Sustainability

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15 pages, 2999 KiB

Open AccessArticle

Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval

by Fengjie Fu, Dianhai Wang, Meng Sun, Rui Xie and Zhengyi Cai

Sustainability 2024, 16(5), 1818; https://doi.org/10.3390/su16051818 - 22 Feb 2024

Cited by 1 | Viewed by 1937

Predicting short-term urban traffic flow is a fundamental and cost-effective strategy in traffic signal control systems. However, due to the interrupted, periodic, and stochastic characteristics of urban traffic flow influenced by signal control, there are still unresolved issues related to the selection of [...] Read more.

Predicting short-term urban traffic flow is a fundamental and cost-effective strategy in traffic signal control systems. However, due to the interrupted, periodic, and stochastic characteristics of urban traffic flow influenced by signal control, there are still unresolved issues related to the selection of the optimal aggregation time interval and the quantifiable uncertainties in prediction. To tackle these challenges, this research introduces a method for predicting urban interrupted traffic flow, which is based on Bayesian deep learning and considers the optimal aggregation time interval. Specifically, this method utilizes the cross-validation mean square error (CVMSE) method to obtain the optimal aggregation time interval and to establish the relationship between the optimal aggregation time interval and the signal cycle. A Bayesian LSTM-CNN prediction model, which extends the LSTM-CNN model under the Bayesian framework to a probabilistic model to better capture the stochasticity and variation in the data, is proposed. Experimental results derived from real-world data demonstrate gathering traffic flow data based on the optimal aggregation time interval significantly enhances the prediction accuracy of the urban interrupted traffic flow model. The optimal aggregation time interval for urban interrupted traffic flow data corresponds to a multiple of the traffic signal control cycle. Comparative experiments indicate that the Bayesian LSTM-CNN prediction model outperforms the state-of-the-art prediction models. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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16 pages, 288 KiB

Open AccessArticle

A Framework for Providing Information about Parking Spaces

by Navid Nadimi, Mohammad Ali Zayandehroodi, Rosalia Camporeale and Morteza Asadamraji

Sustainability 2023, 15(19), 14505; https://doi.org/10.3390/su151914505 - 5 Oct 2023

Cited by 1 | Viewed by 984

Abstract

There is a serious imbalance between parking demand and capacity in cities due to limitations in their parking facilities. It is important for drivers to know about parking vacancies before their trips. Meanwhile, administrators need information about parking capacity and demand before a [...] Read more.

There is a serious imbalance between parking demand and capacity in cities due to limitations in their parking facilities. It is important for drivers to know about parking vacancies before their trips. Meanwhile, administrators need information about parking capacity and demand before a week begins to improve parking management. A method is proposed here for predicting parking demand and capacity by utilizing a Naïve Bayes model and different variables such as drivers’ characteristics and their trips, environmental conditions, parking attributes, and vehicle specifications. Tehran (Iran) is used as a case study etfor testing the model. Using the proposed model, it is possible to identify which parking facilities (and when) might experience spillover. For parking management and policy, demand management, and providing information about parking availability for drivers before their trips, this can be helpful. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

22 pages, 6475 KiB

Open AccessArticle

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

by Krit Jedwanna, Chuthathip Athan and Saroch Boonsiripant

Sustainability 2023, 15(17), 13042; https://doi.org/10.3390/su151713042 - 29 Aug 2023

Cited by 1 | Viewed by 1277

Abstract

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel [...] Read more.

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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19 pages, 4115 KiB

Open AccessArticle

Research on Renewal and Transformation of Smart Building in Luoyang Based on Reducing Energy Usage and Collective Memory

by Fang Yan and Litao Zhang

Sustainability 2023, 15(11), 8592; https://doi.org/10.3390/su15118592 - 25 May 2023

Viewed by 1302

Abstract

Collective memory is a specific carrier for interpreting historical space and local emotions. With the help of collective memory theory, this paper constructs the industrial heritage system of an old industrial zone. Taking the old industrial area in Jianxi District of Luoyang as [...] Read more.

Collective memory is a specific carrier for interpreting historical space and local emotions. With the help of collective memory theory, this paper constructs the industrial heritage system of an old industrial zone. Taking the old industrial area in Jianxi District of Luoyang as an example, this paper uses two types of memory representations, the material memory field and spiritual memory field, as the carriers of collective memory. It is divided into 11 types of spatial fields, such as production space, living space, landscape space and spiritual culture, and 76 memory connotations to construct a collective memory classification model of industrial culture. Through the investigation and analysis of the memory of the collective memory subject, it was found that: ① the collective production space is the most impressive. ② The living space is distributed in patches due to celebrity effects; and ③ People are more prominent in the symbolization of collective memory. On the foundations of memory analysis, Take the luoyang bearing factory for example, to research the sustainable development of intelligent and energy-efficient buildings. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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30 pages, 5741 KiB

Open AccessArticle

Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)

by Xianwang Li, Zhongxiang Huang, Saihu Liu, Jinxin Wu and Yuxiang Zhang

Sustainability 2023, 15(10), 7949; https://doi.org/10.3390/su15107949 - 12 May 2023

Cited by 4 | Viewed by 2012

Abstract

The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on [...] Read more.

The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on improved variational mode decomposition (IVMD) and multi-model combination is proposed. Firstly, the mixed-strategy improved sparrow search algorithm (MSSA) is used to adaptively determine the parameters of the VMD with envelope entropy as the fitness value. Then, IVMD is applied to decompose the original passenger flow time series into several sub-series adaptively. Meanwhile, the sample entropy is utilized to divide the sub-series into high-frequency and low-frequency components, and different models are established to predict the sub-series with different frequencies. Finally, the MSSA is employed to determine the weight coefficients of each sub-series to combine the prediction results of the sub-series and get the final passenger flow prediction results. To verify the prediction performance of the established model, passenger flow datasets from four different types of Nanning Metro stations were taken as examples for carrying out experiments. The experimental results showed that: (a) The proposed hybrid model for short-term passenger flow prediction is superior to several baseline models in terms of both prediction accuracy and versatility. (b) The proposed hybrid model is excellent in multi-step prediction. Taking station 1 as an example, the MAEs of the proposed model are 3.677, 5.7697, and 8.1881, respectively, which can provide technical support for subway operations management. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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14 pages, 357 KiB

Open AccessArticle

Wireless Secret Sharing Game for Internet of Things

by Lei Miao, Dingde Jiang and Hongbo Zhang

Sustainability 2023, 15(9), 7427; https://doi.org/10.3390/su15097427 - 30 Apr 2023

Viewed by 1161

Abstract

In the era of Internet of Things (IoT), billions of small but smart wireless devices work together to make our cities more intelligent and sustainable. One challenge is that many IoT devices do not have human interfaces and are very difficult for humans [...] Read more.

In the era of Internet of Things (IoT), billions of small but smart wireless devices work together to make our cities more intelligent and sustainable. One challenge is that many IoT devices do not have human interfaces and are very difficult for humans to manage. This creates sustainability and security issues. Enabling automatic secret sharing across heterogeneous devices for cryptography purposes will provide the needed security and sustainability for the underlying IoT infrastructure. Therefore, wireless secret sharing is crucial to the success of smart cities. One secret sharing method is to utilize the effect of the randomness of the wireless channel in the data link layer to generate the common secret between legitimate users. This paper models this secret sharing mechanism from the perspective of game theory. In particular, we formulate a non-cooperative zero-sum game between the legitimate users (Alice and Bob) and an eavesdropper (Eve). Alice and Bob’s strategy is deciding how to exchange packets to protect the secret, and Eve’s strategy is choosing where to stay to better intercept the secret. In a symmetrical game where Eve has the same probability of successfully receiving a packet from Alice and Bob when the transmission distance is the same, we show that both pure and mixed strategy Nash equilibria exist. In an asymmetric game where Eve has different probabilities of successfully receiving a packet from Alice and Bob, a pure strategy may not exist; in this case, we show how a mixed strategy Nash equilibrium can be found. We run simulations to show that our results are better than other approaches. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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17 pages, 1080 KiB

Open AccessArticle

Performance Evaluation and Comparison of Cooperative Frameworks for IoT-Based VDTN

by Ghani Ur Rehman, Muhammad Zubair, Wael Hosny Fouad Aly, Haleem Farman, Zafar Mahmood, Julian Hoxha and Naveed Anwer Butt

Sustainability 2023, 15(6), 5454; https://doi.org/10.3390/su15065454 - 20 Mar 2023

Cited by 1 | Viewed by 1712

Abstract

The term “Internet of Things” (IoT) refers to an architecture in which digital objects have identification, sensing, connectivity, and processing capabilities that allow them to connect with other devices as well as perform tasks on the internet. There are many applications of IoT, [...] Read more.

The term “Internet of Things” (IoT) refers to an architecture in which digital objects have identification, sensing, connectivity, and processing capabilities that allow them to connect with other devices as well as perform tasks on the internet. There are many applications of IoT, among which Vehicle Delay-Tolerant Networks (VDTNs) are one of the best known. This new generation of vehicular networks can be applied in a variety of circumstances. For example, it can be employed to make data connections possible in densely crowded cities and as well as in remote and sparsely populated places with weak connectivity. These environments are characterized by frequent network partitioning, inconsistent connectivity, considerable propagation delays, high error rates, and short contact duration. Most of these behaviours are due to node selfishness. This task is crucial because selfish behaviour by nodes may make other nodes hesitant to cooperate. Selfish nodes have significant negative impacts on the effectiveness and efficiency of the network as a whole. To solve these issues, cooperative strategies that motivate nodes to share their resources must be considered. Important contributions to cooperation for vehicular networks are presented in this article, which investigates the effects of six different cooperative techniques on network performance and makes corresponding suggestions for their use in IoT-based VDTNs. Across all simulations, our results show that the studied strategies are all able to increase overall network performance by improving throughput and packet delivery probability, which in turn reduces average packet delivery time, energy consumption, overhead ratio, and the number of packets dropped. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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11 pages, 2760 KiB

Open AccessArticle

Research on Demand Price Elasticity Based on Expressway ETC Data: A Case Study of Shanghai, China

by Yunyi Li, Minhua Shao, Lijun Sun, Xinmiao Wang and Shizhao Song

Sustainability 2023, 15(5), 4379; https://doi.org/10.3390/su15054379 - 1 Mar 2023

Cited by 3 | Viewed by 4112

Abstract

The research on price elasticity of demand, especially in the field of transportation, has high theoretical and application value. Based on the perspective of price elasticity of demand, the study presents the impact of adjusting expressway rates on the traffic flow of cars [...] Read more.

The research on price elasticity of demand, especially in the field of transportation, has high theoretical and application value. Based on the perspective of price elasticity of demand, the study presents the impact of adjusting expressway rates on the traffic flow of cars with seven seats or less. The data are from the measured data of the Shanghai expressway Electronic Toll Collection (ETC) from 2019 to 2020. In order to eliminate the impact of the surge of ETC users in 2019 on the results, Empirical Mode Decomposition (EMD) is used to optimize the data. The research shows that the price elasticity of demand will increase with the increase in charge amount (distance). Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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16 pages, 9869 KiB

Open AccessArticle

Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

by Tianhe Lan, Xiaojing Zhang, Dayi Qu, Yufeng Yang and Yicheng Chen

Sustainability 2023, 15(2), 1374; https://doi.org/10.3390/su15021374 - 11 Jan 2023

Cited by 7 | Viewed by 1899

Abstract

Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, in order to improve the accuracy of short-term traffic flow prediction, a prediction model (GWO-attention-LSTM) based on the combination of optimized attention mechanism and long short-term memory (LSTM) [...] Read more.

Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, in order to improve the accuracy of short-term traffic flow prediction, a prediction model (GWO-attention-LSTM) based on the combination of optimized attention mechanism and long short-term memory (LSTM) is proposed. The model is based on LSTM and uses the attention mechanism to assign individual weight to the feature information extracted via LSTM. This can increase the prediction model’s focus on important information. The initial weight parameters of the attention mechanism are also optimized using the grey wolf optimizer (GWO). By simulating the hunting process of grey wolves, the GWO algorithm calculates the hunting position of the grey wolf and maps it to the initial weight parameters of the attention mechanism. In this way, the short-time traffic flow prediction model is constructed. The traffic flow data of the trunk roads in the center of Qingdao (China) are used as the research object. Multiple sets of comparison models are set up for prediction analysis. The results show that the GWO-attention-LSTM model has obvious advantages over other models. The prediction error MAE values of the GWO-attention-LSTM model decreased by 7.32% and 14.35% on average compared with the attention-LSTM model and LSTM model. It is concluded that the GWO-attention-LSTM model has better model performance and can provide effective help for traffic management control and traffic flow theory research. Full article

(This article belongs to the Special Issue Advances in Smart City and Intelligent Transportation Systems)

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