Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Mobility Sustainability: Ecological, Smart and Connected Transportation Systems
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Mobility Sustainability: Ecological, Smart and Connected Transportation Systems

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 42672

Special Issue Editors

Dr. Xiaobei Jiang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: road user behavior; Intelligent Vehicles and traffic safety.
Prof. Dr. Haixiang Lin

E-Mail Website
Guest Editor
Department of Applied Mathematical Analysis, Delft University of Technology, Delft, The Netherlands
Interests: high performance computing; parallel and distributed algorithms; big data in urban traffic; modeling and simulation of large scale and complex models; sparse matrix computation and parallel numerical solvers.
Dr. Fei Yan

E-Mail Website
Guest Editor
Department of Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany
Interests: modeling driver uncertainty; trust in automation; adaptive driver assistance in automated driving; cross-cultural studies of driving behavior.
Dr. Qian Cheng

E-Mail Website
Guest Editor
Institute of Ergonomics, Technical University of Munich, Munich, Germany
Interests: human factors in intelligent network vehicle system; spatio-temporal data mining.

Special Issue Information

Dear Colleagues,

A powerful combination of Artificial Intelligence and Big Data is now triggering spectacular advancements in the Internet of Vehicles (IoV), smart road, and Autonomous Vehicles (AVs) areas. Previous works pave the way for improving the mobility and transport sustainability, which contribute to a safe, efficient and comfortable driving environment. Automated intelligent agents, following their technological evolution, have become the main characters of the global mobility industry.

However, there are still challenges prior to the deployment of fully autonomous vehicles, such as how to balance the driver’s ability and the self-driving system considering other road users, road traffic infrastructures and integrated environmental information; how to improve traffic efficiency by means of Artificial Intelligence and Machine Learning; how to access AV’s capabilities in different driving situations depending on the cultural background.

Accordingly, ecological, smart and connected transportation systems are the important characteristics of the future. Environmental pollutions can be greatly reduced by electrical vehicles (EVs) and shared AVs. Increasing monitoring, intelligent coordination and smarter use of road networks can largely reduce traffic congestion and traffic accidents. We are now at the point of breakthrough of many innovative technologies which are defining the future sustainable transport systems.

This Special Issue aims to discuss novel approaches in ecological, smart and connected transportation systems, especially to the latest theoretical and practical achievements that will contribute to this research field.

The main topics of the Special Issue include but are not limited to:

  • Risk and emergency solution of transportation systems;
  • Approaches and methodologies in smart road network;
  • Ecological driving behavior;
  • System architecture and modeling of transportation systems;
  • Sustainable traffic policies and economics;
  • Urban public and shared transportation systems;
  • Energy and emission solution of transportation systems;
  • Modeling and simulation of mobility as service;
  • Infrastructure construction and management;
  • Smart operation, management and control of intelligent vehicles.

Dr. Xiaobei Jiang
Prof. Dr. Haixiang Lin
Dr. Fei Yan
Dr. Qian Cheng
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. 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (18 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 1714 KiB  
Article
Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory
by Min Li, Wuhong Wang, Zhen Liu, Mingjun Qiu and Dayi Qu
Sustainability 2022, 14(11), 6901; https://doi.org/10.3390/su14116901 - 6 Jun 2022
Cited by 8 | Viewed by 2035
Abstract
Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the [...] Read more.
Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

10 pages, 3139 KiB  
Article
A Novel DC-AC Fast Charging Technology for Lithium-Ion Power Battery at Low-Temperatures
by Shanshan Guo, Zhiqiang Han, Jun Wei, Shenggang Guo and Liang Ma
Sustainability 2022, 14(11), 6544; https://doi.org/10.3390/su14116544 - 27 May 2022
Cited by 6 | Viewed by 2280
Abstract
There are several drawbacks for lithium-ion batteries at low temperatures, including weak electrolyte conductivity, low chemical reaction rate and greatly increased impedance. Thus, it is inefficient to charge lithium-ion batteries at low temperatures. This work proposes an AC incentive fast charging strategy at [...] Read more.
There are several drawbacks for lithium-ion batteries at low temperatures, including weak electrolyte conductivity, low chemical reaction rate and greatly increased impedance. Thus, it is inefficient to charge lithium-ion batteries at low temperatures. This work proposes an AC incentive fast charging strategy at low-temperatures for lithium-ion batteries based on the analysis and comparison of the existing charging and heating methods. The charging speed, temperature variation, the capacity loss of the constant current constant voltage (CCCV) charging strategy and the proposed method with different current and frequency conditions are compared and analyzed. The results show that it takes about 1400 s for the proposed method to fully charge a lithium-ion battery in the case of 2.2 A current beginning at 25% state of charge (SOC). In addition, the temperature rises about 8 °C. In contrast, the charging time of the CCCV method is 400 s slower than the proposed method and the temperature of the CCCV method increases only about 2 °C. In the case of 1.5 A current beginning at 0% SOC, the charging time of the proposed method is 500 s faster than the CCCV method. The results indicate that the proposed charging method can significantly improve the charging efficiency of lithium-ion batteries at low temperatures. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

19 pages, 43713 KiB  
Article
Automation and Remote Control of an Aquatic Harvester Electric Vehicle
by Emil Tudor, Mihai-Gabriel Matache, Ionuț Vasile, Ion-Cătălin Sburlan and Vasilica Stefan
Sustainability 2022, 14(10), 6360; https://doi.org/10.3390/su14106360 - 23 May 2022
Cited by 7 | Viewed by 3184
Abstract
Electric boats are evolving, following the trend of imposing electric powered vehicles in all transportation solutions. For a research project, a reed and aquatic weed harvester, the author’s goal is to develop an experimental electrical vehicle aimed at solving several particular problems such [...] Read more.
Electric boats are evolving, following the trend of imposing electric powered vehicles in all transportation solutions. For a research project, a reed and aquatic weed harvester, the author’s goal is to develop an experimental electrical vehicle aimed at solving several particular problems such as: small speed, big throttle, high maneuverability, big load capacity, small draught and affordable cost. The solution comprises of one electric motor powered by a converter supplied from Li-Ion batteries, which drives a hydraulic pump for simultaneous operation of two lateral-placed paddle wheels and one complex mechanism of cutter and conveyor. The control system of this vehicle consists of one remote controller, with bidirectional radio communication to three on-board controllers used for the management of the electro-hydraulic actuators, the electric motor and the battery storage system. The hardware and the software architectures are presented, underlining the automated operations designed to increase the safety, the maneuverability and the predictability of the vehicle. The advantages of the use of control electronics is the increasing operability of the vehicle by supervising the available stored energy and the predicted consumption of energy, the fast and remote assistance in case of operational failure using online diagnose and the operation optimization by selecting the best load profile for the cutter and for the paddles. The results of this research are the validation of the proposed hardware and software architectures used for the control of an electro-hydraulic vehicle and the feasibility of using radio communication and remote diagnose for vehicle control. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

21 pages, 2654 KiB  
Article
Research on Multi Unmanned Aerial Vehicles Emergency Task Planning Method Based on Discrete Multi-Objective TLBO Algorithm
by Miao Tang, Minghua Hu, Honghai Zhang and Long Zhou
Sustainability 2022, 14(5), 2555; https://doi.org/10.3390/su14052555 - 23 Feb 2022
Cited by 8 | Viewed by 1683
Abstract
The outbreak of unexpected events such as floods and geological disasters often produces a large number of emergency material requirements, and when common logistics methods are often ineffective, emergency logistics unmanned aerial vehicles (UAVs) become an important means. How to rationally plan multiple [...] Read more.
The outbreak of unexpected events such as floods and geological disasters often produces a large number of emergency material requirements, and when common logistics methods are often ineffective, emergency logistics unmanned aerial vehicles (UAVs) become an important means. How to rationally plan multiple UAVs to quickly complete the emergency logistics tasks in many disaster-stricken areas has become an urgent problem to be solved. In this paper, an optimization model is established with the goal of minimizing the task completion time and the penalty cost of advance/delay, and a discrete multi-objective teaching–learning-based optimization (DMOTLBO) algorithm is proposed. The Pareto frontier approximation problem is transformed into a set of single objective sub-problems by the decomposition mechanism of the algorithm, and each sub-problem is solved by the improved discrete TLBO algorithm. According to the characteristics of the problem, TLBO algorithm is improved by discretization, and an individual update method is constructed based on probability fusion of various mutation evolution operators. At the same time, variable neighborhood descent search is introduced to enhance the local search ability. Based on the multi-level comparative experiment, the improvement measures and effectiveness of DMOTLBO are verified. Finally, combined with specific case analysis, the practicability and efficiency of the DMOTLBO algorithm in solving the multi-objective emergency logistics task planning problem of multiple UAVs are further verified. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

17 pages, 9530 KiB  
Article
The Impacts of COVID-19 and Policies on Spatial and Temporal Distribution Characteristics of Traffic: Two Examples in Beijing
by Weiwei Guo, Yan Feng, Wenxiu Luo, Yilong Ren, Jiyuan Tan, Xiaobei Jiang and Qingwan Xue
Sustainability 2022, 14(3), 1733; https://doi.org/10.3390/su14031733 - 2 Feb 2022
Cited by 6 | Viewed by 2024
Abstract
The global closure policy to limit the spread of the new coronavirus (COVID-19) in 2020 was based on public safety and health considerations. In the implementation of arrangements to prevent the epidemic, the function of the transportation system as a basis for securing [...] Read more.
The global closure policy to limit the spread of the new coronavirus (COVID-19) in 2020 was based on public safety and health considerations. In the implementation of arrangements to prevent the epidemic, the function of the transportation system as a basis for securing cities has been severely affected. After summarizing the domestic and international literature on epidemic policies and travel, this study analyzes the changes of the spatial and temporal distribution characteristics of people’s travel and the impacts in the context of the two epidemic phases in Beijing and abroad. During the epidemic, traffic volume into and out of Beijing showed a downward trend. In our study, we found that total travel volume in Beijing during the Spring Festival in 2020 was down by about 70% year-on-year, the distribution of daily traffic trips during the day was not affected by the outbreak, and six urban areas in the center of Beijing experienced greater declines in travel volume compared to other urban areas. The conclusions of the study can provide a reference for the sustainability and recovery of urban areas and formulation of policies in the subsequent pandemic era in terms of the relationship between public travel and epidemic control. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

23 pages, 6741 KiB  
Article
Analysis of Driving Control Characteristics in Typical Road Types
by Baicang Guo, Qiang Hua, Lisheng Jin, Xianyi Xie, Zhen Huo and Huanhuan Wang
Sustainability 2022, 14(2), 782; https://doi.org/10.3390/su14020782 - 11 Jan 2022
Cited by 3 | Viewed by 1479
Abstract
Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running [...] Read more.
Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

17 pages, 6601 KiB  
Article
Impact of Penetrations of Connected and Automated Vehicles on Lane Utilization Ratio
by Xiaoyuan Wang, Shijie Liu, Huili Shi, Hui Xiang, Yang Zhang, Guowen He and Hanqing Wang
Sustainability 2022, 14(1), 474; https://doi.org/10.3390/su14010474 - 2 Jan 2022
Cited by 5 | Viewed by 2108
Abstract
Lane Utilization Ratio (LUR), affected by lane selection behavior directly, represents the traffic distribution on different lanes of road section for a single direction. The research on LUR, especially under Penetration Conditions of Connected and Automated Vehicles (PCCAV), is not comprehensive enough. Considering [...] Read more.
Lane Utilization Ratio (LUR), affected by lane selection behavior directly, represents the traffic distribution on different lanes of road section for a single direction. The research on LUR, especially under Penetration Conditions of Connected and Automated Vehicles (PCCAV), is not comprehensive enough. Considering the difficulty in the conduction of real vehicle experiment and data collection under PPCAV, the lane selection model based on phase-field coupling and set pair logic, which considers the full-information of lanes, was used to carry out microscopic traffic simulation. From the analysis of microsimulation results, the basic relationships between Penetration of Connected and Automated Vehicles (PCAV), traffic volume, and Lane-Changing Times, also that between PCAV, traffic volume, and LUR in the basic section of the urban expressway were studied. Moreover, the influence of driving propensity on the effect of PCAVs was also studied. The research results could enrich the traffic flow theory and provide the theoretical basis for traffic management and control. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

17 pages, 4126 KiB  
Article
An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS
by Ziwen Song, Feng Sun, Rongji Zhang, Yingcui Du and Guiliang Zhou
Sustainability 2021, 13(24), 13626; https://doi.org/10.3390/su132413626 - 9 Dec 2021
Cited by 2 | Viewed by 2192
Abstract
In this paper, we propose an improved cellular automaton model for the traffic operation characteristics of variable direction lanes in an Intelligent Vehicle Infrastructure Cooperation System (I-VICS). According to the proposed flow of variable oriented lane operation in the I-VICS environment, the idea [...] Read more.
In this paper, we propose an improved cellular automaton model for the traffic operation characteristics of variable direction lanes in an Intelligent Vehicle Infrastructure Cooperation System (I-VICS). According to the proposed flow of variable oriented lane operation in the I-VICS environment, the idea for the improved model has been determined. According to an analysis of different signal states, an improved STCA model is proposed, in combination with the speed induction method of I-VICS and the variable direction lane switching strategy. In the assumed regular simulation environment, the STCA and STCA-V models are simulated under different vehicular densities. The results indicated that traffic parameters such as traffic flow and average speed of the variable direction lanes in the I-VICS environment are better than those in the conventional environment according to the operating rules of the proposed model. Moreover, lane utilization increased for the same density. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

18 pages, 2865 KiB  
Article
Characterizing the Economic and Environmental Benefits of LNG Heavy-Duty Trucks: A Case Study in Shenzhen, China
by Qian Zhao, Wenke Huang, Mingwei Hu, Xiaoxiao Xu and Wenlin Wu
Sustainability 2021, 13(24), 13522; https://doi.org/10.3390/su132413522 - 7 Dec 2021
Cited by 6 | Viewed by 3939
Abstract
Heavy-duty trucks (HDTs) in road freight are a primary contributor of PM2.5 and NOX emissions in many cities. Shenzhen, a megacity of China, has already made great efforts to promote the green transport transition, including via the Liquefied Natural Gas (LNG) [...] Read more.
Heavy-duty trucks (HDTs) in road freight are a primary contributor of PM2.5 and NOX emissions in many cities. Shenzhen, a megacity of China, has already made great efforts to promote the green transport transition, including via the Liquefied Natural Gas (LNG) HDTs program, which may be the largest alternative fuel vehicle promotion program in the world. In order to fully understand the actual efficiency of such program, the economic and environmental impacts of LNG HDTs were analyzed in this study. The results revealed that, while the capital cost of LNG HDTs is higher than that of diesel HDTs, the aggregated cost during the entire operation period of LNG HDTs is 10% to 17% lower than that of diesel HDTs. By replacing existing diesel HDTs mode (including China-I to China-V) with LNG HDTs (100%), environmental impact analysis showed that PM2.5 and NOX emissions could be reduced by 96.7% and 73.2% in the city level, respectively. Moreover, the environmental benefits of using purely LNG HDTs versus just China-V diesel HDTs were also compared, which indicated that LNG substitution is superior to China-V, with a reduction of 20.9% for PM2.5 and 35.4% for NOX, respectively. Overall, the effectiveness of the promotion of LNG HDTs is notable in Shenzhen, and these findings could provide references for other cities to promote LNG HDTs and beyond. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

10 pages, 1767 KiB  
Article
Operating Safety Evaluation of Battery-Electric Taxi Based on Spatio-Temporal Speed Parameters
by Xueyu Mi, Chunjiao Dong, Ning Li, Yi Lin, Chunfu Shao and Bosong Fan
Sustainability 2021, 13(23), 13446; https://doi.org/10.3390/su132313446 - 4 Dec 2021
Cited by 1 | Viewed by 1797
Abstract
The battery-electric taxis have the features of larger mass, low operating noise, and great speed, and the drivers of battery-electric taxis have various driving behaviors and low safety awareness, which leads to higher safety risks. In the paper, the driving and speed characteristics [...] Read more.
The battery-electric taxis have the features of larger mass, low operating noise, and great speed, and the drivers of battery-electric taxis have various driving behaviors and low safety awareness, which leads to higher safety risks. In the paper, the driving and speed characteristics of battery-electric taxis, conventional taxis, and private cars are compared and analyzed through conducting a GPS trajectory survey and a cross-section traffic flow parameter survey. An evaluation index system that is based on the spatio-temporal speed parameters is proposed, and a MEW-VIKOR method is developed for the operatiing safety evaluation of the battery-electric taxi. The results show that the operating speed of battery-electric taxis is significantly higher than that of conventional taxis on weekdays and weekends, and there is a relatively common speeding phenomenon on urban local roads. The proposed safety evaluation index system that is based on the spatio-temporal speed parameters and the MEW-VIKOR evaluation method can effectively evaluate the operatiing safety of battery-electric taxis. In addition, the ranking results show that, according to the spatio-temporal speed parameters, the operating safety of battery-electric taxis is lower than that of conventional taxis and private cars. The research provides theoretical insights for strategies and policies making to reduce the unsafe driving behaviors of battery-electric taxis. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

13 pages, 5380 KiB  
Article
The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions
by Xiaoyuan Wang, Yongqing Guo, Chenglin Bai, Quan Yuan, Shanliang Liu and Xuegang (Jeff) Ban
Sustainability 2021, 13(23), 13292; https://doi.org/10.3390/su132313292 - 1 Dec 2021
Cited by 2 | Viewed by 2242
Abstract
Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This [...] Read more.
Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

12 pages, 931 KiB  
Article
Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles
by Xiaobei Jiang, Wenlin Yu, Wenjie Li, Jiawen Guo, Xizheng Chen, Hongwei Guo, Wuhong Wang and Tao Chen
Sustainability 2021, 13(23), 13272; https://doi.org/10.3390/su132313272 - 30 Nov 2021
Cited by 7 | Viewed by 2452
Abstract
The emergence of automated vehicles (AVs) is expected to have a huge impact on traffic safety and environmental improvement. In order to promote the sustainable development of AVs, it is urgent to study the public’s acceptance of and willingness-to-pay for automated vehicles and [...] Read more.
The emergence of automated vehicles (AVs) is expected to have a huge impact on traffic safety and environmental improvement. In order to promote the sustainable development of AVs, it is urgent to study the public’s acceptance of and willingness-to-pay for automated vehicles and their influencing factors. Based on a questionnaire survey and descriptive research, this paper investigates the public’s general views on AVs. A psychological model considering technical trust (TT), perceived benefit (PB), perceived risk (PR), and perceived ease of use (PU) was constructed to study the factors that influence the public’s acceptance of and willingness-to-pay for AVs. Logistic regression models based on demographic factors such as monthly income (MI) and driving experience (DE) and psychological factors were established to predict end-users’ acceptance and willingness-to-pay. The accuracy of the two models is 93.2% and 87.9%, respectively. Based on the results, the following policies can be put forward to promote the development of AVs: (1) more information to enhance TT; (2) pricing and easy maintenance based on PU; (3) education and training based on TT and PB; and (4) personalized sales based on DE and MI. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

14 pages, 2280 KiB  
Article
Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations
by Xuesong Feng, Zhibin Tao, Xuejun Niu and Zejing Ruan
Sustainability 2021, 13(23), 13219; https://doi.org/10.3390/su132313219 - 29 Nov 2021
Cited by 6 | Viewed by 1744
Abstract
Taking into consideration the overlapped influences of multiple rail transit stations upon land use characteristics, this study newly develops a multi-objective land use allocation optimization model to decide the land use type and intensity of every undeveloped land block of an urban area. [...] Read more.
Taking into consideration the overlapped influences of multiple rail transit stations upon land use characteristics, this study newly develops a multi-objective land use allocation optimization model to decide the land use type and intensity of every undeveloped land block of an urban area. The new model is solved by successively utilizing the non-dominated sorting genetic algorithm and the technique for order performance by similarity to ideal solution to obtain the least biased Pareto-optimal land development scheme. The study area is an urban region around two metro stations in Beijing of China. The influencing scopes of these two stations are overlapped in part, and many of the land blocks in the study area are not yet developed. It is shown that the newly developed land use allocation optimization model is able to rationally achieve multi-objectives in coordination to the most extents for the sustainable urban development in view of the integrated effect of multiple rail transit stations. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

17 pages, 2867 KiB  
Article
Signal Control Method for Through and Left-Turn Shared Lane by Setting Left-Turn Waiting Area at Signalized Intersections
by Xiancai Jiang, Li Yao, Yao Jin and Runting Wu
Sustainability 2021, 13(23), 13154; https://doi.org/10.3390/su132313154 - 27 Nov 2021
Cited by 4 | Viewed by 2134
Abstract
This paper proposes a signal control method for the through and left-turn shared lanes at signalized intersections to solve traffic conflicts between left-turn vehicles and opposing through vehicles by setting left-turn waiting area (LWA). Delays and stops are weighted to form an integrated [...] Read more.
This paper proposes a signal control method for the through and left-turn shared lanes at signalized intersections to solve traffic conflicts between left-turn vehicles and opposing through vehicles by setting left-turn waiting area (LWA). Delays and stops are weighted to form an integrated performance index (PI) in a vehicle-to-infrastructure cooperation system. The PI models pertaining to all vehicles are established based on the LWA intersection. In addition, an optimized method of signal timing parameters is constructed by minimizing the average PI. VISSIM simulation shows that the average PI decreases by 6.51% compared with the original layout and signal timing plan of the intersection, since the increased delay of the side-road left-turn vehicles is insufficient to offset the reduced delay of the side-road through vehicles after the improvement. The sensitivity analysis shows that the greater the traffic volume of the phase including the through and left-turn shared lanes, the higher the operation efficiency of the LWA intersection compared with the typical permitted phase intersection. When the left-turn vehicles of the shared lanes in each cycle are less than the stop spaces, the LWA intersection can effectively reduce the average PI of the shared lanes. Furthermore, the more the stop spaces in the LWA, the lower the average PI in the same traffic conditions. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

14 pages, 6628 KiB  
Article
An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace
by Shan Li, Honghai Zhang, Zhuolun Li and Hao Liu
Sustainability 2021, 13(23), 13079; https://doi.org/10.3390/su132313079 - 25 Nov 2021
Cited by 24 | Viewed by 3426
Abstract
Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is [...] Read more.
Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

11 pages, 2515 KiB  
Article
Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data
by Hui Chen, Zhaoming Chu and Chao Sun
Sustainability 2021, 13(23), 13057; https://doi.org/10.3390/su132313057 - 25 Nov 2021
Cited by 3 | Viewed by 1662
Abstract
Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal [...] Read more.
Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

14 pages, 4082 KiB  
Article
UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction
by Honghai Zhang, Yongjie Yan, Shan Li, Yuxin Hu and Hao Liu
Sustainability 2021, 13(22), 12528; https://doi.org/10.3390/su132212528 - 12 Nov 2021
Cited by 12 | Viewed by 2197
Abstract
Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted [...] Read more.
Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

17 pages, 4583 KiB  
Article
Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity
by Xingliang Liu, Jian Wang, Tangzhi Liu and Jin Xu
Sustainability 2021, 13(21), 12195; https://doi.org/10.3390/su132112195 - 4 Nov 2021
Cited by 3 | Viewed by 2499
Abstract
Emergency events can induce serious traffic congestions in a local area which may propagate to the upstream roads, and even the whole network. Until now, the methodology forecasting spatiotemporal boundary propagation of emergency-event-based traffic congestions, with both explicitness and road network availability, has [...] Read more.
Emergency events can induce serious traffic congestions in a local area which may propagate to the upstream roads, and even the whole network. Until now, the methodology forecasting spatiotemporal boundary propagation of emergency-event-based traffic congestions, with both explicitness and road network availability, has not been found. This study develops a new method for predicting spatiotemporal boundary of the congestion caused by emergency events, which is more applicable and practical than cell transmission model (CTM)-derived methods. This method divides the expressway network into different sections based on their functions and the shockwave direction caused by the emergency events. It characterizes the velocity of the moving congestion boundary based on kinetic wave theory and volume–density relationship. After determining whether the congestion will spread into the network level through an interchange using a new concept, highway node acceptance capacity (HNAC), we can predict the spatiotemporal boundary and corresponding traffic condition within the boundary. The proposed method is tested under four traffic incident cases with corresponding traffic data collected through field observations. We also compare its prediction performances with other methods used in the literature. Full article
(This article belongs to the Special Issue Mobility Sustainability: Ecological, Smart and Connected Transportation Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-18
Back to TopTop