Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process.

/pdf/big-data-analytics-and-mining-for-effective-visualization-47o6oa4oea.pdf

Big data analytics and mining for effective visualization and trends forecasting of crime data

Traffic speed forecasting is one of the core problems in Intelligent Transportation Systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focus on modeling the spatial dependencies only with the distance. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the smallest building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into prediction network for traffic forecasting. We evaluate the proposed model with two large-scale real-world datasets, and find 7.40% average improvement for 1-hour forecasting in highly complex urban networks.

DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

To meet the extremely stringent but diverse requirements of 5G, cost-effective network deployment and traffic-aware adaptive utilization of network resources are becoming essential. In this paper, a hotspot prediction based virtual small cell (VSC) operation scheme is adopted to improve both the cost efficiency and operational efficiency of 5G networks. This paper focuses on how to predict the hotspots by using deep learning, and then demonstrates how the predictions can be leveraged to support adaptive beamforming and VSC operation. We first leverage the feature extraction capabilities of deep learning and exploit use of a long short-term memory (LSTM) neural network to achieve hotspot prediction for the potential formation of the VSCs. To support the operation of VSCs, large-scale antenna array enabled hybrid beamforming is adaptively adjusted for highly directional transmission to cover these hotspot-based VSCs. Within each VSC, an appropriate user equipment is selected as a cell head to collect the intra-cell traffic in the unlicensed band and relays the aggregated traffic to the macro-cell base station by using the licensed band. Our simulation results illustrate that the proposed LSTM-based method can extract spatial and temporal traffic features of hotspot with higher accuracy, compared with some existing deep and non-deep learning approaches. Numerical results also show that VSCs with hotspot prediction and hybrid beamforming can improve the energy efficiency dramatically with flexible deployment and low latency, compared with the scenario of the convolutional fixed small cells.

Deep Learning Based Hotspot Prediction and Beam Management for Adaptive Virtual Small Cell in 5G Networks

With the development of information technique and wireless communication, a vast number of taxis' and ride-sharing cars' trajectory data that provide a rich and detailed source to study on-demand services have been collected. The increasing available trajectory data bring benefits and new challenges to the studies of on-demand services. To provide an overview of the benefits and challenges brought by the trajectory data, we provide a survey on recent studies of trajectory analysis (refer to analyzing trajectory datasets) for on-demand services in this paper. Our purposes are at least trifold. First, we highlight the value of trajectory data in understanding on-demand services and discuss the procedures of retrieving information for the demand part and the supply part from raw trajectory data. Second, we categorize related studies into three parts (the demand part, the supply part, and the mixed part) and review the significant findings. For the demand part, we focus on the models proposed for describing and explaining the spatial-temporal characteristics of observed trips. Methods or models proposed for describing trip statistics, scaling laws of trips, and dynamics of ridership are reviewed. We summarize four types of factors that influence the spatial-temporal patterns of demands. For the supply part, we focus on the models proposed for describing the spatial-temporal characteristics of available taxis/ride-sharing cars and modeling the behavior of drivers (i.e., passenger-search behavior and route choice behavior) to explain the spatial-temporal patterns of taxi/ride-sharing supplies. For the mixed part, we focus on studies that apply the uncovered demands/supplies patterns to design recommendation systems and pricing strategies. Third, we discuss the future directions on collecting/releasing trajectory data and future research directions to advance the understanding of on-demand services.

Trajectory analysis for on-demand services: A survey focusing on spatial-temporal demand and supply patterns

Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focused on modeling the spatial dependencies using the distance only. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the fundamental building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into deep neural networks. We evaluate the proposed model with two large-scale real-world datasets, and find positive improvements for long-term forecasting in highly complex urban networks. The improvement can be larger for commute hours, but it can be also limited for short-term forecasting. 

/pdf/ddp-gcn-multi-graph-convolutional-network-for-spatiotemporal-282d0vdp.pdf

DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting

The GPS trajectories are rich with potential information that could be used to explore the regulation of traffic to serve the public. While that past approaches for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. In this paper, we propose a novel neural network, named L-CNN based on CNN and LSTM, and develop an effective real-time prediction model to forecast the most likely potential passenger for taxi drivers. It is noteworthy that our model can be easily extended to other real-time traffic prediction problems, such as road traffic and flow prediction. Finally, we test our method based on GPS trajectories generated by Cheng Du taxi. The method presented provides passenger prediction over 15-min intervals for up to 1 h in advance and the results prove the efficiency of our predicting system.

Real-Time Taxi-Passenger Prediction With L-CNN

City-scale traffic speed prediction provides significant data foundation for the intelligent transportation system, which enriches commuters with up-to-date information about traffic condition. However, predicting on-road vehicle speed accurately is challenging, as the speed of the vehicle on the urban road is affected by various types of factors. These factors can be categorized into three main aspects, which are temporal, spatial, and other latent information. In this paper, we propose a novel spatio-temporal model named L-U-Net based on U-Net as well as long short-term memory architecture and develop an effective speed prediction model, which is capable of forecasting city-scale traffic conditions. It is worth noting that our model can avoid the high complexity and uncertainty of subjective features extraction and can be easily extended to solve other spatio-temporal prediction problems such as flow prediction. The experimental results demonstrate that the prediction model we proposed can forecast urban traffic speed effectively.

/pdf/a-novel-spatio-temporal-model-for-city-scale-traffic-speed-2nndrovd7a.pdf

A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction

NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network Spectrum

For smartphone service providers, it is of vital importance to recognize characteristics of customers. The process of recognizing these characteristics is generally referred to as user profile, which provides knowledge basis for business decisions, enables intelligent services, and brings unique competitiveness. As a basic component of user profile, bedtime could reflect lifestyle, health condition, and occupation of people. This paper presents a flexible algorithm named BTP (Bedtime Prediction), which is designed for predicting wake time and bedtime by analysing screen status of smartphone. BTP first collects screen status log data of user’s smartphone and conducts preprocessing with a series of auxiliary user profiles. Then, it detects and records users’ wake time and bedtime of one day by searching and combining major screen extinguish periods in the past 24 hours. Finally, BTP predicts future bedtime by matching current screen status sequence with all historical records. By applying BTP, most of night and morning scenario-based applications could provide more considerate services, rather than following fixed execution time like alarm clock. Experiments on practical applications prove that BTP can effectively predict wake time and bedtime without applying complicated machine learning algorithms or uploading data to server.

/pdf/btp-a-bedtime-predicting-algorithm-via-smartphone-screen-4l0cesmkgy.pdf

BTP: A Bedtime Predicting Algorithm via Smartphone Screen Status

With the popularity of smart phones, plenty of mobile phone applications are developed to meet people's various needs, and mobile application recommendation has become a popular and challenging topic. Most studies focus on learning user preferences from various information both on user-side and APP-side, and recommending based on user similarity or app similarity. However, these methods all have a high probability to cause serious homogenization problems that can not meet users' unknown/new needs. Therefore, recommending diverse apps is more likely to cover users' all preferences, and even guide users to discover new needs and interests. To this end, we give the definition of Application Diversity that taking into account the similarity between apps and the relevance of categories, and propose a novel application recommendation approach that consists of two parts, P-Stair Neural Network (P-SNN) and Dynamic Adjustment Method (DAM). First, P-SNN learns user preferences from multi-dimensional data by using deep neural networks techniques, and predicts users' ratings for uninstalled applications. Then, DAM selects TOP-N applications as the final recommend list with considering both user preferences and recommend diversity. Several experiments on different datasets shows that our algorithm effectively improves the diversity of recommendations in the case of similar accuracy.

Cheng Cheng

Papers

Real-Time Taxi-Passenger Prediction With L-CNN

A Novel Spatio-Temporal Model for City-Scale Traffic Speed Prediction

NFRR: A Novel Family Relationship Recognition Algorithm Based on Telecom Social Network Spectrum

BTP: A Bedtime Predicting Algorithm via Smartphone Screen Status

A Novel Learning Approach to Improve Mobile Application Recommendation Diversity