The emergence of electric vehicles (EV) presents new opportunities for transportation decarburization and sustainable transportation in cities worldwide. However, previous research has primarily focused on EV charging infrastructures within urban areas, with limited attention to those launched in expressway service areas. Addressing this gap is crucial in alleviating EV users’ range anxiety on expressway journeys. This study investigates the BEV users’ reuse behavior toward charging infrastructures on expressways. Focusing on the BEV users who had used the charging infrastructures on the expressway, the structural equation modeling and the multi-group analysis are employed to reveal the effect of psychological factors on BEV users’ reuse intention and explore the heterogeneity across different socio-demographic groups. Results reveal that Attitude and Subjective Norm drive the reuse intention. Perceived Risk has an indirect negative effect on reuse intention. Attitude has a more significant effect on reuse intention among elder users, high-frequency users, and low remaining State of Charge (SOC) users. This paper offers new insights for charging infrastructures’ planning and operation in expressway service areas.

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Accurate recognition of driver behaviours is the basis for a reliable driver assistance system. This paper proposes a novel fusion framework for driver behaviour recognition that utilises the traffic scene and driver gaze information. The proposed framework is based on the graph neural network (GNN) and contains three modules, namely, the gaze analysing (GA) module, scene understanding (SU) module and the information fusion (IF) module. The GA module is used to obtain gaze images of drivers, and extract the gaze features from the images. The SU module provides trajectory predictions for surrounding vehicles, motorcycles, bicycles and other traffic participants. The GA and SU modules are parallel and the outputs of both modules are sent to the IF module that fuses the gaze and scene information using the attention mechanism and recognises the driving behaviours through a combined classifier. The proposed framework is verified on a naturalistic driving dataset. The comparative experiments with the state-of-the-art methods demonstrate that the proposed framework has superior performance for driving behaviour recognition in various situations.

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Reliable assessment of the fatigue resistance of asphalt pavement with a long-term service is critically crucial for the rational formulation of original pavement utilization strategies in reconstruction and expansion projects. Currently, the pavement performance evaluation indicators are mainly used to guide pavement preventive maintenance, and its applicability in reconstruction and expansion projects of the freeway is limited. This paper aims to propose an evaluation method of fatigue resistance of asphalt concrete utilizing semi-circular bending (SCB) tests and stereo digital image correlation (stereo-DIC) techniques. A total of 27 asphalt concrete cores were drilled from the three freeways (K84, K124, and K165) with a service life of more than 20 years, and the SCB specimens were produced to conduct the SCB fracture and fatigue tests. During the SCB test, the stereo-DIC technique was employed to monitor the evolution process of the strain distribution and crack length for the specimens. K-dimension tree neighbor-searching algorithm (K-d tree algorithm) was used to effectively measure the change of crack length corresponding to each fatigue load cycle. Meanwhile, the strain threshold of asphalt concrete crack initiation was determined by the bilinear softening cohesive zone model (CZM) to ensure the accuracy of the crack length calculated by the K-d tree algorithm. Furthermore, the relationship between crack growth rate and stress intensity, which was used to fit the Paris law parameters, was determined. The CZM and DIC results indicated that the strain threshold of asphalt concrete crack should be set as 2000 με when using the K-d tree algorithm to determine the crack length. With the stress ratio increase, the Paris law parameter A increased wavily, and the parameter n decreased steadily, while the threshold of the stress intensity factor increased steadily. The Paris law master curves could characterize the fatigue performance of various road sections at a wide load range. The residual fatigue life of K84, K124, K165 the road sections were 2.13E + 08, 3.57E + 08, and 1.02E + 07, respectively.

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Large data centers are currently the mainstream infrastructures for big data processing. As one of the most fundamental tasks in these environments, the efficient execution of distributed data operators (e.g., join and aggregation) are still challenging current data systems, and one of the key performance issues is network communication time. State-of-the-art methods trying to improve that problem focus on either application-layer data locality optimization to reduce network traffic or on network-layer data flow optimization to increase bandwidth utilization. However, the techniques in the two layers are totally independent from each other, and performance gains from a joint optimization perspective have not yet been explored. In this article, we propose a novel approach called NEAL (NEtwork-Aware Locality scheduling) to bridge this gap, and consequently to further reduce communication time for distributed big data operators. We present the detailed design and implementation of NEAL, and our experimental results demonstrate that NEAL always performs better than current approaches for different workloads and network bandwidth configurations.

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Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.

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Measuring risk is critical for collision avoidance. The paper aims to develop an online risk level classification algorithm for forward collision avoidance systems. Assuming risk levels are reflected by braking profiles, deceleration curves from critical evasive braking events from the Virginia “100-car” database were first extracted. The curves are then clustered into different risk levels based on spectrum clustering, using curve distance and curve changing rate as dissimilarity metrics among deceleration curves. Fuzzy logic rules of safety indicators at critical braking onset for risk classification were then extracted according to the clustered risk levels. The safety indicators include time to collision, time headway, and final relative distance under emergency braking, which characterizes three kinds of uncertain critical conditions respectively. Finally, the obtained fuzzy risk level classification algorithm was tested and compared with other Automatic Emergency Braking (AEB) algorithms under Euro-NCAP testing scenarios in simulation. Results show the proposed algorithm is promising in balancing the objectives of avoiding collision and reducing interference with driver's normal driving compared with other algorithms.

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Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks, for instance in terms of reduced management complexity and more fine-grained traffic engineering. However, the design and implementation of SDN within wireless sensor networks (WSN) have received far less attention. Unfortunately, because of the multi-hop type of communication in WSN, a direct reuse of the wired SDN architecture could lead to excessive commu- nication overhead. In this paper, we propose a cluster-based flow management approach that makes a trade-off between the granularity of monitoring by an SDN controller and the communication overhead of flow management. A network is partitioned into clusters with a minimum number of border nodes. Instead of having to handle the individual flows of all nodes, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes. Our proof-of- concept implementations in software and hardware show that, when compared with benchmark solutions, our approach is significantly more efficient with respect to the number of nodes that must be managed and the number of control messages exchanged.@en

Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the network
communication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved by using state-of-the-art methods, such as reducing network traffic designed in the data management domain, and data flow scheduling in the data communications domain.
However, the proposed techniques in both fields just view each other as a black box, and performance gains from a co-optimization perspective have not yet been explored.
In this paper, based on current research in coflow scheduling,
we propose a novel Coflow-based Co-optimization Framework
(CCF), which can co-optimize application-level data movement
and network-level data communications for distributed operators,
and consequently contribute to their performance in
large distributed environments. We present the detailed design
and implementation of CCF, and conduct an experimental
evaluation of CCF using large-scale simulations on large data
joins. Our results demonstrate that CCF can always perform
faster than current approaches on network communications in
large-scale distributed scenarios.@en