j.ins.2018.07.044 ◽

2018 ◽

Vol 467 ◽

pp. 59-73 ◽

Cited By ~ 8

Author(s):

Li-Li Wang ◽

Henry Y.T. Ngan ◽

Nelson H.C. Yung

Keyword(s):

Large Scale ◽

Traffic Data ◽

Adaptive Boosting

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CUR decomposition for compression and compressed sensing of large-scale traffic data

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) ◽

10.1109/itsc.2013.6728438 ◽

2013 ◽

Cited By ~ 16

Author(s):

Nikola Mitrovic ◽

Muhammad Tayyab Asif ◽

Umer Rasheed ◽

Justin Dauwels ◽

Patrick Jaillet

Keyword(s):

Compressed Sensing ◽

Large Scale ◽

Traffic Data

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Optimization Model of Transit Signal Priority Control for Intersection and Downstream Bus Stop

Mathematical Problems in Engineering ◽

10.1155/2016/9487190 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Rui Li ◽

Changjiang Zheng ◽

Wenquan Li

Keyword(s):

Optimization Model ◽

System Reliability ◽

Optimization Method ◽

Traffic Data ◽

Transit Signal Priority ◽

Traffic Condition ◽

Transit Service ◽

Bus Stop ◽

Bus Service ◽

Positive Effect

Transit signal priority has a positive effect on improving traffic condition and level of transit service in the urban area. In this paper, a passenger-based transit signal priority (TSP) optimization model is formulated to optimize intersection signal phasing based on minimizing accessibility-based passenger delay at the intersection and increased waiting-delay at the downstream bus stop simultaneously. Genetic Algorithm is utilized to calculate passenger-based optimization model that is calibrated by evening rush hour actual traffic data (17:30–18:30, October 13th–October 15th, 2015) along Shuiximen Boulevard in Nanjing, China. The performance of the proposed optimization model in decreasing delay and improving system reliability is simulated and evaluated by VISSIM-based simulation platform, and the results illustrate that the proposed optimization model presents promising outcomes in decreasing accessibility-based passenger delay at intersection (average reduction of 12%) and passenger waiting-delay at downstream bus service stop (average reduction of 18%) compared with traditional vehicle-based TSP optimization method in rush hour.

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A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

Discrete Dynamics in Nature and Society ◽

10.1155/2014/323764 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Liang Fu Lu ◽

Zheng-Hai Huang ◽

Mohammed A. Ambusaidi ◽

Kui-Xiang Gou

Keyword(s):

Network Traffic ◽

Large Scale ◽

Low Rank ◽

Prototype System ◽

Traffic Data ◽

Proximal Gradient Method ◽

Large Scale Network ◽

Network Operation ◽

Efficient Data ◽

Scale Network

With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.

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A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

PROMET - Traffic&Transportation ◽

10.7307/ptt.v33i4.3709 ◽

2021 ◽

Vol 33 (4) ◽

pp. 593-608

Author(s):

Chuhao Zhou ◽

Peiqun Lin ◽

Xukun Lin ◽

Yang Cheng

Keyword(s):

Neural Network ◽

Road Network ◽

Large Scale ◽

Prediction Models ◽

Short Term Memory ◽

Traffic Data ◽

Traffic Operations ◽

Forecast Horizon ◽

Temporal Characteristics ◽

Long Short Term Memory

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

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Measuring Traffic in Cities Through a Large-Scale Online Platform

Journal of Big Data Analytics in Transportation ◽

10.1007/s42421-019-00007-7 ◽

2019 ◽

Vol 1 (2-3) ◽

pp. 161-173 ◽

Cited By ~ 3

Author(s):

Vilhelm Verendel ◽

Sonia Yeh

Keyword(s):

Real Time ◽

Large Scale ◽

Data Availability ◽

Traffic Information ◽

Traffic Data ◽

Online Platform ◽

Real Time Traffic ◽

Data Source ◽

Road Segments

Abstract Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.

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