scholarly journals Constructing a Network Fundamental Diagram: Synthetic Origin–Destination Approach

2019 ◽  
Vol 2673 (7) ◽  
pp. 479-488 ◽  
Author(s):  
Jianhe Du ◽  
Hesham A. Rakha
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Traffic Monitoring ◽  
Traffic Volume ◽  
Network Control ◽  
Volume Data ◽  
Monitoring And Control ◽  
Fundamental Diagram ◽  
Probe Vehicle ◽  
Network Fundamental Diagram

The network fundamental diagram (NFD) is increasingly used in traffic monitoring and control. One obstacle to a wider application of NFDs in network control is the difficulty of obtaining data from all vehicles traveling in the network to construct an accurate NFD. One solution is to estimate the NFD using data from only a fraction of vehicles (i.e., probe vehicles), where the probe vehicle market penetration rate (MPR) needs to be estimated. A previous study conducted by the authors demonstrated that a distance or time-weighted harmonic mean was needed to estimate the flow- and density-based MPRs, respectively, using a pairing k-mean clustering approach. This paper proposes another approach that utilizes probe vehicle and observed link volume data to estimate the MPR. A heuristic model is proposed to identify the optimum locations from which to collect link traffic volume data for use in the MPR estimation. The estimated MPR can then be used to construct the NFD. Results show that these models can accurately estimate the NFD with limited probe vehicle and link traffic volume data. Accordingly, the models can be used in the field to estimate the NFD using readily available loop detector and probe vehicle data. The ideal locations for traffic volume data collection can also be proactively chosen to generate optimum estimation results. As the models proposed here show no significant gains with an increased magnitude of collected data after a certain threshold, they will be helpful, particularly when large-scale data collection is not affordable or realistic.

Remote Network Control System for Web Service

2014 ◽  
Vol 484-485 ◽  
pp. 348-352
Author(s):  
Fang Wang ◽  
Chang Liu ◽  
Li Lin
Keyword(s):  
Control System ◽  
Information Exchange ◽  
Large Scale ◽  
Network Control ◽  
Monitoring And Control ◽  
Industrial Monitoring ◽  
Field Device ◽  
Control And Management ◽  
Communication Control ◽  
And Control

Now computer terminals, communication control, and network technology continues rapidly developed, the rapidly expanding range of information exchange, has been covering the field device to the control and management at all levels of networking, industrial control monitoring is not only limited to on-site monitoring, on-site scheduling. The development of industrial monitoring control system is a history of development of centralized monitoring and control to the network monitoring control. The previous system, the various important instruments status were monitored by the large-scale instruments.

Exploring Vehicle Probe Data as a Resource to Enhance Network-Wide Traffic Volume Estimates

2021 ◽  
Author(s):  
Giuseppe Grande ◽  
Matthew Lesniak ◽  
Louis-Paul Tardif ◽  
Jonathan D Regehr
Keyword(s):  
Travel Speed ◽  
Traffic Monitoring ◽  
Flow Speed ◽  
Traffic Volume ◽  
Volume Data ◽  
Annual Average Daily Traffic ◽  
Traffic Volumes ◽  
The Mean ◽  
Volume Estimates ◽  
Potential Use

Annual average daily traffic (AADT) is a fundamental input for numerous civil engineering applications, yet generating reliable estimates of AADT at a network-wide level poses challenges. This article explores the potential use of vehicle probe data to enhance conventional traffic monitoring practice for generating network-wide estimates of AADT by exploring relationships between site-specific traffic volume data and vehicle probe data collected in Manitoba, Canada. The analysis revealed that mean travel speed cannot be used to predict traffic volumes on Manitoba highways, since the mean travel speed did not deviate from the free-flow speed regardless of the volume measured. The quantity of probe data observations showed moderate correlation with traffic volume at some sites (R-squared up to 0.65), but these correlations were stronger (R-squared up to 0.9) when considering trucks only. These findings suggest that probe data could be used to estimate truck volumes at certain locations.

scholarly journals BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

2020 ◽  
Vol 19 (2) ◽  
pp. 087
Author(s):  
Natalija Stojanović ◽  
Dragan Stojanović
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Smart Cities ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Monitoring And Control ◽  
Dynamic Configuration ◽  
Large Cities ◽  
Traffic Lights ◽  
Mobility Data

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

scholarly journals Smart Devices in Airbnbs: Considering Privacy and Security for both Guests and Hosts

2020 ◽  
Vol 2020 (2) ◽  
pp. 436-458 ◽  
Author(s):  
Shrirang Mare ◽  
Franziska Roesner ◽  
Tadayoshi Kohno
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Security And Privacy ◽  
Smart Devices ◽  
Monitoring And Control ◽  
Data Types ◽  
Privacy And Security ◽  
Large Scale Survey ◽  
Internet History ◽  
And Control

AbstractConsumer smart home devices are becoming increasingly pervasive. As Airbnb hosts deploy smart devices in spaces shared with guests, we seek to understand the security and privacy implications of these devices for both hosts and guests. We conducted a large-scale survey of 82 hosts and 554 guests to explore their current technology practices, their preferences for smart devices and data collection/sharing, and their privacy and security concerns in the context of Airbnbs. We found that guests preferred smart devices, even viewed them as a luxury, but some guests were concerned that smart devices enable excessive monitoring and control, which could lead to repercussions from hosts (e.g., locked thermostat). On average, the views of guests and hosts on data collection in Airbnb were aligned, but for the data types where differences occur, serious privacy violations might happen. For example, 90% of our guest participants did not want to share their Internet history with hosts, but one in five hosts wanted access to that information. Overall, our findings surface tensions between hosts and guests around the use of smart devices and in-home data collection. We synthesize recommendations to address the surfaced tensions and identify broader research challenges.

Analisis Kinerja dan Pengaruh Pelebaran Jalan serta Pembuatan Median Jalan (Studi Kasus Jalan Temanggung Tilung Kota Palangkaraya)

2018 ◽  
Vol 7 (1) ◽  
pp. 51-60
Author(s):  
Fitri Wulandari ◽  
Nirwana Puspasari ◽  
Noviyanthy Handayani
Keyword(s):  
Data Retrieval ◽  
Service Level ◽  
Traffic Volume ◽  
Primary Data ◽  
Volume Data ◽  
Saturation Degree ◽  
The Road ◽  
Before And After ◽  
Road Widening ◽  
The Way

Jalan Temanggung Tilung is a 2/2 UD type road (two undirected two-way lanes) with a road width of 5.5 meters, which is a connecting road between two major roads, namely the RTA road. Milono and the path of G. Obos. Over time, the volume of traffic through these roads increases every year, plus roadside activities that also increase cause congestion at several points of the way. To overcome this problem, the local government carried out road widening to increase the capacity and level of road services. The study was conducted to determine the amount of traffic volume, performance, service level of the Temanggung Tilung road section at peak traffic hours before and after road widening. Data retrieval is done by the direct survey to the field to obtain primary data in the form of geometric road data, two-way traffic volume data, and side obstacle data. Performance analysis refers to the 1997 Indonesian Road Capacity Manual (MKJI) for urban roads. From the results of data processing, before increasing the road (Type 2/2 UD), the traffic volume that passes through the path is 842 pcs/hour and after road widening (Type 4/2 UD) the traffic volume for two directions is 973 pcs/hour, with route A equaling 528 pcs/hour and direction B equaling 445 pcs/hour. Based on the analysis of road performance before road enhancement, the capacity = 2551 pcs/hour, saturation degree = 0.331, and the service level of the two-way road are level B. Based on the analysis of the performance of the way after increasing the way, the direction capacity A = 2686 pcs/hour and direction B = 2674 pcs /hour, saturation degree for direction A = 0.196 and direction B = 0.166, service level for road direction A and direction B increase to level A

LARGE-SCALE PREDICTOR VALIDATION IN PROJECT A: DATA COLLECTION PROCEDURES AND DATA BASE PREPARATION

1990 ◽  
Vol 43 (2) ◽  
pp. 301-311 ◽  
Author(s):  
WINNIE Y. YOUNG ◽  
JANIS S. HOUSTON ◽  
JAMES H. HARRIS ◽  
R. GENE HOFFMAN ◽  
LAURESS L. WISE
Keyword(s):  
Data Collection ◽  
Data Base ◽  
Large Scale
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