scholarly journals Integral Index of Traffic Planning: Case-Study of Moscow City’s Transportation System

2020 ◽  
Vol 12 (18) ◽  
pp. 7395 ◽  
Author(s):  
Tatiana Petrova ◽  
Andrey Grunin ◽  
Arthur Shakhbazyan
Keyword(s):  
Traffic Management ◽  
Transportation System ◽  
Data Availability ◽  
Primary Data ◽  
Traffic Planning ◽  
Integral Index ◽  
Management Quality ◽  
Traffic Volumes ◽  
Complex Development ◽  
Key Indicators

The integral indexes are used to measure trends and monitor progress in transportation complex development. The selection of the indicators, included in indexes, is related to the data availability (depends on existence of a specific data sources). The aim of this paper is to provide a development methodology of Integral Index of Traffic Planning (Integral TP Index), which is based on the primary data on vehicle speeds, traffic volumes, number of accidents, etc., for Moscow and allows for basic assessment of transport situation in Russian’s capital. The proposed methodology is a combination of economic and urban approaches to analyze the key indicators of transportation planning efficiency in the metropolis. Four groups of indicators are considered: traffic management efficiency, traffic management quality, transit efficiency and road safety. The integral index considers traffic volumes for various roads and their contributions to the overall transportation system. Division of streets by type (highways, rings, center) makes it possible to take into account specifications the radial-ring streets structure of Moscow. The constructed index is applied to the analysis of Moscow transportation statistics in 2012–2017 provided by the Moscow Traffic Management Center, Yandex and TomTom.

Bicycle Transportation in Shanghai: Status and Prospects

1996 ◽  
Vol 1563 (1) ◽  
pp. 8-15 ◽  
Author(s):  
Cai Jin Xin
Keyword(s):  
Public Transportation ◽  
Traffic Management ◽  
Motor Vehicle ◽  
Transportation System ◽  
Transportation Infrastructure ◽  
Urban Region ◽  
Economic Changes ◽  
Traffic Volumes ◽  
Long Time ◽  
Basic Infrastructure

Shanghai is China's largest city and has a dense concentration of population served by limited transportation infrastructure. Traditionally, bicycles and public transportation have been the dominant modes used by residents, but recent social and economic changes have resulted in a dramatic growth in motor vehicle use and overall traffic volumes. The use of bicycles as a mode of transportation in China and in Shanghai will continue for a long time to come. However, the role of bicycles within Shanghai is changing and will continue to evolve in the future as efforts to enhance basic transportation infrastructure are undertaken and economic and social conditions improve. The complexity of transportation patterns generated by high population densities, coupled with a shortage in basic infrastructure for an urban region of its nature, size, and importance, has created increasingly serious conflicts between motorized and nonmotorized vehicles throughout central Shanghai. These conflicts decrease overall safety and the functional capability of the existing transportation system to meet rapidly increasing travel demands in the municipality. Major improvements are being implemented that will upgrade Shanghai's urban transportation system significantly. They will, however, require a substantial commitment of time and financial resources to complete. In the interim, a comprehensive traffic management effort will be followed that recognizes the importance of bicycles and supports their continued use within the municipality in an appropriate balance with public transportation and other modes consistent with actual conditions and changing demands.

Urban Vehicle Emissions Management under Uncertainty - A Traffic Planning Model with Interval-Parameter Programming

2013 ◽  
Vol 864-867 ◽  
pp. 1586-1591
Author(s):  
Hong Liang Zhang
Keyword(s):  
Vehicle Emissions ◽  
Optimization Methods ◽  
Transportation System ◽  
Programming Method ◽  
Planning Model ◽  
Traffic Planning ◽  
Interval Parameter ◽  
Interval Linear ◽  
Virtual City

In this study, an interval-parameter programming method has been used for urban vehicle emissions management under uncertainty. The model improves upon the existing optimization methods with advantages in uncertainty reflection, system costs and limitation of emission. Moreover, the model is applied to a case study of urban vehicle emissions management in a virtual city. The results indicate that the interval linear traffic planning model can effectively reduce the vehicles emission and provide strategies for authorities to deal with problems of transportation system.

scholarly journals Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management

2020 ◽  
Vol 21 (1) ◽  
pp. 47-60 ◽  
Author(s):  
Jorge M. Bandeira ◽  
Pavlos Tafidis ◽  
Eloísa Macedo ◽  
João Teixeira ◽  
Behnam Bahmankhah ◽  
...  
Keyword(s):  
Traffic Management ◽  
Satellite System ◽  
Pollutant Emissions ◽  
Specific Power ◽  
Linear Regression Models ◽  
Data Logger ◽  
Alternative Source ◽  
Traffic Volumes ◽  
Noise Assessment ◽  
Global Navigation Satellite

AbstractThis paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.

scholarly journals Economic valuation and mapping of Ecosystem Services in the context of protected area management (Natural Park of Serra de São Mamede, Portugal)

One Ecosystem ◽  
2018 ◽  
Vol 3 ◽  
Author(s):  
Cristina Marta-Pedroso ◽  
Lia Laporta ◽  
Ivo Gama ◽  
Tiago Domingos
Keyword(s):  
Decision Making ◽  
Ecosystem Services ◽  
Protected Areas ◽  
Crop Production ◽  
Economic Value ◽  
Market Price ◽  
Economic Benefits ◽  
Data Availability ◽  
Primary Data ◽  
Natural Park

Demonstrating economic benefits generated by protected areas is often pointed out as pivotal for supporting decision-making. We argue in this paper that the concept of ecosystem services (ES), defined as the benefits humans derive from ecosystems, provides a consistent framework to approach this issue as it links ecosystem functioning and benefits, including benefits with economic value. This study aimed at providing evidence on how to bring the economic value of protected areas to the decision-making process and contributing to extend current EU Member States' experience in mapping and assessing the economic value of ES in the context of the EU Biodiversity Strategy to 2020 (Action 5). In doing so, we used the Natural Park of Serra de S. Mamede (PNSSM), located in the Alentejo NUTS II region, as a case study. We followed a three-step approach to pursue our goals, entailing stakeholders' engagement for selecting relevant ES (through a participatory workshop), biophysical mapping of ES flows (based on a multi-tiered approach depending on data availability) and spatial economic estimation of such flows (using value transfer, willingness-to-pay and market price methods). Our results indicate that the ES with highest economic value are not always the ones with higher perceived value by stakeholders. For most ES, the economic value increased with increasing protection level within the park, except for the crop production service. Although no formal uncertainty or sensitivity analysis has been performed, the following range is based on a critical assessment of non-primary data used. We estimated the aggregate annual value of PNSSM to be 11 to 33M€/year (representing 0.1 to 0.3% of the regional NUTSII Alentejo Gross Domestic Product). Our findings reinforce the need to adopt mixes of monetary and non-monetary valuation processes and not to rely just on one approach or measure of value while bringing ES into protected areas management.

scholarly journals Kinerja Fungsi Terminal Giri Adipura dalam Sistem Transportasi di Kabupaten Wonogiri

Arsitektura ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 92
Author(s):  
Nur Laila Fitriana ◽  
Galing Yudana ◽  
Winny Astuti
Keyword(s):  
Traffic Management ◽  
Research Method ◽  
Transportation Network ◽  
Transportation System ◽  
Network System ◽  
Surrounding Area ◽  
Performance Category ◽  
Average Performance ◽  
The Public ◽  
Analysis Technique

<div><p><em>Terminal is included in the transportation network system which functions as a place for passengers to take the bus and a place to drop the passengers. Terminal is also the element of </em><em>spatial</em><em> arrangement which influences the structure and system of a town’s movement.</em><em> </em><em>The development of Giri Adipura Terminal in Singodutan Village which was done from 2010 to 2014 is aimed to recover the performance of previous terminal and to develop the surrounding area. Phenomenon which was happened in Giri Adipura Terminal is there were still many passengers who were not utilizing the terminal function and the public transportations were not entering the terminal in order. This research was done to know the performance of Giri Adipura Terminal function in transportation system in Wonogiri Regency. The used research method was quantitative with deductive approach.</em><em> </em><em>The used analysis technique was scoring, by determined the parameter and indicator in each variabel. The result of scoring calculation from the performance of Giri Adipura Terminal function in transportation system in Wonogiri Regency was 9.33. Based on the performance of terminal function category, 9.33 score was in range of 6.68—9.35, so it was included in the average performance category. Thus, the performance of Giri Adipura Terminal function in transportation system in Wonogiri Regency has not reach a good performance yet and it needs some things which should be done to reach that performance. For the performance of each variabl</em><em>e</em><em>, variables which were in the good performance category were accessibility and traffic management. Meanwhile, variables which were in the average category were terminal service and transportation movement access.</em><em></em></p><p><strong><em> </em></strong></p><p><strong><em>Keywords:</em></strong><em> </em><em>performance</em><em>, </em><em>terminal function</em><em>, </em><em>transportation system</em></p></div>

scholarly journals IOT Based Traffic Management System

2021 ◽  
pp. 184-187
Author(s):  
Shashank S ◽  
Kiran P ◽  
Nischay D ◽  
Vinay Kumar M ◽  
B R Vatsala ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Transportation System ◽  
Raspberry Pi ◽  
Urban Residents ◽  
System Of Cities ◽  
Traffic Management System ◽  
Global Population

In 2014, 54% of the total global population was urban residents. The prediction was a growth of nearly 2% each year until 2020 leading to more pressure on the transportation system of cities. Cities should be making their streets run smarter instead of just making them bigger or building more roads. This leads to the proposed system which will use a Raspberry pi and Camera for tracking the number of vehicles leading to time-based monitoring of the system.

Secondary Data

2018 ◽  
pp. 306-320
Author(s):  
Aaike De Wever ◽  
Astrid Schmidt-Kloiber ◽  
Vanessa Bremerich ◽  
Joerg Freyhof
Keyword(s):  
Biodiversity Conservation ◽  
Secondary Data ◽  
Data Availability ◽  
Data Sources ◽  
Primary Data ◽  
Freshwater Ecology ◽  
Biodiversity Data ◽  
Biodiversity Change ◽  
Freshwater Management ◽  
Management And Conservation

Understanding biodiversity change and addressing questions in freshwater management and conservation requires access to biodiversity data and information. Unfortunately, large, comprehensive data sources on freshwater ecology and biodiversity are largely lacking. In this chapter, we explain how to take advantage of secondary data and improve data availability for supporting freshwater ecology research and biodiversity conservation. We emphasise the importance of secondary data, give an overview of existing databases (e.g., taxonomy, molecular or occurrence databases), discuss problems in understanding and caveats when using such data, and explain the need to make primary data publicly available.

Is the Perfect the Enemy of the Good?: Using the Data You Have

2019 ◽  
pp. 205-218
Author(s):  
Theresa Chapple-McGruder ◽  
Jaime Slaughter-Acey ◽  
Jennifer Kmet ◽  
Tonia Ruddock
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Secondary Data ◽  
Policy Decision ◽  
Data Availability ◽  
Primary Data ◽  
Health Improvement ◽  
Improvement Program ◽  
Policy Decision Making ◽  
Health Question

This chapter offers instructions on how to find the data needed for a particular public health improvement program. The chapter starts by defining two systems of data collection: primary and secondary. However, it is important to remember that all data has limitations. There is no such thing as perfect data. The use of primary data in practice or policy decision-making is often constrained by resources and time, as collecting robust data typically takes years. Although secondary data poses limits, such that it might be data not collected specifically for a particular health question, or not being representative of the population of interest, or perhaps there is a lag in data availability. However, the chapter concludes, things can always be improved even if perfection is never reached.

Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Byron J. Gajewski ◽  
Shawn M. Turner ◽  
William L. Eisele ◽  
Clifford H. Spiegelman
Keyword(s):  
Traffic Management ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation System ◽  
Monitoring Data ◽  
Statistical Techniques ◽  
Parameter Distribution ◽  
Data Archiving ◽  
Wide Range

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

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