Journal of Advanced Transportation
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 Journal metrics
Acceptance rate36%
Submission to final decision106 days
Acceptance to publication75 days
CiteScore3.400
Journal Citation Indicator0.520
Impact Factor2.419

Article of the Year 2020

Identifying Big Five Personality Traits through Controller Area Network Bus Data

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 Journal profile

Journal of Advanced Transportation publishes theoretical and innovative papers on analysis, design, operations, optimization and planning of multi-modal transport networks, transit & traffic systems, transport technology and traffic safety.

 Editor spotlight

Chief Editor, Dr Gonçalo Homem de Almeida Correia, is based at Delft University of Technology, The Netherlands. His main research interest is in the planning and operations of transport systems in urban environments.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model

This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020–2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value.

Research Article

The Effect of Parenting Styles on Children’s Familiarity with Traffic Signs

The community, and especially the family, affects children’s traffic safety. Parents influence children’s current and future traffic behaviors. Numerous studies have demonstrated a relationship between parenting style and children’s behavioral problems such as antisocial behaviors and delinquency, so the modification of parenting styles could have a positive impact on the interactions between parents and children. In the literature on children’s traffic safety, parental influence has long been recognized as an important aspect of research, but parent-related factors are mostly unknown. In particular, a factor that can affect parents’ attitudes and children’s views of road safety is parenting style. Therefore, this study aims to examine children’s knowledge of traffic signs utilizing a parenting styles’ perspective. The determining role of demographic characteristics in traffic skills is critical and is investigated in this study. In this study, 1011 preschool, first-, second-, and third-grade students were interviewed and information about parenting styles and demographic characteristics were collected from questionnaires completed by parents. Through interviews, children’s familiarity with law enforcement and informative signs was assessed. Results indicated that older children and those with higher socioeconomic status had better skills in this field. The results also showed that parents could improve their children’s understanding of signs by less use of inconsistent discipline and corporal punishments. Parental negligence, contradictory use of corporal punishment, and nonuse of positive behaviors are some factors which are most likely related to children’s knowledge of traffic signs and rules. The findings of this study can guide parents and assist relevant authorities to implement policies to more effectively train young children by developing practical and targeted resources.

Research Article

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.

Research Article

MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit

Passenger flow forecasting plays an important role in urban rail transit (URT) management. However, complex spatial and temporal correlations make this task extremely challenging. Previous work has been done by capturing spatiotemporal correlations of historical data. However, the spatiotemporal relationship between stations not only is limited to geospatial adjacency, but also lacks different perspectives of station correlation analysis. To fully capture the spatiotemporal correlations, we propose a deep learning model based on graph convolutional neural networks called MDGCN. Firstly, we identify the heterogeneity of stations under two spaces by the Multi-graph convolutional layer. Secondly, we designed the Diff-graph convolutional layer to identify the changing trend of heterogeneous features and used the attention mechanism unit with the LSTM unit to achieve adaptive fusion of multiple features and modeling of temporal correlation. We evaluate this model on real datasets. Compared to the best baselines, the root-mean-square errors of MDGCN are improved by 1%–15% for different prediction intervals.

Research Article

Truck-Lifting Prevention System Based on Vision Tracking for Container-Lifting Operation

Truck-lifting accidents are common in container-lifting operations. Previously, the operation sites are needed to arrange workers for observation and guidance. However, with the development of automated equipment in container terminals, an automated accident detection method is required to replace manual workers. Considering the development of vision detection and tracking algorithms, this study designed a vision-based truck-lifting prevention system. This system uses a camera to detect and track the movement of the truck wheel hub during the operation to determine whether the truck chassis is being lifted. The hardware device of this system is easy to install and has good versatility for most container-lifting equipment. The accident detection algorithm combines convolutional neural network detection, traditional image processing, and a multitarget tracking algorithm to calculate the displacement and posture information of the truck during the operation. The experiments show that the measurement accuracy of this system reaches 52 mm, and it can effectively distinguish the trajectories of different wheel hubs, meeting the requirements for detecting lifting accidents.

Research Article

Prediction of Road Network Traffic State Using the NARX Neural Network

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Journal of Advanced Transportation
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate36%
Submission to final decision106 days
Acceptance to publication75 days
CiteScore3.400
Journal Citation Indicator0.520
Impact Factor2.419
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.