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Electronics
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AI-Based Transportation Planning and Operation
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AI-Based Transportation Planning and Operation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 18777

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Keemin Sohn

E-Mail Website
Guest Editor
Laboratory of Big-data applications in public sector, Department of urban engineering, Chung-Ang University, Seoul 156-756, Korea
Interests: transportation; transportation planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With drastic urbanization, cities around the world are facing transportation-induced problems such as congestion, accidents, and air pollution. Transportation planning and operation provides opportunities to satisfy the demand for the movement of people and goods in a safe, economical, convenient, and sustainable manner. Previous studies of transportation planning and operation have depended upon econometrics- or engineering-based modeling, which cannot fully incorporate the power of data-driven or AI-based approaches. Recently, artificial intelligence (AI) technologies, such as deep learning, reinforcement learning, and Bayesian modeling, have provided powerful tools to deal with the complexity and high nonlinearity in the problems of transportation planning and operations. More specifically, AI-based technologies in decision-making, planning, modeling, estimation, and control have facilitated the process of transportation planning and operations. The purpose of this Special Issue is to provide an academic platform to publish high-quality research papers on the applications of innovative AI algorithms to transportation planning and operation. Prospective authors are invited to submit original research and review articles related to the applications of AI or machine learning techniques to transportation planning and operation. The potential topics of interest include but are not limited to the following:

  • Big data analytics in transportation
  • Data-driven transportation modeling and simulation
  • AI-based traffic surveillance
  • Traffic operations and management
  • Road safety enhancement
  • AI-based transportation network design
  • Decision-making on transportation issues
  • Car sharing technologies
  • Pedestrian movement analysis
  • Vehicle emission management
  • Mobility data analysis for evacuation

Prof. Dr. Keemin Sohn
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • transportation planning and operation
  • artificial intelligence
  • big data
  • machine learning
  • data-driven approach

Published Papers (7 papers)

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Research

14 pages, 2975 KiB  
Article
The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks
by Hojun Lee, Minhee Kang, Jaein Song and Keeyeon Hwang
Electronics 2020, 9(12), 2178; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9122178 - 18 Dec 2020
Cited by 9 | Viewed by 3455
Abstract
Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive [...] Read more.
Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive design” for accidents is continuously required. Accordingly, the “preventive design” that prevents accidents in advance is continuously required to enhance the safety of AVs. Specially, black ice with characteristics that are difficult to identify with the naked eye—the main cause of major accidents in winter vehicles—is expected to cause serious injuries in the era of AVs, and measures are needed to prevent them. Therefore, this study presents a Convolutional Neural Network (CNN)-based black ice detection plan to prevent traffic accidents of AVs caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, we augmented image data and learned road environment images. Tests showed that the proposed CNN model detected black ice with 96% accuracy and reproducibility. It is expected that the CNN model for black ice detection proposed in this study will contribute to improving the safety of AVs and prevent black ice accidents in advance. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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18 pages, 3788 KiB  
Article
Estimating Micro-Level On-Road Vehicle Emissions Using the K-Means Clustering Method with GPS Big Data
by Hyejung Hu, Gunwoo Lee, Jae Hun Kim and Hyunju Shin
Electronics 2020, 9(12), 2151; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9122151 - 15 Dec 2020
Cited by 7 | Viewed by 2131
Abstract
Due to the advanced spatial data collection technologies, the locations of vehicles on roads are now being collected nationwide, so there is a demand for applying a micro-level emission calculation methods to estimate regional and national emissions. However, it is difficult to apply [...] Read more.
Due to the advanced spatial data collection technologies, the locations of vehicles on roads are now being collected nationwide, so there is a demand for applying a micro-level emission calculation methods to estimate regional and national emissions. However, it is difficult to apply this method due to the low data collection rate and the complicated calculation procedure. To solve these problems, this study proposes a vehicle trajectory extraction method for estimating micro-level vehicle emissions using massive GPS data. We extracted vehicle trajectories from the GPS data to estimate the emission factors for each link at a specific time period. Vehicle trajectory data was divided into several groups through a k-means clustering method, in which the ratios of each operating mode were used as variables for clustering similar vehicle trajectories. The results showed that the proposed method has an acceptable accuracy in estimating emissions. Furthermore, it was also confirmed that the estimated emission factors appropriately reflected the driving characteristics of links. If the proposed method were utilized to update the link-based micro-level emission factors using continuously accumulated trajectory data for the road network, it would be possible to efficiently calculate the regional- or national-level emissions only using traffic volume. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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15 pages, 1490 KiB  
Article
For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks
by Minhee Kang, Jaein Song and Keeyeon Hwang
Electronics 2020, 9(11), 1829; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111829 - 02 Nov 2020
Cited by 8 | Viewed by 1920
Abstract
Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, [...] Read more.
Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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14 pages, 5338 KiB  
Article
An Application of Reinforced Learning-Based Dynamic Pricing for Improvement of Ridesharing Platform Service in Seoul
by Jaein Song, Yun Ji Cho, Min Hee Kang and Kee Yeon Hwang
Electronics 2020, 9(11), 1818; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111818 - 02 Nov 2020
Cited by 9 | Viewed by 2672
Abstract
As ridesharing services (including taxi) are often run by private companies, profitability is the top priority in operation. This leads to an increase in the driver’s refusal to take passengers to areas with low demand where they will have difficulties finding subsequent passengers, [...] Read more.
As ridesharing services (including taxi) are often run by private companies, profitability is the top priority in operation. This leads to an increase in the driver’s refusal to take passengers to areas with low demand where they will have difficulties finding subsequent passengers, causing problems such as an extended waiting time when hailing a vehicle for passengers bound for these regions. The study used Seoul’s taxi data to find appropriate surge rates of ridesharing services between 10:00 p.m. and 4:00 a.m. by region using a reinforcement learning algorithm to resolve this problem during the worst time period. In reinforcement learning, the outcome of centrality analysis was applied as a weight affecting drivers’ destination choice probability. Furthermore, the reward function used in the learning was adjusted according to whether the passenger waiting time value was applied or not. The profit was used for reward value. By using a negative reward for the passenger waiting time, the study was able to identify a more appropriate surge level. Across the region, the surge averaged a value of 1.6. To be more specific, those located on the outskirts of the city and in residential areas showed a higher surge, while central areas had a lower surge. Due to this different surge, a driver’s refusal to take passengers can be lessened and the passenger waiting time can be shortened. The supply of ridesharing services in low-demand regions can be increased by as much as 7.5%, allowing regional equity problems related to ridesharing services in Seoul to be reduced to a greater extent. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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16 pages, 1331 KiB  
Article
Context-Aware Link Embedding with Reachability and Flow Centrality Analysis for Accurate Speed Prediction for Large-Scale Traffic Networks
by Chanjae Lee and Young Yoon
Electronics 2020, 9(11), 1800; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111800 - 29 Oct 2020
Cited by 4 | Viewed by 2041
Abstract
This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks. We first analyze how traffic flows in and out of every link through the lowest cost reachable paths. We aggregate the traffic flow conditions of [...] Read more.
This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks. We first analyze how traffic flows in and out of every link through the lowest cost reachable paths. We aggregate the traffic flow conditions of the links on every hop of the inbound and outbound reachable paths to represent the traffic flow dynamics. We compute a new measure called traffic flow centrality (i.e., the Z value) for every link to capture the inherently complex mechanism of the traffic links influencing each other in terms of traffic speed. We combine the features regarding the traffic flow centrality with the external conditions around the links, such as climate and time of day information. We model how these features change over time with recurrent neural networks and infer traffic speed at the subsequent time windows. Our feature representation of the traffic flow for every link remains invariant even when the traffic network changes. Furthermore, we can handle traffic networks with thousands of links. The experiments with the traffic networks in the Seoul metropolitan area in South Korea reveal that our unique ways of embedding the comprehensive spatio-temporal features of links outperform existing solutions. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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20 pages, 1604 KiB  
Article
Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory
by Ei Ei Mon, Hideya Ochiai, Chaiyachet Saivichit and Chaodit Aswakul
Electronics 2020, 9(9), 1412; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091412 - 01 Sep 2020
Cited by 1 | Viewed by 2693
Abstract
The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. [...] Read more.
The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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14 pages, 3831 KiB  
Article
Measuring Traffic Volumes Using an Autoencoder with No Need to Tag Images with Labels
by Seungbin Roh, Johyun Shin and Keemin Sohn
Electronics 2020, 9(5), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9050702 - 25 Apr 2020
Viewed by 2822
Abstract
Almost all vision technologies that are used to measure traffic volume use a two-step procedure that involves tracking and detecting. Object detection algorithms such as YOLO and Fast-RCNN have been successfully applied to detecting vehicles. The tracking of vehicles requires an additional algorithm [...] Read more.
Almost all vision technologies that are used to measure traffic volume use a two-step procedure that involves tracking and detecting. Object detection algorithms such as YOLO and Fast-RCNN have been successfully applied to detecting vehicles. The tracking of vehicles requires an additional algorithm that can trace the vehicles that appear in a previous video frame to their appearance in a subsequent frame. This two-step algorithm prevails in the field but requires substantial computation resources for training, testing, and evaluation. The present study devised a simpler algorithm based on an autoencoder that requires no labeled data for training. An autoencoder was trained on the pixel intensities of a virtual line placed on images in an unsupervised manner. The last hidden node of the former encoding portion of the autoencoder generates a scalar signal that can be used to judge whether a vehicle is passing. A cycle-consistent generative adversarial network (CycleGAN) was used to transform an original input photo of complex vehicle images and backgrounds into a simple illustration input image that enhances the performance of the autoencoder in judging the presence of a vehicle. The proposed model is much lighter and faster than a YOLO-based model, and accuracy of the proposed model is equivalent to, or better than, a YOLO-based model. In measuring traffic volumes, the proposed approach turned out to be robust in terms of both accuracy and efficiency. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation)
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