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50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations
The impacts of autonomous vehicles, coupled with greater inter-vehicle and system connectivity, may be far-reaching on several levels. They entail changes to (1) the demand and behavior side, (2) the supply of mobility services, and (3) network and facility operational performance. We focus here on their impact on traffic flow and operations, especially in mixed traffic situations in which autonomous vehicles share the road with regular, human-driven vehicles, along with connected vehicles that may also have some automated functions. These mixed traffic situations correspond to likely deployment scenarios of the technologies, especially in the long transition towards 100% deployment. We explain using elementary traffic science concepts how autonomous vehicles and connected vehicles are expected to increase the throughput of highway facilities, as well as improve the stability of the traffic stream. A microsimulation framework featuring varying behavioral mechanisms for the three classes of vehicles is introduced. The framework is used to examine the throughput and stability questions through a series of experiments under varying market penetration rates of autonomous and/or connected vehicles; at low market shares, the impacts are relatively minor on either throughput or stability. However, as market shares increase, autonomous vehicles exert a greater influence on both dimensions compared to the same shares of connected vehicles. Applications of the framework to examine the effectiveness of selected traffic management approaches are discussed, including dedicated lanes for autonomous vehicles (good only if its use is optional and when the market share of autonomous vehicles is greater than the percentage of nominal capacity represented by that lane), and speed harmonization.
Journal Article
Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison
Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.
Journal Article
Streetfight : handbook for an urban revolution
As New York Citys transportation commissioner, Janette Sadik-Khan managed the seemingly impossible and transformed the streets of one of the worlds greatest, toughest cities into dynamic spaces safe for pedestrians and bikers. Her approach was dramatic and effective: Simply painting a part of the street to make it into a plaza or bus lane not only made the street safer, but it also lessened congestion and increased foot traffic, which improved the bottom line of businesses. Real-life experience confirmed that if you know how to read the street, you can make it function better by not totally reconstructing it but by reallocating the space thats already there. Breaking the street into its component parts, Streetfight demonstrates, with step-by-step visuals, how to rewrite the underlying source code of a street, with pointers on how to add protected bike paths, improve crosswalk space, and provide visual cues to reduce speeding. Achieving such a radical overhaul wasnt easy, and Streetfight pulls back the curtain on the battles Sadik-Khan won to make her approach work. She includes examples of how this new way to read the streets has already made its way around the world, from pocket parks in Mexico City and Los Angeles to more pedestrian-friendly streets in Auckland and Buenos Aires, and innovative bike-lane designs and plazas in Austin, Indianapolis, and San Francisco. Many are inspired by the changes taking place in New York City and are based on the same techniques. Streetfight deconstructs, reassembles, and reinvents the street, inviting readers to see it in ways they never imagined.
Book
Traffic flow prediction models - A review of deep learning techniques
Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios.
Journal Article
Dead end : suburban sprawl and the rebirth of American urbanism
'Dead End' traces how the ideal of a safe, green, orderly retreat where hardworking members of the middle class could raise their children away from the city mutated into the McMansion and strip mall-ridden suburbs of today.
Book
A deep-learning model for urban traffic flow prediction with traffic events mined from twitter
Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic parameter prediction by complementing traffic data with data representing events capable of disrupting regular traffic patterns reported in social media posts. This paper proposes a deep learning urban traffic prediction model that combines information extracted from tweet messages with traffic and weather information. The predictive model adopts a deep Bi-directional Long Short-Term Memory (LSTM) stacked autoencoder (SAE) architecture for multi-step traffic flow prediction trained using tweets, traffic and weather datasets. The model is evaluated on an urban road network in Greater Manchester, United Kingdom. The findings from extensive empirical analysis using real-world data demonstrate the effectiveness of the approach in improving prediction accuracy when compared to other classical/statistical and machine learning (ML) state-of-the-art models. The improvement in predictive accuracy can lead to reduced frustration for road users, cost savings for businesses, and less harm to the environment.
Journal Article
An AutoEncoder and LSTM-Based Traffic Flow Prediction Method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.
Journal Article
Spatial dynamic graph convolutional network for traffic flow forecasting
The complex traffic network spatial correlation and the characteristic of high nonlinear and dynamic traffic conditions in the time are the challenges to accurate traffic flow forecasting. Existing spatiotemporal models attempt to utilize the static graph to explore spatial dependency and employ RNN-based model to capture temporal dependency. However, the static graph fails to reflect the dynamic changeable correlation between each node. That is some nodes have a strong connection in a real traffic network, whereas a weak connection is in a static predefined graph. To overcome the above problems, we propose a spatial dynamic graph convolutional network (SDGCN) for traffic flow forecasting. With the support of an attention fusion network in graph learning, SDGCN generates the dynamic graph at each time step, which can model the changeable spatial correlation from traffic data. By embedding dynamic graph diffusion convolution into gated recurrent unit, our model can explore spatio-temporal dependency simultaneously. Moreover, to handle long sequence forecasting, ReZero transformer is utilized to detect the global temporal correlation capturing. The experiments are conducted on two public datasets. The experimental results demonstrate the superior performance of our network.
Journal Article