IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Analysis of Mexico City transportation systems to address climate change, traffic, social equity, safety, and air pollution health risks

This research presents an analysis of Mexico City\u27s transportation systems and how they impact climate change, traffic, social equity, safety, and health risks. The purpose of this research is to propose transportation and energy management strategies to the government of Mexico City to reduce the effects of climate change, traffic, social equity, safety, and health risks. The methodology used in the research includes a traffic analysis, environmental and social impact analysis across Mexico City transportation, an equity analysis, and a SWOT analysis of policies. Through the traffic analysis of the research found that traffic congestion occurs in the northwest region of Mexico City. Traffic is a major problem in Mexico City due to the increase in population since the year 2015. Traffic is considered the central problem of air pollution in Mexico City. The environmental and social impact analysis across Mexico City transportation found that low-socioeconomic status sectors tend to deal with more health, safety, and pollution problems in Mexico City. Through the equity analysis the research recommends that transportation electrification is convenient in the eastern and northeastern areas of Mexico City to reduce air pollution and improve the quality of the transportation modes in the vulnerable zones. Through the SWOT analysis the research found that the policy “Don’t Drive Today” is not bringing down emissions and is increasing the number of vehicles on the road. Recommendations for Mexico City to improve the policy are: 1) Creation of policies or incentives to make citizens invest in electric vehicles (EVs), 2) Carpooling systems, and 3) Intelligent Transportation Systems (ITS)

Wireless magnetic sensor network for road traffic monitoring and vehicle classification

Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.Comment: 8,

A survey of sustainable development of intelligent transportation system based on urban travel demand

This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development

An integrated urban systems model with GIS

The purpose of the research is to develop an integrated urban systems model, which will assist in formulating a better land use-transportation policy by simulating the relationships between land use patterns and travel behavior, integrated with geographic information systems (GISs). In order to make an integrated land use-transportation model possible with the assistance of GISs technologies, the following four sub-systems have been developed: (1) an effective traffic analysis zone generation system; (2) an iterative land use and transportation modeling system; (3) efficient interfaces between GIS and land use, and GIS and transportation models; and (4) a user-friendly graphic user interface (GUI) system. By integrating these sub-systems, a variety of alternative land use-transportation policies can be evaluated through the modification of input parameters in each simulation. Eventually, the developed model using a GIS will assist in formulating an effective land use policy by obtaining robust simulation results for both land use-transportation planners and decision makers. The model has been applied to the Urbana-Champaign area as well as to the Seoul region in Korea for a demonstration of the workings of the model.

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013