•A comprehensive study of data was undertaken, including the examination of key highways, accident-prone areas, and causes contributing to accidents.•Establishing a comprehensive centralized database ...system to enhance the documenting and management of accidents.•Conducting traffic calming measures in locations with a high incidence of accidents.•Establishing clear legislative guidelines pertaining to fatal accidents.•The study's findings and recommendations are essential for developing effective strategies to improve road safety in Rawalpindi.
Road Traffic Accidents (RTAs) provide a substantial risk to both public safety and infrastructure within severe earthquake zones. This research aims to provide a thorough analysis of the many elements that contribute to RTAs in the area. Although research on RTAs has been conducted in numerous settings, there is a lack of localized research specifically focused on developing countries. Therefore, this study is necessary to address this gap in literature. Our study focuses on examining the distinct issues and elements that are particular to this specific location. The objective is to provide customized solutions that effectively reduce the occurrence of accidents. The objective of this study is to examine the status of RTAs, ascertain the main factors contributing to RTAs, and provide viable approaches to improve road safety. A comprehensive study of data was undertaken, which included the examination of key highways, accident-prone areas, and causes contributing to accidents. Insights were derived by the use of statistical analysis, hotspot mapping, and the categorization of incidents. The results revealed a distressing roadway are afflicted by a significant frequency of accidents, even those occurring on prominent thoroughfares. Excessive speed has been revealed as the primary contributing factor, closely followed by negligence and recklessness. The common factor seen in these events was the insufficient enforcement of traffic regulations. The research highlights several practical consequences. Firstly, it is advised to implement urgent actions such as the upgrading of the traffic database, the adoption of contemporary traffic management software, and the enforcement of rigorous traffic laws. The establishment of a comprehensive centralized database system is crucial in order to enhance the documenting and management of accidents. Furthermore, the use of cost-benefit analysis provides justification for the adoption of traffic calming measures in locations with a high incidence of accidents. Furthermore, the implementation of continuous road safety awareness efforts and the establishment of clear legislative guidelines pertaining to fatal accidents are imperative measures in promoting enhanced road safety.
The comprehensive access to road traffic patterns in the continuously growing urban areas is key to achieve a sustainable mobility. However, the inherent complexity of urban traffic poses many ...challenges to achieve this goal, including: i) the need to integrate heterogeneous views of road traffic (such as speed limits, jam size, delay, throughput) from available sources; ii) the complex spatiotemporal intricacies of geolocalized speed and loop counter data; iii) the need to mine congestion patterns robust to the inherent traffic variability and unexpected occurrence of events, taking also into consideration the varying degrees of congestion severity; and iv) the need to guarantee the statistical significance and interpretability of the target patterns. In the context of our work, a road traffic pattern is a recurrent congestion profile (w.r.t. speed limits, jam extent and flow) that can span multiple locations and time periods within a day. Biclustering, the discovery of coherent subspaces (local patterns) within real-valued data, has unique properties of interest, being positioned to unravel such traffic patterns, while satisfying the aforementioned challenges. Despite its relevance, the potentialities of applying biclustering in mobility domains remain unexplored. This work proposes a structured view on why, when and how to apply biclustering for mining traffic patterns of road mobility, a subject remaining largely unexplored up to date. Using the city of Lisbon as a guiding case, we illustrate the relevance of biclustering geolocalized speed data and loop counter data. The gathered results confirm the role of biclustering in comprehensively finding statistically significant and actionable spatiotemporal associations of road mobility.
Motorcycle taxis in Guatemala City are a form of transportation in which the customer pays to ride as the pillion. For a modest amount of money, Guatemalans can avoid road traffic by riding in ...between idling cars. Clients and drivers alike call this maneuver "eating traffic." The colloquialism captures a reversal in which commuters dominate traffic rather than traffic dominating them. This article, in response, assesses ethnographically the affective terrain of traffic in Guatemala City. Of interest is how eating traffic opens a window into the city as a field of emotions characterized by a constant, deeply felt desire to flee, to escape - to be anywhere but here.
Context: The use of Geographic Information System (GIS) as a real-time monitoring system for the control and management of accident events is well known as it provides a platform to perform various ...spatial and network analysis and also in the presentation of descriptive data.
Aim and Objective: This paper presents a GIS-based approach to find out areas prone to road traffic accidents based on spatial analysis and to analyze the spatial accessibility using network analysis
Material and Methods Accident particulars like date, location, time and outcome for the year 2015 were included in the GIS database. Moran’s I method of spatial autocorrelation was used for the assessment of spatial clustering of accidents and hotspots spatial densities. The clusters of high and low values of the severity of accidents were obtained using cluster and outlier analysis. Location-allocation was performed as part of network analysis to find the nearest hospital to these high-value clusters.
Results: Spatial autocorrelation showed that there was overall clustering present, cluster and outlier analysis gave clusters of severe accidents at NH-1 and Fortis hospital was the nearest facility to these clusters.
Conclusion: This system is highly useful and provides information about accident locations, accident and service diagnosis and fast delivery of emergency services.
Background: Road transport is predisposing people to the risk of road accidents, injuries and fatalities. Road traffic injuries caused an estimated 1.24 million deaths worldwide, of which India alone ...accounted for 73%. This study was conducted to determine the improvement in the awareness of road safety measures post educational intervention.
Methods: This was a Cross-sectional study with a follow-up component. The study was conducted among undergraduate medical students in Chennai. The sample size was calculated as 220. Study tool consisted of two questionnaires; one of which was given as pre-test and post-test and other 4 weeks after educational intervention to assess behaviour. Frequency and paired t test were used for analysis.
Results: 46 (21%) did not have the habit of wearing helmet/seatbelt during every drive. 56 (25.5%) of them had used mobile phones while driving. Overall non-compliance to traffic rules was 25- 30%. 31 (14.1 %) faced road traffic accidents. The knowledge on road safety measures improved to 96% after educational intervention.
Conclusion: An educational intervention presented in this study is effective to improve knowledge, attitude and behaviour about road safety among the study participants.
•We propose SNSJam, which is a system to detect and predict road traffic jams by leveraging multiple data sources, specifically Twitter and Instagram.•SNSJam supports multiple languages, specifically ...Arabic and English. It also supports Standard Arabic and UAE local Dialect.•We developed a location recognizer that identifies locations from the text of posts and/or GPS locations. SNSJam supports user-defined locations, which are common names among people but different from the official names. SNSJam is the first such system to define and support user-defined locations.•We developed a context-aware classifier to detect traffic jams. The classifier is able to identify the cause of traffic jams. The detected traffic jams can be visualized through a dynamic map.•SNSJam employs a linear regression model to predict future traffic jams by leveraging current and historical posts.
The increased popularity of micro-blogging applications together with the widespread of location-aware devices have resulted in the creation of large streams of geo-tagged data. Such data provides a great opportunity for researchers to explore event detection and prediction. In particular, road traffic detection and prediction are of great importance to various applications, i.e. Intelligent Transportation Systems. Current works proposed traffic jam detection from a single data source with a single language. However, for countries where the residents are speaking two, or more, languages and are interacting with more than one online social platform, single-language and single-source systems are insufficient to capture the necessary online information. Therefore, in this paper, we introduce SNSJam, an effective system to detect and predict road traffic jams using cross-lingual (English and Arabic) data collected from multiple dynamic sources, such as Twitter and Instagram. SNSJam classifier not only detect traffic events, but also identifies the causes of traffic jams. To identify the location of a traffic event, a Location Recognizer is developed that extracts locations from text and GPS of the post. Additionally, the Location Recognizer supports user-defined locations, which are common names among people. Our experiments show that by combining Arabic and English data streams, the accuracies of traffic events detection and prediction are significantly improved as compared with that of the individual languages. Additionally, combining data streams from multiple sources (Twitter and Instagram) further improved the accuracies of event detection and prediction over any individual source. A visualization interface was developed to show the detected spatio-temporal traffic events on a dynamic map. The detection and prediction results are validated against ground truth data obtained from the concerned authorities in the UAE.
Reliable and flexible emergency networks are of paramount essence for disaster relief during or after the event of disasters due to the destruction or lack of terrestrial communication ...infrastructures. Thanks to fast deployment and flexible mobilities, unmanned aerial vehicles (UAVs) emerge as a promising paradigm to efficiently establish emergency networks and perform immediate disaster relief tasks in affected areas. However, in such UAV-assisted disaster relief networks (UDRNs), the limited onboard batteries and computational capacities of UAVs hinder them from performing computation-intensive missions. Moreover, critical security vulnerabilities arise in data transmission among UAVs owing to the untrusted environment, open communication channels, and unreliable misbehavior tracing. To this end, this article investigates UDRNs based on blockchain and machine learning to achieve secure and efficient data transmission. Specifically, we first present a lightweight blockchain-enabled collaborative aerial-ground networking framework to safeguard data delivery under disasters, where a credit-based delegated proof-of-stake consensus protocol is further devised to enhance consensus efficiency while promoting UAVs' honest behaviors. In addition, by harnessing the idle computing power of ground vehicles (referred to as vehicular fog computing), a novel reinforcement learning-based algorithm is developed to intelligently offload UAVs' computation missions to the moving vehicles in the dynamic environment. Experimental results demonstrate that the proposed framework outperforms the existing approaches, in terms of consensus security, user utility, task latency, and energy consumption. Finally, future research directions in this emerging area are discussed.
Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient ...pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an “out-of-limit map” tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents’ exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.
Autonomous vehicles continue to advance, primarily due to the continuous development and improvement of deep learning methods. Motion prediction of road users is a function that humans perform ...naturally while driving, such as anticipating when a pedestrian is likely to cross an intersection or when a vehicle would like to merge into a lane. Similarly, the ability to predict the motion of other road users has become a key function for autonomous vehicles to optimally plan their path. This publication explores the recent advancements in motion prediction of road users propelled by both deep learning, as well as the release of large datasets. These datasets provide many challenging real-world scenarios which can be leveraged to train and test state-of-the-art deep learning models. This study provides an overview of motion prediction, state-of-the-art datasets, and state-of-the-art models along with a deep dive into their methods. Finally, there is a comparison of model performance, followed by recommendations into future research and concluding remarks.
Over the years, road traffic accidents keep claiming lives of people all over the world and alcohol impaired driving is one major cause of these road crashes. While most countries aim at curbing ...alcohol impaired driving by educating the masses on drunk driving through rallied campaigns, most developed and some developing countries have coupled mass education with technology which includes breathalyzers and ignition interlock devices. This technology has proven to reduce Driving While Intoxicated (DWI) recidivism impressively in countries that have deployed it. This paper aims at proposing a smart vehicle ignition interlock named digiLock to help curb alcohol impaired driving in Ghana and beyond.
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