scholarly journals Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 629
Author(s):  
Maria V. Peppa ◽  
Tom Komar ◽  
Wen Xiao ◽  
Phil James ◽  
Craig Robson ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Weather Conditions ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Transport Systems ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Approaches To Study ◽  
End To End

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

The importance of public support in the implementation of green transportation in smart cities using smart vehicle bicycle communication transport

2020 ◽  
Vol 38 (5/6) ◽  
pp. 997-1011
Author(s):  
Ning Li ◽  
Parthasarathy R. ◽  
Harshila H. Padwal
Keyword(s):  
Smart Cities ◽  
Optimization Technique ◽  
Public Support ◽  
Weather Conditions ◽  
Transport Systems ◽  
Content Type ◽  
Signaling Systems ◽  
Traffic Conditions ◽  
Green Transportation ◽  
Smart Vehicle

Purpose Smart mobility is a major guideline in the development of Smart Cities’ transport systems and management. The issue of transition into green, secure and sustainable transport modes, such as using bicycles, should be implemented in this case, along with the subjectivism of management. Design/methodology/approach The proposed technology reflects the Smart Bicycle vehicle model, which tracks cyclists and weather conditions and turns to electric motors in critical circumstances. Findings This reduces the physical load and battery consumption of cyclists which affects the Smart Cities’ ecology positively. Originality/value In Smart Vehicle Bicycle Communication Transport, the vehicle movement optimization technique is used for traffic scenarios to analyze traffic signaling systems that give better results in variable and dense traffic conditions.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

2017 ◽  
Vol 2667 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Zihan Hong ◽  
Hani S. Mahmassani ◽  
Xiang Xu ◽  
Archak Mittal ◽  
Ying Chen ◽  
...  
Keyword(s):  
Traffic Management ◽  
Demand Management ◽  
Management Strategies ◽  
Weather Conditions ◽  
Active Transportation ◽  
Operational Conditions ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Traffic Performance ◽  
The Impact

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

MACHINE LEARNING ON CONGESTION ANALYSIS BASED REAL-TIME NAVIGATION SYSTEM

2011 ◽  
Vol 20 (04) ◽  
pp. 753-781
Author(s):  
KAI CHEN ◽  
KIA MAKKI ◽  
NIKI PISSINOU
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Real Time ◽  
Navigation System ◽  
Prediction Models ◽  
Urban Traffic ◽  
Route Guidance ◽  
Highway Traffic ◽  
Data Set ◽  
Guidance Systems

In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,1 it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment.

scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

Field Test of Nonintrusive Traffic Detection Technologies

1998 ◽  
Vol 1643 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Stephen J. Bahler ◽  
James M. Kranig ◽  
Erik D. Minge
Keyword(s):  
Field Test ◽  
Intelligent Transportation System ◽  
General System ◽  
Weather Conditions ◽  
Test Site ◽  
Urban Traffic ◽  
Minimal Impact ◽  
Traffic Conditions ◽  
Traffic Detection ◽  
Detection Technologies

The results of a 2-year field test of nonintrusive traffic detection technologies are presented. Seventeen devices representing the following eight technologies were evaluated: passive infrared, active infrared, magnetic, radar, doppler microwave, pulse ultrasonic, passive acoustic, and video. The devices were tested in a variety of environmental and traffic conditions at both intersection and freeway test sites. Emphasis was placed on urban traffic conditions, such as heavy congestion; locations that typify temporary counting situations, such as 48-hour or peak hour counts; and performance in the wide variety of weather conditions found in Minnesota. The evaluation also focused on the ease of system set-up and general system reliability. The results show that nonintrusive technologies are capable of performing as well as conventional methods in some, but not all, situations. At the freeway test site, most nonintrusive devices counted within 3 percent of baseline data. At the intersection test site, however, congested stop-and-go traffic hindered the performance of the majority of the devices. Weather and other environmental variables were found to have minimal impact on the majority of devices. This test is the first phase of an ongoing project to evaluate new, nonintrusive technologies and devices. Further research will expand into areas such as real-time datacollection to support intelligent transportation system applications.

Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways

2018 ◽  
Vol 2672 (38) ◽  
pp. 184-198 ◽  
Author(s):  
Kai Wang ◽  
Shanshan Zhao ◽  
Eric Jackson
Keyword(s):  
Model Prediction ◽  
Prediction Models ◽  
Weather Conditions ◽  
Parameter Estimates ◽  
Prediction Ability ◽  
Crash Prediction ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Crash Prediction Models ◽  
Crash Types

Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies. To account for the variations in crash count among different adverse weather conditions, crash types, and crash severities for both rain- and snow-related crashes, crash data on freeways was collected from the State of Connecticut, and crash prediction models were developed to estimate crash counts by crash type and severity for each weather condition. To account for the potential correlations among crash type and severity counts due to the common unobserved factors, integrated nested Laplace approximation (INLA) multivariate Poisson lognormal (MVPLN) models were developed to estimate weather-related crashes counts by crash type and severity simultaneously (four MVPLN models were estimated in total). To verify the model prediction ability, univariate Poisson lognormal (UPLN) models were estimated and compared with the MVPLN models. The results show that the effects of factors contributing to crashes, including median width, horizontal curve, lane width, and shoulder width, vary not only among different adverse weather conditions, but also among different crash types and severities. The crash types and severities are shown to be highly correlated and the model comparison verifies that the MVPLN models significantly improve the model prediction accuracy compared with the UPLN models. Therefore, the MVPLN model is recommended to provide more unbiased parameter estimates when estimating weather-related crashes by crash type and severity.

scholarly journals An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

2021 ◽  
Vol 13 (21) ◽  
pp. 11893
Author(s):  
Abdul Rauf Bhatti ◽  
Ahmed Bilal Awan ◽  
Walied Alharbi ◽  
Zainal Salam ◽  
Abdullah S. Bin Humayd ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Energy System ◽  
Weather Data ◽  
Training Performance ◽  
Proposed Model ◽  
Artificial Neural Network Ann

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

scholarly journals Change of car driver`s stress index during different periods of the day in urban traffic conditions

2020 ◽  
Vol 2020 (2) ◽  
pp. 23-32
Author(s):  
Yuri Davidich ◽  
◽  
Yevhen Kush ◽  
Denys Ponkratov ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Transport Process ◽  
Well Being ◽  
Urban Traffic ◽  
Transport Systems ◽  
Stress Index ◽  
Traffic Conditions ◽  
Driving Assistance ◽  
Almost All

Nowadays, the transport industry plays an important role in human well-being and the functioning of any settlement. Transport systems are involved in almost all areas of production and services. Therefore, any failure in its operation can lead to significant material costs. One of the most important such systems is “driver - vehicle - road - environment”. It should be noted, that the main link in it is “driver”. The correctness and duration of decision-making in different road situations depend on the driver`s functional state. This directly affects the level of traffic safety. Consequently, the tasks of modern transport research are the introduction of methods of the vehicle driver`s conditions monitoring and the detection of his fatigue in its early stages. That`s why the actuality of studying the human operator role in the transport process and the creation of modern means of driving assistance are increasing now.

scholarly journals Multi-Level Multi-Constraint Multi-Priority Fuzzy Sensor Routing (M3L-C-PFSR)

2019 ◽  
Vol 8 (9) ◽  
pp. 1428-1435
Keyword(s):  
Real Time ◽  
Routing Protocol ◽  
Work Performance ◽  
Resource Constraints ◽  
Buffer Management ◽  
Traffic Conditions ◽  
End To End Delay ◽  
Multi Level ◽  
End To End

In the field of Wireless Sensor Network (WSN), real time applications, the interesting research emerged in the field of QoS routing. Networks must address resource constraints although providing an accurate guarantee of quality of service (PDR, throughput, energy, End-to-End delay).This document presents the routing protocol compatible with QoS, namely [M3 L-C-PFSR] Multilevel, Multi constraint Multi Priority Fuzzy Sensor Routing Protocol which gives priority for important packets of important applications and also ensures the PDR, delay and throughput. Applying a scheme of multi level fuzzy based efficient buffer management to limit packet loss due to overflow. It supports lower throughput for packets which are lower in priority and higher throughput will be given for real-time prioritized packets which are assigned privileged higher priority, thus reducing end-to-end delay. The proposed work performance will be appraised [M3 L-C-PFSR] in NS2. The results of the simulation depicts that the proposed protocol [M3 L-C-PFSR] effectively minimizes the losses of critical packet, with different traffic conditions, ensuring the reliability and required delivery of data.

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