scholarly journals Use of Taxi-Trip Data in Analysis of Demand Patterns for Detection and Explanation of Anomalies

2017 ◽  
Vol 2643 (1) ◽  
pp. 129-138 ◽  
Author(s):  
Ioulia Markou ◽  
Filipe Rodrigues ◽  
Francisco C. Pereira
Keyword(s):  
New York ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Economic Stress ◽  
Transport Systems ◽  
Data Set ◽  
Special Events ◽  
Public Data ◽  
Kernel Density Analysis ◽  
Demand Patterns

Because of environmental and economic stress, current strong investment in adaptive transport systems can efficiently use capacity, minimizing costs and environmental impacts. The common vision is of a system that dynamically changes itself (the supply) to anticipate the needs of travelers (the demand). In some occasions, unexpected and unwanted demand patterns are noticed in the traffic network; these patterns lead to system failures and cost implications. Significantly, low speeds or excessively low flows at an unforeseeable time are only some of the phenomena that are often noticed and need to be explained for a transport system to develop a better future response. The objective of this research was the formulation of a methodology that could identify anomalies on traffic networks and correlate them with special events by using Internet data. The main subject of interest in this study was the investigation of why traffic congestion was occurring as well as why demand fluctuated on days when there were no apparent reasons for such phenomena. The system was evaluated by using Google’s public data set for taxi trips in New York City. A “normality” baseline was defined at the outset and then used in the subsequent study of the demand patterns of individual days to detect outliers. With the use of this approach it was possible to detect fluctuations in demand and to analyze and correlate them with disruptive event scenarios such as extreme weather conditions, public holidays, religious festivities, and parades. Kernel density analysis was used so that the affected areas, as well as the significance of the observed differences compared with the average day, could be depicted.

scholarly journals Advances in lane marking detection algorithms for all-weather conditions

2021 ◽  
Vol 11 (4) ◽  
pp. 3365
Author(s):  
Hadhrami Ab Ghani ◽  
Rosli Besar ◽  
Zamani Md Sani ◽  
Mohd Nazeri Kamaruddin ◽  
Syabeela Syahali ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Research Work ◽  
General System ◽  
Weather Conditions ◽  
Lane Detection ◽  
Transport Systems ◽  
Detection Algorithms ◽  
Lane Marking ◽  
Angle Method

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.

Enhancing the Performance of Vehicle Passenger Detection under Adverse Weather Conditions Using Augmented Reality-Based Machine Learning Approach

2021 ◽  
pp. 036119812110312
Author(s):  
Jaeyun Lee ◽  
Sangcheol Kang ◽  
Jaedeok Lim ◽  
Seong Geon Kim ◽  
Changmo Kim
Keyword(s):  
Augmented Reality ◽  
Traffic Congestion ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Training Data ◽  
Weather Data ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Adverse Weather Conditions ◽  
Adverse Weather

In response to extreme traffic congestion in metropolitan areas that causes unnecessarily long travel times, high fuel consumption, and excessive greenhouse gas emissions, transportation agencies have implemented various strategies to mitigate traffic congestion. Managed lanes—one of the measures applied worldwide—provide benefits to road users and operating agencies by integrating advanced technologies such as electronic and dynamic tolling systems. However, those agencies already implementing or considering implementing the managed lane strategy are seeking a solution to effectively and properly charge toll rates based on vehicle occupancy and penalize violating vehicles. Vehicle passenger detection systems (VPDSs) have been developed and evaluated worldwide, but limitations still inhibit their full implementation. This study confirms that the performance of the deep learning algorithm, a core VPDS technology, declines under certain adverse weather conditions because of lack of training data sets. The performance of the “you only look once” (YOLOv3) model trained with a normal weather data set decreased by as much as 8.5% when it was tested for adverse weather conditions. In this study, augmented reality (AR) models are developed to enhance the accuracy of vehicle passenger detection (VPDA) by the VPDS by training the algorithm with AR images representing virtual adverse weather conditions. Models trained with AR image sets of various weather categories (fog, rain, and snow) attained VPDA enhanced by up to 7.9%. The final model significantly improves VPDA under adverse weather conditions. The proposed models could be considered for implementation with road weather information systems under adverse weather conditions.

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.

The Changing Landscape of Asian Entrepreneurship, Minority Banks, and Community Development

2011 ◽  
Vol 9 (1-2) ◽  
pp. 78-90
Author(s):  
Tarry Hum
Keyword(s):  
New York ◽  
New York City ◽  
Data Collection ◽  
Community Development ◽  
Real Estate ◽  
York City ◽  
Commercial Real Estate ◽  
Data Sources ◽  
Real Estate Development ◽  
Public Data

This policy brief examines minority banks and their lending practices in New York City. By synthesizing various public data sources, this policy brief finds that Asian banks now make up a majority of minority banks, and their loans are concentrated in commercial real estate development. This brief underscores the need for improved data collection and access to research minority banks and the need to improve their contributions to equitable community development and sustainability.

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.

Urban Impacts of Mobile Fuel Delivery Service

2020 ◽  
pp. 036119812097541
Author(s):  
Andrea Broaddus
Keyword(s):  
San Francisco ◽  
Traffic Congestion ◽  
Data Set ◽  
Gas Stations ◽  
Bay Area ◽  
Urban Impacts ◽  
Fuel Delivery ◽  
Declining Demand ◽  
Multiple Vehicles ◽  
Gas Station

Mobile fuel delivery (MFD) uses a fueling truck to fill up personal and commercial fleet vehicles while they are parked overnight. This study used a sample data set provided by a San Francisco Bay Area company to explore the potential impacts on vehicle miles traveled (VMT), carbon dioxide (CO2) emissions, and traffic congestion. An analysis of vehicle travel associated with gas station trips was conducted to establish a basis for comparison. Future scenarios comparing the potential impacts of scaled-up MFD services in 2030 were also developed. The study concluded that MFD services compared favorably to gas stations in relation to environmental and traffic benefits in the longer term, even though personal fueling trips tended to generate low VMT. Benefits stemmed from efficiencies achieved by fueling multiple vehicles per delivery trip, replacing car share vehicle fueling trips, and removing trips from the network during peak hours. This analysis estimated that total annual CO2 emissions associated with fuel delivery operations in the Bay Area were 76 metric tons, which is less than a typical gas station with 97 metric tons. Under assumptions of declining demand for gasoline and significantly fewer gas stations, and with highly efficient optimized operations, mobile delivery could gain up to 5% market share for gas and not add additional VMT over the business as usual scenario.

scholarly journals Behavior of Traffic Congestion and Public Transport in Eight Large Cities in Latin America during the COVID-19 Pandemic

2021 ◽  
Vol 11 (10) ◽  
pp. 4703
Author(s):  
Renato Andara ◽  
Jesús Ortego-Osa ◽  
Melva Inés Gómez-Caicedo ◽  
Rodrigo Ramírez-Pisco ◽  
Luis Manuel Navas-Gracia ◽  
...  
Keyword(s):  
Latin American ◽  
Public Transport ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Urban Transport ◽  
Control Measures ◽  
Johns Hopkins Hospital ◽  
Large Cities ◽  
Public Data ◽  
The Impact

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities.

scholarly journals Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Malik Doole ◽  
Joost Ellerbroek ◽  
Victor L. Knoop ◽  
Jacco M. Hoekstra
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Large Scale ◽  
Cost Savings ◽  
Future Research ◽  
Fast Time ◽  
Potential Cost ◽  
Horizontal Structure ◽  
Traffic Demand ◽  
Demand Patterns

Large-scale adoption of drone-based delivery in urban areas promise societal benefits with respect to emissions and on-ground traffic congestion, as well as potential cost savings for drone-based logistic companies. However, for this to materialise, the ability of accommodating high volumes of drone traffic in an urban airspace is one of the biggest challenges. For unconstrained airspace, it has been shown that traffic alignment and segmentation can be used to mitigate conflict probability. The current study investigates the application of these principles to a highly constrained airspace. We propose two urban airspace concepts, applying road-based analogies of two-way and one-way streets by imposing horizontal structure. Both of the airspace concepts employ heading-altitude rules to vertically segment cruising traffic according to their travel direction. These airspace configurations also feature transition altitudes to accommodate turning flights that need to decrease the flight speed in order to make safe turns at intersections. While using fast-time simulation experiments, the performance of these airspace concepts is compared and evaluated for multiple traffic demand densities in terms of safety, stability, and efficiency. The results reveal that an effective way to structure drone traffic in a constrained urban area is to have vertically segmented altitude layers with respect to travel direction as well as horizontal constraints imposed to the flow of traffic. The study also makes recommendations for areas of future research, which are aimed at supporting dynamic traffic demand patterns.

Preparing Paraprofessionals to Become Teachers: A Study Examining the Effect of Catalytic Interventions

2021 ◽  
pp. 105678792110036
Author(s):  
Tonya Johnson ◽  
Edward Lehner
Keyword(s):  
New York ◽  
Skill Acquisition ◽  
Social Reproduction ◽  
Teacher Candidates ◽  
New York State ◽  
Student Population ◽  
School Students ◽  
Preparation Programs ◽  
Data Set ◽  
Public School Students

New York State, in all of its regions, has a growing number of diverse public-school students, including many immigrant groups, accounting for a significant change in the ethnic and racial demographics of the state’s student population. Despite the rapidly changing student population, the teaching force nonetheless remains disproportionately White and populated primarily by women. A growing body of research suggests that the ranks of paraprofessionals, many of whom are already working in schools, may be the ideal population from which to develop a more fully diversified teaching candidate pool. Informed by a theoretical lens of social reproduction and drawing from an expansive data set, this research surveys the unique barriers that paraprofessionals face in accessing information about licensure and navigating local and state requirements. Specifically, the current work examines the needs of candidates at an urban community college and presents findings from a pilot support program designed to increase paraprofessional credentialing leading to teaching licensure. The findings of this work highlight not only the need for teacher and paraprofessional preparation programs to alter recruiting and skill-acquisition practices but also the need for continued research to better understand how to support multiethnic, multilingual, and multiracial teacher candidates.

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