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Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 26847

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Special Issue Editors

Prof. Dr. Dianhai Wang

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Guest Editor

Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
Interests: traffic control optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Zhenyu Mei

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Guest Editor

Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: transportation simulation; parking system optimization; transportation system; sustainability

Dr. Lihui Zhang

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Guest Editor

Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China
Interests: connected and autonomous vehicles; traffic signal optimization; transportation network design; road congestion pricing; transit system optimization

Dr. Xiaofei Ye

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Guest Editor

Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Interests: transportation planning and design; traffic data analysis and modeling

Special Issue Information

Dear Colleagues,

At present, the simulation model can effectively evaluate and optimize, reduce the emission of, and realize the sustainable development of the transportation system. The advantages of simulation modeling of traffic flow, parking, intelligent connectivity, and driving behavior of the transportation system can help us to better understand its transportation policy or behavior and compare different design options to achieve more sustainable solutions. The advantages are well known. In addition, simulation technology provides a more realistic representation of traffic behavior and its surrounding environment. These models can include not only the vehicle itself, but also important features related to the interaction between driving behavior and the environment, such as inducement information, control information, and so on. All these improvements can more effectively improve operational efficiency and safety. The main goal of this Special Issue is to publish the latest developments around how simulation models can improve sustainability. The topics of interest include (but are not limited to) the following:

Traffic flow simulation modeling;
Parking model;
Framework design of traffic simulation system;
Traffic optimization modeling;
Traffic model in a connected vehicle environment;
Traffic safety simulation model;
Modeling of driving behavior in an intelligent transportation environment.

Prof. Dr. Dianhai Wang
Dr. Zhenyu Mei
Dr. Lihui Zhang
Dr. Xiaofei Ye
Guest Editors

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Keywords

traffic flow simulation
parking
optimization
connected vehicle
safety
traffic flow models
driving behavior

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Published Papers (12 papers)

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Research

24 pages, 8773 KiB

Open AccessArticle

Investigation of Analyzable Solutions for Left-Turn-Centered Congestion Problems in Urban Grid Networks

by Taraneh Ardalan, Denis Sarazhinsky, Nemanja Dobrota and Aleksandar Stevanovic

Sustainability 2024, 16(11), 4777; https://doi.org/10.3390/su16114777 - 4 Jun 2024

Cited by 1 | Viewed by 1026

Abstract

Traffic congestion caused by left-turning vehicles in a coordinated corridor is a multifaceted problem requiring tailored solutions. This study explores the impact of shared left-turn lanes within one-way couplets, particularly during peak hours, where high left-turn volumes, limited side street storage, and the overlapped green time between pedestrians and left-turners contribute to queue spillbacks, coordination interruption, and network congestion. The focus of this paper is on the solutions that can be easily analyzed by practitioners, here called “analyzable solutions”. This approach stands in contrast to solutions derived from “non-transparent” optimization tools, which do not allow for a clear assessment of the solution’s adequacy or the ability to predict its impact in real-world applications. This paper investigates the effects of employing two analyzable signal timing strategies: Lagging Pedestrian (LagPed) phasing and Left-Turn Progression (LTP) offsets. Using high-fidelity microsimulation, the authors evaluated different scenarios, assessing pedestrian delays, queue lengths, travel time index, area average travel time index, and environmental impacts such as Fuel Consumption (FC) and CO₂ emissions. The effectiveness of the proposed strategies was comprehensively evaluated against the base case scenario, demonstrating considerable improvements in various performance measures, including approximately a 5% reduction in FC and CO₂ emissions. Implementation of the LTP strategy alone yields substantial reductions in delays, the number of stops, the queue length for left-turning vehicles, travel times for all road users, and ultimately FC and CO₂ emissions. This study offers innovative approach to addressing the complex and multifaceted problem of left-turn-centered congestion in urban grid networks using efficient and down-to-earth analyzable solutions. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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23 pages, 4923 KiB

Open AccessArticle

The Development of Modeling Shared Spaces to Support Sustainable Transport Systems: Introduction to the Integrated Pedestrian–Vehicle Model (IPVM)

by Delilah Slack-Smith, Kasun P. Wijayaratna and Michelle Zeibots

Sustainability 2024, 16(10), 4227; https://doi.org/10.3390/su16104227 - 17 May 2024

Viewed by 1221

Abstract

The significance of developing shared road infrastructure in cities throughout the world is growing. Driven by the need to improve traffic management in ways that enhance multiple sustainability outcomes, developing the tools needed to test shared space proposals is becoming more sought after by responsible agencies. This paper reviews approaches to simulation modeling focused on representing and assessing shared spaces, culminating in a new approach presented here called the Integrated Pedestrian–Vehicle Model (IPVM)—a novel framework that combines social force models, car-following models and other algorithms from the robotics domain to better describe both mobility and activity within a shared space. The IPVM recognizes that while shared spaces are inherently multimodal, past efforts have tended to use pedestrian models as a starting point. Most consider the interaction of pedestrians with other pedestrians and static road infrastructure. Shared space models are generally microscopic models that integrate a social force model with a variety of car-following models to describe the interaction between vehicles and pedestrians. However, there is little research and few practical methodologies that address the long-range conflict avoidance between vehicles and pedestrians. This aspect is crucial for accurately representing the desire lines and pathways of pedestrians and active transport users in complex environments like shared spaces. The IPVM describes and visualizes shared road infrastructure with an absence of separating infrastructure between users and outputs. It generates metrics that can be used in conjunction with the latest evaluation approaches to gauge the sustainability credentials of shared space road proposals. Enhanced modeling of shared space solutions can lead to more effective implementation, which can potentially reduce the presence of cars, increase public and active transport use and lead to a more sustainable transport system. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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24 pages, 18096 KiB

Open AccessArticle

A Dual-Stage Attention-Based Vehicle Speed Prediction Model Considering Driver Heterogeneity with Fuel Consumption and Emissions Analysis

by Rongjun Cheng, Qinyin Li, Fuzhou Chen and Baobin Miao

Sustainability 2024, 16(4), 1373; https://doi.org/10.3390/su16041373 - 6 Feb 2024

Viewed by 1106

Abstract

With the development of intelligent transportation systems (ITSs), personalized driving systems are receiving more and more attention, and the development of advanced systems cannot be separated from the practical exploration of drivers’ heterogeneous driving behaviors. An important foundation for subsequent driver-targeted research is how to mine the key influencing factors that characterize drivers through real driving data and how to appropriately classify drivers as a whole. This study took heterogeneous drivers as the object, based on a dual-stage attention-based vehicle speed prediction model, and carried out research on the speed prediction of traffic flow and the impact of fuel consumption and emissions in the car-following state considering the heterogeneity of drivers. Specifically, first, Spearman’s correlation analysis and K-means clustering were used to classify different types of drivers. Then, speed predictions for different types of drivers were separated via the dual-stage attention-based encoder–decoder (DAED) model and the prediction results between models and drivers were compared. Finally, the heterogeneous drivers’ fuel consumption and emissions were further analyzed via the VT-micro model. The results show that the proposed speed prediction model can effectively discriminate the influences of heterogeneous drivers on the prediction model, and the aggressive type presents the best effect. In addition, from the experiments on traffic fuel consumption and emissions, it can be concluded that the timid driver is the friendliest to the environment. By researching individual drivers’ driving characteristics, this study may help sustainable development in traffic management. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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12 pages, 6211 KiB

Open AccessArticle

Assessing the Impact of Travel Restrictions on the Spread of the 2020 Coronavirus Epidemic: An Advanced Epidemic Model Based on Human Mobility

by Xiaofei Ye, Yi Zhu, Tao Wang, Xingchen Yan, Jun Chen and Pengjun Zheng

Sustainability 2023, 15(16), 12597; https://doi.org/10.3390/su151612597 - 19 Aug 2023

Cited by 2 | Viewed by 1502

Abstract

Infectious disease transmission can be greatly influenced by human mobility. During the COVID-19 pandemic, the Chinese Government implemented travel restriction policies to mitigate the impact of the disease or even block the transmission chain of it. In order to quantify the impact of these policies on the number of infections and the peak time of transmission, this research modified the traditional SIR model by considering human mobility. The proposed model was validated using a Baidu Qianxi dataset and the results indicate that the number of total infections would have increased by 1.61 to 2.69 times the current value and the peak time would have moved forward by 3 to 8 days if there were no such restriction policies. Furthermore, a mixing index α added in the proposed model showed that the proportion of residents using public transport to travel between different areas had a positive relationship with the number of infections and the duration of the epidemic. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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21 pages, 2740 KiB

Open AccessArticle

Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration

by Tasneem Miqdady, Rocío de Oña and Juan de Oña

Sustainability 2023, 15(13), 9990; https://doi.org/10.3390/su15139990 - 23 Jun 2023

Cited by 5 | Viewed by 1849

Abstract

Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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17 pages, 2953 KiB

Open AccessArticle

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

by Ming Li, Wei Yu and Jun Zhang

Sustainability 2023, 15(12), 9409; https://doi.org/10.3390/su15129409 - 12 Jun 2023

Cited by 4 | Viewed by 1299

Abstract

Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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27 pages, 8983 KiB

Open AccessArticle

Capacity Drop at Freeway Ramp Merges with Its Replication in Macroscopic and Microscopic Traffic Simulations: A Tutorial Report

by Yibing Wang, Long Wang, Xianghua Yu and Jingqiu Guo

Sustainability 2023, 15(3), 2050; https://doi.org/10.3390/su15032050 - 20 Jan 2023

Cited by 3 | Viewed by 2517

Abstract

Capacity drop (CD) at overloaded bottlenecks is a puzzling traffic flow phenomenon with some internal and complicated mechanisms at the microscopic level. Capacity drop is not only important for traffic flow theory and modelling, but also significant for traffic control. A traffic model evaluating traffic control measures needs to be able to reproduce capacity drop in order to deliver reliable evaluation results. This paper delivers a comprehensive overview on the subject from the behavioral mechanism perspective, as well as from microscopic and macroscopic simulation points of view. The paper also conducts comparable studies to replicate capacity drop at freeway ramp merges from both macroscopic and microscopic perspectives. Firstly, the subject is studied using the macroscopic traffic flow model METANET with respect to ramp merging scenarios with and without ramp metering. Secondly, one major weakness of commercial microscopic traffic simulation tools in creating capacity drop at ramp merges is identified and a forced lane changing model for ramp-merging vehicles is studied and incorporated into the commercial traffic simulation tool AIMSUN. The extended AIMSUN carefully calibrated against real data is then examined for its capability of reproducing capacity drop in a complicated traffic scenario with merging bottlenecks. The obtained results demonstrate that reproducible capacity drop can be delivered for the targeted bottlenecks using both macroscopic and microscopic simulation tools. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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16 pages, 3548 KiB

Open AccessArticle

Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand

by Hongxia Ge, Siteng Li, Rongjun Cheng and Zhenlei Chen

Sustainability 2022, 14(12), 7371; https://doi.org/10.3390/su14127371 - 16 Jun 2022

Cited by 10 | Viewed by 2482

Abstract

As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing demand services. A self-attention mechanism in convolutional long short-term memory (ConvLSTM) is proposed to accurately predict the online car-hailing demand. It can more effectively address the disadvantage that ConvLSTM is not good at capturing spatial correlation over a large spatial extent. Furthermore, it can generate features by aggregating pair-wise similarity scores of features at all positions of input and memory, and thus obtain the function of long-range spatiotemporal dependencies. First, the online car-hailing trajectories dataset was converted into images after geographic grid matching, and image enhancement was performed by cropping. Then, the effectiveness of the ConvLSTM embedded with a self-attention mechanism (SA-ConvLSTM) was demonstrated by comparing it to existing models. The experimental results showed that the proposed model performed better than the existing models, and including spatiotemporal information in images would perform better predictions than including spatial information in time-series pixels. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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20 pages, 7178 KiB

Open AccessArticle

Stabilization Strategy of a Novel Car-Following Model with Time Delay and Memory Effect of the Driver

by Yifan Pan, Yongjiang Wang, Baobin Miao and Rongjun Cheng

Sustainability 2022, 14(12), 7281; https://doi.org/10.3390/su14127281 - 14 Jun 2022

Cited by 8 | Viewed by 1721

Abstract

In this paper, a novel car-following model is established integrating the drivers’ memory of previous information. The drivers’ memory of the vehicle ahead is introduced as an influencing factor on the drivers’ expected behavior. The time delay feedback control term is added to the model to increase the stability interval of the system. By comparing the stability intervals of the controlled and uncontrolled models, the necessity of adding a delay feedback control item is demonstrated. The validity and feasibility of the time delay feedback control strategy are proved by numerical simulations. In this paper, the stability interval of the system is determined by the definite integral stability method (DISM) and the Hopf bifurcation analysis method. According to the number of unstable eigenvalues derived from the system eigenvalue equation, the appropriate time delay feedback control parameters are set. By choosing the optimal parameters, the new model can optimize the traffic flow to the maximum extent, eliminate the stop-and-go of vehicles, and make the traffic stable. Numerical examples close to actual traffic conditions are given to verify the feasibility of the control strategy using the verified design steps. Next generation simulation (NGSIM) measurements are used to conduct parameter calibration of the new model. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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12 pages, 1741 KiB

Open AccessArticle

Analytical Solution of the Mixed Traffic Flow Cellular Automaton FI Model with the Next-Nearest-Neighbor Interaction

by Yanxin Zhang, Yu Xue, Yanfeng Qiao and Bingling Cen

Sustainability 2022, 14(12), 7127; https://doi.org/10.3390/su14127127 - 10 Jun 2022

Cited by 1 | Viewed by 2988

Abstract

Based on a one-dimensional (1D) traffic flow cellular automaton (CA) FI model, a deterministic next-nearest-neighbor interaction FI model (NIFI model) is proposed. Using the mean-field analysis, the analytical solution of the NIFI model in one-dimensional traffic flow is derived under periodic boundary conditions. For the mixed traffic flow, the occupancy and the mixing ratio are introduced to describe the mixing effect. Similarly, using the mean-field method, the exact solution of the mixed traffic flow is derived from the long-time evolution to reach the steady state. The numerical simulations are carried out for the mixed traffic flow with different vehicle lengths, maximum velocities, and mixing ratios to verify the analytical solutions. The results show that the numerical simulation results agree well with the analytical solution. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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15 pages, 3683 KiB

Open AccessArticle

Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model

by Qianru Qi, Rongjun Cheng and Hongxia Ge

Sustainability 2022, 14(7), 4083; https://doi.org/10.3390/su14074083 - 30 Mar 2022

Cited by 3 | Viewed by 1986

Abstract

In recent years, online ride-hailing has become an indispensable part of residents’ travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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14 pages, 573 KiB

Open AccessEditor’s ChoiceArticle

Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme

by Yu Yamada, Abu Saleh Md Bakibillah, Kotaro Hashikura, Md Abdus Samad Kamal and Kou Yamada

Sustainability 2022, 14(3), 1807; https://doi.org/10.3390/su14031807 - 5 Feb 2022

Cited by 7 | Viewed by 5470

Abstract

Traffic congestion or accidents may occur as a consequence of the difficulty of performing a safe, comfortable, and efficient overtaking in a timely manner when there is a slow or stopped vehicle, cyclist, or partial lane blockage on the road. Specifically, most drivers find it challenging to overtake a sluggish vehicle on a single-lane road in the presence of vehicles coming from other directions. To resolve such overtaking concerns, this paper proposes a novel optimal trajectory generating scheme for autonomous vehicle overtaking that is both smooth and safe and can be used in a variety of traffic scenarios. The proposed scheme is based on the solution of an optimal predictive problem with the goal of minimizing driving costs while limiting collision risks in the presence of any opposite vehicle on the overtaking lane. The computational burden of the scheme is almost negligible and can be implemented in real-time. The scheme is evaluated in a variety of traffic conditions, including stopped and slow vehicles in the lane, as well as the presence or absence of a nearby opposite vehicle. The simulation results show that the proposed scheme effectively obtains the optimal trajectories even in the difficult overtaking contexts considering various constraints imposed by the road curve, opposite vehicles, and slow preceding vehicles. Finally, the optimal overtaking costs are obtained for various states of the associated vehicles, which provide an indication of the best state to initiate the overtake. The proposed technology can be employed as a fully automated system or an advanced driver assistance system (ADAS) to improve the vehicle flows at challenging driving conditions and enhance transportation sustainability. Full article

(This article belongs to the Special Issue Application of Emerging Simulation Technologies in Achieving Sustainable Transportation Systems)

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