Traffic flow forecasting with particle swarm optimization and support vector regression

Hu, Jianming; Gao, Pan; Yao, Yunfei; Xie, Xudong

doi:10.1109/itsc.2014.6958049

Cited by 24 publications

(7 citation statements)

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“…Moreover, it is expected that a distributed computational solution, such as the multi-agent architecture, will outperform centralized modeling systems owing to its autonomy and flexibility [32]. Multi-agent computing was applied to overcome several transport challenges, including urban traffic control [33][34][35][36], fleet management [37,38], and route planning and guidance [39,40]. Different agent-based frameworks were used in these applications to implement multi-agent environments, e.g., MATsim [41].…”

Section: State-of-the-art Intelligent Transport Management Systemsmentioning

confidence: 99%

“…Artificial intelligence-based systems proposed for managing and optimizing the transport network have employed bird swarm optimizer, rule-based fuzzy logic, and artificial neural networks, among other techniques [39,40,42,43]. In essence, these systems are trained on the previous traffic data to prognosticate the traffic stream variables in the transport network.…”

Section: State-of-the-art Intelligent Transport Management Systemsmentioning

confidence: 99%

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An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

et al. 2021

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The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

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Section: State-of-the-art Intelligent Transport Management Systemsmentioning

confidence: 99%

Section: State-of-the-art Intelligent Transport Management Systemsmentioning

confidence: 99%

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

et al. 2021

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“…Non-parametric methods can be divided into two distinct types: non-parametric regression such as support vector regression (SVR) [15]- [18] and artificial neural networks (ANNs) [19]- [23]. SVR has been successfully applied to predict traffic data, e.g., flow [16] and travel times [15], as SVR models have powerful representation learning ability by using kernels. Alternatively, ANNs are among the first non-parametric methods that have been applied to traffic prediction, and thus there is vast literature on the subject, which extend from the simple multilayer perceptrons (MLP) [19] to more complicated structures as generative adversarial networks (GAN) [24], recurrent neural networks (RNNs) [21], convolutional neural networks (CNNs) [22] and the combination of RNNs and CNNs [23], [25].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Traffic Forecasting Using High-Resolution Traffic Data

Li,

Yang,

Jabari

2020

Preprint

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This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy fluctuations from one time step to the next (typically on the order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of traffic conditions are critical for traffic operations applications (e.g., adaptive signal control). But traffic forecasting tools in the literature deal predominantly with 3-5 minute aggregated data, where the typical signal cycle is on the order of 2 minutes. This renders such forecasts useless at the operations level. To this end, we model the traffic forecasting problem as a matrix completion problem, where the forecasting inputs are mapped to a higher dimensional space using kernels. The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs. These dependencies correspond to correlations between different locations in the network. We further employ adaptive boosting to enhance the training accuracy and capture historical patterns in the data. The performance of the proposed methods is verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.

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“…The basic idea behind non-parametric techniques is that they learn a general form from the historical data and use it to predict future data. Non-parametric methods can be divided into two types: non-parametric regression such as support vector regression (SVR) [13,21,55] and artificial neural networks (ANN) [25,32,[34][35][36]38]. Compared with time series models which assume that traffic data vary linearly over time, SVR and ANN techniques can capture nonlinear variations in traffic data.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of SVR models is that they can learn representative features by using various kernels. For this reason, SVR has been successfully applied to predict traffic data such as flow [13,21], headway [58], and travel time [5,6,17,55]. Ma et al [21] further proposed an online version of SVR, which can efficiently update the model when new data is added.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning

Li,

Yang,

Jabari

2020

Preprint

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We focus on short-term traffic forecasting for traffic operations management. Specifically, we focus on forecasting traffic network sensor states in high-resolution (second-by-second). Most work on traffic forecasting has focused on predicting aggregated traffic variables, typically over intervals that are no shorter than 5 minutes. We develop a (big) data-driven methodology for forecasting sensor states in high-resolution. Our contributions can be summarized as offering three major insights: first, we show how the forecasting problem can be modeled as a matrix completion problem. Second, we employ a block-coordinate descent algorithm and demonstrate that the algorithm converges in sub-linear time to a block coordinate-wise optimizer. This allows us to capitalize on the "bigness" of high-resolution data in a computationally feasible way. Third, we develop an ensemble learning (or adaptive boosting) approach to reduce the training error to within any arbitrary error threshold. The latter utilizes past days, so that the boosting can be interpreted as capturing periodic patterns in the data. The performance of the proposed method is analyzed theoretically and tested empirically using a real-world high-resolution traffic dataset from Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.

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Traffic flow forecasting with particle swarm optimization and support vector regression

Cited by 24 publications

References 1 publication

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

Short-Term Traffic Forecasting Using High-Resolution Traffic Data

Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning

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