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Traffic Prediction and Route Guidance
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Traffic Prediction and Route Guidance

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 21417

Special Issue Editor

Dr. Sehyun Tak

E-Mail Website1 Website2
Guest Editor
Korea Transport Institute, Sejong 30147, Republic of Korea
Interests: automated vehicles; intelligent transportation system; traffic control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic congestion costs people valuable time, fuel, and frustration every single day. At the same time, large amounts of congestion impact governments who need to keep traffic flowing for the movement of goods, reducing pollution in certain areas, and for the safety of those on the road. Congestion is a global problem that impacts all levels of society.  Intelligent transportation systems (ITS)  including infrastructure and services, as well as the planning, operation, and control methods is to optimize transportation and to increase the safety of transportation.  More and more drivers are relying on navigation applications and devices to guide them through the fastest routes and avoid hitting congestion. The best navigation devices use advanced traffic prediction services for accurate estimated times of arrivals (ETAs) and optimized routes during a driver’s journey.

Traffic prediction is the task of forecasting real-time traffic information based on floating car data and historical traffic data, such as traffic flow, average traffic speed and traffic incidents. Recent technological advances applying the various methodologies of AI,  machine learning in navigation systems for vehicles have the capability to provide drivers with route information. These technological advances, together with two-way radio communication of digital information, automatic measurement of traffic flows, and supercomputer technology, could be combined to provide useful information to drivers concerning expected travel times, best routes, and best departure times.

The aim of this Special Issue is to collect papers describing technological tools currently applied in traffic prediction and route guidance.

Dr. Sehyun Tak
Guest Editor

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Keywords

  • traffic prediction
  • route guidance
  • traffic control
  • traffic simulation
  • pedestrian simulation
  • autonomous vehicle
  • traffic congestion
  • intelligent transportation
  • traffic flow
  • traffic data
  • machine learning
  • Big Data transportation

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Published Papers (7 papers)

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Research

20 pages, 2279 KiB  
Article
Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions
by Vytautas Paulauskas, Lawrence Henesey, Birute Plačiene, Martynas Jonkus, Donatas Paulauskas, Raimondas Barzdžiukas, Artur Kaulitzky and Martynas Simutis
Appl. Sci. 2022, 12(20), 10652; https://doi.org/10.3390/app122010652 - 21 Oct 2022
Cited by 6 | Viewed by 3489
Abstract
Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation [...] Read more.
Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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19 pages, 4711 KiB  
Article
A Two-Stage Decomposition-Reinforcement Learning Optimal Combined Short-Time Traffic Flow Prediction Model Considering Multiple Factors
by Dayi Qu, Kun Chen, Shaojie Wang and Qikun Wang
Appl. Sci. 2022, 12(16), 7978; https://doi.org/10.3390/app12167978 - 9 Aug 2022
Cited by 8 | Viewed by 1701
Abstract
Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to [...] Read more.
Accurate short-term traffic flow prediction is a prerequisite for achieving an intelligent transportation system to proactively alleviate traffic congestion. Considering the complex and variable traffic environment, so that the traffic flow contains a large number of non-linear characteristics, which makes it difficult to improve the prediction accuracy, a combined prediction model that reduces the unsteadiness of traffic flow and fully extracts the traffic flow features is proposed. Firstly, decompose the traffic flow data into multiple components by the seasonal and trend decomposition using loess (STL); these components contain different features, and the optimized variational modal decomposition (VMD) is used for the second decomposition of the component with large fluctuation frequencies, and then the components are reconstructed according to the fuzzy entropy and Lempel-Ziv complexity index and the Pearson correlation coefficient is used to filter the traffic flow features. Then light gradient boosting machine (LightGBM), long short-term memory with attention mechanism (LA), and kernel extreme learning machine with genetic algorithm optimization (GA-KELM) are built for prediction. Finally, we use reinforcement learning to integrate the advantages of each model, and the weights of each model are determined to obtain the best prediction results. The case study shows that the model established in this paper is better than other models in predicting urban road traffic flow, with an average absolute error of 2.622 and a root mean square error of 3.479, both of which are lower than the prediction errors of other models, indicating that the model can fully extract the features in complex traffic flow. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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13 pages, 3556 KiB  
Article
Early Gapping and Platoon Merging Strategies for Autonomous Vehicles using Local Controllers
by Nir Shvalb, Shlomo Geller and Idit Avrahami
Appl. Sci. 2022, 12(13), 6328; https://doi.org/10.3390/app12136328 - 21 Jun 2022
Viewed by 1548
Abstract
Autonomous vehicle merging schemes require a central control or a complex communication system between the vehicles. We suggest an alternative local traffic control method based on distance sensors and roadside units which provides the vehicles with the desired gap profile without the need [...] Read more.
Autonomous vehicle merging schemes require a central control or a complex communication system between the vehicles. We suggest an alternative local traffic control method based on distance sensors and roadside units which provides the vehicles with the desired gap profile without the need for vehicle-to-vehicle communication. The gap profile aims to open gaps between the vehicles before an upcoming junction. To explore the profiles’ governing parameters, 140,000 simulation cases with varying conditions were run. Results show that, for a speed limit of 100 km/h and high inlet density (of 1–1.5 s between vehicles), the best strategy with respect to flow and merging percentage (of ~90%) is to use early gapping and platoon merging using linear profiles with long stabilization sections (>0.6 km). Moreover, the gapping process should start when the vehicle ahead attains a velocity of 75 km/h. In this way, fluent traffic can be sustained without perpetuating upstream traffic jams. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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21 pages, 1341 KiB  
Article
Forecasting of Bicycle and Pedestrian Traffic Using Flexible and Efficient Hybrid Deep Learning Approach
by Fouzi Harrou, Abdelkader Dairi, Abdelhafid Zeroual and Ying Sun
Appl. Sci. 2022, 12(9), 4482; https://doi.org/10.3390/app12094482 - 28 Apr 2022
Cited by 17 | Viewed by 2479
Abstract
Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential [...] Read more.
Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential use of bicycle and pedestrian infrastructure and improving bicyclists’ safety and comfort. Advances in sensory technology enable collecting massive traffic flow data, including road traffic, bicycle, and pedestrian traffic flow. This paper introduces a novel deep hybrid learning model with a fully guided-attention mechanism to improve bicycles and pedestrians’ traffic flow forecasting. Notably, the proposed approach extends the modeling capability of the Variational Autoencoder (VAE) by merging a long short-term memory (LSTM) model with the VAE’s decoder and using a self-attention mechanism at multi-stage of the VAE model (i.e., decoder and before data resampling). Specifically, LSTM improves the VAE decoder’s capacity in learning temporal dependencies, and the guided-attention units enable selecting relevant features based on the self-attention mechanism. This proposed deep hybrid learning model with a multi-stage guided-attention mechanism is called GAHD-VAE. Proposed methods were validated with traffic measurements from six publicly available pedestrian and bicycle traffic flow datasets. The proposed method provides promising forecasting results but requires no assumptions that the data are drawn from a given distribution. Results revealed that the GAHD-VAE methodology can efficiently enhance the traffic forecasting accuracy and achieved better performance than the deep learning methods VAE, LSTM, gated recurrent units (GRUs), bidirectional LSTM, bidirectional GRU, convolutional neural network (CNN), and convolutional LSTM (ConvLSTM), and four shallow methods, linear regression, lasso regression, ridge regression, and support vector regression. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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18 pages, 45739 KiB  
Article
The Planning Process of Transport Tasks for Autonomous Vans—Case Study
by Jacek Caban, Aleksander Nieoczym, Agnieszka Dudziak, Tomasz Krajka and Mária Stopková
Appl. Sci. 2022, 12(6), 2993; https://doi.org/10.3390/app12062993 - 15 Mar 2022
Cited by 15 | Viewed by 2604
Abstract
Transport is an area that is developing at a tremendous pace. This development applies not only to electric and hybrid cars appearing more and more often on the road but also to those of an autonomous or semi-autonomous nature. This applies to both [...] Read more.
Transport is an area that is developing at a tremendous pace. This development applies not only to electric and hybrid cars appearing more and more often on the road but also to those of an autonomous or semi-autonomous nature. This applies to both passenger cars and vans. In many different publications, you can find a description of a number of benefits of using automated guided vehicles (AGV) for logistics and technical tasks, e.g., in the workplace. An important aspect is the use of knowledge management and machine learning, i.e., artificial intelligence (AI), to design these types of processes. An important issue in the construction of autonomous vehicles is the IT connection of sensors receiving signals from the environment. These signals are data for deep learning algorithms. The data after IT processing enable the decision-making by AI systems, while the used machine learning algorithms and neural networks are also needed for video image analysis in order to identify and classify registered objects. The purpose of this article is to present and verify a mathematical model used to respond to vehicles’ demand for a transport service under set conditions. The optimal conditions of the system to perform the transport task were simulated, and the efficiency of this system and benefits of this choice were determined. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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26 pages, 7492 KiB  
Article
Optimization of Multimodal Discrete Network Design Problems Based on Super Networks
by Yaling Zhou, Chengxuan Cao and Ziyan Feng
Appl. Sci. 2021, 11(21), 10143; https://doi.org/10.3390/app112110143 - 29 Oct 2021
Cited by 6 | Viewed by 2128
Abstract
In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to [...] Read more.
In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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19 pages, 3759 KiB  
Article
Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
by Aleksandra Romanowska and Kazimierz Jamroz
Appl. Sci. 2021, 11(21), 9914; https://doi.org/10.3390/app11219914 - 23 Oct 2021
Cited by 11 | Viewed by 5806
Abstract
The fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow [...] Read more.
The fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow facilities. The objective of the article was to analyze and compare existing models of the fundamental relationship. To that end, we proposed a universal and quantitative method for assessing models of the fundamental relationship based on real traffic data from a Polish expressway. The proposed methodology seeks to address the problem of finding the best deterministic model to describe the empirical relationship between fundamental traffic flow parameters: average speed, flow, and density based on simple and transparent criteria. Both single and multi-regime models were considered: a total of 17 models. For the given data, the results helped to identify the best performing models that meet the boundary conditions and ensure simplicity, empirical accuracy, and good estimation of traffic flow parameters. Full article
(This article belongs to the Special Issue Traffic Prediction and Route Guidance)
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