Subjects -> TRANSPORTATION (Total: 214 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (43 journals)
    - TRANSPORTATION (117 journals)

TRANSPORTATION (117 journals)                     

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Hybrid Journal   (Followers: 129)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 8)
Applied Mobilities     Hybrid Journal   (Followers: 5)
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: 13)
Cities in the 21st Century     Open Access   (Followers: 17)
Communications in Transportation Research     Open Access  
Danish Journal of Transportation Research / Dansk Tidsskrift for Transportforskning     Open Access   (Followers: 1)
Decision Making : Applications in Management and Engineering     Open Access   (Followers: 1)
Economics of Transportation     Partially Free   (Followers: 16)
Emission Control Science and Technology     Hybrid Journal   (Followers: 1)
eTransportation     Open Access   (Followers: 1)
EURO Journal of Transportation and Logistics     Open Access   (Followers: 12)
European Journal of Transport and Infrastructure Research (EJTIR)     Open Access   (Followers: 2)
European Transport Research Review     Open Access   (Followers: 22)
Geosystem Engineering     Hybrid Journal  
IATSS Research     Open Access  
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 4)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
IET Electrical Systems in Transportation     Open Access   (Followers: 13)
IET Intelligent Transport Systems     Open Access   (Followers: 11)
IET Smart Cities     Open Access  
IFAC-PapersOnLine     Open Access  
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 5)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 10)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 8)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 13)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 8)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 1)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 11)
International Journal of Services Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 18)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 12)
International Journal of Transportation Engineering     Open Access   (Followers: 2)
International Journal of Transportation Science and Technology     Open Access   (Followers: 9)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 2)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 12)
Journal of Big Data Analytics in Transportation     Hybrid Journal   (Followers: 2)
Journal of Intelligent and Connected Vehicles     Open Access   (Followers: 1)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 7)
Journal of Navigation     Hybrid Journal   (Followers: 178)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 10)
Journal of Supply Chain Management Science (JSCMS)     Open Access   (Followers: 3)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 4)
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: 10)
Journal of Transport Geography     Hybrid Journal   (Followers: 22)
Journal of Transport History     Hybrid Journal   (Followers: 12)
Journal of Transportation and Logistics     Open Access   (Followers: 3)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 9)
Journal of Transportation Security     Hybrid Journal   (Followers: 3)
Journal of Transportation Technologies     Open Access   (Followers: 13)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 7)
Journal on Vehicle Routing Algorithms     Hybrid Journal  
Les Dossiers du Grihl     Open Access   (Followers: 1)
LOGI ? Scientific Journal on Transport and Logistics     Open Access  
Logistics     Open Access   (Followers: 1)
Logistics & Sustainable Transport     Open Access   (Followers: 4)
Logistique & Management     Hybrid Journal  
Maritime Transport Research     Open Access  
Mobility in History     Full-text available via subscription   (Followers: 7)
Modern Transportation     Open Access   (Followers: 11)
Nonlinear Dynamics     Hybrid Journal   (Followers: 19)
Open Journal of Safety Science and Technology     Open Access   (Followers: 16)
Open Transportation Journal     Open Access   (Followers: 1)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
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: 11)
Promet : Traffic &Transportation     Open Access  
Public Transport     Hybrid Journal   (Followers: 18)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 4)
Revista Transporte y Territorio     Open Access  
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
Sport, Education and Society     Hybrid Journal   (Followers: 12)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 2)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 1)
Transactions on Transport Sciences     Open Access   (Followers: 4)
Transport     Open Access   (Followers: 16)
Transport and Telecommunication     Open Access   (Followers: 4)
Transport in Porous Media     Hybrid Journal  
Transport Problems     Open Access   (Followers: 4)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 11)
Transportation     Hybrid Journal   (Followers: 32)
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: 16)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 4)
Transportation Research Interdisciplinary Perspectives     Open Access   (Followers: 2)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 38)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 38)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 29)
Transportation Research Procedia     Open Access   (Followers: 6)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 29)
Transportation Safety and Environment     Open Access   (Followers: 1)
Transportation Science     Full-text available via subscription   (Followers: 26)
Transportation Systems and Technology     Open Access  
TRANSPORTES     Open Access   (Followers: 3)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 7)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 1)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 9)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
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   (Followers: 3)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 5)
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  [2469 journals]
  • Optimization Models for Estimating Transit Network Origin–Destination
           Flows with Big Transit Data

    • Free pre-print version: Loading...

      Abstract: Abstract The increasing adoption of automatic vehicle location and automatic passenger count technologies by transit agencies produces passenger boarding and alighting count data on a continuous basis. This data provides new opportunities for origin–destination (O–D) flow estimation. However, the state-of-the-art methodologies generated flows within routes and barely considered linked trips. This paper proposes optimization models to identify transfers and approximate network-level O–D flows by: a quadratic integer program (QIP), a feasible rounding procedure for the quadratic convex programming (QCP) relaxation of the QIP, and an integer program (IP). A case study for Ann Arbor-Ypsilanti area in Michigan suggests that: The IP model outperforms the QCP in terms of accuracy and remains tractable from an efficiency standpoint, contrary to the QIP. Its O–D estimation achieves an R-Squared metric of \(95.57\%\) at the traffic analysis zone level and \(92.39\%\) at the stop level, compared to the ground-truths inferred from the state-of-the-practice trip-chaining methods.
      PubDate: 2021-12-01
      DOI: 10.1007/s42421-021-00050-3
       
  • Large-Scale Data-Driven Traffic Sensor Health Monitoring

    • Free pre-print version: Loading...

      Abstract: Abstract Accurate traffic data collection is essential for supporting advanced traffic management system operations. This study investigated a large-scale data-driven sequential traffic sensor health monitoring (TSHM) module that can be used to monitor sensor health conditions over large traffic networks. Our proposed module consists of three sequential steps for detecting different types of abnormal sensor issues. The first step detects sensors with abnormally high missing data rates, while the second step uses clustering anomaly detection to detect sensors reporting abnormal records. The final step introduces a novel Bayesian changepoint modeling technique to detect sensors reporting abnormal traffic data fluctuations by assuming a constant vehicle length distribution based on average effective vehicle length (AEVL). Our proposed method is then compared with two benchmark algorithms to show its efficacy. Results obtained by applying our method to the statewide traffic sensor data of Iowa show it can successfully detect different classes of sensor issues. This demonstrates that sequential TSHM modules can help transportation agencies determine traffic sensors’ exact problems, thereby enabling them to take the required corrective steps.
      PubDate: 2021-09-23
      DOI: 10.1007/s42421-021-00049-w
       
  • Correction to: Short‑Term Prediction of Demand for Ride‑Hailing
           Services: A Deep Learning Approach

    • Free pre-print version: Loading...

      Abstract: A correction to this paper has been published: https://doi.org/10.1007/s42421-021-00046-z
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00046-z
       
  • Correction to: Seasonal Disorder in Urban Traffic Patterns: A Low Rank
           Analysis

    • Free pre-print version: Loading...

      Abstract: A correction to this paper has been published: https://doi.org/10.1007/s42421-021-00045-0
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00045-0
       
  • Scalable Framework for Enhancing Raw GPS Trajectory Data: Application to
           Trip Analytics for Transportation Planning

    • Free pre-print version: Loading...

      Abstract: Abstract Transportation analysts and planners are beginning to leverage GPS trajectory data to draw additional insight into travel behavior and enhance data-driven decision-making capabilities. However, raw trajectory data cannot be utilized directly; they require extensive processing prior to analysis. This paper presents a scalable approach for enhancing raw GPS trajectory data by snapping and routing waypoints along a user-defined target road network that may have discontinuities and missing links, thus enabling trajectory datasets to be used in conjunction with the types of non-routable road networks often employed by transportation agencies. The proposed approach fuses a well-established map matching solution with a custom waypoint conflation procedure, and provides a framework to execute the trajectory processing in parallel to efficiently leverage available computing resources for large GPS datasets. To demonstrate its capability, four months of 2018 trajectory data from Maryland (2.5 billion waypoints from 46 million trips) are processed in this manner and assigned to a Traffic Message Channel road network. The enhanced trajectory data are then used to demonstrate a real-world use case, identifying key travel patterns along the I-270 spur in Maryland—a key commuting corridor currently being considered by the Maryland Department of Transportation for a congestion mitigation investment.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00040-5
       
  • Deep LSTM Recurrent Neural Networks for Arterial Traffic Volume Data
           Imputation

    • Free pre-print version: Loading...

      Abstract: Abstract This research investigates the performance of deep learning to perform traffic data imputation using Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) layers. Investigation of real-time traffic volume data received from a connected corridor revealed the presence of intermittent data gaps. Such data gaps in data streams could impact applications that utilize connected corridor data. To improve the utility of the connected corridor real-time data streams a deep learning algorithm that can use historic and current high frequency data to learn and provide accurate estimations for imputation is realized. In this study traffic volume data streams are received in 6-min aggregate bins from the corridor detectors. Univariate time series models based on only the given detector and multivariate time series models based on the given detector and a similar detector are trained on LSTM RNN layers using current and historic data. To investigate the performance of these models in imputing missing data gaps two experiments are conducted. The first experiment investigates the performance of models to impute consecutive missing data under typical traffic conditions. Results indicate comparable performance of the multivariate and univariate models for shorter consecutive missing data gap imputations, while for longer consecutive missing data gaps, multivariate outperforms univariate for several cases. The second experiment compared the models performance under atypical traffic conditions. Results indicate improved performance of the multivariate over univariate models, further demonstrating the potential advantages of using recent information from other similar detectors in a multivariate model, under both typical and atypical conditions.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00043-2
       
  • A Proactive Approach to Evaluating Intersection Safety Using Hard-Braking
           Data

    • Free pre-print version: Loading...

      Abstract: Abstract Typical safety improvements at signalized intersections are identified and prioritized using crash data over 3–5 years. Enhanced probe data that provides date, time, heading, and location of hard-braking events has recently become available to agencies. In a typical month, over six million hard-braking events are logged in the state of Indiana. This study compared rear-end crash data over a period of 4.5 years at 8 signalized intersections with weekday hard-braking data from July 2019. Using Spearman’s rank-order correlation, results indicated a strong correlation between hard-braking events and rear-end crashes occurring more than 400 ft upstream of an intersection. The paper concludes that using a month or two of hard-braking events occurring upstream from the stop bar may be a useful tool to screen potential locations with elevated rear-end crashes. Using these techniques described in this paper, new commercially available hard-braking data sources will provide an opportunity for agencies to follow up with mitigation measures addressing emerging problems much quicker than typical practices that rely on 3–5 years of crash data.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00039-y
       
  • Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning
           Approach

    • Free pre-print version: Loading...

      Abstract: Abstract As ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00041-4
       
  • Inferring Twitters’ Socio-demographics to Correct Sampling Bias of
           Social Media Data for Augmenting Travel Behavior Analysis

    • Free pre-print version: Loading...

      Abstract: Abstract Many studies demonstrated that social media data, especially Twitter data, have significant potentials to develop models for estimating travel demand, managing operation, and conducting long-term planning purposes. However, it is well known that research with social media data is facing a looming challenge in sampling bias. The Twitter user’s population has huge discrepancies compared with the overall population. Therefore, social media data, when it is directly used for travel behavior analysis, contains biases and errors to some degree. The objective of this study is to correct sampling bias of Twitter data for travel behavior analysis by inferring Twitter users’ socio-demographics. This study first links travelers’ Twitter account with their Facebook account, and verifies their socio-demographics by Facebook data, assuming that one’s Facebook information is real. Second, several models are proposed for predicting socio-demographics, including gender, age, ethnicity, and education levels. Afterward, this paper resamples social media data and compares it to the 2009 California Household Travel Survey data. The resampled data show comparable characteristics to the survey data. This research shed light on tackling sampling bias issues when social media data are incorporated for augmenting travel behavior analysis and urban planning.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00037-0
       
  • Real-Time Traffic Counter Using Mobile Devices

    • Free pre-print version: Loading...

      Abstract: Abstract Automatic traffic counting and classification (ATCC) is a salient step in many applications such as accessing the contribution of traffic to air pollution for clean air strategies and computing the passenger car unit (PCU) for urban road infrastructure planning and management. This work focuses on developing an ATCC system that is low cost, privacy-preserving, and auditable using state-of-the-art AI technology on mobile phones. The camera unit and the GPU compute available within a mobile phone are used to capture the video feed and run the required analytics for detection, tracking and counting in real time. On the target device, we have been able to achieve 12 FPS. On the test data composed of four videos, the solution achieved a counting precision and recall of 0.96 ± 0.02 and 0.86 ± 0.03, respectively.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00044-1
       
  • Deep Learning Techniques for Vehicle Trajectory Extraction in Mixed
           Traffic

    • Free pre-print version: Loading...

      Abstract: Abstract Vehicle trajectories provide very useful empirical data for studying traffic phenomena such as vehicle following behavior, lane changing behavior, traffic oscillations, capacity drop, safety analysis, etc. However, there are a very limited number of studies on extracting trajectory data from mixed traffic and for congested conditions. This paper presents a deep learning-based framework to extract vehicle trajectories in mixed traffic under both free-flow and congested conditions. The popular YOLOv3 deep learning architecture is used and trained on a hybrid dataset generated from two different sets of frames with different scales and orientations. The anchor boxes for vehicle detection and classification are customized to improve accuracy and efficiency. The SORT algorithm is used to track the identified vehicles and the extracted trajectory data are benchmarked with a popular trajectory extraction portal that showed that the proposed model performs well for trajectory extraction. The paper also presents a methodology based on numerical integration techniques to impute missing trajectory data. Finally, the trajectory data obtained from the adjacent road sections are aligned and scaled to the real-world coordinates using coordination transformation and error correction methods to make it useful for research purposes. The extracted trajectories show remarkable accuracy with approximately 0.25–0.35 m of precision. It is expected that these trajectories capture traffic and driving behavior phenomena for a better understanding of mixed traffic conditions.
      PubDate: 2021-08-01
      DOI: 10.1007/s42421-021-00042-3
       
  • Predicting the Use of Managed Lanes Using Machine Learning

    • Free pre-print version: Loading...

      Abstract: Abstract The use of managed lanes (MLs) is currently predicted using discrete choice models. However, recent research discovered that many travelers were not making choices in their use of MLs—they always used one set of lanes and never varied. Therefore, choice models may not be appropriate. This research examined if machine learning could be used to model ML use by travelers. Machine learning techniques were used to classify approximately 125,000 travelers who made approximately 3.5 million trips on the Katy Freeway over a 2-year period. Different machine learning models were able to classify travelers into those who always used MLs, always used toll-free general purpose lanes (GPLs), and those who used both types of lanes (choosers). Travel time savings, total number of trips made, and the start and end location of the trip were key predictors of the user class. Neural networks with LSTM could classify the trips conducted by choosers as ML trips or GPL trips based on the start and end location of the trips and the drivers’ travel history. A comparison with logit models showed that machine learning models performed better in classifying travelers and trips made by choosers. However, trends obtained from both models were same. This research has shown that a multi-level classification of travelers and machine learning techniques were able to predict real-world traveler choice on a ML facility. This may be an important first step in shifting travel models from how we think travelers are behaving to what is actually happening.
      PubDate: 2021-07-14
      DOI: 10.1007/s42421-021-00048-x
       
  • Semi-supervised GANs to Infer Travel Modes in GPS Trajectories

    • Free pre-print version: Loading...

      Abstract: Abstract This study experiments with the use of adversarial networks to classify travel mode based on one-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (or variables). We develop different GANs architectures and compare their prediction results with convolutional neural networks (CNNs). The best semi-supervised GANs model led to a prediction accuracy of 83.4%, while the best CNN model was able to achieve the prediction accuracy of 81.3%. The results compare favorably with previous studies, especially when taking the large, real-world nature of the dataset into account. The developed semi-supervised GANs models share the same architectural innovations used in the image recognition literature, that we show can be used in travel information inference from smartphone travel survey data, not only to generate more labeled samples but also to improve the prediction performance of the classifier. Future work will allow exploration of better-performing models either with more channels and/or improved architectures.
      PubDate: 2021-07-10
      DOI: 10.1007/s42421-021-00047-y
       
  • Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

    • Free pre-print version: Loading...

      Abstract: Abstract This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.
      PubDate: 2021-04-01
      DOI: 10.1007/s42421-021-00033-4
       
  • Model Free Identification of Traffic Conditions Using Unmanned Aerial
           Vehicles and Deep Learning

    • Free pre-print version: Loading...

      Abstract: Abstract The purpose of this paper is to provide a methodological framework to identify traffic conditions based on non-calibrated video recordings captured from unmanned aerial vehicles (UAV) using deep learning. To this end, we propose two complementary to each other approaches: (i) identify in real time, with minimal computational cost, traffic conditions, (ii) localize, classify vehicles and approximate traffic variables (volume, speed, density) on a road segment from video captured by UAVs. Both problems are formulated as classification problems and tackled using Convolutional Neural Networks (CNN). The use of pre-trained CNNs is also investigated. Both approaches are, then, analysed based on their accuracy and feasibility in implementation. Findings indicate that all models developed achieve a detection accuracy of 89% and higher. The CNN with the best performance can classify traffic conditions between constrained and unconstrained traffic with 91% accuracy higher than what a pretrained model achieved and with significantly faster training times. Furthermore, findings indicated that pretrained neural network for traffic localization was able to predict the position and type of vehicles with a precision of 0.91. Based on the fundamental traffic diagram, it was shown that the two approaches provide compatible results and a feasible representation of traffic on the study area. Finally, possible applications in the field of transportation and traffic monitoring are discussed.
      PubDate: 2021-04-01
      DOI: 10.1007/s42421-021-00038-z
       
  • Analyzing Parking Sentiment and its Relationship to Parking Supply and the
           Built Environment Using Online Reviews

    • Free pre-print version: Loading...

      Abstract: Abstract This study examines positive and negative sentiments associated with parking experiences reported in online Yelp reviews for four metropolitan areas in North America, leveraging large location-based social network (LBSN) data to understand parking sentiment as a measure of parking search or post-parking experiences. Demand from travelers and business owners for more parking is a significant issue for local transportation planners and decision-makers, but to date, there has been little study of how local parking management strategies or built environment characteristics modify parking experiences and sentiments. To understand this relationship, we first conduct a sentiment analysis (SA) to identify the emotional, affective content of parking-related reviews embedded in the Yelp reviews. We then use generalized mixed effects (GLME) models to examine the associations between parking sentiment and (a) parking management practices, and (b) characteristics of the built environment. The SA results show that positive and negative parking sentiments are significantly spatially clustered in each metropolitan area. GLME models show that sentiments are significantly associated with destination activity types, parking management strategies, and several built environment factors. The results of this study indicate how different interventions advocated by transportation policies may influence perceptions of parking in commercial and mixed-use districts with implications for overall support for neighborhood transportation planning best practice. Furthermore, the findings represent that emerging data mining and statistical methods can successfully leverage big data to reveal travel experiences and their relationship to urban contexts, providing an effective solution to obtaining experiential transportation information.
      PubDate: 2021-04-01
      DOI: 10.1007/s42421-021-00036-1
       
  • Correlating Hard-Braking Activity with Crash Occurrences on Interstate
           Construction Projects in Indiana

    • Free pre-print version: Loading...

      Abstract: Abstract The Federal Highway Administration (FHWA) reported between 2016 and 2017, fatal crashes in work zones increased by 3%, while fatal crashes outside of work zones decreased by 1.5%. The FHWA also reported that work zones account for approximately 10% of the nation’s overall congestion and 24% of unexpected interstate delays. This paper reports on a study of 23 construction work zones that covered approximately 150 centerline miles of Indiana interstate roadway in the summer of 2019. Approximately 50% of all interstate crashes for the period of May to September 2019 occurred within or in an approach upstream or downstream of one of these work zones. Commercially available vehicle hard-braking event data is used for the study and geofenced to the work zone approaches and limits. This research examined 196,215 hard-braking events over a 2-month period in the summer of 2019 and 3132 crashes over the same 2-month period in 2018 and 2019 for the 23 interstate work zones. The study found there were approximately 1 crash/mile for every 147 hard-braking events in and around a construction site. The R2 was approximately 0.85. The paper concludes by recommending that hard-braking event data be used by agencies to quickly identify emerging work zone locations that show relatively large number of hard-braking events for further evaluation.
      PubDate: 2021-04-01
      DOI: 10.1007/s42421-020-00024-x
       
  • Sensor-Based Transportation Mode Recognition Using Variational Autoencoder

    • Free pre-print version: Loading...

      Abstract: Abstract We present a data augmentation technique that can improve the classification of transportation modes when the training data is insufficient. The proposed method uses a variational autoencoder (VAE) based synthetic data generation algorithm for smartphone data. Often the data collected by individuals for research is limited due to practical constraints. The algorithm discussed would aid in generating similar data from a handful of collected data to give a substantial dataset for any machine learning models. We propose a VAE, the decoder of which can help generate this synthetic data. We show that the synthetic data closely follows the pattern of the real data. We also show that classification accuracy is improved with the use of this type of data. Our method would also be a useful tool to boost the samples of an underrepresented class in a dataset. The detection of activity recognition using smartphone sensors could be applied to multiple aspects of vehicle to pedestrian P2V systems and smart mobility.
      PubDate: 2021-04-01
      DOI: 10.1007/s42421-021-00035-2
       
  • Correction to: Deep Learning in Transport Studies: A Meta-analysis on the
           Prediction Accuracy

    • Free pre-print version: Loading...

      Abstract: The final version of the published manuscript had several instances of error in referencing, especially in references with authors of the same name and year.
      PubDate: 2020-12-01
      DOI: 10.1007/s42421-021-00034-3
       
  • Deep Learning in Transport Studies: A Meta-analysis on the Prediction
           Accuracy

    • Free pre-print version: Loading...

      Abstract: Abstract Deep learning methods are being increasingly applied in transport studies, while the methods require modellers to go through a try-and-error model tuning process particularly on choosing neural network structure. Moreover, the accuracy level also depends on other factors such as the type of data, sample size, region of data collection, and time of prediction. To efficiently facilitate such a model tuning process, this study attempts to summarize the relationship between the prediction accuracy of deep learning models and the factors which influence it. We conducted a comprehensive review of the literature by adopting a detailed search strategy, followed by a meta-analysis on prediction accuracy. Four separate linear mixed effects models, taking into account unobserved heterogeneities in prediction accuracy across studies, were developed to statistically test the impacts of influential factors on prediction accuracy for (a) all observations (136 studies; 2314 cases), (b) studies with MAPE, MRE, and average accuracy indicators (86 studies; 1,878 cases), (c) classification-based studies with accuracy indicator (29 studies; 220 cases), and (d) traffic forecasting studies with MAPE, MRE, and average accuracy indicators (36 studies, 991 cases). The final model includes additional factors to test the influence of sample size and time horizon of prediction variables. The findings showed that, as expected, deep learning models, particularly ones that consider spatiotemporal dependencies of transport phenomena, show better prediction accuracies compared to conventional machine learning models. We also found that, on average, the prediction accuracy is improved by 5.90% with 100 million additional data, while the accuracy is reduced by 5.28% with 100 min increase in time horizon of prediction in traffic forecasting studies. We concluded this paper with a comprehensive summary of the existing findings on the applications of deep learning to transport studies.
      PubDate: 2020-12-01
      DOI: 10.1007/s42421-020-00030-z
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 35.170.82.159
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-