Application of Batch and Stream Collaborative Computing in Urban Traffic Data Processing

2017 ◽  
pp. 725-734
Author(s):  
Tao Zhang ◽  
Shuai Zhao
Keyword(s):  
Data Processing ◽  
Urban Traffic ◽  
Collaborative Computing ◽  
Traffic Data

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

Big traffic data processing framework for intelligent monitoring and recording systems

2016 ◽  
Vol 181 ◽  
pp. 139-146 ◽  
Author(s):  
Yingjie Xia ◽  
Jinlong Chen ◽  
Xindai Lu ◽  
Chunhui Wang ◽  
Chao Xu
Keyword(s):  
Data Processing ◽  
Traffic Data ◽  
Intelligent Monitoring ◽  
Big Traffic Data ◽  
Processing Framework

Long-range correlation analysis of urban traffic data

2010 ◽  
Vol 19 (8) ◽  
pp. 080205 ◽  
Author(s):  
Sheng Peng ◽  
Wang Jun-Feng ◽  
Tang Tie-Qiao ◽  
Zhao Shu-Long
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Urban Traffic ◽  
Traffic Data ◽  
Range Correlation

scholarly journals Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

2021 ◽  
Vol 13 (23) ◽  
pp. 13068
Author(s):  
Akbar Ali ◽  
Nasir Ayub ◽  
Muhammad Shiraz ◽  
Niamat Ullah ◽  
Abdullah Gani ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Low Cost ◽  
Urban Traffic ◽  
Traffic Data ◽  
Mobile Crowdsensing ◽  
Traffic Efficiency ◽  
Crowd Sensing ◽  
Management Schemes ◽  
Mobile Crowd

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

scholarly journals Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Lemonde ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Big Data ◽  
City Council ◽  
Urban Traffic ◽  
Integrative Analysis ◽  
Context Aware ◽  
Traffic Data ◽  
Situational Context ◽  
Transport Modes ◽  
The City ◽  
Urban Data

AbstractWorldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

scholarly journals Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 1509 ◽  
Author(s):  
Liang Ge ◽  
Siyu Li ◽  
Yaqian Wang ◽  
Feng Chang ◽  
Kunyan Wu
Keyword(s):  
Weather Conditions ◽  
Urban Traffic ◽  
Traffic Data ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Temporal Correlations ◽  
Speed Prediction ◽  
External Component ◽  
Temporal Graph ◽  
Traffic Speed

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

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