TARA-Net: A Fusion Network for Detecting Takeaway Rider Accidents

2021 ◽  
Vol 12 (6) ◽  
pp. 1-19
Author(s):  
Yifan He ◽  
Zhao Li ◽  
Lei Fu ◽  
Anhui Wang ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Information Structure ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Sequence Data ◽  
Contextual Information ◽  
Spatial Prediction ◽  
Food Delivery ◽  
Temporal Sequence ◽  
Trajectory Data ◽  
Feed Forward Neural Network

In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a TakeAway Rider Accident detection fusion network TARA-Net to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world Ele.me dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.

QARTA

2021 ◽  
Vol 14 (11) ◽  
pp. 2273-2282
Author(s):  
Mashaal Musleh ◽  
Sofiane Abbar ◽  
Rade Stanojevic ◽  
Mohamed Mokbel
Keyword(s):  
Machine Learning ◽  
Road Network ◽  
Contextual Information ◽  
Time Of Day ◽  
Food Delivery ◽  
Machine Learning Techniques ◽  
Navigation Systems ◽  
Query Result ◽  
Learning Techniques ◽  
Map Service

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.

Hybrid Group Anomaly Detection for Sequence Data: Application to Trajectory Data Analytics

2021 ◽  
pp. 1-12
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Gautam Srivastava ◽  
Alberto Cano ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Anomaly Detection ◽  
Data Analytics ◽  
Sequence Data ◽  
Trajectory Data ◽  
Data Application ◽  
Hybrid Group

Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning

2018 ◽  
Vol 2672 (45) ◽  
pp. 173-184 ◽  
Author(s):  
Seongjin Choi ◽  
Hwasoo Yeo ◽  
Jiwon Kim
Keyword(s):  
Deep Learning ◽  
Sequence Data ◽  
Urban Traffic ◽  
Base Case ◽  
Traffic Patterns ◽  
Trajectory Data ◽  
Urban Networks ◽  
Recent Success ◽  
Finite Set ◽  
Vehicle Trajectory

This paper proposes a deep learning approach to learning and predicting network-wide vehicle movement patterns in urban networks. Inspired by recent success in predicting sequence data using recurrent neural networks (RNN), specifically in language modeling that predicts the next words in a sentence given previous words, this research aims to apply RNN to predict the next locations in a vehicle’s trajectory, given previous locations, by viewing a vehicle trajectory as a sentence and a set of locations in a network as vocabulary in human language. To extract a finite set of “locations,” this study partitions the network into “cells,” which represent subregions, and expresses each vehicle trajectory as a sequence of cells. Using large amounts of Bluetooth vehicle trajectory data collected in Brisbane, Australia, this study trains an RNN model to predict cell sequences. It tests the model’s performance by computing the probability of correctly predicting the next [Formula: see text] consecutive cells. Compared with a base-case model that relies on a simple transition matrix, the proposed RNN model shows substantially better prediction results. Network-level aggregate measures such as total cell visit count and intercell flow are also tested, and the RNN model is observed to be capable of replicating real-world traffic patterns.

scholarly journals Managerial Self-Attribution Bias and Banks’ Future Performance: Evidence from Emerging Economies

2019 ◽  
Vol 12 (2) ◽  
pp. 73 ◽  
Author(s):  
Iqbal
Keyword(s):  
Emerging Economies ◽  
Performance Prediction ◽  
Attribution Theory ◽  
Prediction Models ◽  
Contextual Information ◽  
Numerical Data ◽  
Annual Reports ◽  
Future Performance ◽  
Attribution Bias ◽  
Expected Outcomes

The objective of the study was to predict the future performance of banks based on the contextual information provided in annual reports. The European Central Bank has observed that performance prediction models in earlier studies mainly rely on quantitative financial data, which are insufficient for the comprehensive assessment of banks’ performance. There is a need to incorporate the qualitative information along with numerical data for better prediction. In this context, this study employed the attribution theory for understanding the contextual information of behavioral biases of management towards the expected outcomes. The sample consisted of 58 banks of 16 emerging economies, and the period covered from 2007–2015. Unsupervised hierarchical clustering was performed to identify the latent groups of banks within the data. For performance prediction, system GMM was employed, because it helped to deal with the endogeneity and heterogeneity problems. The results of the study were consistent with the attribution theory that management took credit for favorable expected outcomes and distanced from bad outcomes. An important policy implication of the study is that the prevalence of self-attribution bias of management in annual reports provides an additional source of information for the regulators to identify the banks at risks and take preventive measures to avoid the expected cost of failure. It can also help investors, and gives analysts a better tool for a comprehensive analysis of the profitability of prospective investments.

scholarly journals Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models

2019 ◽  
Vol 57 (3) ◽  
pp. 271-286 ◽  
Author(s):  
Gema Casal ◽  
Paul Harris ◽  
Xavier Monteys ◽  
John Hedley ◽  
Conor Cahalane ◽  
...  
Keyword(s):  
Prediction Models ◽  
Spatial Prediction ◽  
Sentinel 2

Mapping (un)certainty of machine learning-based spatial prediction models based on predictor space distances

2020 ◽  
Author(s):  
Hanna Meyer ◽  
Edzer Pebesma
Keyword(s):  
Machine Learning ◽  
Spatial Patterns ◽  
Environmental Science ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Predictor Variable ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Field Samples

<p>Spatial mapping is an important task in environmental science to reveal spatial patterns and changes of the environment. In this context predictive modelling using flexible machine learning algorithms has become very popular. However, looking at the diversity of modelled (global) maps of environmental variables, there might be increasingly the impression that machine learning is a magic tool to map everything. Recently, the reliability of such maps have been increasingly questioned, calling for a reliable quantification of uncertainties.</p><p>Though spatial (cross-)validation allows giving a general error estimate for the predictions, models are usually applied to make predictions for a much larger area or might even be transferred to make predictions for an area where they were not trained on. But by making predictions on heterogeneous landscapes, there will be areas that feature environmental properties that have not been observed in the training data and hence not learned by the algorithm. This is problematic as most machine learning algorithms are weak in extrapolations and can only make reliable predictions for environments with conditions the model has knowledge about. Hence predictions for environmental conditions that differ significantly from the training data have to be considered as uncertain.</p><p>To approach this problem, we suggest a measure of uncertainty that allows identifying locations where predictions should be regarded with care. The proposed uncertainty measure is based on distances to the training data in the multidimensional predictor variable space. However, distances are not equally relevant within the feature space but some variables are more important than others in the machine learning model and hence are mainly responsible for prediction patterns. Therefore, we weight the distances by the model-derived importance of the predictors. </p><p>As a case study we use a simulated area-wide response variable for Europe, bio-climatic variables as predictors, as well as simulated field samples. Random Forest is applied as algorithm to predict the simulated response. The model is then used to make predictions for entire Europe. We then calculate the corresponding uncertainty and compare it to the area-wide true prediction error. The results show that the uncertainty map reflects the patterns in the true error very well and considerably outperforms ensemble-based standard deviations of predictions as indicator for uncertainty.</p><p>The resulting map of uncertainty gives valuable insights into spatial patterns of prediction uncertainty which is important when the predictions are used as a baseline for decision making or subsequent environmental modelling. Hence, we suggest that a map of distance-based uncertainty should be given in addition to prediction maps.</p>

scholarly journals Modeling geo-homopholy in online social networks for population distribution projection

2017 ◽  
Vol 1 (3) ◽  
pp. 249-269 ◽  
Author(s):  
Yuanxing Zhang ◽  
Zhuqi Li ◽  
Kaigui Bian ◽  
Yichong Bai ◽  
Zhi Yang ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Dirichlet Process ◽  
Prediction Models ◽  
Population Distribution ◽  
Data Availability ◽  
Floating Population ◽  
Trajectory Data ◽  
Content Type ◽  
Geographical Regions

Purpose Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks. Design/methodology/approach In this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions. Findings By experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time. Originality/value This paper tries to project population distribution by modeling geo-homophily in OSNs.

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