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Jinjun Tang
Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha, China

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Article
Published: 03 August 2021 in Transportation
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Understanding the relationships between commuting demand and built environment (BE) features is advantageous in alleviating travel pressure during the commuting period. Numerous works have explored these relationships under either a single spatial unit or travel modes. However, few efforts have attempted to examine how variations in both spatial units and transport modes affect the relationships. To address the above issues, we investigate the impact of the modifiable areal unit problem (MAUP) on the modeling results across three partitioning schemes (i.e., hexagonal-based, square-based, and administrative boundary) and nine spatial units under four commuting modes (i.e., taxi, bike-sharing, ride-hailing, and bus). A geographically weighted regression is applied to estimate commuting demands considering spatial autocorrelation and heterogeneity. The experiment is conducted using smart card data and vehicle GPS trajectories collected in Shenzhen City, China. The contributions of this study include the following three parts. First, it evaluates the pros and cons of different spatial units for commuting demand modeling. Second, it summarizes the effects of the MAUP on relationships among land use, demographic, and traffic network characteristics and commuting demand according to the partitioning scale and schemes. Third, it examines the similarities and differences in the effect of the MAUP on commuting demand modeling among four travel modes. The findings of this study contribute to the division of traffic analysis zones and the allocation of commuting trips.

ACS Style

Fan Gao; Jinjun Tang; Zhitao Li. Effects of spatial units and travel modes on urban commuting demand modeling. Transportation 2021, 1 -27.

AMA Style

Fan Gao, Jinjun Tang, Zhitao Li. Effects of spatial units and travel modes on urban commuting demand modeling. Transportation. 2021; ():1-27.

Chicago/Turabian Style

Fan Gao; Jinjun Tang; Zhitao Li. 2021. "Effects of spatial units and travel modes on urban commuting demand modeling." Transportation , no. : 1-27.

Journal article
Published: 30 July 2021 in Transportmetrica A: Transport Science
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A lane-changing process is complicated due to multiple factors in the driving environment, and unsafe lane-changing behaviour may lead to a severe crash. This study proposes a method for the driving angle prediction of lane changes based on extremely randomized decision trees. First, the harmonic potential is defined to characterize the interaction between the lane-changing vehicle and the surrounding vehicles. Next, we construct extremely randomized decision trees to predict driving angles considering relative velocity, relative acceleration, and potential as input variables. Then, the NGSIM dataset is used to verify the method proposed, and the lane-changing process is divided into two stages by different environments. Furthermore, a comparison of prediction performance with several traditional machine learning methods further demonstrates the superior learning ability of the proposed method. Finally, we conduct a sensitivity analysis on the significant variables and discuss the effects of these variables on the prediction results.

ACS Style

Zhe Wang; Helai Huang; Jinjun Tang; Jaeyoung Lee; Xianwei Meng. Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method. Transportmetrica A: Transport Science 2021, 1 -25.

AMA Style

Zhe Wang, Helai Huang, Jinjun Tang, Jaeyoung Lee, Xianwei Meng. Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method. Transportmetrica A: Transport Science. 2021; ():1-25.

Chicago/Turabian Style

Zhe Wang; Helai Huang; Jinjun Tang; Jaeyoung Lee; Xianwei Meng. 2021. "Driving angle prediction of lane changes based on extremely randomized decision trees considering the harmonic potential field method." Transportmetrica A: Transport Science , no. : 1-25.

Original article
Published: 18 June 2021 in Computer-Aided Civil and Infrastructure Engineering
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The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.

ACS Style

Jinjun Tang; Jie Zeng. Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer-Aided Civil and Infrastructure Engineering 2021, 1 .

AMA Style

Jinjun Tang, Jie Zeng. Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data. Computer-Aided Civil and Infrastructure Engineering. 2021; ():1.

Chicago/Turabian Style

Jinjun Tang; Jie Zeng. 2021. "Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data." Computer-Aided Civil and Infrastructure Engineering , no. : 1.

Original research paper
Published: 12 June 2021 in IET Intelligent Transport Systems
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Trip destination prediction plays an important role in exploring urban travel patterns. Accurate prediction can improve the efficiency of traffic management and the quality of location-based services. Here, a deep learning structure that contains three components: travel information extraction, classification learning mechanism, and output module is proposed. Three types of information (the partial trajectory of on-going trips, historical trajectories, and related external information) are extracted in the first component. Then, the classification learning mechanism chooses different methods (i.e. Long Short-Term Memory network and Embedding technology) according to the characteristic of variables. Finally, an output layer that integrates the prior information about destinations is constructed. Two open-source trajectory datasets are used to validate the effectiveness of the proposed model. Results show that the proposed model outperforms benchmark models using only part of the information or using all of the information but ignore the classification learning mechanism. The performance of the proposed model under different call types and travel durations is further explored. The result of this study will help understand travel behaviour in urban cities.

ACS Style

Jinjun Tang; Jian Liang; Tianjian Yu; Yong Xiong; Guoliang Zeng. Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories. IET Intelligent Transport Systems 2021, 1 .

AMA Style

Jinjun Tang, Jian Liang, Tianjian Yu, Yong Xiong, Guoliang Zeng. Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories. IET Intelligent Transport Systems. 2021; ():1.

Chicago/Turabian Style

Jinjun Tang; Jian Liang; Tianjian Yu; Yong Xiong; Guoliang Zeng. 2021. "Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories." IET Intelligent Transport Systems , no. : 1.

Research article
Published: 17 May 2021 in Journal of Advanced Transportation
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This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k-means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k-means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.

ACS Style

Yong Wang; Xiuwen Wang; Xiangyang Guan; Jinjun Tang. Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment. Journal of Advanced Transportation 2021, 2021, 1 -21.

AMA Style

Yong Wang, Xiuwen Wang, Xiangyang Guan, Jinjun Tang. Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment. Journal of Advanced Transportation. 2021; 2021 ():1-21.

Chicago/Turabian Style

Yong Wang; Xiuwen Wang; Xiangyang Guan; Jinjun Tang. 2021. "Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment." Journal of Advanced Transportation 2021, no. : 1-21.

Research article
Published: 11 March 2021 in Journal of Advanced Transportation
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Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered.

ACS Style

Yajie Zou; Bo Lin; Xiaoxue Yang; Lingtao Wu; Malik Muneeb Abid; Jinjun Tang. Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management. Journal of Advanced Transportation 2021, 2021, 1 -9.

AMA Style

Yajie Zou, Bo Lin, Xiaoxue Yang, Lingtao Wu, Malik Muneeb Abid, Jinjun Tang. Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management. Journal of Advanced Transportation. 2021; 2021 ():1-9.

Chicago/Turabian Style

Yajie Zou; Bo Lin; Xiaoxue Yang; Lingtao Wu; Malik Muneeb Abid; Jinjun Tang. 2021. "Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management." Journal of Advanced Transportation 2021, no. : 1-9.

Research article
Published: 04 February 2021 in Journal of Advanced Transportation
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Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.

ACS Style

Fang Liu; Wei Bi; Wei Hao; Fan Gao; Jinjun Tang. An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns. Journal of Advanced Transportation 2021, 2021, 1 -13.

AMA Style

Fang Liu, Wei Bi, Wei Hao, Fan Gao, Jinjun Tang. An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns. Journal of Advanced Transportation. 2021; 2021 ():1-13.

Chicago/Turabian Style

Fang Liu; Wei Bi; Wei Hao; Fan Gao; Jinjun Tang. 2021. "An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns." Journal of Advanced Transportation 2021, no. : 1-13.

Research article
Published: 05 December 2020 in Transportmetrica A: Transport Science
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Exploring characteristics in traffic flow and predicting its variation patterns are the main steps to realize applications of Intelligent Transportation Systems. Most existing studies focused on highway or freeway traffic flow predictions and achieved good performance. However, due to the intermittent characteristics and intense fluctuation on short-term scales, it is a challenge to accurately predict future variation patterns of traffic flow on the urban road network. A hybrid model: Genetic Algorithm with Attention-based Long Short-Term Memory (GA-LSTM), combining with spatial-temporal correlation analysis, is proposed in this study to predict traffic volume on urban roads. In the temporal correlation modeling, the attention mechanism is introduced to represent the significance of historical observations in the LSTM model. In the spatial correlation modeling, a new weight matrix is constructed to combine the volume transition matrix estimated from vehicle trajectories and network weight matrix quantified from different detectors. Finally, the Genetic Algorithm is improved to optimize the attention weight in the deep learning structure. In the experiment, traffic flow data collected from License Plate Recognition (LPR) devices located at 19 urban intersections in Changsha City, China, is utilized to validate the effectiveness of the established model. A model comparison is further conducted with several widely used prediction models to show the superiority of the proposed model with higher accuracy and better stability under different prediction steps, especially advantage in capturing intense fluctuation of traffic volume at the short-term interval.

ACS Style

Jinjun Tang; Jie Zeng; Yuwei Wang; Hang Yuan; Fang Liu; Helai Huang. Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science 2020, 17, 1217 -1243.

AMA Style

Jinjun Tang, Jie Zeng, Yuwei Wang, Hang Yuan, Fang Liu, Helai Huang. Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm. Transportmetrica A: Transport Science. 2020; 17 (4):1217-1243.

Chicago/Turabian Style

Jinjun Tang; Jie Zeng; Yuwei Wang; Hang Yuan; Fang Liu; Helai Huang. 2020. "Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm." Transportmetrica A: Transport Science 17, no. 4: 1217-1243.

Journal article
Published: 01 December 2020 in Analytic Methods in Accident Research
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Many studies have been devoted to investigate the spatial variations (heterogeneities) in the effects of risk factors on crash likelihood. However, investigations mainly target the safety effects on the mean of the crash data (distribution). Less attention was paid to investigate the spatial nonstationary effects on the different quantiles of the crash data distribution. In this study, a conditional quantile-based Bayesian hierarchical random parameter Tobit model is proposed to investigate the regional varying effects of road-related factors on crash rate at different quantiles of the crash rate distribution. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road related data for a three-year period is used for a case study. The results show that: 1) the regression coefficients of all of the selected risk factors vary over a wide range among 34 counties on every investigated quantiles of crash rate distribution; 2) in each county, the regression coefficients of all of the factors vary over investigated quantiles of the crash rate distribution, and for the same factor, the coefficients present different ranges of the variation in different counties; 3) the 50th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model outperforms the conditional mean-based Bayesian hierarchical random parameter Tobit model, Bayesian quantile Tobit model and Bayesian Tobit model in terms of the prediction accuracy measured by the MAE, and 75th-quantile conditional quantile-based Bayesian hierarchical random parameter Tobit model is outstanding in terms of the goodness-of-fit measured by the DIC. These findings suggest the importance of investigating the regional nonstationary effects of risk factors for different quantiles of the crash rate distribution. The practical implications of the proposed conditional quantile-based Bayesian hierarchical random parameter Tobit model in terms of data prediction, parameters interpretation and safety effects explanation are highlighted at the end of this paper.

ACS Style

Jinjun Tang; Weiqi Yin; Chunyang Han; Xinyuan Liu; Helai Huang. A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates. Analytic Methods in Accident Research 2020, 29, 100153 .

AMA Style

Jinjun Tang, Weiqi Yin, Chunyang Han, Xinyuan Liu, Helai Huang. A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates. Analytic Methods in Accident Research. 2020; 29 ():100153.

Chicago/Turabian Style

Jinjun Tang; Weiqi Yin; Chunyang Han; Xinyuan Liu; Helai Huang. 2020. "A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates." Analytic Methods in Accident Research 29, no. : 100153.

Research article
Published: 09 October 2020 in Journal of Advanced Transportation
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Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

ACS Style

Yue Zhang; Yajie Zou; Lingtao Wu; Jinjun Tang; Malik Muneeb Abid. Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency. Journal of Advanced Transportation 2020, 2020, 1 -9.

AMA Style

Yue Zhang, Yajie Zou, Lingtao Wu, Jinjun Tang, Malik Muneeb Abid. Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency. Journal of Advanced Transportation. 2020; 2020 ():1-9.

Chicago/Turabian Style

Yue Zhang; Yajie Zou; Lingtao Wu; Jinjun Tang; Malik Muneeb Abid. 2020. "Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency." Journal of Advanced Transportation 2020, no. : 1-9.

Journal article
Published: 29 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.

ACS Style

Jinjun Tang; Yifan Yang; Wei Hao; Fang Liu; Yinhai Wang. A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -13.

AMA Style

Jinjun Tang, Yifan Yang, Wei Hao, Fang Liu, Yinhai Wang. A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-13.

Chicago/Turabian Style

Jinjun Tang; Yifan Yang; Wei Hao; Fang Liu; Yinhai Wang. 2020. "A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-13.

Journal article
Published: 23 March 2020 in Journal of Advanced Transportation
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Efficient parking tends to be challenging in most large cities in China. Drivers often spend substantial amounts of time looking for parking lots while driving at low speeds, thereby resulting in interference with road traffic. This paper focuses on efficiently allocating parking spaces to the demanders. A double-objective model is proposed that considers both the utilizing rate and the walking distance. First, managers want to utilize parking resources fully. Therefore, they tend to prioritize the efficient distribution of parking spaces in response to parking demands. However, demanders typically choose parking spaces according to convenience. The second objective is the acceptable walking distance from the parking space to the destination. The particle swarm optimization (PSO) algorithm is used to solve this model. We collected parking demand and supply data in a central business district (CBD) of Harbin in China and evaluated the feasibility of the model. The results demonstrate that the proposed model increases the occupying rates of parking lots in residential zones while decreasing the walking distance. The shared use of parking spaces maximizes the utility and alleviates the shortage of parking spaces in downtown.

ACS Style

Wenhui Zhang; Fan Gao; Shurui Sun; Qiuying Yu; Jinjun Tang; Bohang Liu. A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance. Journal of Advanced Transportation 2020, 2020, 1 -11.

AMA Style

Wenhui Zhang, Fan Gao, Shurui Sun, Qiuying Yu, Jinjun Tang, Bohang Liu. A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance. Journal of Advanced Transportation. 2020; 2020 ():1-11.

Chicago/Turabian Style

Wenhui Zhang; Fan Gao; Shurui Sun; Qiuying Yu; Jinjun Tang; Bohang Liu. 2020. "A Distribution Model for Shared Parking in Residential Zones that Considers the Utilization Rate and the Walking Distance." Journal of Advanced Transportation 2020, no. : 1-11.

Journal article
Published: 03 March 2020 in IEEE Access
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Ship tracking provides crucial on-site microscopic kinematic traffic information which benefits maritime traffic flow analysis, ship safety enhancement, traffic control, etc., and thus has attracted considerable research attentions in the maritime surveillance community. Conventional ship tracking methods yield satisfied results by exploring distinct visual ship features in maritime images, which may fail when the target ship is partially or fully sheltered by obstacles (e.g., ships, waves, etc.) in maritime videos. To overcome the difficulty, we propose an augmented ship tracking framework via the kernelized correlation filter (KCF) and curve fitting algorithm. First, the KCF model is introduced to track ships in the consecutive maritime images and obtain raw ship trajectory dataset. Second, the data anomaly detection and rectification procedure are implemented to rectify the contaminated ship positions. For the purpose of performance evaluation, we implement the proposed framework and another three popular ship tracking models on the four typical ship occlusion videos. The experimental results show that our proposed framework successfully tracks ships in maritime video clips with high accuracy (i.e., the average root mean square error (RMSE), root mean square percentage error (RMSPE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) are less than 10), which significantly outperforms the other popular ship trackers.

ACS Style

Xinqiang Chen; XueQian Xu; Yongsheng Yang; Huafeng Wu; Jinjun Tang; Jiansen Zhao. Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos. IEEE Access 2020, 8, 42884 -42897.

AMA Style

Xinqiang Chen, XueQian Xu, Yongsheng Yang, Huafeng Wu, Jinjun Tang, Jiansen Zhao. Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos. IEEE Access. 2020; 8 (99):42884-42897.

Chicago/Turabian Style

Xinqiang Chen; XueQian Xu; Yongsheng Yang; Huafeng Wu; Jinjun Tang; Jiansen Zhao. 2020. "Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos." IEEE Access 8, no. 99: 42884-42897.

Journal article
Published: 28 February 2020 in Journal of Navigation
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Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition.

ACS Style

Xinqiang Chen; Yongsheng Yang; Shengzheng Wang; Huafeng Wu; Jinjun Tang; Jiansen Zhao; Zhihuan Wang. Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network. Journal of Navigation 2020, 73, 813 -832.

AMA Style

Xinqiang Chen, Yongsheng Yang, Shengzheng Wang, Huafeng Wu, Jinjun Tang, Jiansen Zhao, Zhihuan Wang. Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network. Journal of Navigation. 2020; 73 (4):813-832.

Chicago/Turabian Style

Xinqiang Chen; Yongsheng Yang; Shengzheng Wang; Huafeng Wu; Jinjun Tang; Jiansen Zhao; Zhihuan Wang. 2020. "Ship Type Recognition via a Coarse-to-Fine Cascaded Convolution Neural Network." Journal of Navigation 73, no. 4: 813-832.

Journal article
Published: 17 February 2020 in Sustainability
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Individual mobility patterns are an important factor in urban traffic planning and traffic flow forecasting. How to understand the spatio-temporal distribution of passengers deeply and accurately, so as to provide theoretical support for the planning and operation of the metro network, is an urgent issue of wide concern. In this paper, we applied NCP decomposition to uncover the characteristics of travel patterns from temporal and spatial dimensions in the metro network of Shenzhen City. Utilizing matrix factorization and correlation analysis, we extracted several stable components from the collective mobility and find that the departure and arrival mobility patterns have different characteristics in both the temporal and spatial dimension. According to the point of interest (POI) data in the Shenzhen City, the function attributes of the station are identified and then we found that the spatial distribution characteristics of different patterns are different. We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.

ACS Style

Jinjun Tang; Xiaolu Wang; Fang Zong; Zheng Hu. Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China. Sustainability 2020, 12, 1475 .

AMA Style

Jinjun Tang, Xiaolu Wang, Fang Zong, Zheng Hu. Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China. Sustainability. 2020; 12 (4):1475.

Chicago/Turabian Style

Jinjun Tang; Xiaolu Wang; Fang Zong; Zheng Hu. 2020. "Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China." Sustainability 12, no. 4: 1475.

Research article
Published: 20 January 2020 in Journal of Advanced Transportation
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Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety.

ACS Style

Xinqiang Chen; Lei Qi; Yongsheng Yang; Qiang Luo; Octavian Postolache; Jinjun Tang; Huafeng Wu. Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis. Journal of Advanced Transportation 2020, 2020, 1 -12.

AMA Style

Xinqiang Chen, Lei Qi, Yongsheng Yang, Qiang Luo, Octavian Postolache, Jinjun Tang, Huafeng Wu. Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis. Journal of Advanced Transportation. 2020; 2020 ():1-12.

Chicago/Turabian Style

Xinqiang Chen; Lei Qi; Yongsheng Yang; Qiang Luo; Octavian Postolache; Jinjun Tang; Huafeng Wu. 2020. "Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis." Journal of Advanced Transportation 2020, no. : 1-12.

Research article
Published: 20 January 2020 in Journal of Advanced Transportation
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Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

ACS Style

Xiaoxue Yang; Yajie Zou; Jinjun Tang; Jian Liang; Muhammad Ijaz. Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models. Journal of Advanced Transportation 2020, 2020, 1 -16.

AMA Style

Xiaoxue Yang, Yajie Zou, Jinjun Tang, Jian Liang, Muhammad Ijaz. Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models. Journal of Advanced Transportation. 2020; 2020 ():1-16.

Chicago/Turabian Style

Xiaoxue Yang; Yajie Zou; Jinjun Tang; Jian Liang; Muhammad Ijaz. 2020. "Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models." Journal of Advanced Transportation 2020, no. : 1-16.

Journal article
Published: 06 January 2020 in IEEE Access
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In the Intelligent Transportation Systems (ITS), highly accurate traffic flow prediction is considered as key technology to evaluate traffic state of the urban road network. However, due to disturbing from environment, the original traffic flow data may be influenced by noise and finally cause the decline of prediction accuracy. This study design a hybrid prediction model combining Ensemble Empirical Mode Decomposition (EEMD) denoising schemes and classifying learning algorithm based on Fuzzy C-means Neural Network (FCMNN) to improve prediction accuracy. In the model training process, several key parameters in EEMD and FCMNN are determined according to prediction errors based on traffic volume detected from highway network in the Minneapolis city. In the model validation, three widely used indicators for error evaluation are applied to estimate the prediction accuracy of four candidate models under single and multi step, including Artificial Neural Network (ANN), EEMD+ANN, FCMNN and EEMD+FCMNN. The results shown in the case study indicate that the prediction models combined with denoising methods are superior to the models without adopting denoising algorithm. Furthermore, the model using classifying learning method FCMNN can produce higher prediction accuracy than traditional ANN model. In addition, the long-term prediction performance of FCMNN is also much better than that of ANN because that sub-NN system is trained according to each classifying patterns to obtain better optimization effect. Results summarized in this study could be helpful for administration to design managing and controlling strategies according to high prediction accuracy.

ACS Style

Jinjun Tang; Fan Gao; Fang Liu; Xinqiang Chen. A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network. IEEE Access 2020, 8, 11546 -11559.

AMA Style

Jinjun Tang, Fan Gao, Fang Liu, Xinqiang Chen. A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network. IEEE Access. 2020; 8 (99):11546-11559.

Chicago/Turabian Style

Jinjun Tang; Fan Gao; Fang Liu; Xinqiang Chen. 2020. "A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network." IEEE Access 8, no. 99: 11546-11559.

Journal article
Published: 17 December 2019 in Physica A: Statistical Mechanics and its Applications
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In order to improve traffic safety, a large amount of works focusing on crash prediction and identifying factors contributing to crashes. However, the ignorance of data unobserved heterogeneity in some traditional models may lead to biased parameter estimation and erroneous inferences. To investigate the relationship between crash and the potential contributing factors, the crash data occurred in 3-year survey period on Interstate highways in Washington, including 134 fatal crashes, 13936 injury crashes, and 34,084 property damage only (PDO) crashes were collected. A data quality control method based on sensitivity analysis is used to determine the road segments. Then a negative binomial (NB) model and a random negative binomial (RENB) model are constructed to predict crash number. The inverse stepwise procedure was applied to examine the significance of explanatory variable. The horizontal alignment type, speed limit, visibility, road surface condition, and AADT are identified as significant factors on the crash. In the comparison, four standard errors are designed as indicators, and the results show that the errors of RENB model are lower than that of NB model. The comparing results illustrate that the RENB model outperforms the NB model in crash number prediction and safety service level prediction

ACS Style

Ying Yan; Ying Zhang; Xiangli Yang; Jin Hu; Jinjun Tang; Zhongyin Guo. Crash prediction based on random effect negative binomial model considering data heterogeneity. Physica A: Statistical Mechanics and its Applications 2019, 547, 123858 .

AMA Style

Ying Yan, Ying Zhang, Xiangli Yang, Jin Hu, Jinjun Tang, Zhongyin Guo. Crash prediction based on random effect negative binomial model considering data heterogeneity. Physica A: Statistical Mechanics and its Applications. 2019; 547 ():123858.

Chicago/Turabian Style

Ying Yan; Ying Zhang; Xiangli Yang; Jin Hu; Jinjun Tang; Zhongyin Guo. 2019. "Crash prediction based on random effect negative binomial model considering data heterogeneity." Physica A: Statistical Mechanics and its Applications 547, no. : 123858.

Articles
Published: 17 November 2019 in Traffic Injury Prevention
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Objective: Driving behavior is the key feature for determining the nature of traffic stream qualities and reflecting the risk of operating environments. However, evaluating the driving risk accurately and practically in continuous tunnels (tunnels with a space more than 250 m and less than 1000 m) still faces severe challenges due to the complex driving conditions. The objective of this study is to predict the driving risk indicators and determine different risk levels. Methods: The naturalistic driving system equipped with a road environment and driving behavior data acquisition system combined with the fixed-point test method was used for data collection in 130 tunnels on four highways. A traditional AASHTO braking model and convex hull algorithm were adopted to predict the critical safety speed and the critical time headway of each risk feature point in tunnels. According to the risk constraints under free-flow, car-following and lane-changing conditions, the average traffic flow risk index (TFRI) representing six risk levels and the safety threshold of the corresponding risk indicators were determined. Results: The findings of this study revealed that the critical safety speed at nighttime is slower than in other daytime conditions in continuous tunnels. The time headway slightly changes under 90 km/h. As the speed continues to increase, speed has a significant influence on the critical time headway. The only reliable interaction involved the different adverse weather conditions on the mean critical safety speed in the continuous tunnels (short plus long) (F = 9.730, p<0.05) and single long tunnels (F = 12.365, p<0.05). Conclusions: It can be concluded that driving behaviors significantly vary in different tunnel risk feature points and the combined effect of high speed and luminance variation may result in high driving risk. The performance validation indicted that the risk assessment level determined by the proposed approach is consistent with the real safety situations. The study provides an effective and generally acceptable method for identifying driving risk criteria that can also be applied for traffic management and safety countermeasures with a view to possible implementation in continuous tunnels.

ACS Style

Ying Yan; Youhua Dai; Xiaodong Li; Jinjun Tang; Zhongyin Guo. Driving risk assessment using driving behavior data under continuous tunnel environment. Traffic Injury Prevention 2019, 20, 807 -812.

AMA Style

Ying Yan, Youhua Dai, Xiaodong Li, Jinjun Tang, Zhongyin Guo. Driving risk assessment using driving behavior data under continuous tunnel environment. Traffic Injury Prevention. 2019; 20 (8):807-812.

Chicago/Turabian Style

Ying Yan; Youhua Dai; Xiaodong Li; Jinjun Tang; Zhongyin Guo. 2019. "Driving risk assessment using driving behavior data under continuous tunnel environment." Traffic Injury Prevention 20, no. 8: 807-812.