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The COVID-19 outbreak led to significant changes in daily commuting. As lockdowns were imposed to metropolitan areas throughout the globe, travelers refrained heavily from using public transport, to maintain social distancing. Based on data from Athens, Greece, this paper investigates the anticipated, post-pandemic behavior of travelers with respect to public transport use. Focus is given on analyzing those factors that affect post-pandemic recovery time of public transport users, i.e. the time travelers would refrain from using public transport, following a gradual exit from the pandemic outbreak and relaxation of lockdowns. The analysis is performed using both a clustering algorithm and a discrete duration model. Both methodologies highlighted the fact that the frequency of using public transport before the pandemic along with the travelers’ age, influence their behavior in terms of recovery time. Results from the discrete duration model suggest also that self-employed and travelers who mostly use private vehicles, are less likely to use public transport after the outbreak. Concerning the psychological factors that shape COVID-19 safety-related perceptions that affect public transport use, travelers who would be willing to use protection gear when traveling with are also less likely to return to public transport. Findings of this study could be useful for policy making, suggesting that efficient marketing strategies toward promoting public transport usage in a post-pandemic era should focus on travelers with specific socio-demographic and travel characteristics.
Athanasios Kopsidas; Christina Milioti; Konstantinos Kepaptsoglou; Eleni I. Vlachogianni. How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece. Transportation Letters 2021, 13, 344 -352.
AMA StyleAthanasios Kopsidas, Christina Milioti, Konstantinos Kepaptsoglou, Eleni I. Vlachogianni. How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece. Transportation Letters. 2021; 13 (5-6):344-352.
Chicago/Turabian StyleAthanasios Kopsidas; Christina Milioti; Konstantinos Kepaptsoglou; Eleni I. Vlachogianni. 2021. "How did the COVID-19 pandemic impact traveler behavior toward public transport? The case of Athens, Greece." Transportation Letters 13, no. 5-6: 344-352.
The purpose of this paper is to provide a methodological framework to identify traffic conditions based on non-calibrated video recordings captured from unmanned aerial vehicles (UAV) using deep learning. To this end, we propose two complementary to each other approaches: (i) identify in real time, with minimal computational cost, traffic conditions, (ii) localize, classify vehicles and approximate traffic variables (volume, speed, density) on a road segment from video captured by UAVs. Both problems are formulated as classification problems and tackled using Convolutional Neural Networks (CNN). The use of pre-trained CNNs is also investigated. Both approaches are, then, analysed based on their accuracy and feasibility in implementation. Findings indicate that all models developed achieve a detection accuracy of 89% and higher. The CNN with the best performance can classify traffic conditions between constrained and unconstrained traffic with 91% accuracy higher than what a pretrained model achieved and with significantly faster training times. Furthermore, findings indicated that pretrained neural network for traffic localization was able to predict the position and type of vehicles with a precision of 0.91. Based on the fundamental traffic diagram, it was shown that the two approaches provide compatible results and a feasible representation of traffic on the study area. Finally, possible applications in the field of transportation and traffic monitoring are discussed.
Eleni I. Vlahogianni; Javier Del Ser; Konstantinos Kepaptsoglou; Ibai Laña. Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning. Journal of Big Data Analytics in Transportation 2021, 3, 1 -13.
AMA StyleEleni I. Vlahogianni, Javier Del Ser, Konstantinos Kepaptsoglou, Ibai Laña. Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning. Journal of Big Data Analytics in Transportation. 2021; 3 (1):1-13.
Chicago/Turabian StyleEleni I. Vlahogianni; Javier Del Ser; Konstantinos Kepaptsoglou; Ibai Laña. 2021. "Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning." Journal of Big Data Analytics in Transportation 3, no. 1: 1-13.
Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.
Ibai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors 2021, 21, 1121 .
AMA StyleIbai Laña, Javier Sanchez-Medina, Eleni Vlahogianni, Javier Del Ser. From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability. Sensors. 2021; 21 (4):1121.
Chicago/Turabian StyleIbai Laña; Javier Sanchez-Medina; Eleni Vlahogianni; Javier Del Ser. 2021. "From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability." Sensors 21, no. 4: 1121.
Eco-driving is a multidimensional concept that includes driving behavior, route selection and all other choices or behaviors related to the vehicles’ fuel consumption (e.g., the use of quality fuel, the use of air conditioning, driving at peak hours, etc.). The scope of this paper is to present an overview of recent literature referring to eco-driving and developed models for calculating fuel consumption, as well as the most important factors affecting it. Recent literature contains a large number of models that estimate fuel consumption, based on naturalistic driving data, which are collected using smartphones and OBDs. In this work, the existing literature is critically assessed in relation to conceptual, methodological and data related aspects. The analyses result to a set of limitations and challenges that are further discussed in the framework of system wide implementations for deriving policies that increase drivers’ awareness, but also improve system performance.
Panagiotis Fafoutellis; Eleni Mantouka; Eleni Vlahogianni. Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. Sustainability 2020, 13, 226 .
AMA StylePanagiotis Fafoutellis, Eleni Mantouka, Eleni Vlahogianni. Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods. Sustainability. 2020; 13 (1):226.
Chicago/Turabian StylePanagiotis Fafoutellis; Eleni Mantouka; Eleni Vlahogianni. 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods." Sustainability 13, no. 1: 226.
Electromobility and alternative fuels are considered as key solutions towards the reduction of emissions and energy savings in order to achieve more sustainable and environmentally friendly transportation systems. European countries are trying to achieve significant progress in promoting these new technologies in order to ensure better life and air quality for their residents. However, the number of electric vehicles is still low and not all European cities have made worth mentioning progress towards this direction. The objective of this research is to identify the gaps hindering the promotion of electromobility and alternative fuels in 9 European regions from 8 European countries: Italy, Slovenia, Greece, Finland, Norway, Latvia, Belgium and Romania. The analysis is focusing on three thematic areas: Business, Governance and Research and Innovation Strategies for Smart Specialization (RIS3) and each area is divided in various aspects such as charging Infrastructure, e-Vehicle fleet, incentives, technology, campaigns, legislation, enforcement, education, research and Innovation. The analysis is based on estimating the progress achieved in each aspect using a scale from 1 to 10 (10 indicates the highest level of performance and level 1 the lowest). Results indicate that there are regions with significant progress in the field of electromobility and others that still should elaborate a lot to increase their performance in most of the analyzed aspects. The findings assessment will help policy makers to prioritize their effort on the areas that have the highest gap until the desired level in electromobility is achieved and identify other regions with similar electromobility issues.
Foteini Orfanou; Panagiotis Papantoniou; Eleni Vlahogianni; George Yannis. A Comparative Gap Analysis for Electromobility and Alternative Fuels. Advances in Intelligent Systems and Computing 2020, 606 -615.
AMA StyleFoteini Orfanou, Panagiotis Papantoniou, Eleni Vlahogianni, George Yannis. A Comparative Gap Analysis for Electromobility and Alternative Fuels. Advances in Intelligent Systems and Computing. 2020; ():606-615.
Chicago/Turabian StyleFoteini Orfanou; Panagiotis Papantoniou; Eleni Vlahogianni; George Yannis. 2020. "A Comparative Gap Analysis for Electromobility and Alternative Fuels." Advances in Intelligent Systems and Computing , no. : 606-615.
Urban congestion pricing is widely regarded as an effective way of traffic management in order to relief metropolitan centers from heavy traffic, reduce emissions, air pollution and as a means to promote public transport usage. Until recently, pricing strategies were formed mostly by taking into account peak hours and exemption provisions (e.g., for taxis or clean-fuel vehicles). However, recent advancements in smartphone sensing have enabled the monitoring of driving behavior, a practice which can act as an additional parameter for the determination of urban tolls. The aim of this paper is to investigate drivers’ perception towards PHYD urban pricing schemes and identify factors that may affect their acceptance in the city of Athens using a questionnaire survey. Our preliminary results have shown that men tend to be more aggressive and exceed speed limits than female drivers do. The findings reveal that women are more likely to accept PHYD urban pricing schemes. In addition, commuters are also more likely to accept such a measure contrary to those travelling for other purposes. Finally, eco-minded drivers are more willing to accept PHYD pricing systems. Such results can be really useful for both researchers and decision and policy makers who are willing to design user friendly and easy acceptable pricing systems.
Kyriaki Christovasili; Eleni Mantouka; Eleni Vlahogianni. A User Acceptance Survey of Pay-How-You-Drive Urban Pricing Schemes. Advances in Intelligent Systems and Computing 2020, 584 -594.
AMA StyleKyriaki Christovasili, Eleni Mantouka, Eleni Vlahogianni. A User Acceptance Survey of Pay-How-You-Drive Urban Pricing Schemes. Advances in Intelligent Systems and Computing. 2020; ():584-594.
Chicago/Turabian StyleKyriaki Christovasili; Eleni Mantouka; Eleni Vlahogianni. 2020. "A User Acceptance Survey of Pay-How-You-Drive Urban Pricing Schemes." Advances in Intelligent Systems and Computing , no. : 584-594.
The autonomous vehicles are expected to bring unprecedented changes in the labor sector and the workforce. Traditional jobs will be alleviated, new will be created while people involved in the autonomous vehicle operation should be qualified with additional skills and knowledge in order to be able to deal with the new technology and the various systems. Furthermore, the impact on the role of the ‘driver’ is anticipated to be significant in all transportation modes. The purpose of the present research is to identify the skills and knowledge required for an efficient and proper operation of any autonomous vehicle. Both professional and private operators and all transportation sectors (road, rail, maritime, aviation) and autonomous levels will be considered as each one has different requirements.
Foteini Orfanou; Eleni Vlahogianni; George Yannis. A Taxonomy of Skills and Knowledge for Efficient Autonomous Vehicle Operation. Advances in Intelligent Systems and Computing 2020, 305 -315.
AMA StyleFoteini Orfanou, Eleni Vlahogianni, George Yannis. A Taxonomy of Skills and Knowledge for Efficient Autonomous Vehicle Operation. Advances in Intelligent Systems and Computing. 2020; ():305-315.
Chicago/Turabian StyleFoteini Orfanou; Eleni Vlahogianni; George Yannis. 2020. "A Taxonomy of Skills and Knowledge for Efficient Autonomous Vehicle Operation." Advances in Intelligent Systems and Computing , no. : 305-315.
The articles in this special section focus on data driven optimization for transportation and smart mobility applications. We live in an era of major societal and technological changes. Transportation de-carbonization and postindustrial demographic trends, such as massive migrations and an aging society, generate new challenges for cities, making the efficient and sustainable management of services and resources more necessary than ever. Cities must evolve, transform, and become smart to cope with these realities. According to the literature, a city can be referred to as smart when investments in human and social capital and traditional (transportation) and modern [information and communications technology (ICT)] communication infrastructure fuel sustainable economic growth and high quality of life, with a wise management of natural resources, through participatory government.
Eneko Osaba; Javier J. Sanchez Medina; Eleni I. Vlahogianni; Xin-She Yang; Antonio D. Masegosa; Joshue Perez Rastelli; Javier Del Ser. Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]. IEEE Intelligent Transportation Systems Magazine 2020, 12, 6 -9.
AMA StyleEneko Osaba, Javier J. Sanchez Medina, Eleni I. Vlahogianni, Xin-She Yang, Antonio D. Masegosa, Joshue Perez Rastelli, Javier Del Ser. Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]. IEEE Intelligent Transportation Systems Magazine. 2020; 12 (4):6-9.
Chicago/Turabian StyleEneko Osaba; Javier J. Sanchez Medina; Eleni I. Vlahogianni; Xin-She Yang; Antonio D. Masegosa; Joshue Perez Rastelli; Javier Del Ser. 2020. "Data-Driven Optimization for Transportation Logistics and Smart Mobility Applications [Guest Editorial]." IEEE Intelligent Transportation Systems Magazine 12, no. 4: 6-9.
The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.
Anna-Maria Stavrakaki; Dimitrios I. Tselentis; Emmanouil Barmpounakis; Eleni I. Vlahogianni; George Yannis. Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior. Sensors 2020, 20, 2600 .
AMA StyleAnna-Maria Stavrakaki, Dimitrios I. Tselentis, Emmanouil Barmpounakis, Eleni I. Vlahogianni, George Yannis. Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior. Sensors. 2020; 20 (9):2600.
Chicago/Turabian StyleAnna-Maria Stavrakaki; Dimitrios I. Tselentis; Emmanouil Barmpounakis; Eleni I. Vlahogianni; George Yannis. 2020. "Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior." Sensors 20, no. 9: 2600.
Using a Powered Two-Wheeler (PTW) for everyday commuting has become particularly attractive in large metropolitan areas. The industry has also picked upon the rising interest for PTW oriented services using crowdsourcing and smartphones. However, current navigation or other travel information services are not at all customized to the needs of PTWs, which are systematically being neglected from the target group. To address the needs of this diverse mode, one should first be able to identify PTWs from the rest of the vehicles. The aim of the specific study is to develop models to reveal the contributing factors which are related to the identification of PTWs using solely smartphone sensors’ data and tree-based machine learning approaches. To boost the accuracy of the identification task, popular feature selection algorithms and meta-modeling strategies are implemented and evaluated. Additionally, in order to deal with the unbalanced dataset different oversampling techniques are examined. Results show that high accuracy models can be developed for the specific classification problem with the proper feature representation.
Emmanouil Barmpounakis; Eleni I. Vlahogianni. Powered Two-Wheeler Detection Using Crowdsourced Smartphone Data. IEEE Transactions on Intelligent Vehicles 2020, 5, 575 -584.
AMA StyleEmmanouil Barmpounakis, Eleni I. Vlahogianni. Powered Two-Wheeler Detection Using Crowdsourced Smartphone Data. IEEE Transactions on Intelligent Vehicles. 2020; 5 (4):575-584.
Chicago/Turabian StyleEmmanouil Barmpounakis; Eleni I. Vlahogianni. 2020. "Powered Two-Wheeler Detection Using Crowdsourced Smartphone Data." IEEE Transactions on Intelligent Vehicles 5, no. 4: 575-584.
The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure.
Christina Iliopoulou; Christina P. Milioti; Eleni I. Vlahogianni; Konstantinos L. Kepaptsoglou. Identifying spatio-temporal patterns of bus bunching in urban networks. Journal of Intelligent Transportation Systems 2020, 24, 365 -382.
AMA StyleChristina Iliopoulou, Christina P. Milioti, Eleni I. Vlahogianni, Konstantinos L. Kepaptsoglou. Identifying spatio-temporal patterns of bus bunching in urban networks. Journal of Intelligent Transportation Systems. 2020; 24 (4):365-382.
Chicago/Turabian StyleChristina Iliopoulou; Christina P. Milioti; Eleni I. Vlahogianni; Konstantinos L. Kepaptsoglou. 2020. "Identifying spatio-temporal patterns of bus bunching in urban networks." Journal of Intelligent Transportation Systems 24, no. 4: 365-382.
In the last years, the arrival and progressively gained maturity of technological paradigms such as Connected Vehicles, the Internet of Things, Sensor Networks, Urban Computing, Smart Cities, Cloud Computing, Edge Computing, Big Data and others alike have ignited the role historically played by data-based learning techniques to levels never seen before. This sharp increase has been particularly noticed in the design and management of intelligent systems for transportation and mobility, as processes, services and applications deployed in these systems are fed with data substrates captured at unprecedented rates and scales. Legacy sensing equipment installed on the roads’ infrastructure (e.g., induction loops and cameras) are nowadays complemented by alternative means to sense the transportation and mobility context of interest in real time and ubiquitously, as can be exemplified by data collected in a crowd-sourced way by using ad-hoc smart applications, as well as floating car data and/or social media.
Javier Del Ser; Javier J. Sanchez-Medina; Eleni I. Vlahogianni. Introduction to the Special Issue on Online Learning for Big-Data Driven Transportation and Mobility. IEEE Transactions on Intelligent Transportation Systems 2019, 20, 4621 -4623.
AMA StyleJavier Del Ser, Javier J. Sanchez-Medina, Eleni I. Vlahogianni. Introduction to the Special Issue on Online Learning for Big-Data Driven Transportation and Mobility. IEEE Transactions on Intelligent Transportation Systems. 2019; 20 (12):4621-4623.
Chicago/Turabian StyleJavier Del Ser; Javier J. Sanchez-Medina; Eleni I. Vlahogianni. 2019. "Introduction to the Special Issue on Online Learning for Big-Data Driven Transportation and Mobility." IEEE Transactions on Intelligent Transportation Systems 20, no. 12: 4621-4623.
This paper aims to provide a methodological framework for the comparative evaluation of driving safety efficiency based on Data Envelopment Analysis (DEA). The analysis considers each driver as a Decision Making Unit (DMU) and aims to provide a relative safety efficiency measure to compare different drivers based on their driving performance. The last is defined based on a set of driving analytics (e.g. distance travelled, speed, accelerations, braking, cornering and smartphone usage) collected using an innovative data collection scheme, which is based on the continuous recording of driving behavior analytics in real time, using smartphone device sensors. Safety efficiency is examined in terms of speed limit violation, driving distraction, aggressiveness and safety on urban, rural and highway road and in an overall model. DEA models are identifying the most efficient drivers that lie on the efficiency frontier and act as peers for the rest of the non-efficient drivers. The proposed methodological framework is tested on data from fifty-six (56) drivers during a 7-months period. Findings help distinguish the most efficient drivers from those that are less efficient. Moreover, the efficient level of inputs and outputs to switch from non-efficiency to the efficiency frontier is identified. Results also provide a potential for classification of the driving sample based on drivers’ comparative safety efficiency. The main characteristics of the most and less efficient drivers are consequently analyzed and presented. Most common inefficient driving practices are identified (aggressive, risky driving, etc.) and driving behavior is comparatively evaluated and analyzed.
Dimitrios Tselentis; Eleni Vlahogianni; George Yannis. Driving safety efficiency benchmarking using smartphone data. Transportation Research Part C: Emerging Technologies 2019, 109, 343 -357.
AMA StyleDimitrios Tselentis, Eleni Vlahogianni, George Yannis. Driving safety efficiency benchmarking using smartphone data. Transportation Research Part C: Emerging Technologies. 2019; 109 ():343-357.
Chicago/Turabian StyleDimitrios Tselentis; Eleni Vlahogianni; George Yannis. 2019. "Driving safety efficiency benchmarking using smartphone data." Transportation Research Part C: Emerging Technologies 109, no. : 343-357.
Konstantinos Gkolias; Eleni I. Vlahogianni. Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks. IEEE Transactions on Intelligent Transportation Systems 2018, 20, 4318 -4327.
AMA StyleKonstantinos Gkolias, Eleni I. Vlahogianni. Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks. IEEE Transactions on Intelligent Transportation Systems. 2018; 20 (12):4318-4327.
Chicago/Turabian StyleKonstantinos Gkolias; Eleni I. Vlahogianni. 2018. "Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks." IEEE Transactions on Intelligent Transportation Systems 20, no. 12: 4318-4327.
The objective of the present research is to better understand mobility in university campus areas, using local and transnational data, policies and planning instruments. This analysis looks at integrating student’s mobility flows to/from and inside Campus areas with urban mobility. Within this framework, a survey was developed for seven Southern European universities including a mobility questionnaire on current mobility, desired mobility, mobility problems, proposed measures/policies/tools as well as demographic characteristics of the participants which were mainly undergraduate students, post graduate students, academic/faculty members and administrative staff. For the purpose of the survey, 1,090 questionnaires were collected and further analyse. Regarding the mobility to/from the city, campuses are further distinguished into those that are inside and outside the city. Results highlight differences in the policies that are most critical based on the location of each University. More specifically, for campuses located inside urban area, the most important transport measures include public transport and environmental issues. On the other hand, for mobility in campuses located outside urban areas, results indicate that measures should address public transport and road infrastructure, to help accessibility to and from the campus areas.
Eleni Vlahogianni; Panagiotis Papantoniou; George Yannis; Maria Attard; Alberto Regattieri; Francesco Piana; Francesco Pilati. Analysis of Mobility Patterns in Selected University Campus Areas. Advances in Intelligent Systems and Computing 2018, 426 -433.
AMA StyleEleni Vlahogianni, Panagiotis Papantoniou, George Yannis, Maria Attard, Alberto Regattieri, Francesco Piana, Francesco Pilati. Analysis of Mobility Patterns in Selected University Campus Areas. Advances in Intelligent Systems and Computing. 2018; ():426-433.
Chicago/Turabian StyleEleni Vlahogianni; Panagiotis Papantoniou; George Yannis; Maria Attard; Alberto Regattieri; Francesco Piana; Francesco Pilati. 2018. "Analysis of Mobility Patterns in Selected University Campus Areas." Advances in Intelligent Systems and Computing , no. : 426-433.
The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making.
Steven M. Lavrenz; Eleni I. Vlahogianni; Konstantina Gkritza; Yue Ke. Time series modeling in traffic safety research. Accident Analysis & Prevention 2018, 117, 368 -380.
AMA StyleSteven M. Lavrenz, Eleni I. Vlahogianni, Konstantina Gkritza, Yue Ke. Time series modeling in traffic safety research. Accident Analysis & Prevention. 2018; 117 ():368-380.
Chicago/Turabian StyleSteven M. Lavrenz; Eleni I. Vlahogianni; Konstantina Gkritza; Yue Ke. 2018. "Time series modeling in traffic safety research." Accident Analysis & Prevention 117, no. : 368-380.
Dionysia Panagoulia; Eleni I. Vlahogianni. Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece. Atmospheric Research 2018, 205, 33 -47.
AMA StyleDionysia Panagoulia, Eleni I. Vlahogianni. Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece. Atmospheric Research. 2018; 205 ():33-47.
Chicago/Turabian StyleDionysia Panagoulia; Eleni I. Vlahogianni. 2018. "Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece." Atmospheric Research 205, no. : 33-47.
Eleni G. Mantouka; Emmanouil N. Barmpounakis; Christina P. Milioti; Eleni I. Vlahogianni. Gamification in mobile applications: The case of airports. Journal of Intelligent Transportation Systems 2018, 23, 417 -426.
AMA StyleEleni G. Mantouka, Emmanouil N. Barmpounakis, Christina P. Milioti, Eleni I. Vlahogianni. Gamification in mobile applications: The case of airports. Journal of Intelligent Transportation Systems. 2018; 23 (5):417-426.
Chicago/Turabian StyleEleni G. Mantouka; Emmanouil N. Barmpounakis; Christina P. Milioti; Eleni I. Vlahogianni. 2018. "Gamification in mobile applications: The case of airports." Journal of Intelligent Transportation Systems 23, no. 5: 417-426.
Curbside parking is associated with various adverse impacts on urban traffic networks and is rarely recommended. However, there are cases where parking demand dictates the establishment of on-street parking lanes. Proper planning of the number and type of curbside parking lanes to be located is essential for maximizing roadway capacity and minimizing the resulting impacts of parking operations on the network’s performance. This paper develops a bi-level mathematical programming model for planning and sizing curbside parking lanes in an urban network. The model is solved using a genetic algorithm and demonstrated for a medium-sized urban network.
Chrysanthi Gkini; Christina Iliopoulou; Konstantinos Kepaptsoglou; Eleni I. Vlahogianni. Model for Planning and Sizing Curbside Parking Lanes in Urban Networks. Transportation Research Record: Journal of the Transportation Research Board 2018, 2672, 1 -11.
AMA StyleChrysanthi Gkini, Christina Iliopoulou, Konstantinos Kepaptsoglou, Eleni I. Vlahogianni. Model for Planning and Sizing Curbside Parking Lanes in Urban Networks. Transportation Research Record: Journal of the Transportation Research Board. 2018; 2672 (20):1-11.
Chicago/Turabian StyleChrysanthi Gkini; Christina Iliopoulou; Konstantinos Kepaptsoglou; Eleni I. Vlahogianni. 2018. "Model for Planning and Sizing Curbside Parking Lanes in Urban Networks." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 20: 1-11.
Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.
Ibai Lana; Javier Del Ser; Manuel Velez; Eleni I. Vlahogianni. Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intelligent Transportation Systems Magazine 2018, 10, 93 -109.
AMA StyleIbai Lana, Javier Del Ser, Manuel Velez, Eleni I. Vlahogianni. Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intelligent Transportation Systems Magazine. 2018; 10 (2):93-109.
Chicago/Turabian StyleIbai Lana; Javier Del Ser; Manuel Velez; Eleni I. Vlahogianni. 2018. "Road Traffic Forecasting: Recent Advances and New Challenges." IEEE Intelligent Transportation Systems Magazine 10, no. 2: 93-109.