Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Intelligent Transportation Systems: Fusing Computer Vision and Sensor Networks for Traffic Management

Intelligent Transportation Systems (ITS) represent a pivotal approach to addressing the complex challenges posed by modern-day urban mobility. By seamlessly integrating computer vision and sensor networks, ITS offer a comprehensive solution for traffic management, safety enhancement, and environmental sustainability. This paper delves into the synergistic fusion of computer vision and sensor networks within the framework of ITS, emphasizing their collective role in optimizing traffic flow, mitigating congestion, and enhancing overall road safety. Leveraging cutting-edge technologies such as machine learning, image processing, and Internet of Things (IoT), ITS harness real-time data acquisition and analytics capabilities to facilitate informed decision-making by transportation authorities. Through a comprehensive review of recent advancements, challenges, and opportunities, this paper illuminates the transformative potential of integrating computer vision and sensor networks in ITS. Furthermore, it presents compelling case studies and exemplary applications, showcasing the tangible benefits of this fusion across diverse traffic management scenarios. Ultimately, this paper advocates for the widespread adoption of integrated ITS solutions as a means to usher in a new era of smarter, safer, and more sustainable urban transportation systems

Map-Reduce for Processing GPS Data from Public Transport in Montevideo, Uruguay

This article addresses the problem of processing large volumes of historical GPS data from buses to compute quality-of-service metrics for urban transportation systems. We designed and implemented a solution to distribute the data processing on multiple processing units in a distributed computing infrastructure. For the experimental analysis we used historical data from Montevideo, Uruguay. The proposed solution scales properly when processing large volumes of input data, achieving a speedup of up to 22× when using 24 computing resources. As case studies, we used the historical data to compute the average speed of bus lines in Montevideo and identify troublesome locations, according to the delay and deviation of the times to reach each bus stop. Similar studies can be used by control authorities and policy makers to get an insight of the transportation system and improve the quality of service.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Scalable learning with a structural recurrent neural network for short-term traffic prediction

This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal relationship of the big data often requires a significant amount of computational burden or an ad-hoc design aiming for a specific type of road network. To tackle the problem, we combine a road network graph with recurrent neural networks (RNNs) to construct a structural RNN (SRNN). The SRNN employs a spatio-temporal graph to infer the interaction between adjacent road segments as well as the temporal dynamics of the time series data. The model is scalable thanks to two key aspects. First, the proposed SRNN architecture is built by using the semantic similarity of the spatio-temporal dynamic interactions of all segments. Second, we design the architecture to deal with fixed-length tensors regardless of the graph topology. With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14.1% and the convolutional neural network (CNN) by 5.87%, respectively, in terms of root mean squared error (RMSE). Moreover, we show that the proposed model is scalable. The SRNN model trained with data of a road network is able to predict traffic data of different road networks, with the fixed number of parameters to train

MaaS surveillance : Privacy considerations in mobility as a service

Funding Information: König, D., Eckhardt, J., Aapaoja, A., Sochor, J.L., Karlsson, M., 2016. Deliverable 3: Business and operator models for MaaS. MAASiFiE project funded by CEDR. Submitted to: CEDR Conference of European Directors of Roads. Publisher Copyright: © 2019 Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Peer reviewedPostprin

Congestions and big data: a review on the predictive solutions

Discusses how big data is applied in predicting traffic congestions, and preempting their costs. For first, recurring concepts are explained. Secondly, different traffic related data's sources are reviewed. Thirdly comes the present commercial applications. And for last, conclusions on the topic

Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches