Editorial

5 pages, 205 KiB

Open AccessEditorial

Special Issue on Future Intelligent Transportation System (ITS) for Tomorrow and Beyond

by Sarvar Hussain Nengroo, Hojun Jin, Inhwan Kim and Dongsoo Har

Appl. Sci. 2022, 12(12), 5994; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125994 - 13 Jun 2022

Cited by 1 | Viewed by 1251

Intelligent Transportation System (ITS) has evolved into a system for provision of traffic information and traffic control with the help of advanced IT technologies [...] Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

Research

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18 pages, 3440 KiB

Open AccessArticle

Travel Time Prediction on Long-Distance Road Segments in Thailand

by Rathachai Chawuthai, Nachaphat Ainthong, Surasee Intarawart, Niracha Boonyanaet and Agachai Sumalee

Appl. Sci. 2022, 12(11), 5681; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115681 - 02 Jun 2022

Cited by 4 | Viewed by 2065

Abstract

This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment [...] Read more.

This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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26 pages, 3763 KiB

Open AccessArticle

Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station

by Hojun Jin, Sangkeum Lee, Sarvar Hussain Nengroo and Dongsoo Har

Appl. Sci. 2022, 12(9), 4786; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094786 - 09 May 2022

Cited by 17 | Viewed by 2712

Abstract

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an [...] Read more.

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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20 pages, 807 KiB

Open AccessArticle

Evaluating the Impact of Drone Signaling in Crosswalk Scenario

by Sana Bouassida, Najett Neji, Lydie Nouvelière and Jamel Neji

Appl. Sci. 2021, 11(1), 157; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010157 - 26 Dec 2020

Cited by 5 | Viewed by 2083

Abstract

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is [...] Read more.

The characteristic pillars of a city are its economy, its mobility, its environment, its inhabitants, its way of life, and its organization. Since 1980, the concept of smart city generally consists of optimizing costs, organization, and the well-being of inhabitants. The idea is to develop means and solutions capable of meeting the needs of the population, while preserving resources and the environment. Owing to their little size, their flexibility, and their low cost, Unmanned Aerial Vehicles (UAV) are today used in a huge number of daily life applications. UAV use cases can be classified into three categories: data covering (like surveillance and event covering), data relaying (like delivery and emergency services), and data dissemination (like cartography and precise agriculture). In addition, the interest to Cooperative Intelligent Transportation Systems (C-ITS) has risen in these recent years, especially in the context of smart cities. In such systems, both drivers and traffic managers share the information and cooperate to coordinate their actions to ensure safety, traffic efficiency, and environment preservation. In this work, we aimed at introducing a UAV in a use case that is likely to happen in C-ITS. A conflict is considered involving a car and a pedestrian. A UAV observes from the top of the scene and will play the role of the situation controller, the information collector, and the assignment of the instructions to the car driver in case of a harmful situation to avoid car-pedestrian collision. To this end, we highlight interactions between the UAV and the car vehicle (U2V communication), as well as between the UAV and infrastructure (U2I communication). Hence, the benefit of using UAV is emphasized to reduce accident gravity rate, braking distance, energy consumption, and occasional visibility reduction. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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24 pages, 15179 KiB

Open AccessArticle

Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction

by Zain Ul Abideen, Heli Sun, Zhou Yang, Rana Zeeshan Ahmad, Adnan Iftekhar and Amir Ali

Appl. Sci. 2021, 11(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010017 - 22 Dec 2020

Cited by 16 | Viewed by 3507

Abstract

This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching [...] Read more.

This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system’s performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer’s primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company’s service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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20 pages, 5098 KiB

Open AccessFeature PaperArticle

Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization

by Sungkwan Kim, Inhwan Kim, Luiz Felipe Vecchietti and Dongsoo Har

Appl. Sci. 2020, 10(24), 8876; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248876 - 11 Dec 2020

Cited by 4 | Viewed by 2425

Abstract

Lately, pose estimation based on learning-based Visual Odometry (VO) methods, where raw image data are provided as the input of a neural network to get 6 Degrees of Freedom (DoF) information, has been intensively investigated. Despite its recent advances, learning-based VO methods still [...] Read more.

Lately, pose estimation based on learning-based Visual Odometry (VO) methods, where raw image data are provided as the input of a neural network to get 6 Degrees of Freedom (DoF) information, has been intensively investigated. Despite its recent advances, learning-based VO methods still perform worse than the classical VO that consists of feature-based VO methods and direct VO methods. In this paper, a new pose estimation method with the help of a Gated Recurrent Unit (GRU) network trained by pose data acquired by an accurate sensor is proposed. The historical trajectory data of the yaw angle are provided to the GRU network to get a yaw angle at the current timestep. The proposed method can be easily combined with other VO methods to enhance the overall performance via an ensemble of predicted results. Pose estimation using the proposed method is especially advantageous in the cornering section which often introduces an estimation error. The performance is improved by reconstructing the rotation matrix using a yaw angle that is the fusion of the yaw angles estimated from the proposed GRU network and other VO methods. The KITTI dataset is utilized to train the network. On average, regarding the KITTI sequences, performance is improved as much as 1.426% in terms of translation error and 0.805 deg/100 m in terms of rotation error. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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25 pages, 11535 KiB

Open AccessArticle

The Deep 3D Convolutional Multi-Branching Spatial-Temporal-Based Unit Predicting Citywide Traffic Flow

by Zain Ul Abideen, Heli Sun, Zhou Yang and Amir Ali

Appl. Sci. 2020, 10(21), 7778; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217778 - 03 Nov 2020

Cited by 7 | Viewed by 2354

Abstract

Recently, for public safety and traffic management, traffic flow prediction is a crucial task. The citywide traffic flow problem is still a big challenge in big cities because of many complex factors. However, to handle some complex factors, e.g., spatial-temporal and some external [...] Read more.

Recently, for public safety and traffic management, traffic flow prediction is a crucial task. The citywide traffic flow problem is still a big challenge in big cities because of many complex factors. However, to handle some complex factors, e.g., spatial-temporal and some external factors in the intelligent traffic flow forecasting problem, spatial-temporal data for urban applications (i.e., travel time estimation, trajectory planning, taxi demand, traffic congestion, and the regional rainfall) is inherently stochastic and unpredictable. In this paper, we proposed a deep learning-based novel model called “multi-branching spatial-temporal attention-based long-short term memory residual unit (MBSTALRU)” for the citywide traffic flow from lower-level layers to high-level layers, simultaneously. In our work, initially, we have modeled the traffic flow with spatial correlations multiple 3D volume layers and propose the novel multi-branching scheme to control the spatial-temporal features. Our approach is useful for exploring temporal dependencies through the 3D convolutional neural network (CNN) multiple branches, which aim to merge the spatial-temporal characteristics of historical data with three-time intervals, namely closeness, daily, and weekly, and we have embedded features by attention-based long-short term memory (LSTM). Then, we capture the correlation between traffic inflow and outflow with residual layers units. In the end, we merge the external factors dynamically to predict citywide traffic flow simultaneously. The simulation results have been performed on two real-world datasets, BJTaxi and NYCBike, which show better performance and effectiveness of the proposed method than previous state-of-the-art models. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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20 pages, 356 KiB

Open AccessArticle

Efficient Delivery Services Sharing with Time Windows

by Wanyuan Wang, Hansi Tao and Yichuan Jiang

Appl. Sci. 2020, 10(21), 7431; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217431 - 22 Oct 2020

Cited by 1 | Viewed by 1698

Abstract

Delivery service sharing (DSS) has made an important contribution in the optimization of daily order delivery applications. Existing DSS algorithms introduce two major limitations. First, due to computational reasons, most DSS algorithms focus on the fixed pickup/drop-off time scenario, which is [...] Read more.

Delivery service sharing (DSS) has made an important contribution in the optimization of daily order delivery applications. Existing DSS algorithms introduce two major limitations. First, due to computational reasons, most DSS algorithms focus on the fixed pickup/drop-off time scenario, which is inconvenient for real-world scenarios where customers can choose the pickup/drop-off time flexibly. On the other hand, to address the intractable DSS with the flexible time windows (DSS-Fle), local search-based heuristics are widely employed; however, they have no theoretical results on the advantage of order sharing. Against this background, this paper designs a novel algorithm for DSS-Fle, which is efficient on both time complexity and system throughput. Inspired by the efficiency of shareability network on the delivery service routing (DSR) variant where orders cannot be shared and have the fixed time window, we first consider the variant of DSR with flexible time windows (DSR-Fle). For DSR-Fle, the order’s flexible time windows are split into multiple virtual fixed time windows, one of which is chosen by the shareability network as the order’s service time. On the other hand, inspired by efficiency of local search heuristics, we further consider the variant of DSS with fixed time window (DSS-Fix). For DSS-Fix, the beneficial sharing orders are searched and inserted to the shareability network. Finally, combining the spitting mechanism proposed in DSR-Fle and the inserting mechanism proposed in DSS-Fix together, an efficient algorithm is proposed for DSS-Fle. Simulation results show that the proposed DSS-Fle variant algorithm can scale to city-scale scenarios with thousands of regions, orders and couriers, and has the significant advantage on improving system throughput. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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24 pages, 10092 KiB

Open AccessArticle

Camera Geolocation Using Digital Elevation Models in Hilly Area

by Zhibin Pan, Jin Tang, Tardi Tjahjadi, Xiaoming Xiao and Zhihu Wu

Appl. Sci. 2020, 10(19), 6661; https://0-doi-org.brum.beds.ac.uk/10.3390/app10196661 - 23 Sep 2020

Cited by 3 | Viewed by 1955

Abstract

The geolocation of skyline provides an important application in unmanned vehicles, unmanned aerial vehicles, and other fields. However, the existing methods are not effective in hilly areas. In this paper, we analyze the difficulties to locate in hilly areas and propose a new [...] Read more.

The geolocation of skyline provides an important application in unmanned vehicles, unmanned aerial vehicles, and other fields. However, the existing methods are not effective in hilly areas. In this paper, we analyze the difficulties to locate in hilly areas and propose a new geolocation method. According to the vegetation in hilly area, two new skyline features, enhanced angle chain code and lapel point, are proposed. In order to deal with the skyline being close to the camera, we also propose a matching method which incorporates skyline distance heatmap and skyline pyramid. The experimental results show that the proposed method is highly effective in hilly area and has a robust performance against noise and rotation effects. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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18 pages, 5013 KiB

Open AccessArticle

Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm

by Zifei Nie and Hooman Farzaneh

Appl. Sci. 2020, 10(15), 5271; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155271 - 30 Jul 2020

Cited by 41 | Viewed by 5890

Abstract

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of [...] Read more.

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system. Full article

(This article belongs to the Special Issue Future Intelligent Transportation System for Tomorrow and Beyond)

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