Subjects -> TRANSPORTATION (Total: 216 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (39 journals)
    - TRANSPORTATION (123 journals)

TRANSPORTATION (123 journals)                     

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Hybrid Journal   (Followers: 122)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 9)
Applied Mobilities     Hybrid Journal   (Followers: 3)
Archives of Transport     Open Access   (Followers: 18)
Asian Transport Studies     Open Access   (Followers: 1)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 16)
Cities in the 21st Century     Open Access   (Followers: 16)
Danish Journal of Transportation Research / Dansk Tidsskrift for Transportforskning     Open Access   (Followers: 3)
Decision Making : Applications in Management and Engineering     Open Access   (Followers: 2)
Economics of Transportation     Partially Free   (Followers: 14)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
eTransportation     Open Access   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 15)
European Transport Research Review     Open Access   (Followers: 24)
Geosystem Engineering     Hybrid Journal   (Followers: 2)
IATSS Research     Open Access  
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
IET Electrical Systems in Transportation     Open Access   (Followers: 11)
IET Intelligent Transport Systems     Open Access   (Followers: 12)
IET Smart Cities     Open Access   (Followers: 1)
IFAC-PapersOnLine     Open Access   (Followers: 1)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 11)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 12)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 6)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 11)
International Journal of Electronic Transport     Hybrid Journal   (Followers: 9)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 7)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 16)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 9)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 12)
International Journal of Services Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 19)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 19)
International Journal of Transportation Engineering     Open Access   (Followers: 2)
International Journal of Transportation Science and Technology     Open Access   (Followers: 12)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 16)
Journal of Big Data Analytics in Transportation     Hybrid Journal   (Followers: 2)
Journal of Intelligent and Connected Vehicles     Open Access   (Followers: 2)
Journal of KONES     Open Access  
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 9)
Journal of Navigation     Hybrid Journal   (Followers: 281)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 12)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 3)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 12)
Journal of Transport and Land Use     Open Access   (Followers: 26)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 16)
Journal of Transport Geography     Hybrid Journal   (Followers: 28)
Journal of Transport History     Hybrid Journal   (Followers: 13)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 10)
Journal of Transportation Security     Hybrid Journal   (Followers: 2)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 12)
Journal of Transportation Technologies     Open Access   (Followers: 15)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 8)
Journal on Vehicle Routing Algorithms     Hybrid Journal  
Les Dossiers du Grihl     Open Access   (Followers: 1)
LOGI ? Scientific Journal on Transport and Logistics     Open Access   (Followers: 1)
Logistics     Open Access   (Followers: 3)
Logistics & Sustainable Transport     Open Access   (Followers: 6)
Logistique & Management     Hybrid Journal  
Mobility in History     Full-text available via subscription   (Followers: 5)
Modern Transportation     Open Access   (Followers: 12)
Nonlinear Dynamics     Hybrid Journal   (Followers: 20)
Open Journal of Safety Science and Technology     Open Access   (Followers: 17)
Open Transportation Journal     Open Access   (Followers: 1)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 4)
Periodica Polytechnica Transportation Engineering     Open Access  
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 15)
Promet : Traffic &Transportation     Open Access  
Public Transport     Hybrid Journal   (Followers: 20)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 8)
Revista Transporte y Territorio     Open Access   (Followers: 1)
Revue Marocaine de Management, Logistique et Transport     Open Access  
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 2)
Sport, Education and Society     Hybrid Journal   (Followers: 13)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 3)
Streetnotes     Open Access   (Followers: 1)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 1)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 7)
Transport     Open Access   (Followers: 17)
Transport and Telecommunication     Open Access   (Followers: 5)
Transport in Porous Media     Hybrid Journal   (Followers: 2)
Transport Problems     Open Access   (Followers: 5)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 10)
Transport technic and technology     Open Access   (Followers: 1)
Transportation     Hybrid Journal   (Followers: 34)
Transportation Engineering     Open Access   (Followers: 1)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Transportation in Developing Economies     Hybrid Journal  
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportation Journal     Full-text available via subscription   (Followers: 17)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 6)
Transportation Research Interdisciplinary Perspectives     Open Access   (Followers: 3)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 41)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 39)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 31)
Transportation Research Procedia     Open Access   (Followers: 7)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 37)
Transportation Safety and Environment     Open Access   (Followers: 2)
Transportation Science     Full-text available via subscription   (Followers: 26)
Transportation Systems and Technology     Open Access  
TRANSPORTES     Open Access   (Followers: 6)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 9)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 1)
Transportrecht     Hybrid Journal   (Followers: 1)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 12)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 4)
Urban Development Issues     Open Access   (Followers: 3)
Urban, Planning and Transport Research     Open Access   (Followers: 33)
Vehicles     Open Access  
Vehicular Communications     Full-text available via subscription   (Followers: 4)
World Electric Vehicle Journal     Open Access  
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 6)
Транспортні системи та технології перевезень     Open Access  

           

Similar Journals
Journal Cover
Journal of Big Data Analytics in Transportation
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2523-3556 - ISSN (Online) 2523-3564
Published by Springer-Verlag Homepage  [2656 journals]
  • GPU-Enabled Visual Analytics Framework for Big Transportation Datasets
    • Abstract: Transportation agencies rely on a variety of data sources for condition monitoring of their assets and making critical decisions such as infrastructure investments and project prioritization. Recent exponential increase in the volumes of these datasets has been causing significant information overload problems for data analysts; data curation process has increasingly become time consuming as legacy CPU-based systems are reaching their limits for processing and visualizing relevant trends in these massive datasets. There is a need for new tools that can consume these new datasets and provide analytics at rates resonant with the speed of human thought. The current paper proposes a new framework that allows for both multidimensional visualization and analytics to be carried seamlessly on large transportation datasets. The framework stores data in a massively parallel database and leverages the immense computational power available in graphical processing units (GPUs) to carry out data analytics and rendering on the fly via a Structured Query Language which interacts with the underlying GPU database. A front-end is designed for near-instant rendering of queried results on simple charts and maps to enable decision makers to drill down insights quickly. The framework is used to develop applications for analyzing big transportation datasets with over 100 million rows. Performance benchmarking experiments conducted showed that the methodology developed is able to provide real-time visual updates for big data in less than 100 ms. The performance of the developed framework was also compared with CPU-based visual analytics platforms such as Tableau and D3.
      PubDate: 2019-10-24
      DOI: 10.1007/s42421-019-00010-y
       
  • Clustering Activity–Travel Behavior Time Series using Topological
           Data Analysis
    • Abstract: Over the last few years, traffic data have been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new divide and combine-based approach to do K-means clustering on activity–travel behavior time series using features that are derived using tools in time series analysis and topological data analysis. Our approach facilitates a case study, where each individual’s daily activity–travel behavior is characterized as a categorical time series consisting of three different levels. Clustering data from five waves of the National Household Travel Survey ranging from 1990 to 2017 suggests that activity–travel patterns of individuals over the last 3 decades can be grouped into three clusters. Results also provide evidence in support of recent claims about differences in activity–travel patterns of different survey cohorts. The proposed method is generally applicable and is not limited only to activity–travel behavior analysis in transportation studies. Driving behavior, travel mode choice, household vehicle ownership, when being characterized as categorical time series, can all be analyzed using the proposed method.
      PubDate: 2019-10-23
      DOI: 10.1007/s42421-019-00008-6
       
  • Efficient Data Collection and Accurate Travel Time Estimation in a
           Connected Vehicle Environment Via Real-Time Compressive Sensing
    • Abstract: Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve safety and mobility of transportation systems, which can overburden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial–temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data.
      PubDate: 2019-10-18
      DOI: 10.1007/s42421-019-00009-5
       
  • Transportation Mode Detection from Low-Power Smartphone Sensors Using
           Tree-Based Ensembles
    • Abstract: Recently, a considerable amount of research has focused on understanding transportation mobility patterns from crowdsourced smartphone data. To this end, transportation mode detection is an indispensable, yet challenging task towards deriving meaningful information from large datasets collected using smartphones. Most studies to date use Global Navigation Satellite Systems (GNSS) derived data such as speed to detect transportation mode. Limited research relies on sensors that do not depend on external sources, such as accelerometer and gyroscope. The present work proposes a methodological framework based on machine learning for identifying the transportation mode using accelerometer, gyroscope and orientation data in the absence of battery consuming sensors, such as GNSS. Different models are developed and compared based on random forest and gradient boosting machine algorithms. A comparative study between GNSS free and GNSS based algorithms is also established. Results are further discussed and possible research directions are provided.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00004-w
       
  • Visualizing and Evaluating Interdependent Regional Traffic Congestion and
           System Resiliency, a Case Study Using Big Data from Probe Vehicles
    • Abstract: Big data from probe vehicles is increasingly becoming an important contributor for determining the regional performance of a transportation roadway network. Recent research has applied aggregated speed data from probe vehicles to quantify travel time variations as a result of recurring congestion, incidents, weather events and other non-recurring congestion. Through the establishment of a base travel time for all roadway segments in a region, any increase in travel time characteristics in the regional networks can be quantified temporally and spatially. This characterization is especially important when determining a region’s congestion resiliency, which is being defined as the ability of a roadway network accommodate failures and return to a baseline congestion after a major capacity reduction to the roadway network. This paper demonstrates how aggregated big data on vehicle speeds obtained from regionally deployed probe vehicles could be used to characterize and visualize the interdependent congestion impacts between regions and across roadway types (interstate, arterial, and local). To demonstrate the models and methodologies, an in-depth analysis of the I-276 Bridge closure incident in Burlington County, NJ near Philadelphia, PA was conducted. The bridge was clzosed after a routine inspected identified a crack in one of the structural members. In total, 90 days of data, which included 90-million speed records, were commercially collected for 1765 roadway segments, was analyzed. A novel performance metric was developed to allow an impact analysis by comparing Burlington County to two adjacent counties, Mercer and Camden. The results showed that the bridge closure did have a definitive, quantifiable impact on the primary road network of the adjacent counties. Subsequent analysis identified specific roadways that were most impacted by the closure. Although this research explores historic speed data, the methodologies presented can be applied to real-time speed data to assist in developing efficient traffic operation plans during major incidents, lane closures and weather events.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00002-y
       
  • Transfer Learning Using Deep Neural Networks for Classification of Truck
           Body Types Based on Side-Fire Lidar Data
    • Abstract: Vehicle classification is one of the most essential aspects of highway performance monitoring as vehicle classes are needed for various applications including freight planning and pavement design. While most of the existing systems use in-pavement sensors to detect vehicle axles and lengths for classification, researchers have also explored traditional approaches for image-based vehicle classification which tend to be computationally expensive and typically require a large amount of data for model training. As an alternative to these image-based methods, this paper investigates whether it is possible to transfer the learning (or parameters) of a highly accurate pre-trained (deep neural network) model for classifying truck images generated from 3D-point cloud data from a LiDAR sensor. In other words, without changing the parameters of several well-known convolutional neural networks (CNNs), such as AlexNet, VggNet and ResNet, this paper shows how they can be adopted to extract the needed features to classify trucks, in particular trucks with different types of trailers. This paper demonstrates the applicability of these CNNs for solving the vehicle classification problem through an extensive set of experiments conducted on images created based on data from a LIDAR sensor. Results show that using pre-trained CNN models to extract low-level features within images yield significantly accurate results, even with a relatively small size of training data that are needed for the classification step at the end.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00005-9
       
  • Traffic Dynamics Exploration and Incident Detection Using Spatiotemporal
           Graphical Modeling
    • Abstract: To discover the spatial and temporal traffic patterns, this paper proposes a spatiotemporal graphical modeling approach, spatiotemporal pattern network (STPN), to explore traffic dynamics in large traffic networks. A measurement based on Granger causality is used to identify the characteristics of spatial and temporal traffic patterns. An anomaly score is estimated to detect and locate traffic incidents in diverse types and severities, and also to quantify the influence of incidents on traffic flow fluctuations. Built upon symbolic dynamics filtering, the proposed approach implements spatial and temporal feature extraction via discovering causal dependencies among road segments using STPN, system-wide pattern learning through an energy-based model, restricted Boltzmann machine, and inference using a newly developed root-cause analysis algorithm. Case studies are carried out using the probe vehicle data collected on Interstate Highway 80 in Iowa and the results show that the proposed approach is capable of (1) discovering and representing causal interactions among sub-systems (e.g., road segment) of a traffic network that provide valuable information for developing and applying customized traffic management strategies, (2) adaptively handling multiple nominal patterns mixed with anomalous data for effectively differentiating abnormal traffic system status and locating traffic incidents, and (3) quantifying the fluctuation of traffic flow and the severity of the detected incident via anomaly scores estimated from traffic speed behaviors. The findings from the case studies reiterate the importance of incorporating both temporal and spatial features for pattern analysis and incident detection. The proposed approach is built for real-time application and can be utilized for on-line incident detection.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00003-x
       
  • A Perspective on the Challenges and Opportunities for Privacy-Aware Big
           Transportation Data
    • Abstract: In recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potential to redefine how transportation system (i.e., design, planning and operations) is done. While researchers in both academia and industry are making advances in using this data to transportation system ends (e.g., information inference from collected data), little attention has been paid to four larger scale challenges that will need to be overcome if the potential for Big Transportation Data is to be harnessed for transportation decision-making purposes. This paper aims to provide awareness of these large-scale challenges and provides insight into how we believe these challenges are likely to be met.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00001-z
       
  • A Cyberinfrastructure for Big Data Transportation Engineering
    • Abstract: Big data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, and decrease construction worker injuries, among others. Despite these benefits, research on big data-driven transportation engineering is difficult today due to the computational expertise required to get started. This work proposes BoaT, a transportation-specific programming language, and its big data infrastructure that is aimed at decreasing this barrier to entry. Our evaluation, that uses over two dozen research questions from six categories, shows that research is easier to realize as a BoaT computer program, an order of magnitude faster when this program is run, and exhibits 12–14× decrease in storage requirements.
      PubDate: 2019-06-01
      DOI: 10.1007/s42421-019-00006-8
       
 
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