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17 pages, 2833 KiB

Open AccessArticle

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

by Jae-joon Chung and Hyun-Jung Kim

Sustainability 2020, 12(6), 2475; https://doi.org/10.3390/su12062475 - 21 Mar 2020

Cited by 36 | Viewed by 5776

Abstract

This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO₂ concentrations in [...] Read more.

This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO₂ concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO₂, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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14 pages, 531 KiB

Open AccessArticle

On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments

by Dongqing Zhang and Zhaoxia Guo

Sustainability 2020, 12(1), 238; https://doi.org/10.3390/su12010238 - 27 Dec 2019

Cited by 5 | Viewed by 2155

Abstract

This research addresses how the stochasticity and correlation of travel speeds affect the shortest path solutions in sustainable environments. We consider a shortest path problem with the objective function of minimizing a linear combination of the mean and standard deviation of carbon emissions. [...] Read more.

This research addresses how the stochasticity and correlation of travel speeds affect the shortest path solutions in sustainable environments. We consider a shortest path problem with the objective function of minimizing a linear combination of the mean and standard deviation of carbon emissions. By adjusting the proportion of the standard deviation in the objective function, the effects of speed stochasticity and correlation are studied under different preferences of the decision-makers on the fluctuations of carbon emissions. Based on 102-day real speed data from the Los Angeles freeway network, this research conducts extensive numerical experiments on 200 randomly chosen origin-destination pairs. Experimental results demonstrate the necessity of considering speed stochasticity and correlation, especially when the standard deviation of carbon emissions takes a large proportion in the objective function. As the weight of the standard deviation in the objective function increases from 0 to 1.5, the reduction of emission objective values increases from 0.03% to 0.13% by considering speed stochasticity, and increases from 0.02% to 0.20% by considering speed correlation. Taking the city Los Angeles with about 2361 taxis and about 525,945 passenger orders in January 2017 as an example, 0.03% and 0.02% reductions respond to about 3156 kg and 2630 kg carbon emission, respectively. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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15 pages, 10036 KiB

Open AccessArticle

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

by Charalampos Bratsas, Kleanthis Koupidis, Josep-Maria Salanova, Konstantinos Giannakopoulos, Aristeidis Kaloudis and Georgia Aifadopoulou

Sustainability 2020, 12(1), 142; https://doi.org/10.3390/su12010142 - 23 Dec 2019

Cited by 56 | Viewed by 8581

Abstract

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic [...] Read more.

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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13 pages, 3186 KiB

Open AccessArticle

Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms

by Yuan Yuan, Chunfu Shao, Zhichao Cao, Wenxin Chen, Anteng Yin, Hao Yue and Binglei Xie

Sustainability 2019, 11(24), 7230; https://doi.org/10.3390/su11247230 - 19 Dec 2019

Cited by 9 | Viewed by 3203

Abstract

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World [...] Read more.

Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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26 pages, 7688 KiB

Open AccessArticle

An Integrated Variable Speed Limit and ALINEA Ramp Metering Model in the Presence of High Bus Volume

by Nima Dadashzadeh and Murat Ergun

Sustainability 2019, 11(22), 6326; https://doi.org/10.3390/su11226326 - 11 Nov 2019

Cited by 9 | Viewed by 4450

Abstract

Under many circumstances, when providing full bus priority methods, urban transport officials have to operate buses in mixed traffic based on their road network limitations. In the case of Istanbul’s Metrobus lane, for instance, when the route comes to the pre-designed Bosphorus Bridge, [...] Read more.

Under many circumstances, when providing full bus priority methods, urban transport officials have to operate buses in mixed traffic based on their road network limitations. In the case of Istanbul’s Metrobus lane, for instance, when the route comes to the pre-designed Bosphorus Bridge, it has no choice but to merge with highway mixed traffic until it gets to the other side. Much has been written on the relative success of implementing Ramp Metering (RM), for example ALINEA (‘Asservissement line´ aire d’entre´ e autoroutie’) and Variable Speed Limits (VSL), two of the most widely-used “merging congestion” management strategies, in both a separate and combined manner. However, there has been no detailed study regarding the combination of these systems in the face of high bus volume. This being the case, the ultimate goal of this study is to bridge this gap by developing and proposing a combination of VSL and RM strategies in the presence of high bus volume (VSL+ALINEA/B). The proposed model has been coded using microscopic simulation software—VISSIM—and its vehicle actuated programming (VAP) feature; referred to as VisVAP. For current traffic conditions, the proposed model is able to improve total travel time by 9.0%, lower the number of average delays of mixed traffic and buses by 29.1% and 81.5% respectively, increase average speed by 12.7%, boost bottleneck throughout by 2.8%, and lower fuel consumption, Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Volatile Organic Compounds (VOC) emissions by 17.3% compared to the existing “VSL+ALINEA” model. The results of the scenario analysis confirmed that the proposed model is not only able to decrease delay times on the Metrobus system but is also able to improve the adverse effects of high bus volume when subject to adjacent mixed traffic flow along highway sections. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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13 pages, 3368 KiB

Open AccessArticle

Eco-Speed Guidance for the Mixed Traffic of Electric Vehicles and Internal Combustion Engine Vehicles at an Isolated Signalized Intersection

by Kai Liu, Dong Liu, Cheng Li and Toshiyuki Yamamoto

Sustainability 2019, 11(20), 5636; https://doi.org/10.3390/su11205636 - 12 Oct 2019

Cited by 6 | Viewed by 2850

Abstract

Although electric vehicles (EVs) have been regarded as promising to reduce tailpipe emissions and energy consumption, a mixed traffic flow of EVs and internal combustion engine vehicles (ICEVs) makes the energy/emissions reduction objective more difficult because EVs and ICEVs have various general characteristics. [...] Read more.

Although electric vehicles (EVs) have been regarded as promising to reduce tailpipe emissions and energy consumption, a mixed traffic flow of EVs and internal combustion engine vehicles (ICEVs) makes the energy/emissions reduction objective more difficult because EVs and ICEVs have various general characteristics. This paper proposes a low-emission-oriented speed guidance model to address the energy/emission reduction issue under a mixed traffic flow at an isolated signalized intersection to achieve the objective of reducing emissions and total energy consumption while reducing vehicle delay and travel time. The total energy/emissions under different market penetration rates of EVs with various traffic volumes are analyzed and compared. Numerical examples demonstrate that the proposed speed guidance model has better performance than those without considering the impact of queues. For a certain traffic volume, the energy/emission reduction effects under speed guidance will increase with an increasing share of EVs. This paper also explores the impact of the time interval for guidance renewal on vehicle emissions in practice. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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12 pages, 762 KiB

Open AccessArticle

Research on Comprehensive Multi-Infrastructure Optimization in Transportation Asset Management: The Case of Roads and Bridges

by Zhang Chen, Yuanlu Liang, Yangyang Wu and Lijun Sun

Sustainability 2019, 11(16), 4430; https://doi.org/10.3390/su11164430 - 16 Aug 2019

Cited by 13 | Viewed by 3176

Abstract

Optimization is the core of transportation asset management, but current optimization approaches are still in the stage of single infrastructure management, which seriously hinders the development and application of transportation asset management. This paper establishes a comprehensive multi-infrastructure optimization model for transportation assets [...] Read more.

Optimization is the core of transportation asset management, but current optimization approaches are still in the stage of single infrastructure management, which seriously hinders the development and application of transportation asset management. This paper establishes a comprehensive multi-infrastructure optimization model for transportation assets consisting of roads and bridges, which is aimed at achieving the goal of transportation asset comfort, integrity, and security, taking budget funds as constraint conditions, and applying the optimization technique of goal programming and integer programming. An interactive fuzzy linear-weighted optimum-order algorithm is presented to solve the comprehensive optimization model. Finally, the comprehensive multi-infrastructure optimization model and algorithm are verified to be effective by practical data in a case study. The results indicate that the model and algorithm can provide a satisfactory and reasonable maintenance and rehabilitation schedule for transportation asset management agencies. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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15 pages, 3142 KiB

Open AccessArticle

Mining Public Opinion on Transportation Systems Based on Social Media Data

by Dawei Li, Yujia Zhang and Cheng Li

Sustainability 2019, 11(15), 4016; https://doi.org/10.3390/su11154016 - 25 Jul 2019

Cited by 25 | Viewed by 4648

Abstract

Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order [...] Read more.

Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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26 pages, 7649 KiB

Open AccessArticle

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

by Tamás Bányai, Béla Illés, Miklós Gubán, Ákos Gubán, Fabian Schenk and Ágota Bányai

Sustainability 2019, 11(14), 3850; https://doi.org/10.3390/su11143850 - 15 Jul 2019

Cited by 8 | Viewed by 4624

Abstract

The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply [...] Read more.

The just-in-sequence inventory strategy, as an important part of the supply chain solutions in the automotive industry, is based on feedback information from the manufacturer. The performance, reliability, availability and cost efficiency are based on the parameters of the members of the supply chain process. To increase the return on assets (ROA) of the manufacturer, the optimization of the supply process is unavoidable. Within the frame of this paper, the authors describe a flower pollination algorithm-based heuristic optimization model of just-in-sequence supply focusing on sustainability aspects, including fuel consumption and emission. After a systematic literature review, this paper introduces a mathematical model of just-in-sequence supply, including assignment and scheduling problems. The objective of the model is to determine the optimal assignment and schedule for each sequence to minimize the total purchasing cost, which allows improving cost efficiency while sustainability aspects are taken into consideration. Next, a flower pollination algorithm-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase cost-efficiency in just-in-sequence solutions. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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28 pages, 2997 KiB

Open AccessArticle

Assessing the Socioeconomic Impacts of Intelligent Connected Vehicles in China: A Cost–Benefit Analysis

by Xu Kuang, Fuquan Zhao, Han Hao and Zongwei Liu

Sustainability 2019, 11(12), 3273; https://doi.org/10.3390/su11123273 - 13 Jun 2019

Cited by 25 | Viewed by 4828

Abstract

The deployment of intelligent connected vehicles (ICVs) is regarded as a significant solution to improve road safety, transportation management, and energy efficiency. This study assessed the safety, traffic, environmental, and industrial economic benefits of ICV deployment in China under different scenarios. A bottom-up [...] Read more.

The deployment of intelligent connected vehicles (ICVs) is regarded as a significant solution to improve road safety, transportation management, and energy efficiency. This study assessed the safety, traffic, environmental, and industrial economic benefits of ICV deployment in China under different scenarios. A bottom-up model was established to deal with these impacts within a unified framework, based on the existing theories and literature of ICVs’ cost–benefit analysis, as well as China’s most recent policies and statistics. The results indicate that the total benefits may reach 13.25 to 24.02 trillion renminbi (RMB) in 2050, while a cumulative benefit–cost ratio of 1.15 to 3.06 suggests high cost-effectiveness. However, if the government and industry only focus on their own interests, the break-even point may be delayed by several years. Hence, an effective business model is necessary to enhance public–private cooperation in ICV implementation. Meanwhile, the savings of travel time costs and fleet labor costs play an important part in all socioeconomic impacts. Therefore, the future design of ICVs should pay more attention to the utilization of in-vehicle time and the real substitution for human drivers. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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18 pages, 4303 KiB

Open AccessArticle

Left-Side On-Ramp Metering for Improving Safety and Efficiency in Underground Expressway Systems

by Minhua Shao, Congcong Xie, Lijun Sun, Xiaomin Wan and Zhang Chen

Sustainability 2019, 11(12), 3247; https://doi.org/10.3390/su11123247 - 12 Jun 2019

Cited by 6 | Viewed by 3245

Abstract

As one of the effective measures of intelligent traffic control, on-ramp metering is often used to improve the traffic efficiency of expressways. Existing on-ramp metering research mainly discusses expressways with right-side on-ramps. However, for underground expressway systems (UESs), left-side on-ramps are frequently adopted [...] Read more.

As one of the effective measures of intelligent traffic control, on-ramp metering is often used to improve the traffic efficiency of expressways. Existing on-ramp metering research mainly discusses expressways with right-side on-ramps. However, for underground expressway systems (UESs), left-side on-ramps are frequently adopted to reduce the ground space occupied by ramp construction. Since traffic entering from the left and right sides of the mainline may have different traffic characteristics, on-ramp metering for UESs with left-side on-ramps should be explored specifically. This study examines the impacts of left-side on-ramps on the traffic safety and efficiency of UESs and proposes an effective on-ramp metering strategy. Firstly, using field data, traffic flow fundamental diagrams and speed dispersion are discussed to explore the traffic flow characteristics of the “left-in” UES. The results show that the capacity and critical occupancy are both reduced in left-side on-ramp compared to right-side on-ramp expressways. Meanwhile, the speed dispersion is higher in left-side on-ramp UESs, which means a higher accident risk. Based on this, considering traffic safety and efficiency, a novel two-parameter left-side on-ramp metering strategy for UESs is proposed, in which occupancy and speed are used as the control indicators simultaneously. Additionally, the mechanism of the metering strategy is explained. Finally, the proposed on-ramp metering strategy is simulated on a real UES. The results demonstrate the advantages of the proposed two-parameter on-ramp metering strategy for improving the traffic safety and efficiency of UESs. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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14 pages, 7106 KiB

Open AccessArticle

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

by Fan Yang, Fan Ding, Xu Qu and Bin Ran

Sustainability 2019, 11(11), 3220; https://doi.org/10.3390/su11113220 - 11 Jun 2019

Cited by 25 | Viewed by 4109

Abstract

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential [...] Read more.

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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16 pages, 2773 KiB

Open AccessArticle

Construction of Knowledge Graphs for Maritime Dangerous Goods

by Qi Zhang, Yuanqiao Wen, Chunhui Zhou, Hai Long, Dong Han, Fan Zhang and Changshi Xiao

Sustainability 2019, 11(10), 2849; https://doi.org/10.3390/su11102849 - 19 May 2019

Cited by 28 | Viewed by 6254

Abstract

Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays [...] Read more.

Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the “knowledge island.” This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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18 pages, 2784 KiB

Open AccessArticle

The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation

by Heqing Tan, Muqing Du, Xiaowei Jiang and Zhaoming Chu

Sustainability 2019, 11(7), 2167; https://doi.org/10.3390/su11072167 - 11 Apr 2019

Cited by 7 | Viewed by 3175

Abstract

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at [...] Read more.

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers’ behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans’ two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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15 pages, 2269 KiB

Open AccessArticle

Association of Carbon Emissions and Circular Curve in Northwestern China

by Yaping Dong, Jinliang Xu, Menghui Li, Xingli Jia and Chao Sun

Sustainability 2019, 11(4), 1156; https://doi.org/10.3390/su11041156 - 22 Feb 2019

Cited by 9 | Viewed by 3000

Abstract

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and [...] Read more.

Carbon emissions, produced by automobile fuel consumption, are termed as the key reason leading to global warming. The highway circular curve constitutes a major factor impacting vehicle carbon emissions. It is deemed quite essential to investigate the association existing between circular curve and carbon emissions. On the basis of the IPCC carbon emission conversion methodology, the current research work put forward a carbon emission conversion methodology suitable for China’s diesel status. There are 99 groups’ test data of diesel trucks during the trip, which were attained on 23 circular curves in northwestern China. The test road type was key arterial roads having a design speed greater than or equal to 60 km/h, besides having no roundabouts and crossings. Carbon emission data were generated with the use of carbon emission conversion methodologies and fuel consumption data from field tests. As the results suggested, carbon emissions decline with the increase in the radius of circular curve. A carbon emission quantitative model was established with the radius and length of circular curve, coupled with the initial velocity as the key impacting factors. In comparison with carbon emissions under circular curve section and flat section scenarios, the minimum curve radius impacting carbon emissions is 500 m. This research work provided herein a tool for the quantification of carbon emissions and a reference for a low-carbon highway design. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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15 pages, 2629 KiB

Open AccessArticle

How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis

by Zhichao Li and Jilin Huang

Sustainability 2019, 11(4), 1140; https://doi.org/10.3390/su11041140 - 21 Feb 2019

Cited by 16 | Viewed by 3875

Abstract

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an [...] Read more.

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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16 pages, 4699 KiB

Open AccessArticle

Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM

by Chen Chen, Xiaohua Zhao, Hao Liu, Guichao Ren, Yunlong Zhang and Xiaoming Liu

Sustainability 2019, 11(3), 830; https://doi.org/10.3390/su11030830 - 5 Feb 2019

Cited by 51 | Viewed by 5580

Abstract

The occurrence of adverse weather exacerbates traffic flow conditions, often leading to severe traffic congestions. Many studies have been conducted based on field-collected data to obtain the effects of weather on traffic flow characteristics. However, there is a limitation for filed data-based studies, [...] Read more.

The occurrence of adverse weather exacerbates traffic flow conditions, often leading to severe traffic congestions. Many studies have been conducted based on field-collected data to obtain the effects of weather on traffic flow characteristics. However, there is a limitation for filed data-based studies, in that weather conditions and traffic conditions are both noncontrollable and nonrepeatable, making it difficult to comprehensively assess the influence of weather conditions, especially the rare extreme weather conditions, on traffic flow characteristics. This paper proposes to assess these effects with the combination of driving simulator and traffic simulation. A driving simulator can collect driving behavior by conducting weather-related driving simulation experiments, while a microscopic traffic simulation program can evaluate the changes in traffic flow characteristics by inputting driving behavior parameters coming from the driving simulator. The proposed method can overcome the limitation of the field data-based approach. In this paper, the structure of the assessment platform is introduced at first. Then a verification experiment is conducted to measure the influences of adverse weather conditions on traffic flow characteristics. The verification experiment results show that the influences of adverse weather on traffic flow characteristics have consistent tendencies with outcomes from previous research and demonstrate that the method is practicable for the analysis of the influence of weather on traffic flow characteristics. This paper provides a practical way to analyze the influence of weather on traffic flow from driving behavior’s point of view. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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13 pages, 3129 KiB

Open AccessArticle

An Adaptive Signal Control Method with Optimal Detector Locations

by Senlai Zhu, Ke Guo, Yuntao Guo, Huairen Tao and Quan Shi

Sustainability 2019, 11(3), 727; https://doi.org/10.3390/su11030727 - 30 Jan 2019

Cited by 9 | Viewed by 3283

Abstract

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in [...] Read more.

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in developing countries. This paper proposes a low-cost adaptive signal control method that is easy to implement. Two detectors are installed in each vehicle lane at an optimal location determined by the proposed method to detect green and red redundancy time, based on which the original signal timing is adjusted through a signal controller. The proposed method is evaluated through case studies with low and high volume-to-capacity ratio intersections. The results show that the proposed adaptive signal control method can significantly reduce total traffic delay at intersections. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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20 pages, 4594 KiB

Open AccessArticle

A Parking Space Allocation Method to Make a Shared Parking Strategy for Appertaining Parking Lots of Public Buildings

by Yifei Cai, Jun Chen, Chu Zhang and Bin Wang

Sustainability 2019, 11(1), 120; https://doi.org/10.3390/su11010120 - 26 Dec 2018

Cited by 44 | Viewed by 7453

Abstract

Appertaining parking lots of public buildings provide a large proportion of parking supply in cities. However, these parking lots mainly serve the parking demands of public buildings, leading to a low utilization ratio of parking spaces. It is therefore required to implement a [...] Read more.

Appertaining parking lots of public buildings provide a large proportion of parking supply in cities. However, these parking lots mainly serve the parking demands of public buildings, leading to a low utilization ratio of parking spaces. It is therefore required to implement a shared parking strategy for these parking lots. In this study, a parking space allocation method (PSAM) at the network level is proposed to allocate the parking demand to a parking lot and then the parking space. The users are divided into M-users (users of the buildings) and P-users (public users). The shared parking strategy is analyzed from the aspects of open window, parking fee, and ratio of reservation spaces. The users are allocated to a parking lot by a multinomial logit(MNL) model. Specifically, it is determined whether they can enter parking lot and which space they are allocated according to the specific rules. After all the users are allocated with a parking space, the rejection number of M-users, occupancy rate, and profits of each parking lot are collected and a NSGA-II (non-dominated sorting genetic algorithm II) algorithm is designed to determine the optimal strategy for each parking lot according to the above. Compared with the results of all-time all-space shared parking strategy, our method shows better performance in balancing the interests of all appertaining parking lots and protecting the interests of M-users while obtaining considerable profits for the parking lots. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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27 pages, 2651 KiB

Open AccessArticle

Use of Shared-Mobility Services to Accomplish Emergency Evacuation in Urban Areas via Reduction in Intermediate Trips—Case Study in Xi’an, China

by Menghui Li, Jinliang Xu, Xingliang Liu, Chao Sun and Zhihao Duan

Sustainability 2018, 10(12), 4862; https://doi.org/10.3390/su10124862 - 19 Dec 2018

Cited by 25 | Viewed by 5041

Abstract

Under no-notice evacuation scenarios with limited time horizons, the effectiveness of evacuation can be negatively impacted by intermediate trips that are made by family members and the identification of vulnerable populations. The emergence of shared-mobility companies, such as Uber and DiDi, can be [...] Read more.

Under no-notice evacuation scenarios with limited time horizons, the effectiveness of evacuation can be negatively impacted by intermediate trips that are made by family members and the identification of vulnerable populations. The emergence of shared-mobility companies, such as Uber and DiDi, can be considered as a potential means to address above-mentioned concerns. The proposed study explores the utility of shared-mobility services under emergency-evacuation scenarios and makes recommendations to relevant bodies that are based on the obtained and they are discussed herein. The study investigates attitudes of the public, experts, and drivers towards the use of shared-mobility resources during emergency evacuations based on a stated preference survey. Results of questionnaires, driver interviews, and face-to-face expert interviews have been analyzed to validate the feasibility and identify potential problems of leveraging shared-mobility services during evacuation response, especially in metropolitan areas wherein such services are already ubiquitous. Numerical simulations have been performed to quantify potential improvements in the total trip distance and number of evacuees after incorporating the use of shared mobility into emergency-response operations. However, despite the observed improvement in emergency efficiency, certain realistic roadblocks must be overcome. Realization of the proposed objective heavily depends on actionable policy recommendations, provided herein as a reference for the government, emergency management agencies, and shared-mobility companies. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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19 pages, 1832 KiB

Open AccessEditor’s ChoiceReview

Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

by Ling Zheng, Bijun Li, Bo Yang, Huashan Song and Zhi Lu

Sustainability 2019, 11(16), 4511; https://doi.org/10.3390/su11164511 - 20 Aug 2019

Cited by 34 | Viewed by 6631

Abstract

Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various [...] Read more.

Autonomous driving is experiencing rapid development. A lane-level map is essential for autonomous driving, and a lane-level road network is a fundamental part of a lane-level map. A large amount of research has been performed on lane-level road network generation based on various on-board systems. However, there is a lack of analysis and summaries with regards to previous work. This paper presents an overview of lane-level road network generation techniques for the lane-level maps of autonomous vehicles with on-board systems, including the representation and generation of lane-level road networks. First, sensors for lane-level road network data collection are discussed. Then, an overview of the lane-level road geometry extraction methods and mathematical modeling of a lane-level road network is presented. The methodologies, advantages, limitations, and summaries of the two parts are analyzed individually. Next, the classic logic formats of a lane-level road network are discussed. Finally, the survey summarizes the results of the review. Full article

(This article belongs to the Special Issue Sustainable and Intelligent Transportation Systems)

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