Editorial

5 pages, 180 KiB

Open AccessEditorial

Special Issue on Transportation Big Data and Its Applications

by Xiaolei Ma, Xinqiang Chen and Zhuang Dai

Appl. Sci. 2024, 14(4), 1517; https://doi.org/10.3390/app14041517 - 13 Feb 2024

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Large-scale traffic sensors are strategically deployed across various infrastructures and modes of transportation (e [...] Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

Research

Jump to: Editorial, Review

19 pages, 8013 KiB

Open AccessArticle

Risk Propagation Mechanism and Prediction Model for the Highway Merging Area

by Qing Ye, Yi Li and Ben Niu

Appl. Sci. 2023, 13(14), 8014; https://doi.org/10.3390/app13148014 - 08 Jul 2023

Viewed by 915

Abstract

The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to [...] Read more.

The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to quantify the driving risk in this region. An effective risk prediction model is important for improving traffic control measures in this specific area. In this study, simulation experiments were conducted in SUMO (Simulation of Urban Mobility) to obtain the accident and risk propagation data in merging areas. Firstly, the Gaussian plume model was optimized for the merging area situation to determine and divide the impact range of the accidents. Then, different accident scenarios in the merging area and downstream were simulated with different input flow rates to study the time and speed of risk propagation in the three-level affected areas. Finally, LSTM (long short-term memory) and RNN (recurrent neural network) models were built to predict the accident risk in the merging area. The results showed that the LSTM model had higher accuracy. This study provides an innovative insight into the propagation process of merging area accidents. It is of benefit to the development of post-accident control measures. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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16 pages, 2453 KiB

Open AccessArticle

Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure

by Chunwei Hu, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song and Jin Zhang

Appl. Sci. 2023, 13(12), 7271; https://doi.org/10.3390/app13127271 - 18 Jun 2023

Viewed by 1372

Abstract

Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the [...] Read more.

Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model’s capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model’s sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model’s usefulness for crowd flow prediction tasks. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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21 pages, 2540 KiB

Open AccessArticle

Driving Behavior Risk Measurement and Cluster Analysis Driven by Vehicle Trajectory Data

by Shuyi Chen, Kun Cheng, Junheng Yang, Xiaodong Zang, Qiang Luo and Jiahao Li

Appl. Sci. 2023, 13(9), 5675; https://doi.org/10.3390/app13095675 - 05 May 2023

Cited by 2 | Viewed by 1679

Abstract

The correct identification and timely pre-warning of driving behavior risks can remind drivers to correct their unsafe driving behaviors effectively. First of all, four risk evaluation indicators of driving behavior were defined based on lateral and longitudinal driving characteristics: the lateral stability indicator, [...] Read more.

The correct identification and timely pre-warning of driving behavior risks can remind drivers to correct their unsafe driving behaviors effectively. First of all, four risk evaluation indicators of driving behavior were defined based on lateral and longitudinal driving characteristics: the lateral stability indicator, the longitudinal stability indicator, the car-following risk indicator, and the lane-changing risk indicator. The Pearson correlation coefficient method was used to analyze the correlation of the four indicators, and the conclusion showed that the four indicators were very weakly correlated or presented an irrelevant correlation. Thus, the four indicators can describe different driving behavior risks. Secondly, the criteria importance through intercriteria correlation (CRITIC) method was used to determine the weight of each indicator, and a comprehensive measurement model of driving behavior risk was established. To test the model, this study preprocessed the trajectory data of small vehicles in Lanes 1–5 of the I-80 Expressway from the NGSIM dataset, collected statistical analysis results of vehicle speed and acceleration, and obtained the parameters data required for risk assessment. Then, based on the obtained trajectory data, the variation laws and the thresholds of the four indicators were determined by using the interquartile difference method. Finally, by using the K-means clustering algorithm, the risk types of driving behavior were divided into four categories, namely, dangerous, aggressive, safe, and conservative. The dangerous, aggressive, safe, and conservative driving behaviors accounted for 5.40%, 23.30%, 43.22%, and 28.08% of the total samples, respectively. The expert’s assessment results of the driving behavior risk aligned with the results obtained from the model measurements. This indicated that the driving behavior risk measurement model here described can evaluate a driver’s risk status in real time, provide safety tips for the driver, and offer theoretical support for driving safety warning systems. Full article

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23 pages, 6489 KiB

Open AccessArticle

Exploring the Relative Importance and Interactive Impacts of Explanatory Variables of the Built Environment on Ride-Hailing Ridership by Using the Optimal Parameter-Based Geographical Detector (OPGD) Model

by Zhenbao Wang, Shuyue Liu, Yuchen Zhang, Xin Gong, Shihao Li, Dong Liu and Ning Chen

Appl. Sci. 2023, 13(4), 2180; https://doi.org/10.3390/app13042180 - 08 Feb 2023

Cited by 1 | Viewed by 1580

Abstract

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density [...] Read more.

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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15 pages, 2549 KiB

Open AccessArticle

Optimizing Electric Taxi Battery Swapping Stations Featuring Modular Battery Swapping: A Data-Driven Approach

by Zhengke Liu, Xiaolei Ma, Xiaohan Liu, Gonçalo Homem de Almeida Correia, Ruifeng Shi and Wenlong Shang

Appl. Sci. 2023, 13(3), 1984; https://doi.org/10.3390/app13031984 - 03 Feb 2023

Cited by 3 | Viewed by 1612

Abstract

Optimizing battery swapping station (BSS) configuration is essential to enhance BSS’s energy savings and economic feasibility, thereby facilitating energy refueling efficiency of electric taxis (ETs). This study proposes a novel modular battery swapping mode (BSM) that allows ET drivers to choose the number [...] Read more.

Optimizing battery swapping station (BSS) configuration is essential to enhance BSS’s energy savings and economic feasibility, thereby facilitating energy refueling efficiency of electric taxis (ETs). This study proposes a novel modular battery swapping mode (BSM) that allows ET drivers to choose the number of battery blocks to rent according to their driving range requirements and habits, improving BSS’s economic profitability and operational flexibility. We further develop a data-driven approach to optimizing the configuration of modular BSS considering the scheduling of battery charging at the operating stage under a scenario of time-of-use (ToU) price. We use the travel patterns of taxis extracted from the GPS trajectory data on 12,643 actual taxis in Beijing, China. Finally, we test the effectiveness and performance of our data-driven model and modular BSM in a numerical experiment with traditional BSM as the benchmark. Results show that the BSS with modular BSM can save 38% on the investment cost of purchasing ET battery blocks and is better able to respond to the ToU price than to the benchmark. The results of the sensitivity analysis suggest that when the peak electricity price is too high, additional battery blocks must be purchased to avoid charging during those peak periods. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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19 pages, 4034 KiB

Open AccessArticle

Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data

by Yoon-Ji Kim, Jeong-Seok Lee, Alessandro Pititto, Luigi Falco, Moon-Suk Lee, Kyoung-Kuk Yoon and Ik-Soon Cho

Appl. Sci. 2022, 12(21), 11246; https://doi.org/10.3390/app122111246 - 06 Nov 2022

Cited by 4 | Viewed by 2352

Abstract

For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic [...] Read more.

For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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16 pages, 4260 KiB

Open AccessArticle

Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

by Lei Gao, Lu Wei, Jian Yang and Jinhong Li

Appl. Sci. 2022, 12(12), 5873; https://doi.org/10.3390/app12125873 - 09 Jun 2022

Viewed by 1487

Abstract

Automatic intersection identification and extraction are an important foundation for urban road network updates and traffic network analysis and modeling. Existing intersection extraction methods based on steering angles and stopping points suffer from inadequate sampling amounts and threshold settings. To address this problem, [...] Read more.

Automatic intersection identification and extraction are an important foundation for urban road network updates and traffic network analysis and modeling. Existing intersection extraction methods based on steering angles and stopping points suffer from inadequate sampling amounts and threshold settings. To address this problem, we propose a road network intersection automatic extraction method based on vehicle trajectory intersection clustering. First, the continuous trajectory segments are extracted from trajectory data based on the sampling interval. Second, the maximum reconstruction error method is developed to extract straight-line trajectory segments from continuous trajectory segments. The overlapped straight-line trajectory segments belonging to the same direction are merged to reduce the number of segments and enhance road network patterns. To further improve the calculation efficiency of the intersection points of straight-line segments, bounding box filtering and orthogonal filtering are used to filter the straight-line trajectory segments that do not have an intersection relationship. Finally, the obtained straight-line segment intersection points are clustered using a density peak clustering algorithm. The road intersections are automatically extracted using the clustering center. The experimental results on real vehicle trajectories in Lianyungang City show that the proposed method performs well on intersection recognition and calculation efficiency. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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22 pages, 4475 KiB

Open AccessArticle

Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example

by Ding Liu, Wuyue Rong, Jin Zhang and Ying-En (Ethan) Ge

Appl. Sci. 2022, 12(11), 5755; https://doi.org/10.3390/app12115755 - 06 Jun 2022

Cited by 5 | Viewed by 1836

Abstract

In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% [...] Read more.

In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% predictive power for transfer ridership, followed by closeness centrality and bus-stop density, and their contribution rates are 21.6% and 17.27%, respectively. Local explanations for the model reveal the following conclusions: most built-environment variables have nonlinear and threshold effects on bus–metro ridership. The suggested values for the dominant contributors to bus–metro-transfer ridership are obtained. For example, bus-network density, bus-stop density, and closeness centrality were 12.8 km/sq. km, 11 counts/sq. km, and 0.18 km/sq. km, respectively, for maximizing bus–metro-transfer ridership. The interaction impacts of the bus–metro connection characteristics and the closeness centrality of metro stations on transfer ridership were, also, examined. The result showed that the setting of bus–metro-transfer facilities depended on the location of metro stations. It was necessary to improve the bus–metro-connection system, in metro stations with high closeness centrality. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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14 pages, 1621 KiB

Open AccessArticle

Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis

by Yajie Zou, Lusa Ding, Hao Zhang, Ting Zhu and Lingtao Wu

Appl. Sci. 2022, 12(10), 5259; https://doi.org/10.3390/app12105259 - 23 May 2022

Cited by 29 | Viewed by 3678

Abstract

Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few prediction models consider the characteristics of individual drivers, which may [...] Read more.

Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few prediction models consider the characteristics of individual drivers, which may overlook the potential heterogeneity of driving behavior. In this study, a vehicle acceleration prediction model based on machine learning methods and driving behavior analysis is proposed. First, the driving behavior data are preprocessed, and the relative distance, relative speed, and acceleration of the subject vehicle are selected as feature variables to describe the driving behavior. Then, a finite Mixture of Hidden Markov Model (MHMM) is used to divide the driving behavior semantics. The model can divide heterogeneous data into different behavioral semantic fragments within different time lengths. Next, the similarity of different behavioral semantic fragments is evaluated using the Kolmogorov–Smirnov test. In total, 10 homogenous drivers are classified as the first group, and the remaining 20 drivers are classified as the second group. Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) are used to predict the vehicle acceleration for both groups. The prediction results show that the proposed method in this study can significantly improve the prediction accuracy of vehicle acceleration. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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16 pages, 1003 KiB

Open AccessArticle

Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction

by Weimin Mai, Junxin Chen and Xiang Chen

Appl. Sci. 2022, 12(6), 2842; https://doi.org/10.3390/app12062842 - 10 Mar 2022

Cited by 2 | Viewed by 2058

Abstract

Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on [...] Read more.

Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of prediction. To address this problem and make more accurate predictions, we propose a traffic prediction model named Time-Evolving Graph Convolution Recurrent Network (TEGCRN), which takes advantage of time-evolving graph convolution to capture the dynamic inter-node dependency adaptively at different time slots. Specifically, we first propose a tensor-composing method to generate adaptive time-evolving adjacency graphs. Based on these time-evolving graphs and a predefined distance-based graph, a graph convolution module with mix-hop operation is applied to extract comprehensive inter-node information. Then the resulting graph convolution module is integrated into the Recurrent Neural Network structure to form an general predicting model. Experiments on two real-world traffic datasets demonstrate the superiority of TEGCRN over multiple competitive baseline models, especially in short-term prediction, which also verifies the effectiveness of time-evolving graph convolution in capturing more comprehensive inter-node dependency. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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27 pages, 47768 KiB

Open AccessArticle

Global Assessment of Bridge Passage in Relation to Oversized and Excessive Transport: Case Study Intended for Slovakia

by Jozef Gnap, Juraj Jagelčák, Peter Marienka, Marcel Frančák and Mária Vojteková

Appl. Sci. 2022, 12(4), 1931; https://doi.org/10.3390/app12041931 - 12 Feb 2022

Cited by 5 | Viewed by 1971

Abstract

The development of an economy and, in particular, the construction of new infrastructure as well as industrial enterprises creates demand for the road transport of oversized freight that exceeds the maximum permissible total mass of vehicle combinations with its share on the axles. [...] Read more.

The development of an economy and, in particular, the construction of new infrastructure as well as industrial enterprises creates demand for the road transport of oversized freight that exceeds the maximum permissible total mass of vehicle combinations with its share on the axles. Failure to comply with the defined technological processes and a deficiency in the assessment of permitting such forms of transportation can have a large adverse effect, predominantly on the lifetime of bridges in a road network, which can have international implications as well. There is no legislation adopted by the EU Member States, which would at least partially unify the authorisation procedures of these forms of transportation and, therefore, it results in problems when crossing borders and leads to differences related to the assessment of bridge passages. If there is no systematic inspection of this kind of transportation, it can lead to permanent damage of these bridges as well. Currently, and not only in Slovakia but also in other states, the assessment of bridge passage for certain routes is used for heavy and oversized transportation. It means that if we use 100 transports, 100 assessments of individual routes are needed, although some are the same routes or the same vehicles/vehicle combinations used for a number of transports. Thus, the authors designed a global assessment for bridge passage in relation to heavy and oversized road transport while verifying it in the conditions of the EU Member State from Central Europe–Slovakia. Roads are full of different types of vehicles/vehicle combinations for which the axle loads and distances of the axles (wheelbases) are important. Thus, there were vehicle/vehicle combinations parameters (big data) observed, for which the routes relating to heavy and/or oversized transportation were assessed from 1 January 2016 to 31 December 2020 in Slovakia. The global assessment of bridge passage introduces an entirely new approach within the procedure for obtaining a special permission for road use as well as within transport use itself. Given the low presence of freight with an abnormal axle load or enormous total mass, it is appropriate to define the limited conditions under which it would be possible to implement the global assessment in practice as well. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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17 pages, 4697 KiB

Open AccessArticle

Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach

by Leo Tišljarić, Sofia Fernandes, Tonči Carić and João Gama

Appl. Sci. 2021, 11(24), 12017; https://doi.org/10.3390/app112412017 - 17 Dec 2021

Cited by 8 | Viewed by 3233

Abstract

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related [...] Read more.

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications. Full article

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21 pages, 5717 KiB

Open AccessArticle

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

by Pangwei Wang, Xiao Liu, Yunfeng Wang, Tianren Wang and Juan Zhang

Appl. Sci. 2021, 11(23), 11530; https://doi.org/10.3390/app112311530 - 05 Dec 2021

Cited by 2 | Viewed by 2104

Abstract

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in [...] Read more.

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 min respectively. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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31 pages, 21063 KiB

Open AccessArticle

A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data

by Yitao Wang, Lei Yang, Xin Song, Quan Chen and Zhenguo Yan

Appl. Sci. 2021, 11(21), 10336; https://doi.org/10.3390/app112110336 - 03 Nov 2021

Cited by 2 | Viewed by 2103

Abstract

AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the [...] Read more.

AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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19 pages, 3722 KiB

Open AccessArticle

Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory

by Zhi-Wei Hou, Shijun Yu and Tao Ji

Appl. Sci. 2021, 11(21), 10058; https://doi.org/10.3390/app112110058 - 27 Oct 2021

Cited by 1 | Viewed by 1740

Abstract

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so [...] Read more.

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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Review

Jump to: Editorial, Research

28 pages, 1210 KiB

Open AccessReview

When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

by Xuan Zhou, Ruimin Ke, Hao Yang and Chenxi Liu

Appl. Sci. 2021, 11(20), 9680; https://doi.org/10.3390/app11209680 - 17 Oct 2021

Cited by 26 | Viewed by 5865

Abstract

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation [...] Read more.

The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), as a main building block of modern cities, and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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