scholarly journals Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data

2021 ◽  
Vol 13 (20) ◽  
pp. 11171
Author(s):  
S. Travis Waller ◽  
Sai Chand ◽  
Aleksa Zlojutro ◽  
Divya Nair ◽  
Chence Niu ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Performance Metrics ◽  
Traditional Approach ◽  
Data Availability ◽  
Transport Systems ◽  
Traffic Data ◽  
Travel Times ◽  
Time Data ◽  
Quality Costs ◽  
Household Travel

A traffic assignment model is a critical tool for developing future transport systems, road policies, and evaluating future network upgrades. However, the development of the network and demand data is often highly intensive, which limits the number of cases where some form of the models are available on a global basis. These problems include licensing restrictions, bureaucracy, privacy, data availability, data quality, costs, transparency, and transferability. This paper introduces Rapidex, a novel origin–destination (OD) demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using a capacity-based modification of OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables the fetching of travel time data from pervasive traffic data providers, such as TomTom and Google. With Rapidex, we tailor the genetic-algorithm (GA)-based metaheuristic approach to derive the OD demand pattern. The tool produces critical outputs such as link volumes, link travel times, OD travel times, average trip length and duration, and congestion level, which can also be used for validation. Finally, Rapidex enables the user to perform scenario evaluation, where changes to the network and/or demand data can be made and the subsequent impacts on performance metrics can be identified. In this article, we demonstrate the applicability of Rapidex on the network of Sydney, which has 15,646 directional links, 8708 nodes, and 178 zones. Further, the model was validated using the Household Travel Survey data of Sydney using the aggregated metrics and a novel project selection method. We observed that 88% of the time, the “estimated” and “observed” OD matrices identified the same project (i.e., the rapid process estimated the more intensive traditional approach in 88% of cases). This tool would help practitioners in rapid decision making for strategic long-term planning. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.

scholarly journals A data-driven analysis of the potential of public transport for German commuters using accessibility indicators

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tudor Mocanu ◽  
Jigeeshu Joshi ◽  
Christian Winkler
Keyword(s):  
Public Transport ◽  
Transport Model ◽  
Negative Impact ◽  
Open Data ◽  
Data Driven ◽  
Travel Times ◽  
Time Data ◽  
Mode Shift ◽  
Data Driven Approach ◽  
Household Travel

Abstract Background A significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare. Methodology We investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers. Results Public transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

scholarly journals A model to identify urban traffic congestion hotspots in complex networks

2016 ◽  
Vol 3 (10) ◽  
pp. 160098 ◽  
Author(s):  
Albert Solé-Ribalta ◽  
Sergio Gómez ◽  
Alex Arenas
Keyword(s):  
Complex Networks ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Urban Environments ◽  
Urban Traffic ◽  
Traffic Data ◽  
Time Data ◽  
Proposed Model ◽  
And Control ◽  
Real Traffic

The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities’ road networks, considering, in some experiments, real traffic data, show that the proposed model is capable of identifying susceptible junctions that might become hotspots if mobility demand increases.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

Using novel methodologies to support burden of disease estimates

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Hald
Keyword(s):  
International Development ◽  
Traditional Approach ◽  
Source Population ◽  
Surveillance Systems ◽  
Data Generation ◽  
Time Data ◽  
African Countries ◽  
Indirect Measure ◽  
True Incidence ◽  
Laboratory Capacity

Abstract A challenge to estimating burden of diarrheal diseases, particularly in LMICs, where laboratory capacity and surveillance systems are limited, is obtaining valid estimates of etiology proportions of cases. A commonly used method is systematic review of studies reporting pathogen isolation in diarrhea cases. However, studies often differ in design, source population, timeframe, and pathogens included, hampering extrapolation to the target population. In a study co-funded by the Bill and Melinda Gates Foundation and the UK Department for International Development, we explore a novel approach for estimating diarrhea etiology proportions in urban and rural populations in four African countries. We analyse sewage samples using short-read next-generation sequencing (NGS) to determine abundance of genes that can be mapped to specific bacterial genera, providing an estimate of the relative abundance of specific pathogens in each sample. In parallel to collecting sewage samples, a questionnaire-based population survey will estimate diarrheal incidence. By combining results, pathogen-specific incidence will be estimated and compared with incidence estimates from the traditional approach. The application NGS to human sewage has great potential for surveillance of foodborne infections, particularly in resource-poor settings where laboratory capacity for bacterial isolation is limited. First, NGS is a one method takes all approach, as it is based on detection of RNA/DNA, a language common across pathogens. Second, it is culture independent, allowing for real-time data generation and standardized sharing. Finally, few samples are needed to survey large populations for several pathogens at the same time. Thus, surveillance based on NGS of sewage may prove to be an indirect measure of incidence. Although it will not provide an estimate for the true incidence in the population, it will increase our understanding of the burden and as such be a proxy and novel way of ranking diseases.

scholarly journals Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation

2021 ◽  
Vol 13 (15) ◽  
pp. 8324
Author(s):  
Viacheslav Morozov ◽  
Sergei Iarkov
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Traffic Congestion ◽  
Traffic Management ◽  
System Analysis ◽  
Motor Vehicle ◽  
Experimental Studies ◽  
Transport Systems ◽  
Experiment Planning ◽  
Traffic Light

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.

scholarly journals Evaluation of the Applicability of the Ramp Metering to a Freeway Using Microsimulation: A Case Study in Riyadh, Saudi Arabia

2019 ◽  
Vol 9 (20) ◽  
pp. 4406
Author(s):  
Seongkwan Lee ◽  
Amr Shokri ◽  
Abdullah Al-Mansour
Keyword(s):  
Saudi Arabia ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Fixed Time ◽  
Ramp Metering ◽  
Traffic Data ◽  
Geometrical Data ◽  
Overall Performance ◽  
Length Reduction

Riyadh, the capital of Saudi Arabia, suffers from traffic congestion like other modern societies, during peak hours but also all day long, even without any incidents. To solve this horrible traffic congestion problem, various efforts have been made from the Active Traffic Management (ATM) aspect. Ramp metering (RM) is one of the representative methods of the ATM and has already proven its value in many locations worldwide. Unfortunately, RM has not yet been fully implemented in Saudi Arabia. This research aimed to assess the applicability of RM to a freeway in Riyadh using microsimulation. The widely known software VISSIM (PTV Planung Transport Verkehr AG, Germany, 1992) was chosen to compare the performances of various RM operating scenarios, such as fixedtime operation with different sub-scenarios and traffic-responsive operation using ALINEA (Asservissement Lineaire d’entree Autoroutiere) algorithm. For the simulations, this study targeted Makkah Road, one of the major freeways in Riyadh, and collected geometrical data and traffic data from that freeway. Analysis of four main scenarios and eight sub-scenarios, proved that overall performance of the fixed-time RM operation is generally good. The sub-scenario 4V3R of the fixed-time RM operation was the best in average queue length reduction. However, the traffic-responsive operation was best in average speed improvement.

Source depth estimation using multi-station waveform data

1984 ◽  
Vol 74 (5) ◽  
pp. 1623-1643
Author(s):  
Falguni Roy
Keyword(s):  
Depth Estimation ◽  
The United States ◽  
Digital Data ◽  
United States Geological Survey ◽  
Travel Times ◽  
Source Depth ◽  
Time Data ◽  
Waveform Data ◽  
Level Ii ◽  
Level I

Abstract A depth estimation procedure has been described which essentially attempts to identify depth phases by analyzing multi-station waveform data (hereafter called level II data) in various ways including deconvolution, prediction error filtering, and spectral analysis of the signals. In the absence of such observable phases, other methods based on S-P, ScS-P, and SKS-P travel times are tried to get an estimate of the source depth. The procedure was applied to waveform data collected from 31 globally distributed stations for the period between 1 and 15 October 1980. The digital data were analyzed at the temporary data center facilities of the National Defense Research Institute, Stockholm, Sweden. During this period, a total number of 162 events in the magnitude range 3.5 to 6.2 were defined by analyzing first arrival time data (hereafter called level I data) alone. For 120 of these events, it was possible to estimate depths using the present procedure. The applicability of the procedure was found to be 100 per cent for the events with mb > 4.8 and 88 per cent for the events with mb > 4. A comparison of level I depths and level II depths (the depths as obtained from level I and level II data, respectively) with that of the United States Geological Survey estimates indicated that it will be necessary to have at least one local station (Δ < 10°) among the level I data to obtain reasonable depth estimates from such data alone. Further, it has been shown that S wave travel times could be successfully utilized for the estimation of source depth.

scholarly journals A New Approach of Smart Mobility for Heavy Goods Vehicles in Casablanca

2021 ◽  
Author(s):  
Mohammed Mouhcine Maaroufi ◽  
Laila Stour ◽  
Ali Agoumi
Keyword(s):  
Environmental Sustainability ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Research Work ◽  
Transport Infrastructure ◽  
Transport Systems ◽  
Smart Mobility ◽  
Heavy Goods Vehicles

Managing mobility, both of people and goods, in cities is a thorny issue. The travel needs of urban populations are increasing and put pressure on transport infrastructure. The Moroccan cities are no exception and will struggle, in the short term, to respond to the challenges of the acceleration of the phenomenon of urbanization and the increase in demand for mobility. This will inevitably prevent them from turning into smart cities. The term smart certainly alludes to better use of technologies, but smart mobility is also defined as “a set of coordinated actions intended to improve the efficiency, effectiveness and environmental sustainability of cities” [1]. The term mobility highlights the preponderance of humans over infrastructure and vehicles. Faced with traffic congestion, the solutions currently adopted which consist of fitting out and widening the infrastructures, only encourage more trips and report the problem with more critical consequences. It is true that beyond a certain density of traffic, even Intelligent Transport Systems (ITS) are not useful. The concept of dynamic lane management or Advanced Traffic Management (ATM) opens up new perspectives. Its objective is to manage and optimize road traffic in a variable manner, in space and in time. This article is a summary of the development of a road infrastructure dedicated to Heavy Goods Vehicles (HGV), the first of its kind in Morocco. It aims to avoid the discomfort caused by trucks in the urban road network of the city of Casablanca. This research work is an opportunity to reflect on the introduction of ITS and ATM to ensure optimal use of existing infrastructure before embarking on heavy and irreversible infrastructure projects.

scholarly journals Data Aggregation and Its Impact on Performance Enhancement

2019 ◽  
Vol 9 (2S) ◽  
pp. 316-319
Keyword(s):  
Performance Improvement ◽  
Data Aggregation ◽  
Performance Metrics ◽  
Performance Enhancement ◽  
Data Availability ◽  
Quality Data ◽  
Data Organization ◽  
Data Handling ◽  
Aggregation Techniques ◽  
Important Position

Wireless sensor network incorporates an innovative aspect called as data handling technologies for big data organization. In today’s research the data aggregation occupies an important position and its emerging rapidly. Data aggregation incudes, process of accumulating the data at node, then either store or transfer further to reach out the destination. This survey depicts about the previous work on data aggregation in WSN and also its impact on the different services. There are number of data aggregation techniques available for reducing the data, processing the data and storing the data. Some of them are discussed here as a review. The data aggregation performed using certain techniques can also be aimed in having energy efficiency, time efficient, security could be in the form of confidentiality, unimpaired, authenticate, freshness, quality, data availability, access control, nonrepudiation, secrecy, secrecy. These are the relevant performance metrics to maintain the better Qos in WSNs applications. The goal of this paper is to display an overview of existing techniques for performance improvement in homogenous/ heterogenous networks.

scholarly journals Intelligent Highway Traffic Detection Algorithm based on Deep Learning

2021 ◽  
Author(s):  
ming ji ◽  
Chuanxia Sun ◽  
Yinglei Hu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Traffic Congestion ◽  
Road Network ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Traffic Data ◽  
Vehicle Recognition ◽  
Environment Recognition

Abstract In order to solve the increasingly serious traffic congestion problem, an intelligent transportation system is widely used in dynamic traffic management, which effectively alleviates traffic congestion and improves road traffic efficiency. With the continuous development of traffic data acquisition technology, it is possible to obtain real-time traffic data in the road network in time. A large amount of traffic information provides a data guarantee for the analysis and prediction of road network traffic state. Based on the deep learning framework, this paper studies the vehicle recognition algorithm and road environment discrimination algorithm, which greatly improves the accuracy of highway vehicle recognition. Collect highway video surveillance images in different environments, establish a complete original database, build a deep learning model of environment discrimination, and train the classification model to realize real-time environment recognition of highway, as the basic condition of vehicle recognition and traffic event discrimination, and provide basic information for vehicle detection model selection. To improve the accuracy of road vehicle detection, the vehicle target labeling and sample preprocessing of different environment samples are carried out. On this basis, the vehicle recognition algorithm is studied, and the vehicle detection algorithm based on weather environment recognition and fast RCNN model is proposed. Then, the performance of the vehicle detection algorithm described in this paper is verified by comparing the detection accuracy differences between different environment dataset models and overall dataset models, different network structures and deep learning methods, and other methods.

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