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Sensing and Managing Traffic Flow
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Sensing and Managing Traffic Flow

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 July 2023) | Viewed by 10224

Special Issue Editors

Prof. Dr. Rui Jiang

E-Mail Website
Guest Editor
Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
Interests: transportation systems; traffic flow; bicycles and pedestrians traffic; connected and automated vehicles; cellular automata
Special Issues, Collections and Topics in MDPI journals
Dr. Junfang Tian

E-Mail Website
Guest Editor
College of Management and Economics, Tianjin University, Tianjin 300072, China
Interests: traffic flow; traffic safety; intelligent transportation systems; transport policy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaowei Yu

E-Mail Website
Guest Editor
College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Interests: traffic modelling; traffic simulation; traffic optimization and control
Dr. Xiaopeng Li

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33612, USA
Interests: connected automated traffic analysis and control; sustainable infrastructure systems design; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional traffic flow of human-driven vehicles (HVs) exhibits fascinating phenomena such as traffic breakdown and traffic instability. With the development of automated vehicle (AV) technologies, traditional traffic flow would transit into the mixed traffic flow consisting of HVs and AVs, and finally into the traffic flow of full AVs. To understand and manage all kinds of traffic flow, whether traditional traffic flow, mixed traffic flow, or full AV traffic flow, sensors are indispensable, which range from loop detectors and cameras to GPS, Lidar, V2V and V2I communication devices. Based on the sensor data, it is a challenge task for the traffic community to develop effective operational and control strategies (e.g., ramp metering, signal optimization, trajectory planning, cooperative driving, route guiding) to improve safety, efficiency and driving comfort in the traffic flow.

The scope of this Special Issue is to collect state-of-the-art research papers related to sensor technologies applied in traffic flow management. The topics of interest include, but are not limited to, the following:

  1. Sensor data acquisition and data fusion;
  2. Traffic flow data analysis;
  3. Traffic flow experiment;
  4. Traffic flow modeling and simulation;
  5. Operational and control strategies;
  6. Interactions with pedestrians and bicycles.

Prof. Dr. Rui Jiang
Dr. Junfang Tian
Prof. Dr. Shaowei Yu
Dr. Xiaopeng Li
Guest Editors

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. Sensors 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 2600 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

  • traffic flow
  • automated vehicles
  • traffic breakdown
  • traffic instability
  • ramp metering
  • signal optimization
  • trajectory planning
  • cooperative driving
  • route guiding

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Published Papers (4 papers)

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Research

15 pages, 1417 KiB  
Article
Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy
by Jianshuai Feng, Tianyu Shi, Yuankai Wu, Xiang Xie, Hongwen He and Huachun Tan
Sensors 2023, 23(10), 4659; https://doi.org/10.3390/s23104659 - 11 May 2023
Cited by 2 | Viewed by 2227
Abstract
In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two [...] Read more.
In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway’s throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power. Full article
(This article belongs to the Special Issue Sensing and Managing Traffic Flow)
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18 pages, 6574 KiB  
Article
An Efficient Hybrid Linear Clustering Superpixel Decomposition Framework for Traffic Scene Semantic Segmentation
by Dan Zhong, Tiehu Li and Yuxuan Dong
Sensors 2023, 23(2), 1002; https://doi.org/10.3390/s23021002 - 15 Jan 2023
Cited by 3 | Viewed by 2126
Abstract
Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the [...] Read more.
Superpixel decomposition could reconstruct an image through meaningful fragments to extract regional features, thus boosting the performance of advanced computer vision tasks. To further optimize the computational efficiency as well as segmentation quality, a novel framework is proposed to generate superpixels from the perspective of hybridizing two existing linear clustering frameworks. Instead of conventional grid sampling seeds for region clustering, a fast convergence strategy is first introduced to center the final superpixel clusters, which is based on an accelerated convergence strategy. Superpixels are then generated from a center-fixed online average clustering, which adopts region growing to label all pixels in an efficient one-pass manner. The experiments verify that the integration of this two-step implementation could generate a synergistic effect and that it becomes more well-rounded than each single method. Compared with other state-of-the-art superpixel algorithms, the proposed framework achieves a comparable overall performance in terms of segmentation accuracy, spatial compactness and running efficiency; moreover, an application on image segmentation verifies its facilitation for traffic scene analysis. Full article
(This article belongs to the Special Issue Sensing and Managing Traffic Flow)
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13 pages, 3699 KiB  
Article
Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model
by Yun Xiao, Wei Kong and Zijun Liang
Sensors 2022, 22(23), 9456; https://doi.org/10.3390/s22239456 - 3 Dec 2022
Cited by 3 | Viewed by 2007
Abstract
Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and [...] Read more.
Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application. Full article
(This article belongs to the Special Issue Sensing and Managing Traffic Flow)
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17 pages, 3759 KiB  
Article
Analysis of the Conflict between Car Commuter’s Route Choice Habitual Behavior and Traffic Information Search Behavior
by Kai Liu
Sensors 2022, 22(12), 4382; https://doi.org/10.3390/s22124382 - 9 Jun 2022
Viewed by 2178
Abstract
Motivated by the conflict between travelers’ habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters’ daily [...] Read more.
Motivated by the conflict between travelers’ habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters’ daily route choices. Based on the observed data, participants’ route choices, habit strength, response time, and information search behaviors were analyzed. It is concluded that, in the beginning, the traffic information had a great influence on the habit participants’ route choices, let them think more, and made most of them switch from habit route to the best route (as recommended by traffic information); however, as time went on, the impact of traffic information declined, and several features of habits, such as automatically responding and repeated behavior, would reappear in some participants’ decision-making. Meanwhile, the different way of traffic information search behaviors (i.e., in active performance or in passive reception) could cause different information compliance ratios. These results would help to understand the interrelationship between car commuters’ daily route choice behaviors and traffic information search behaviors in short-term and in long-term, respectively, and provide an interesting starting point for the development of practical traffic information issuing strategies to enhance the impact of traffic information to alleviate traffic congestion during morning commuting. Full article
(This article belongs to the Special Issue Sensing and Managing Traffic Flow)
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