Special Issue "AI-Based Transportation Planning and Operation, Volume II"

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 4144

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 2000 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 (5 papers)

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Research

Article
A Novel Bus Arrival Time Prediction Method Based on Spatio-Temporal Flow Centrality Analysis and Deep Learning
Electronics 2022, 11(12), 1875; https://doi.org/10.3390/electronics11121875 - 14 Jun 2022
Viewed by 552
Abstract
This paper presents a method for predicting bus stop arrival times based on a unique approach that extracts the spatio-temporal dynamics of bus flows. Using a new technique called Bus Flow Centrality Analysis (BFC), we obtain the low-dimensional embedding of short-term bus flow [...] Read more.
This paper presents a method for predicting bus stop arrival times based on a unique approach that extracts the spatio-temporal dynamics of bus flows. Using a new technique called Bus Flow Centrality Analysis (BFC), we obtain the low-dimensional embedding of short-term bus flow patterns in the form of IID (Individual In Degree) and IOD (Individual Out Degree) and TOD (Total Out Degree) at every station in the bus network. The embedding using BFC analysis well captures the characteristics of every individual flow and aggregate pattern. The latent vector returned by the BFC analysis is combined with other essential information such as bus speed, travel time, wait time, dispatch intervals, the distance between stations, seasonality, holiday status, and climate information. We employed a family of recurrent neural networks such as LSTM, GRU, and ALSTM to model how these features change over time and to predict the time the bus takes to reach the next stop in subsequent time windows. We experimented with our solution using logs of bus operations in the Seoul Metropolitan area offered by the Bus Management System (BMS) and the Bus Information System (BIS) of Korea. We predicted arrival times for more than 100 bus routes with a MAPE of 1.19%. This margin of error is 74% lower than the latest work based on ALSTM. We also learned that LSTM performs better than GRU with a 40.5% lower MAPE. This result is even remarkable considering the irregularity in the bus flow patterns and the fact that we did not rely on real-time GPS information. Moreover, our approach scales at a city-wide level by analyzing more than 100 bus routes, while previous studies showed limited experiments on much fewer bus routes. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
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Article
Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow
Electronics 2022, 11(4), 658; https://doi.org/10.3390/electronics11040658 - 20 Feb 2022
Viewed by 584
Abstract
By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow [...] Read more.
By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow is designed to obtain the variance tendency of traffic load. After that, a strategy for adjusting the duration of traffic signal cycle is designed to tackle the problem of overload or lightweight traffic flow in the next-time frame. While predetermining the duration of signal cycle in the next-time frame, from a utilization perspective, an elastic-adaption strategy for scheduling the separate phase’s green traffic lights is derived from the analytical solution, which is obtained from a designed trade-off scheduling optimization problem to increase the adaptability for the upcoming traffic flow. The experiment results show that the proposed framework can effectively reduce the delay and stopping rate of vehicles, and improves the adaptability for the upcoming traffic flow. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
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Article
Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit
Electronics 2021, 10(21), 2653; https://doi.org/10.3390/electronics10212653 - 29 Oct 2021
Viewed by 621
Abstract
This paper addresses the taxi fleet dispatch problem, which is critical for many transport service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue and profit a taxi platform can generate through the dispatch approaches designed with various [...] Read more.
This paper addresses the taxi fleet dispatch problem, which is critical for many transport service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue and profit a taxi platform can generate through the dispatch approaches designed with various criteria. We consider determining the proportion of taxi fleets to different destination zones given the expected rewards from the future states following the distribution decisions learned through reinforcement learning (RL) algorithms. We also take into account more straightforward greedy algorithms that look ahead fewer decision time steps in the future. Our dispatch decision algorithms commonly leverage contextual information and heuristics using a data structure called Contextual Matching Matrix (CMM). The key contribution of our paper is the insight into the trade-off between different design criteria. Primarily, through the evaluation with actual taxi operation data offered by Seoul Metropolitan Government, we challenge the natural expectation that the RL-based approaches yield the best result by showing that a lightweight greedy algorithm can have a competitive advantage. Moreover, we break the norm of dissecting the service area into sub-zones and show that matching passengers beyond arbitrary boundaries generates significantly higher operating income and profit. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
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Article
The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network
Electronics 2021, 10(14), 1737; https://doi.org/10.3390/electronics10141737 - 19 Jul 2021
Cited by 2 | Viewed by 857
Abstract
As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding [...] Read more.
As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
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Article
Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density
Electronics 2021, 10(10), 1189; https://doi.org/10.3390/electronics10101189 - 16 May 2021
Cited by 1 | Viewed by 756
Abstract
Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time [...] Read more.
Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time can be reduced in the field. This model also demands much less human effort to tag images with labels. The number of vehicles in an image becomes the label, rather than bounding boxes drawn around every single vehicle. Nonetheless, the labeling task takes considerable time whenever a CNN model is trained and tested for a new road segment. There are two ways to alleviate the human effort involved in using this method. A previous study used a pseudo label pre-training method, and another study employed an image synthesis method to solve the problem. Besides these two methods, we investigated the model transferability to reduce the labeling effort. Using a CNN that was fully trained on images of a road segment, we devised a robust way to utilize the trained model for another site by transforming the model output with a simple quadratic equation. The utility of the proposed method was confirmed at the expense of a minute amount of deterioration in accuracy. Full article
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
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