Introduction to Intelligent Transportation Systems

Alam, Muhammad; Ferreira, Joaquim; Fonseca, José Alberto

doi:10.1007/978-3-319-28183-4_1

Cited by 133 publications

(75 citation statements)

References 5 publications

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“…In recent years, many countries have invested lots of manpower and resources to carry out the development of management and control technology in the road transportation system. With the development of crowdsourcing for mobile networks and IoT, ITS (Intelligent Transportation System) has been developed rapidly [3,4]. Accurate traffic flow forecasting is the prerequisite and the key step to realize ITS, it is conducive to improving the efficiency of transport operations and the quality of people's travel.…”

Section: Introductionmentioning

confidence: 99%

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Liu¹,

Cheng²,

Liu³

et al. 2018

Preprint

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Accurate and fast traffic flow forecasting is vital in intelligent transportation system because many of the advanced features in intelligent transportation systems are based on it. However, existing methods have poor performance regarding accuracy and computational efficiency in long-term traffic flow forecasting under big data. Hence, we propose an improved Long short-term memory (LSTM) Network and its cluster computing implementation in this paper to address the above challenge. We propose a singular point probability LSTM (SDLSTM) algorithm. The method discards the units of the network according to the singular point probability during the training process and amends the SDLSTM by Autoregressive Integrated Moving Average Model (ARIMA) to achieve the accurate prediction of 24-hour traffic flow data. Furthermore, the paper designs a scheme for implementing this method through cluster computing to shorten the calculation time and improve the system's operating speed. Theoretical analysis and experimental results show that SDLSTM gains a higher accuracy rate and better stability in the long-term traffic flow forecasting compared with previous methods.

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Section: Introductionmentioning

confidence: 99%

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Liu¹,

Cheng²,

Liu³

et al. 2018

Preprint

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show abstract

“…In this paper, different weight matrices W, U were used for AR and MA components and individual road traffic flow observation z i (t), according to Equation (2), was defined to allow every road to have its own weight matrix W i , U i according to spatial location, but not sharing the same weight matrix among all roads, as in Equation (3). Then, the LSTARIMA can be rewritten as follows:…”

Section: Lstar Model Constructionmentioning

confidence: 99%

“…Statistics show that the average annual traffic congestion cost in the US in 2014 was 1433 dollars per auto commuter, or over 5 billion dollars per city for very large urban areas [1]. In order to improve the efficiency of the urban road network and reduce traffic congestion, intelligent transportation systems (ITS) [2] have been developed by integrating information technology, automatic control technology, and geographic information systems (GIS). With the introduction of ITS, real-time traffic information is available to the vehicles in the road network for trip planning through vehicular navigation systems or dynamic route guidance systems.…”

Section: Introductionmentioning

confidence: 99%

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Chen

Zhang

et al. 2018

Applied Sciences

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Abstract:With the rapid increase of private vehicles, traffic congestion has become a worldwide problem. Various models have been proposed to undertake traffic prediction. Among them, autoregressive integrated moving average (ARIMA) models are quite popular for their good performance (simple, low complexity, etc.) in traffic prediction. Localized Space-Time ARIMA (LSTARIMA) improves ARIMA's prediction accuracy by extending the widely used STARIMA with a dynamic weight matrix. In this paper, a localized space-time autoregressive (LSTAR) model was proposed and a new parameters estimation method was formulated based on the LSTARIMA model to reduce computational complexity for real-time prediction purposes. Moreover, two theorems are given and verified for parameter estimation of our proposed LSTAR model. The simulation results showed that LSTAR provided better prediction accuracy when compared to other time series models such as Shift, autoregressive (AR), seasonal moving average (Seasonal MA), and Space-Time AR (STAR). We found that the prediction accuracy of LSTAR was a bit lower than the LSTARIMA model in the simulation results. However, the computational complexity of the LSTAR model was also lower than the LSTARIMA model. Therefore, there exists a tradeoff between the prediction accuracy and the computational complexity for the two models.

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“…Intelligent Transport Systems (ITS) integrate advanced technologies, including computation, storage, communication, sensor, and data analytics, to build comprehensive infrastructure, platform, and innovative applications for transportation [13]. The goal of the ITS aims to increase the performance, effectiveness, efficiency, safety, and resilience of the system.…”

Section: B Intelligent Transport Systems and Cloud Computingmentioning

confidence: 99%

Cloud-Assisted Safe Driving Framework

Kao¹

2017

IJMO

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Abstract-As the technologies for vehicular safety grow and mature, several advanced driving assisting systems are deployed to mitigate negative impacts on driving safety and protect drivers and passengers efficiently. However, the rate of fatalities and serious injuries in traffic accidents is still high. Based on the statistics from the Ministry of the Interior and National Highway Police Bureau in Taiwan, most critical accidents are caused by the circumstance that drivers do not aware the potential danger to their surroundings. Currently, several driving assisting systems deployed in vehicles can sense surrounding information for potential accidents prevention in time. In order to prevent potential accidents in advance for safer driving, a cloud-assisted safe driving framework is proposed in this paper. The framework leverages sensing devices in vehicles to acquire particular information of the front vehicles. The information will be acquired, recognized, and analyzed based on historical data preserved in the cloud. In addition, the information and comments on social network sites based on the recognized information will be collected and analyzed. Based on historical data and social information, a reputation for the recognized vehicle will be calculated in the cloud. Thus, corresponding suggestion based on the reputation is given to the drivers to perform defensive driving if necessary. Through the cloud-assisted safe driving framework, the driving safety can be achieved in advance efficiently.

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Introduction to Intelligent Transportation Systems

Cited by 133 publications

References 5 publications

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Localized Space-Time Autoregressive Parameters Estimation for Traffic Flow Prediction in Urban Road Networks

Cloud-Assisted Safe Driving Framework

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