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Sustainability
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Sustainable Transportation Electrification: Policy, Planning and Operation
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Sustainable Transportation Electrification: Policy, Planning and Operation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 11633

Special Issue Editors

Prof. Dr. Yiming Bie

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Guest Editor
School of Transportation, Jilin University, Changchun 130022, China
Interests: advanced transit operations; traffic design and control
Special Issues, Collections and Topics in MDPI journals
Dr. Shidong Liang

E-Mail Website
Guest Editor
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
Interests: urban transit planning and operation; traffic control
Special Issues, Collections and Topics in MDPI journals
Dr. Weitiao Wu

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: public transport; logistics; traffic flow
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation electrification is widely considered as an effective solution for reducing the fossil fuel dependency and greenhouse gas emissions of the road transportation system. In recent years, many countries have been establishing increasingly stringent and ambitious targets in support of transport electrification. The rapid growth of electric vehicles (EVs) around the world will have a major impact on existing systems. In the era of transportation electrification, the ways in which urban transportation systems are transformed are still a largely unanswered question.

As such, it is important to know how the transportation system will change due to the increase in EVs and how such changes will affect traffic flow operations, the deployment of charging utilities and the service level of urban mobility systems. Information on these is crucial for transportation management and planning.

Further research is required to determine the optimal policies, planning and operation plans for ensuring the sustainability of transportation systems during the electrification process. For this Special Issue, we invite researchers to submit original research and review articles addressing all aspects related to road transportation electrification. Potential topics include but are not limited to the following:

  • Electric vehicle operations with connected self-driving technologies
  • Electric vehicle charging infrastructure planning
  • Eco-friendly mobility for electric vehicles
  • Policies for transportation electrification
  • Traffic flow and human factor changes in electrified transportation systems
  • Data mining and big data in electrified transportation systems
  • Innovative electric transit system solutions
  • Environmental impacts of transportation electrification
  • Evaluation methods for electrified transportation systems

Prof. Dr. Yiming Bie
Dr. Shidong Liang
Dr. Weitiao Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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. Sustainability 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

  • electrified transportation
  • planning and operation
  • electric vehicle
  • charging infrastructure
  • big data
  • electric transit
  • environmental impacts

Published Papers (5 papers)

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Research

20 pages, 3416 KiB  
Article
A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering
by Qiang Shang, Yang Yu and Tian **e
Sustainability 2022, 14(17), 11068; https://doi.org/10.3390/su141711068 - 5 Sep 2022
Cited by 7 | Viewed by 1568
Abstract
As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a [...] Read more.
As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods. Full article
(This article belongs to the Special Issue Sustainable Transportation Electrification: Policy, Planning and Operation)
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16 pages, 3670 KiB  
Article
A Dynamic Regional Partitioning Method for Active Traffic Control
by Yan **ng, Wenqing Li, Weidong Liu, Yachao Li and Zhe Zhang
Sustainability 2022, 14(16), 9802; https://doi.org/10.3390/su14169802 - 9 Aug 2022
Cited by 2 | Viewed by 1513
Abstract
In order to identify the scope of active traffic control regions and improve the effect of active traffic control, this paper proposes a dynamic partitioning method of area boundaries based on benchmark intersections, taking into account the saturation, homogeneity, and correlation of intersections [...] Read more.
In order to identify the scope of active traffic control regions and improve the effect of active traffic control, this paper proposes a dynamic partitioning method of area boundaries based on benchmark intersections, taking into account the saturation, homogeneity, and correlation of intersections in the region. First, a boundary indicator correlation model was established. Next, benchmark intersections were selected based on evaluation indicators, such as traffic speed and queue length. Then, the boundary of the control region is initially defined based on the selected reference intersection, through a combination of the improved Newman algorithm. Subsequently, a spectral clustering algorithm is used to obtain the boundaries of the optimal active control subregions. Finally, a city road network is used as the study object for analysis and verification under the premise of implementing active traffic control. The results show that compared with the intersection clustering algorithm method and the boundary control subdivision method, the control effect indicators, such as the average delay and the average number of stops, have a great optimization improvement. Thus, the proposed method of regional borders combines the actual traffic flow characteristics efficiently to make a more accurate real-time dynamic division of the road network sub-areas. Full article
(This article belongs to the Special Issue Sustainable Transportation Electrification: Policy, Planning and Operation)
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23 pages, 5141 KiB  
Article
Compound Positioning Method for Connected Electric Vehicles Based on Multi-Source Data Fusion
by Lin Wang, Zhenhua Li and Qinglan Fan
Sustainability 2022, 14(14), 8323; https://doi.org/10.3390/su14148323 - 7 Jul 2022
Cited by 3 | Viewed by 1688
Abstract
With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of [...] Read more.
With the development of electrified transportation, electric vehicle positioning technology plays an important role in improving comprehensive urban management ability. However, the traditional positioning methods based on the global positioning system (GPS) or roadside single sensors make it hard to meet requirements of high-precision positioning. Considering the advantages of various sensors in the cooperative vehicle-infrastructure system (CVIS), this paper proposes a compound positioning method for connected electric vehicles (CEVs) based on multi-source data fusion technology, which can provide data support for the CVIS. Firstly, Dempster-Shafer (D-S) evidence theory is used to fuse the position probability in multi-sensor detection information, and screen vehicle existence information. Then, a hybrid neural network model based on a long short-term (LSTM) framework is constructed to fit the map** relationship between measured and undetermined coordinates. Moreover, the fused data are proceeded as the input of the hybrid LSTM model, which can export the vehicular real-time compound positioning information. Finally, an intersection in Shi**gshan District, Bei**g is selected as the test field for trajectory information collection of CEVs. The experimental results have shown that the uncertainty of fusion data can be reduced to 0.38% of the original level, and the maximum error of real-time positioning accuracy is less than 0.0905 m based on the hybrid LSTM model, which can verify the effectiveness of the model. Full article
(This article belongs to the Special Issue Sustainable Transportation Electrification: Policy, Planning and Operation)
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19 pages, 3576 KiB  
Article
Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety
by Huasheng Liu, Yu Li, Chongyu Zhang, ** Li, **aowen Li and Yuqi Zhao
Sustainability 2022, 14(7), 4213; https://doi.org/10.3390/su14074213 - 1 Apr 2022
Cited by 16 | Viewed by 3365
Abstract
Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their [...] Read more.
Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better. Full article
(This article belongs to the Special Issue Sustainable Transportation Electrification: Policy, Planning and Operation)
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17 pages, 1986 KiB  
Article
Collaborative Optimization of Vehicle and Crew Scheduling for a Mixed Fleet with Electric and Conventional Buses
by **g Wang, Heqi Wang, Ande Chang and Chen Song
Sustainability 2022, 14(6), 3627; https://doi.org/10.3390/su14063627 - 19 Mar 2022
Cited by 8 | Viewed by 2442
Abstract
Replacing conventional buses with electric buses is in line with the concept of sustainable development. However, electric buses have the disadvantages of short driving range and high purchase price. Many cities must implement a semi-electrification strategy for bus routes. In this paper, a [...] Read more.
Replacing conventional buses with electric buses is in line with the concept of sustainable development. However, electric buses have the disadvantages of short driving range and high purchase price. Many cities must implement a semi-electrification strategy for bus routes. In this paper, a bi-level, multi-objective programming model is established for the collaborative scheduling problem of vehicles and drivers on a bus route served by the mixed bus fleet. The upper-layer model minimizes the operation cost and economic cost of carbon emission to optimize the vehicle and charging scheme; while the lower-layer model tries to optimize the crew-scheduling scheme with the objective of minimizing driver wages and maximizing the degree of bus-driver specificity, considering the impact of drivers’ labor restrictions. Then, the improved multi-objective particle swarm algorithm based on an ε-constraint processing mechanism is used to solve the problem. Finally, an actual bus route is taken as an example to verify the effectiveness of the model. The results show that the established model can reduce the impact of unbalanced vehicle scheduling in mixed fleets on crew scheduling, ensure the degree of driver–bus specificity to standardize operation, and save the operation cost and driver wage. Full article
(This article belongs to the Special Issue Sustainable Transportation Electrification: Policy, Planning and Operation)
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