Traffic Related Tweets Research Articles

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

Exploring the Potential of Twitter to Understand Traffic Events and Their Locations in Greater Mumbai, India

Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter – a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

Real-Time Traffic Congestion Information from Tweets Using Supervised and Unsupervised Machine Learning Techniques

For many decades automated sensors, such as acoustic, optical, pneumatic, magnetic, inductive sensors and video-based image processing solutions were used for collecting data on traffic flow, speed, density, travel time etc. Using these data, the level of congestion is indirectly estimated. However, most of these sensors are costly and location-based. In addition, they cannot find the reason for congestion. To address these limitations, use of social media as a source of traffic information has been explored recently. Some of them, such as Twitter, are open source and popular throughout the world. Earlier studies that reported the use of Twitter feeds for traffic information are limited and used machine-learning techniques based on supervised learning, which needs labelled data that are difficult to obtain. Hence, this paper compares the unsupervised and supervised approaches to extract traffic-related tweets and develops a method to give the probability of level of congestion in real-time. The methods developed were applied to the tweets from Chennai city to extract the congestion information in real time, the results were validated through newspaper reports of heavy traffic congestion events. The obtained results showed a good correlation with the real congestion events.

Forecasting Citywide Traffic Congestion Based on Social Media

This paper makes the first step towards mining citywide traffic congestion correlation by utilizing traffic related information from social media. Traffic congestion correlation mining, namely studying which road segments close to each other are highly likely to occur congestion simultaneously, is especially important to help many real applications, such as traffic prediction, traffic control, and urban transportation planning. Traditional traffic data collected from various sensors and other equipments are costly to obtain and hard to scale up to cover a entire city. With the rising popularity of social media, it is common for the public transportation systems and governments to share real time traffic information with the public through social media. It provides us great opportunities to study the traffic conditions of a city with the rich and easily available online data. However, it is also very difficult to use social media data to mine the citywide traffic congestion correlation due to the following major challenges: (1) Social media data like tweets in Twitter are usually noisy and hard to process, especially those tweets posted by individuals. (2) There lacks a method to study the citywide traffic congestion correlation. In this paper, instead of crawling all the traffic related tweets of a city, we only focus on utilizing the tweets posted by some particular organizations or governments. Tweets posted by them are more accurate and formal, thus it is much easier for traffic information extraction. We regard the traffic congestion correlation mining task as a spatio-temporal frequent pattern mining problem by considering each tweet reporting the traffic congestion of a particular road segment as a spatio-temporal item. A spatio-temporal frequent pattern mining algorithm TC_Apriori is also proposed to discover the road segment co-occurrence patterns in congestion. We use the tweets reporting the traffic information of Chicago to evaluate the proposed approach, and the results show that the proposed approach can effectively discover the road segment co-occurrence patterns in congestion.

Traffic Detection Using Tweets on Twitter Social Network

Now a days social networking sites are becoming the real time information channel over time. With the help of portable devices many users are able to share the real life events on the internet. This feature of the social networking sites made them more popular and valuable. Basically these social networking sites are used for the maintaining social relationship, finding friends and users with the similar interest .The text message shared by user in social networks that message is called Status Update Message. In that SUM the text, meta-information like timestamp, name of the user, geographic coordinates, links of the other resources are present. The SUM's considered in a specific area may provide the proper information. Social networks is a source of information for event detection such as road traffic congestion and car accidents. Existing system present a real-time monitoring system for traffic event detection from twitter. The system fetches or collect tweets from twitter and then processes tweets using text mining techniques. Lastly performs the classification of tweets. The system aim is to assign the appropriate class label to each tweet, whether it is related to a traffic event or not. used the support vector machine as a classification model. The proposed system uses the semi-supervised approach, which gives training using traffic related dataset. We propose a clustering approach for classification of the tweets in traffic related and non- traffic related tweets. We employ a Euclidean distance to calculate the similarity between the tweets. Keywords: classification, Traffic event detection, Data mining, text mining, and social sensing. Keywords: Twitter-Traffic-Status Language: en SafetyLit note: We believe that the inclusion of references in this case falls under fair use. Why? 1) This article was published as open access and the journal and copyright owner is acknowledged; 2) a link to full text is provided; 3) reproduction of this reference list is relevant to a commentary on what is and is not original thought and 4) the items in this reference list are part of the data upon which an investigation are based.

A methodology for traffic-related Twitter messages interpretation

This paper addresses the problem of interpreting tweets that describe traffic-related events and that are distributed by government agencies in charge of road networks or by news agencies. Processing such tweets is of interest for two reasons. First, albeit phrased in natural language, such tweets use a much more regular and well-behaved prose than generic user-generated tweets. This characteristic facilitates automating their interpretation and achieving high precision and recall. Second, government agencies and news agencies use Twitter channels to distribute real-time traffic conditions and to alert drivers about planned changes on the road network and about future events that may affect traffic conditions. Hence, such tweets provide exactly the kind of information that proactive truck fleet monitoring and similar applications require. The main contribution of the paper is an automatic tweet interpretation tool, based on Machine Learning techniques, that achieves good performance for traffic-related tweets distributed by traffic authorities and news agencies. The paper also covers in detail experiments with real traffic-related tweets to access the precision and recall of the tool.