Traffic congestion has been a major challenge in many urban road networks.
Extensive research studies have been conducted to highlight traffic-related
congestion and address the issue using data-driven approaches. Currently, most
traffic congestion analyses are done using simulation software that offers
limited insight due to the limitations in the tools and utilities being used to
render various traffic congestion scenarios. All that impacts the formulation
of custom business problems which vary from place to place and country to
country. By exploiting the power of the knowledge graph, we model a traffic
congestion problem into the Neo4j graph and then use the load balancing,
optimization algorithm to identify congestion-free road networks. We also show
how traffic propagates backward in case of congestion or accident scenarios and
its overall impact on other segments of the roads. We also train a sequential
RNN-LSTM (Long Short-Term Memory) deep learning model on the real-time traffic
data to assess the accuracy of simulation results based on a road-specific
congestion. Our results show that graph-based traffic simulation, supplemented
by AI ML-based traffic prediction can be more effective in estimating the
congestion level in a road network.

该研究使用知识图谱模拟交通拥堵问题，并利用负载平衡、优化算法来识别无拥堵的道路网络，同时训练 RNN-LSTM 深度学习模型进行交通预测，结果表明基于图形的交通模拟，辅以 AI ML 交通预测可更有效地估计道路网络的拥堵程度。

利用 Neo4j 和深度学习进行交通拥堵模拟和优化

Leveraging Neo4j and deep learning for traffic congestion simulation &  optimization

Scene text recognition has attracted great interests from the computer vision
and pattern recognition community in recent years. State-of-the-art methods use
concolutional neural networks (CNNs), recurrent neural networks with long
short-term memory (RNN-LSTM) or the combination of them. In this paper, we
investigate the intrinsic characteristics of text recognition, and inspired by
human cognition mechanisms in reading texts, we propose a scene text
recognition method with character models on convolutional feature map. The
method simultaneously detects and recognizes characters by sliding the text
line image with character models, which are learned end-to-end on text line
images labeled with text transcripts. The character classifier outputs on the
sliding windows are normalized and decoded with Connectionist Temporal
Classification (CTC) based algorithm. Compared to previous methods, our method
has a number of appealing properties: (1) It avoids the difficulty of character
segmentation which hinders the performance of segmentation-based recognition
methods; (2) The model can be trained simply and efficiently because it avoids
gradient vanishing/exploding in training RNN-LSTM based models; (3) It bases on
character models trained free of lexicon, and can recognize unknown words. (4)
The recognition process is highly parallel and enables fast recognition. Our
experiments on several challenging English and Chinese benchmarks, including
the IIIT-5K, SVT, ICDAR03/13 and TRW15 datasets, demonstrate that the proposed
method yields superior or comparable performance to state-of-the-art methods
while the model size is relatively small.

本文提出了一种基于卷积特征映射中的字符模型的场景文本识别方法，可以避免基于分割的识别方法中的字符分割困难，并且可以识别未知单词，与现有方法相比具有相对较小的模型大小和优秀的性能表现。