scholarly journals Data-Driven Prediction System of Dynamic People-Flow in Large Urban Network Using Cellular Probe Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxuan Chen ◽  
Xia Wan ◽  
Fan Ding ◽  
Qing Li ◽  
Charlie McCarthy ◽  
...  
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Network ◽  
Feature Model ◽  
Data Driven ◽  
Experimental Result ◽  
Prediction System ◽  
Urban Network ◽  
Flow Prediction

Cellular probe data, which is collected by cellular network operators, has emerged as a critical data source for human-trace inference in large-scale urban areas. However, because cellular probe data of individual mobile phone users is temporally and spatially sparse (unlike GPS data), few studies predicted people-flow using cellular probe data in real-time. In addition, it is hard to validate the prediction method at a large scale. This paper proposed a data-driven method for dynamic people-flow prediction, which contains four models. The first model is a cellular probe data preprocessing module, which removes the inaccurate and duplicated records of cellular data. The second module is a grid-based data transformation and data integration module, which is proposed to integrate multiple data sources, including transportation network data, point-of-interest data, and people movement inferred from real-time cellular probe data. The third module is a trip-chain based human-daily-trajectory generation module, which provides the base dataset for data-driven model validation. The fourth module is for dynamic people-flow prediction, which is developed based on an online inferring machine-learning model (random forest). The feasibility of dynamic people-flow prediction using real-time cellular probe data is investigated. The experimental result shows that the proposed people-flow prediction system could provide prediction precision of 76.8% and 70% for outbound and inbound people, respectively. This is much higher than the single-feature model, which provides prediction precision around 50%.

scholarly journals Spatiotemporal Partitioning of Transportation Network Using Travel Time Data

2017 ◽  
Vol 2623 (1) ◽  
pp. 98-107 ◽  
Author(s):  
Clélia Lopez ◽  
Panchamy Krishnakumari ◽  
Ludovic Leclercq ◽  
Nicolas Chiabaut ◽  
Hans van Lint
Keyword(s):  
Travel Time ◽  
Urban Areas ◽  
Large Scale ◽  
Spatial Clustering ◽  
Transportation Network ◽  
Route Guidance ◽  
Time Data ◽  
Normalized Cut ◽  
Urban Network ◽  
Travel Time Data

Today, the deployment of sensing technology permits the collection of massive amounts of spatiotemporal data in urban areas. These data can provide comprehensive traffic state conditions for an urban network and for a particular day. However, data are often too numerous and too detailed to be of direct use, particularly for applications such as delivery tour planning, trip advisors, and dynamic route guidance. A rough estimate of travel times and their variability may be sufficient if the information is available at the full city scale. The concept of the spatiotemporal speed cluster map is a promising avenue for these applications. However, the data preparation for creating these maps is challenging and rarely discussed. In this study, that challenge is addressed by introducing generic methodologies for mapping the data to a geographic information system network, coarsening the network to reduce the network complexity at the city scale, and estimating the speed from the travel time data, including missing data. This methodology is demonstrated on the large-scale urban network of Amsterdam, Netherlands, with real travel time data. The preprocessed data are used to build the spatiotemporal speed cluster by using three partitioning techniques: normalized cut, density-based spatial clustering of applications with noise, and growing neural gas (GNG). A new posttreatment methodology is introduced for density-based spatial clustering and GNG, which are based on data point clustering, to generate connected zones. A preliminary cross comparison of the clustering techniques shows that GNG performs best in generating zones with minimum internal variance, the normalized cut computes three-dimensional zones with the best intercluster dissimilarity, and GNG has the fastest computation time.

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

scholarly journals Integrated Incident Decision-Support using Traffic Simulation and Data-Driven Models

2018 ◽  
Vol 2672 (42) ◽  
pp. 247-256
Author(s):  
Tao Wen ◽  
Adriana-Simona Mihăiţă ◽  
Hoang Nguyen ◽  
Chen Cai ◽  
Fang Chen
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Case Studies ◽  
Situation Awareness ◽  
Large Scale ◽  
Traffic Simulation ◽  
Data Driven ◽  
Incident Management ◽  
Management Platform ◽  
Transport Management

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.

scholarly journals Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
A. Boulmakoul ◽  
L. Karim ◽  
M. Mandar ◽  
A. Idri ◽  
A. Daissaoui
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Added Value ◽  
Traffic Patterns ◽  
Cloud Infrastructure ◽  
Real Time Control ◽  
Time Data ◽  
Urban Network

We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment.

scholarly journals Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Peter O’Donovan ◽  
Ken Bruton ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Information Technology ◽  
Real Time ◽  
Large Scale ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Air Handling Unit ◽  
Significant Effort

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).

scholarly journals Evaluation of Reinforcement Learning Traffic Signalling Strategies for Alternative Objectives: Implementation in the Network of Nicosia, Cyprus

2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou
Keyword(s):  
Reinforcement Learning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Smart Cities ◽  
Control Strategies ◽  
Urban Traffic ◽  
Signal Control ◽  
Urban Network

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.

scholarly journals Using data-driven approaches to improve delivery of animal health care interventions for public health

2021 ◽  
Vol 118 (5) ◽  
pp. e2003722118
Author(s):  
Stella Mazeri ◽  
Jordana L. Burdon Bailey ◽  
Dagmar Mayer ◽  
Patrick Chikungwa ◽  
Julius Chulu ◽  
...  
Keyword(s):  
Real Time ◽  
Vaccination Coverage ◽  
Large Scale ◽  
Animal Health ◽  
Vaccination Campaign ◽  
Sub Saharan Africa ◽  
Data Driven ◽  
Time Data ◽  
Real Time Analysis ◽  
Vaccination Campaigns

Rabies kills ∼60,000 people per year. Annual vaccination of at least 70% of dogs has been shown to eliminate rabies in both human and canine populations. However, delivery of large-scale mass dog vaccination campaigns remains a challenge in many rabies-endemic countries. In sub-Saharan Africa, where the vast majority of dogs are owned, mass vaccination campaigns have typically depended on a combination of static point (SP) and door-to-door (D2D) approaches since SP-only campaigns often fail to achieve 70% vaccination coverage. However, D2D approaches are expensive, labor-intensive, and logistically challenging, raising the need to develop approaches that increase attendance at SPs. Here, we report a real-time, data-driven approach to improve efficiency of an urban dog vaccination campaign. Historically, we vaccinated ∼35,000 dogs in Blantyre city, Malawi, every year over a 20-d period each year using combined fixed SP (FSP) and D2D approaches. To enhance cost effectiveness, we used our historical vaccination dataset to define the barriers to FSP attendance. Guided by these insights, we redesigned our vaccination campaign by increasing the number of FSPs and eliminating the expensive and labor-intensive D2D component. Combined with roaming SPs, whose locations were defined through the real-time analysis of vaccination coverage data, this approach resulted in the vaccination of near-identical numbers of dogs in only 11 d. This approach has the potential to act as a template for successful and sustainable future urban SP-only dog vaccination campaigns.

Sign in / Sign up

Export Citation Format

Share Document