scholarly journals Modeling Traffic Flow, Energy Use, and Emissions Using Google Maps and Google Street View: The Case of EDSA, Philippines

2021 ◽  
Vol 13 (12) ◽  
pp. 6682
Author(s):  
Joshua Ezekiel Rito ◽  
Neil Stephen Lopez ◽  
Jose Bienvenido Manuel Biona
Keyword(s):  
Traffic Flow ◽  
Energy Use ◽  
Road Traffic ◽  
Approximation Error ◽  
The Philippines ◽  
Google Maps ◽  
Flow Energy ◽  
Mobile Emissions ◽  
Total Vehicle ◽  
Future Work

The general framework of the bottom-up approach for modeling mobile emissions and energy use involves the following major components: (1) quantifying traffic flow and (2) calculating emission and energy consumption factors. In most cases, researchers deal with complex and arduous tasks, especially when conducting actual surveys in order to calculate traffic flow. In this regard, the authors are introducing a novel method in estimating mobile emissions and energy use from road traffic flow utilizing crowdsourced data from Google Maps. The method was applied on a major highway in the Philippines commonly known as EDSA. Results showed that a total of 370,855 vehicles traveled along EDSA on average per day in June 2019. In comparison to a government survey, only an 8.63% error was found with respect to the total vehicle count. However, the approximation error can be further reduced to 4.63% if cars and utility vehicles are combined into one vehicle category. The study concludes by providing the limitations and opportunities for future work of the proposed methodology.

scholarly journals Comparison of Driving Forces to Increasing Traffic Flow and Transport Emissions in Philippine Regions: A Spatial Decomposition Study

2021 ◽  
Vol 13 (11) ◽  
pp. 6500
Author(s):  
Geoffrey Udoka Nnadiri ◽  
Anthony S. F. Chiu ◽  
Jose Bienvenido Manuel Biona ◽  
Neil Stephen Lopez
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Economic Activity ◽  
Energy Use ◽  
Driving Forces ◽  
The Philippines ◽  
Transport Infrastructure ◽  
Illustrative Case ◽  
Activity Effect ◽  
Transport Services

The warming of the climate system has raised a lot of concerns for decades, and this is traceable to human activities and energy use. Conspicuously, the transportation sector is a great contributor to global emissions. This is largely due to increasing dependence on private vehicles and a poorly planned public transportation system. In addition to economic impacts, this also has significant environmental and sustainability implications. This study demonstrates a novel approach using spatial logarithmic mean Divisia index (LMDI) to analyze drivers of traffic flow and its corresponding CO2 emissions in regions through an illustrative case study in the Philippines. Population growth is revealed as the main driver to traffic flow in most regions with the exception of a few regions and the national capital which are driven by economic activity. The economic activity effect shows positive trends contributing positively to traffic flow which is greatly linked to income level rise and increase in vehicle ownership. Concerning the impacts, results revealed that an increase in economic activity generally causes traffic intensity to decrease, and switching to more sustainable modes is not a guarantee to reduce carbon emissions. The authors recommend increasing equity on the appropriation of transport infrastructure projects across regions, quality improvement of public transport services and promoting mixed-use development.

scholarly journals Traffic Inequality and Relations in Maritime Silk Road: A Network Flow Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Naixia Mou ◽  
Haonan Ren ◽  
Yunhao Zheng ◽  
Jinhai Chen ◽  
Jiqiang Niu ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Network Flow ◽  
Road Traffic ◽  
Flow Analysis ◽  
Silk Road ◽  
Identification System ◽  
Traffic Network ◽  
Trajectory Data ◽  
Maritime Traffic ◽  
Maritime Silk Road

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.

scholarly journals Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3425
Author(s):  
Huanping Li ◽  
Jian Wang ◽  
Guopeng Bai ◽  
Xiaowei Hu
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Flow Model ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transformation Model ◽  
Hydrodynamic Theory ◽  
Traffic Flow Model ◽  
Traffic System ◽  
The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

2021 ◽  
pp. 036119812199347
Author(s):  
Lei Lin ◽  
Siyuan Gong ◽  
Srinivas Peeta ◽  
Xia Wu
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Road Transportation ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

Study on Energy Consumption Analysis Method of Distributed Cryogenic Separation System

2013 ◽  
Vol 732-733 ◽  
pp. 52-56
Author(s):  
Zhi Guo Wang ◽  
Lei Zhang ◽  
Chai Ling Yin
Keyword(s):  
Energy Use ◽  
Exergy Analysis ◽  
Waste Heat ◽  
Engineering Practice ◽  
Analysis Method ◽  
Analysis Model ◽  
Two Stage ◽  
Separation Plant ◽  
Flow Energy ◽  
Saving Effect

Cryogenic separation method is the main method to recycle NGL (Natural Gas Liquid). Oilfield two-stage expansion NGL cryogenic separation plant is a complex system composed of varieties of material flow, energy flow and equipments, is a typical distributed energy use system composed of three parts, energy supply, energy use and waste heat recovery. In this paper, according to the process characteristics of two-stage expansion cryogenic separation plant, three-box analysis method was used, the system was compartmentalized into six subsystems, represented the exergy analysis model of system—unit—equipment, given the specific analysis process and the assessment rules for the NGL system. Using the practical operational data, the writers conduct the exergy analysis on the operational working condition of Daqing oilfield NGL system. Based on the calculation results, this paper raises some proposals to improve the operational efficiency, and achieved a good energy saving effect in engineering practice.

Towards Optimizing Building Energy Use to Reduce Electric System Carbon Emissions

2010 ◽  
Author(s):  
Erik M. Greensfelder ◽  
Gregor P. Henze ◽  
Vincent J. Cushing
Keyword(s):  
Carbon Emissions ◽  
Energy Use ◽  
Strong Relationship ◽  
Temporal Variations ◽  
Simple Relationship ◽  
System Load ◽  
Electric System ◽  
Plant Emissions ◽  
Building Operation ◽  
Future Work

In spite of heightened interest in anthropogenic climate change, little attention has been paid to optimizing a building’s carbon emissions at the source. Most work in building efficiency has assumed that generating plant carbon emissions are constant at their long-term average values. This study sought to improve our understanding of the temporal variations in carbon emissions on a diurnal time scale and their relation to electric system dispatch and load in order to motivate future work in optimizing building operation to reduce carbon emissions. Hourly fossil fuel plant emissions and load data, available from the EPA, were used to characterize power system performance for four US locations (IL, NY, TX, and CA). The study had set out with a hypothesis hoping to find a simple relationship between electric system load and emissions. It was found that there is a significant correlation between increased system load and decreased emissions rates, yet this correlation is not easily defined. During high load conditions, emissions reductions are related to the increased use of gas generators, or may be related to operating plants at more efficient part load ratios. The work conducted in this study shows that, while more complex than hoped for, there is indeed a strong relationship between electric system load and carbon emissions rates.

scholarly journals Road Traffic Analysis Using Computer Vision

2021 ◽  
Vol 9 (8) ◽  
pp. 2002-2006
Author(s):  
Needhi U. Gaonkar
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Visual Object ◽  
Highway Traffic ◽  
Analysis System

Abstract: Traffic analysis plays an important role in a transportation system for traffic management. Traffic analysis system using computer vision project paper proposes the video based data for vehicle detection and counting systems based on the computer vision. In most Transportation Systems cameras are installed in fixed locations. Vehicle detection is the most important requirement in traffic analysis part. Vehicle detection, tracking, classification and counting is very useful for people and government for traffic flow, highway monitoring, traffic planning. Vehicle analysis will supply with information about traffic flow, traffic summit times on road. The motivation of visual object detection is to track the vehicle position and then tracking in successive frames is to detect and connect target vehicles for frames. Recognising vehicles in an ongoing video is useful for traffic analysis. Recognizing what kind of vehicle in an ongoing video is helpful for traffic analysing. this system can classify the vehicle into bicycle, bus, truck, car and motorcycle. In this system I have used a video-based vehicle counting method in a highway traffic video capture using cctv camera. Project presents the analysis of tracking-by-detection approach which includes detection by YOLO(You Only Look Once) and tracking by SORT(simple online and realtime tracking) algorithm. Keywords: Vehicle detection, Vehicle tracking, Vehicle counting, YOLO, SORT, Analysis, Kalman filter, Hungarian algorithm.

scholarly journals EVALUATING THE EFFECT OF HEAVY VEHICLES ON TRAFFIC PARAMETERS BY USING HEAVY VEHICLE FACTOR

2018 ◽  
Vol 6 (4) ◽  
pp. 95-104
Author(s):  
Parthkumar Patel ◽  
H.R. Varia
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Freight Transport ◽  
Heavy Vehicle ◽  
Mixed Traffic ◽  
Heavy Vehicles ◽  
Highway Traffic ◽  
Ideal Situation ◽  
The Impact

Safe, convenient and timely transportation of goods and passengers is necessary for development of nation. After independence road traffic is increased manifold in India. Modal share of freight transport is shifted from Railway to roadways in India. Road infrastructures continuously increased from past few decades but there is still need for new roads to be build and more than three forth of the roads having mixed traffic plying on it. The impact of freight vehicles on highway traffic is enormous as they are moving with slow speeds. Nature of traffic flow is dependent on various traffic parameters such as speed, density, volume and travel time etc. As per ideal situation these traffic parameters should remain intact, but it is greatly affected by presence of heavy vehicle in mixed traffic due to Svehicles plying on two lane roads. Heavy vehicles affect the traffic flow because of their length and size and acceleration/deceleration characteristics.  This study is aimed to analyse the impact of heavy vehicles on traffic parameters.

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