Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2016.0208

LSTM network: a deep learning approach for short-term traffic forecast

Accurate and real-time traffic forecasting plays an important role in the intelligent traffic system and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an “open” scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time. To capture the spatial and temporal dependences simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutional network (GCN) and the gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures for capturing spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data for capturing temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://www.github.com/lehaifeng/T-GCN .

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Truck platooning refers to a series of trucks driving in close proximity via communication technologies, and it is considered one of the most implementable systems of connected and automated vehicles, bringing huge energy savings and safety improvements. Properly planning platoons and evaluating the potential of truck platooning are crucial to trucking companies and transportation authorities. This study proposes a series of data mining approaches to learn spontaneous truck platooning patterns from massive trajectories. An enhanced map matching algorithm is developed to identify truck headings by using digital map data, followed by an adaptive spatial clustering algorithm to detect instantaneous co-moving truck sets. These sets are then aggregated to find the network-wide maximum platoon duration and size through frequent itemset mining for computational efficiency. We leverage real GPS data collected from truck fleeting systems in Liaoning Province, China, to evaluate platooning performance and successfully extract spatiotemporal platooning patterns. Results show that approximately 36% spontaneous truck platoons can be coordinated by speed adjustment without changing routes and schedules. The average platooning distance and duration ratios for these platooned trucks are 9.6% and 9.9%, respectively, leading to a 2.8% reduction in total fuel consumption. We also distinguish the optimal platooning periods and space headways for national freeways and trunk roads, and prioritize the road segments with high possibilities of truck platooning. The derived results are reproducible, providing useful policy implications and operational strategies for large-scale truck platoon planning and roadside infrastructure construction.

Mining Truck Platooning Patterns Through Massive Trajectory Data

Transit Operation Optimization in the Context of Connected and Automated Vehicles: A Case Study of Beijing

Transit Signal Priority (TSP) is an advanced control mechanism to facilitate transit vehicle operations along signalized arterials. In practical application, TSP systems are integrated with coordinated signal control strategies. People take for granted the reduced transit delays through TSP treatments. However, due to conflicts between TSP control schemes and coordinated signal control strategies along signalized arterials, the benefits achieved by transit vehicles may be washed out to some extent. Our recent study for optimizing the South Snohomish Regional Transit Signal Priority (SS-RTSP) system operations found that transit vehicle delays over a signal-coordinated corridor can be lengthened by TSP treatments in some scenarios. A VISSIM-based simulation model is developed to emulate TSP system operations along the SR-99 arterial covering 13 intersections, in the City of Lynnwood, Washington. Various transit operation scenarios under diverse coordinated signal control plans are designed. Theoretical analysis is also provided to formulate the transit travel process. The research findings indicated that to achieve the best operational efficiency, the compatibility between TSP control schemes and signal control coordination should be strengthened to minimize transit disruption to signal coordination.

A Pitfall to Avoid When Issuing Transit Signal Priority Treatments under Coordinated Control Strategies

Joint Optimization of Frequency and Structure for Transit Network Design

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Xiaolei Ma

Papers

Mining Truck Platooning Patterns Through Massive Trajectory Data

Transit Operation Optimization in the Context of Connected and Automated Vehicles: A Case Study of Beijing

A Pitfall to Avoid When Issuing Transit Signal Priority Treatments under Coordinated Control Strategies

Joint Optimization of Frequency and Structure for Transit Network Design

Sensors in Connected Vehicle Technology: How Sensors Play a Critical Role