Computational Intelligence for Sustainable Transportation and Mobility: Volume 1 E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Computational Intelligence For Data Analysis
- Sprache: Englisch
- Veröffentlichungsjahr: 2006
New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas.
This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends.
Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations.
Key Features:
- Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas
- Covers classification of traffic behavior
- Demonstrates the application of artificial immune system algorithms for traffic prediction
- Covers traffic density estimation using deep learning models
- Covers Fog and edge computing for intelligent transportation systems
- Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers
- Presents a current perspective on an urban hyperloop system for India
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PREFACE
This book begins with the basics of the Computational Intelligence technique and introduces its applications to vehicular traffic prediction, optimization, behaviour analysis, traffic density estimation, etc. New technologies and methodologies are used to improve the existing issues of the traffic system. Due to the development of computational intelligence methods, it is considered a powerful technique to reduce the traffic, transportation, and mobility problems in urban areas. In dynamic and complex situations, an adaptive mechanism is required to enable or facilitate intelligent behaviour which is called Computational Intelligence (CI) technique. The CI technique includes Multiagent system (MAS), Whale optimization, AIS, Deep Neural Networks (DNNs), Fog, and Edge Computation. These CI techniques mimic human behaviour and intelligence; therefore, the concept of intelligence directly links to reasoning and decision making. These CI techniques are used to develop algorithms, models, and approaches for sustainable transportation, traffic, and mobility operations. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation data prediction, traffic behaviour analysis, traffic density estimation and prediction, electric vehicles charging infrastructure, and Industry 4.0. The primary emphasis of this book is to introduce computational intelligence techniques, challenges, issues, and concepts to researchers, scientists, and academicians at large.
OBJECTIVE OF THE BOOK
The objective of this book is to provide a detailed understanding of the Computational Intelligence techniques with the main focus on sustainable transportation and mobility field. The final goal is to connect and utilize the new CI techniques to interdisciplinary areas that can be put to good use.
ORGANIZATION OF THE BOOK
The book is organized into 8 chapters with the following brief description:
1. An Intelligent Vehicular Traffic Flow Prediction Model using Whale Optimization with Multiple Linear Regression
In this chapter, the authors introduce an Intelligent Vehicular Traffic Flow Prediction (IVTFP) model to predict the flow of traffic on the road effectively. The proposed IVTFP model involves two mains stages: 1. Feature selection and classification and 2. Multiple linear regression technique.
2. Intelligent Transportation Systems based Behaviour Characteristics Classification
In this chapter, a layout of Rule-Based Fuzzy Polynomial Neural Networks system based on their behavior in different profiles to regulate the behaviour of the drivers is presented. The work has developed a probability model to show the observer rating works relatively well with the sophisticated models.
3. Artificial Immune Systems Imputation based Traffic Prediction
In this chapter, an AIS algorithms based traffic prediction is designed for long-term root prediction. Predictive assessments of data sensitivity are data on urban data flow. The simulation results show that the proposed sequence achieves the same accuracy as arbitrary predictions and fewer blocks than conventional solutions.
4. An Intelligent Transportation Systems for Traffic Density Estimation and Prediction using Deep Learning Models
This chapter develops a new deep learning (DL) based traffic density estimation and prediction model for ITS. The proposed model involves a set of two DL models, namely convolutional neural network (CNN) and long short term memory (LSTM) for traffic density estimation and prediction.
5. Fog and Edge Computing based Intelligent Transport System
This chapter focuses on reducing the latency with advanced algorithms in data transferring between the fog and edge layers. It discusses its application and the internal processing in the fog layers with advanced algorithms.
6. IoT-based Integration of Sensors with DAQ systems in Intelligent Transport Systems
This chapter constructs smart pavements for the future use of introducing autonomous vehicles to make traveling safer and comfortable for the people. This chapter focuses on the technologies being used for the integration of sensors with DAQ devices via wireless communication technologies in the transportation network.
7. Solar-based Electric Vehicle Charging Infrastructure with Grid Integration and Transient Overvoltage Protection
This chapter aims to counter all the disadvantages by presenting a simulation-based study on standalone Solar DC microgrid for electric vehicle charging. This can be used in the current Indian energy scenario. The usability of the proposed system in the conventional grid is verified by implementing with IEEE 5 bus system.
8. Industry 4.0: Hyperloop Transportation System in India
Hyperloop is a new, better, and more efficient mode of transportation that is being proposed in this chapter as an alternative to India’s railway and airport network with the benefit of better and more efficient performance at lower overall costs.
About the Book
The exponential growth of vehicles and the human population (especially in the urban/metropolitan area) results in many challenges while information collecting, processing, predicting, and integrating various intelligent technologies. To furnish daily work and lead life, everyone is directly dependent upon transportation, which is inter-related to traffic density, mobility, traffic demands, etc. In urban areas, the traffic demands have grown faster than the construction of required infrastructure, reduced the mobility of vehicles, and increased traffic congestion, which is one of the serious problems each city in the country is facing. In addition, the increase in the vehicles population, traffic congestion, etc., leads to degradation of the environment and unnecessary wastage of fuel. These have become serious concerns for the public. This book begins with the basics of the Computational Intelligence techniques required for sustainable transportation and mobility. New technologies and methodologies are used to improve the existing issues of the traffic system. Due to the development of computational intelligence methods, it is considered a powerful technique to reduce the traffic, transportation, and mobility problems in urban areas. In dynamic and complex situations, an adaptive mechanism is required to enable or facilitate an intelligent behavior which is called Computational Intelligence (CI) technique. The CI technique includes Multiagent system (MAS), Whale optimization, AIS, Deep Neural Networks (DNNs), Fog, and Edge Computation. These CI techniques mimic human behavior and intelligence; therefore, the concept of intelligence directly links to reasoning and decision making. These CI techniques are used to develop algorithms, models, and approaches for sustainable transportation, traffic, and mobility operations. This book presents novel techniques developed, new technologies, and computational intelligence for sustainable mobility and transportation data prediction, traffic behaviour analysis, traffic density estimation and prediction, electric vehicles charging infrastructure, and Industry 4.0.
List of Contributors
An Intelligent Vehicular Traffic Flow Prediction Model Using Whale Optimization with Multiple Linear Regression
Hima Bindu Gogineni1,E. Laxmi Lydia2,*,N. Supriya3Abstract
At present, vehicular traffic flow prediction is treated as a crucial issue in the intelligent transportation system. It mainly focuses on the estimation of vehicular traffic flow on roadways or stations in the subsequent time interval ahead of the future. Generally, traffic flow prediction comprises two major stages, namely feature learning and predictive modeling. In this view, this paper introduces an Intelligent Vehicular Traffic Flow Prediction (IVTFP) model to effectively predict the flow of traffic on the road. The proposed IVTFP model involves two main stages, namely feature selection (FS) and classification. At the first level, the whale optimization algorithm (WOA) is applied as a feature selector called WOA-FS to select the useful subset of features. Next, in the second level, the multiple linear regression (MLR) technique is utilized as a prediction model to forecast the traffic flow. The performance of the IVTFP model takes place on the benchmark Brazil dataset. The simulation outcome indicated the effective outcome of the IVTFP model, and it ensured that the application of the WOA-FS model helps attain improved classification outcomes.
Introduction
In transportation management, traffic flow examination is a significant job. When the exact prediction of traffic flow is not processed, then no smart transportation can be operated. Massive studies have concentrated on this detection. The traditi-
onal traffic flow prediction models are classified into 3 classes such as (1) ARIMA method, (2) Probabilistic model, and (3) Non-parametric model. The ARIMA method [1] aims to identify patterns of temporal difference of traffic flow as well as the actual application of this detection. The Probabilistic model is used to develop and examine traffic flow. Finally, in the Non-parametric method, authors have depicted that they usually perform better by capturing in-deterministic as well as tedious nonlinearity of traffic time series. The representative models are Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Local Weighted Learning (LWL) [2-6].
Traffic flow prediction is one of the severe issues in transportation systems. It mainly focuses on evaluating the flow of traffic in specific time intervals. Time intervals are described as short-term intervals that vary from 5 to 30 mins. In an operational examination, the Highway Capacity Manual recommends applying 15 mins time interval. There are 2 kinds of data employed in traffic flow prediction. Initially, the data are gathered by sensors on all roads using an inductive loop method. The main job is to detect traffic flow on all lanes and roads. The alternate type of data is accumulated from the beginning and terminal end of the road. It is named entrance-exit station data. Despite detecting traffic flow on all roads, the other operation is examining traffic flow in all stations, particularly at the existing station.
The steps involved in traffic flow prediction are Feature learning and Predicting model learning. Initially, It is aware of the feature presentation method that filters and chooses the topmost representative features from traffic flow sequence F of each station. The traffic flow sequence is converted into feature space g(F) → X. Prediction task fi, T +1 is denoted by Y. In this point, feature learning is often a hand-crafted objective. Few vital aspects of transportation are speed, the volume of flow, density, and many other aspects that are determined from actual data and applied as variables for forecasting. Furthermore, time-series features are used in the prediction mechanism.
Short-term predicting the traffic flow is a vital computation. Deep learning (DL) is a type of Machine Learning (ML) that is named as a nested hierarchical approach that has Conventional Neural Networks (CNN). Karlaftis and Vlahogianni [2] offer a review of NN models implies that model training is highly costlier with prominent upgrading. Besides, DL has minimum efficiency and identifies a sparse approach that is extended continuously. Also, various analytical models were developed in traffic flows modeling [6-11]. Such models can process better performance on extraction as well as state evaluation. The caveat is very complex to execute on massive scale networks. Bayesian models are highly effective in managing greater-scale transportation network state estimation issues.
Westgate et al. [3] define ambulance traveling duration stability under the application of noisy GPS for path travel time as well as single road segment traveling time distributions. A dynamic Bayesian network (BN) develops physical innovations to collect immediate changes on traffic parameters. Statistical and ML approaches for traffic detection were related in Smith and Demetsky [4]. Sun et al. [5] presented a Bayes network model, in which the conditional possibility of a traffic status on the provided road, with topological components on a road network, has been determined. The final possibility distribution is a combination of Gaussians. Bayes networks to evaluate travel times are recommended by Horvitz et al. that are ultimately designated as commercial objects, which lead to Inrix, a traffic data organization. An ML approach in support vector machine (SVM) predicts the travel times and projects a Fuzzy NN (FNN) model for reporting nonlinearities in traffic information.
Rice and van Zwet [6] discuss that a linear association among next traveling times as well as presently evaluated conditions with time-varying coefficients regression method to detect traveling times. Integrated Auto-regressive Moving Average (ARIMA) as well as Exponential Smoothing (ES) for traffic prediction. A Kohonen self-organizing map is projected as the primary classification model. Van Lint [7
