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Prof. Constantinos Antoniou
Technical University of Munich (TUM)

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0 Big Data Analytics
0 Road Safety
0 Traffic Simulation
0 Transportation systems
0 data-driven approaches

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Road Safety
Traffic Simulation
Transportation systems
data-driven approaches

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Short Biography

Constantinos Antoniou is a Full Professor in the Chair of Transportation Systems Engineering at the Technical University of Munich (TUM), Germany. He holds a Diploma in Civil Engineering from NTUA (1995), a MS in Transportation (1997) and a PhD in Transportation Systems (2004), both from MIT. His research focuses on modelling and optimization of transportation systems, data analytics and machine learning for transportation systems, and human factors for future mobility systems and in his 25 years of experience he has held key positions in a number of research projects in Europe, the US and Asia, while he has also participated in a number of consulting projects. Costas has a proven track record in attracting competitive funding in both national and international levels. He is/has been PI of several research projects (e.g. H2020 iDREAMS, MOMENTUM, Drive2thefuture, DFG DVanPool and Trampa), and has contributed considerably to the preparation of a large number of funded proposals, in the national and international level. He has authored more than 400 scientific publications, including more than 115 papers in international, peer-reviewed journals, 250 in international conference proceedings, 3 books and 20 book chapters. He is a member of several professional and scientific organizations, editorial boards (Member of the Editorial Board of Transportation Research – Part A: Policy and Practice, Transportation Research – Part C, Accident Analysis and Prevention) etc.

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Original research paper
Published: 07 July 2021 in IET Intelligent Transport Systems
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Railway network operations form complex systems. Any disruption adversely impacts the operations, causing long delays. Many studies investigate the effect of a railway incident; however, a holistic quantification is lacking. This study aims to present an estimation framework for flexible traffic management systems, which can help reduce network delays and enable dispatchers to make better-informed decisions. An incident's impact on the network is estimated by creating a sequence of models, which predict two key variables. Firstly, the incident duration is predicted, which is used to predict the second variable: total delay caused by the incident. Various influencing attributes are examined, such as weather, network and railway-related attributes. Their relationship with the response variables is studied in order to understand the incident's impact. Using incident data from the Danish Railways, machine learning models are estimated. The results show that neural networks outperform other competing models for total delay modelling, resulting in improved prediction by the estimation framework, thus giving higher accuracy than the stand-alone models in the study.

ACS Style

Bhagya Shrithi Grandhi; Emmanouil Chaniotakis; Stephan Thomann; Felix Laube; Constantinos Antoniou. An estimation framework to quantify railway disruption parameters. IET Intelligent Transport Systems 2021, 1 .

AMA Style

Bhagya Shrithi Grandhi, Emmanouil Chaniotakis, Stephan Thomann, Felix Laube, Constantinos Antoniou. An estimation framework to quantify railway disruption parameters. IET Intelligent Transport Systems. 2021; ():1.

Chicago/Turabian Style

Bhagya Shrithi Grandhi; Emmanouil Chaniotakis; Stephan Thomann; Felix Laube; Constantinos Antoniou. 2021. "An estimation framework to quantify railway disruption parameters." IET Intelligent Transport Systems , no. : 1.

Journal article
Published: 18 May 2021 in Transportation Research Part C: Emerging Technologies
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Simulation-based Dynamic Traffic Assignment models have important applications in real-time traffic management and control. The efficacy of these systems rests on the ability to generate accurate estimates and predictions of traffic states, which necessitates online calibration. A widely used solution approach for online calibration is the Extended Kalman Filter (EKF), which – although appealing in its flexibility to incorporate any class of parameters and measurements – poses several challenges with regard to calibration accuracy and scalability, especially in congested situations for large-scale networks. This paper addresses these issues in turn so as to improve the accuracy and efficiency of EKF-based online calibration approaches for large and congested networks. First, the concept of state augmentation is revisited to handle violations of the Markovian assumption typically implicit in online applications of the EKF. Second, a method based on graph-coloring is proposed to operationalize the partitioned finite-difference approach that enhances scalability of the gradient computations. Several synthetic experiments and a real world case study demonstrate that application of the proposed approaches yields improvements in terms of both prediction accuracy and computational performance. The work has applications in real-world deployments of simulation-based dynamic traffic assignment systems.

ACS Style

Haizheng Zhang; Ravi Seshadri; A. Arun Prakash; Constantinos Antoniou; Francisco C. Pereira; Moshe Ben-Akiva. Improving the accuracy and efficiency of online calibration for simulation-based Dynamic Traffic Assignment. Transportation Research Part C: Emerging Technologies 2021, 128, 103195 .

AMA Style

Haizheng Zhang, Ravi Seshadri, A. Arun Prakash, Constantinos Antoniou, Francisco C. Pereira, Moshe Ben-Akiva. Improving the accuracy and efficiency of online calibration for simulation-based Dynamic Traffic Assignment. Transportation Research Part C: Emerging Technologies. 2021; 128 ():103195.

Chicago/Turabian Style

Haizheng Zhang; Ravi Seshadri; A. Arun Prakash; Constantinos Antoniou; Francisco C. Pereira; Moshe Ben-Akiva. 2021. "Improving the accuracy and efficiency of online calibration for simulation-based Dynamic Traffic Assignment." Transportation Research Part C: Emerging Technologies 128, no. : 103195.

Original article
Published: 07 May 2021 in Computer-Aided Civil and Infrastructure Engineering
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In this paper, we develop a heuristic model based on Gaussian processes to determine synthetic sets of trips in urban networks, considering only supply‐related information. This is an alternative to the benchmark method used in the literature, which consists of repeating several trials of Monte Carlo simulations and therefore requiring a complex calibration task and large computational resources. The developed heuristic model explicitly leverages the probabilistic nature of Gaussian processes and exploits their properties to iteratively select origin–destination (od) pairs of nodes in the city network. The model then determines the shortest trip in distance for the selected od pairs and appends it to the synthetic set. We discuss the implementation and performance of both the benchmark method and the developed heuristic model on two city networks. We show that the presented model is more robust and computationally efficient than the benchmark method. This is evidenced by its ability to determine synthetic sets with much smaller sizes, naturally reducing the computational burden, when compared to the benchmark method. We also discuss how the choice of the kernel function and calibration of the hyperparameters influence the performance of the presented heuristic model.

ACS Style

S. F. A. Batista; Guido Cantelmo; Mónica Menéndez; Constantinos Antoniou. A Gaussian sampling heuristic estimation model for developing synthetic trip sets. Computer-Aided Civil and Infrastructure Engineering 2021, 1 .

AMA Style

S. F. A. Batista, Guido Cantelmo, Mónica Menéndez, Constantinos Antoniou. A Gaussian sampling heuristic estimation model for developing synthetic trip sets. Computer-Aided Civil and Infrastructure Engineering. 2021; ():1.

Chicago/Turabian Style

S. F. A. Batista; Guido Cantelmo; Mónica Menéndez; Constantinos Antoniou. 2021. "A Gaussian sampling heuristic estimation model for developing synthetic trip sets." Computer-Aided Civil and Infrastructure Engineering , no. : 1.

Journal article
Published: 12 April 2021 in European Transport Research Review
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Background The COVID-19 pandemic is a new phenomenon and has affected the population’s lifestyle in many ways, such as panic buying (the so-called “hamster shopping”), adoption of home-office, and decline in retail shopping. For transportation planners and operators, it is interesting to analyze the spatial factors’ role in the demand patterns at a POI (Point of Interest) during the COVID-19 lockdown viz-a-viz before lockdown. Data and Methods This study illustrates a use-case of the POI visitation rate or popularity data and other publicly available data to analyze demand patterns and spatial factors during a highly dynamic and disruptive event like COVID-19. We develop regression models to analyze the correlation of the spatial and non-spatial attributes with the POI popularity before and during COVID-19 lockdown in Munich by using lockdown (treatment) as a dummy variable, with main and interaction effects. Results In our case-study for Munich, we find consistent behavior of features like stop distance and day-of-the-week in explaining the popularity. The parking area is found to be correlated only in the non-linear models. Interactions of lockdown with POI type, stop-distance, and day-of-the-week are found to be strongly significant. The results might not be transferable to other cities due to the presence of different city-specific factors. Conclusion The findings from our case-study provide evidence of the impact of the restrictions on POIs and show the significant correlation of POI-type and stop distance with POI popularity. These results suggest local and temporal variability in the impact due to the restrictions, which can impact how cities adapt their transport services to the distinct demand and resulting mobility patterns during future disruptive events.

ACS Style

Vishal Mahajan; Guido Cantelmo; Constantinos Antoniou. Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich. European Transport Research Review 2021, 13, 1 -14.

AMA Style

Vishal Mahajan, Guido Cantelmo, Constantinos Antoniou. Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich. European Transport Research Review. 2021; 13 (1):1-14.

Chicago/Turabian Style

Vishal Mahajan; Guido Cantelmo; Constantinos Antoniou. 2021. "Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich." European Transport Research Review 13, no. 1: 1-14.

Research article
Published: 12 April 2021 in Transport Reviews
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The conventional form of traffic interaction undergoes a notable change with the integration of automated driving systems as a new road user, into the public roads. This may be more challenging during the transition phase, while manual-driven vehicles are still on the road, and the road infrastructure is not fully ready for merging such vehicles into the traffic patterns. Therefore, developing a robust interaction method is crucial to ensure the safety of those users interacting with automated driving systems and to ensure the efficiency of these systems on the road. For this purpose, the interaction of automated driving systems with pedestrians, as one of the most vulnerable road user groups, is investigated in this paper. Previous studies have shown the necessity for a comprehensive understanding of pedestrian behaviours and intentions, their responses to different stimuli on the road, the factors influencing their decisions during the interaction, and various external communication techniques among road users. As a result, a wide range of factors related to the communication environment, pedestrian characteristics, and existing communication methods have been found to be significant in the decision-making process of pedestrians.

ACS Style

Roja Ezzati Amini; Christos Katrakazas; Andreas Riener; Constantinos Antoniou. Interaction of automated driving systems with pedestrians: challenges, current solutions, and recommendations for eHMIs. Transport Reviews 2021, 1 -26.

AMA Style

Roja Ezzati Amini, Christos Katrakazas, Andreas Riener, Constantinos Antoniou. Interaction of automated driving systems with pedestrians: challenges, current solutions, and recommendations for eHMIs. Transport Reviews. 2021; ():1-26.

Chicago/Turabian Style

Roja Ezzati Amini; Christos Katrakazas; Andreas Riener; Constantinos Antoniou. 2021. "Interaction of automated driving systems with pedestrians: challenges, current solutions, and recommendations for eHMIs." Transport Reviews , no. : 1-26.

Journal article
Published: 10 March 2021 in Sustainable Cities and Society
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The sustainable and continuous development of public transport systems is crucial to ensuring robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality and pursue a multi-modal shift to greener solutions. The introduction of vehicle automation technology into existing public transport systems has potential impacts on mobility behaviours and may replace conventional bus service in the future. This study examines travellers’ preferences for automated buses versus conventional buses, using a context-dependent stated choice experiment. This experiment measured the effects of context variables (such as trip purpose, travel distance, time of day, weather conditions and travel companion) on the choice of automated buses versus conventional buses. The results were analysed using mixed logit models, and the findings indicate that, in general, choice behaviours do not diverge much between the choice of automated bus and conventional bus. However, individuals’ choices are more elastic towards the changes in automated bus service levels compared to conventional bus service. The results show that poor weather conditions may lower the quality and reliability of public transport service, and the probability of choosing an automated bus over a conventional bus is reduced due to such disruptions. In addition, passengers travelling for work purposes, covering long distances, or travelling with companions are more likely to choose conventional buses than automated buses.

ACS Style

Jia Guo; Yusak Susilo; Constantinos Antoniou; Anna Pernestål. When and why do people choose automated buses over conventional buses? Results of a context-dependent stated choice experiment. Sustainable Cities and Society 2021, 69, 102842 .

AMA Style

Jia Guo, Yusak Susilo, Constantinos Antoniou, Anna Pernestål. When and why do people choose automated buses over conventional buses? Results of a context-dependent stated choice experiment. Sustainable Cities and Society. 2021; 69 ():102842.

Chicago/Turabian Style

Jia Guo; Yusak Susilo; Constantinos Antoniou; Anna Pernestål. 2021. "When and why do people choose automated buses over conventional buses? Results of a context-dependent stated choice experiment." Sustainable Cities and Society 69, no. : 102842.

Journal article
Published: 19 February 2021 in Sustainability
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The advent of electrified, distributed propulsion in vertical take-off and landing (eVTOL) aircraft promises aerial passenger transport within, into, or out of urban areas. Urban air mobility (UAM), i.e., the on-demand concept that utilizes eVTOL aircraft, might substantially reduce travel times when compared to ground-based transportation. Trips of three, pre-existent, and calibrated agent-based transport scenarios (Munich Metropolitan Region, Île-de-France, and San Francisco Bay Area) have been routed using the UAM-extension for the multi-agent transport simulation (MATSim) to calculate congested trip travel times for each trip’s original mode—i.e., car or public transport (PT)—and UAM. The resulting travel times are compared and allow the deduction of potential UAM trip shares under varying UAM properties, such as the number of stations, total process time, and cruise flight speed. Under base-case conditions, the share of motorized trips for which UAM would reduce the travel times ranges between 3% and 13% across the three scenarios. Process times and number of stations heavily influence these potential shares, where the vast majority of UAM trips would be below 50 km in range. Compared to car usage, UAM’s (base case) travel times are estimated to be competitive beyond the range of a 50-minute car ride and are less than half as much influenced by congestion.

ACS Style

Raoul Rothfeld; Mengying Fu; Miloš Balać; Constantinos Antoniou. Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco. Sustainability 2021, 13, 2217 .

AMA Style

Raoul Rothfeld, Mengying Fu, Miloš Balać, Constantinos Antoniou. Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco. Sustainability. 2021; 13 (4):2217.

Chicago/Turabian Style

Raoul Rothfeld; Mengying Fu; Miloš Balać; Constantinos Antoniou. 2021. "Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco." Sustainability 13, no. 4: 2217.

Journal article
Published: 05 February 2021 in Accident Analysis & Prevention
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Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating –for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.

ACS Style

Christos Katrakazas; Athanasios Theofilatos; Ashraful Islam; Eleonora Papadimitriou; Loukas Dimitriou; Constantinos Antoniou. Prediction of rear-end conflict frequency using multiple-location traffic parameters. Accident Analysis & Prevention 2021, 152, 106007 .

AMA Style

Christos Katrakazas, Athanasios Theofilatos, Ashraful Islam, Eleonora Papadimitriou, Loukas Dimitriou, Constantinos Antoniou. Prediction of rear-end conflict frequency using multiple-location traffic parameters. Accident Analysis & Prevention. 2021; 152 ():106007.

Chicago/Turabian Style

Christos Katrakazas; Athanasios Theofilatos; Ashraful Islam; Eleonora Papadimitriou; Loukas Dimitriou; Constantinos Antoniou. 2021. "Prediction of rear-end conflict frequency using multiple-location traffic parameters." Accident Analysis & Prevention 152, no. : 106007.

Journal article
Published: 04 January 2021 in Nature Ecology & Evolution
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Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits. The future challenges and potential opportunities of robotics and autonomous systems in urban ecosystems, and how they may impact biodiversity, are explored and prioritized via a global horizon scan of 170 experts.

ACS Style

Mark A. Goddard; Zoe G. Davies; Solène Guenat; Mark J. Ferguson; Jessica C. Fisher; Adeniran Akanni; Teija Ahjokoski; Pippin M. L. Anderson; Fabio Angeoletto; Constantinos Antoniou; Adam J. Bates; Andrew Barkwith; Adam Berland; Christopher J. Bouch; Christine C. Rega-Brodsky; Loren B. Byrne; David Cameron; Rory Canavan; Tim Chapman; Stuart Connop; Steve Crossland; Marie C. Dade; David A. Dawson; Cynnamon Dobbs; Colleen T. Downs; Erle C. Ellis; Francisco J. Escobedo; Paul Gobster; Natalie Marie Gulsrud; Burak Guneralp; Amy K. Hahs; James D. Hale; Christopher Hassall; Marcus Hedblom; Dieter F. Hochuli; Tommi Inkinen; Ioan-Cristian Ioja; Dave Kendal; Tom Knowland; Ingo Kowarik; Simon J. Langdale; Susannah B. Lerman; Ian MacGregor-Fors; Peter Manning; Peter Massini; Stacey McLean; David D. Mkwambisi; Alessandro Ossola; Gabriel Pérez Luque; Luis Pérez-Urrestarazu; Katia Perini; Gad Perry; Tristan J. Pett; Kate E. Plummer; Raoufou A. Radji; Uri Roll; Simon G. Potts; Heather Rumble; Jon P. Sadler; Stevienna de Saille; Sebastian Sautter; Catherine E. Scott; Assaf Shwartz; Tracy Smith; Robbert P. H. Snep; Carl D. Soulsbury; Margaret C. Stanley; Tim Van de Voorde; Stephen J. Venn; Philip H. Warren; Carla-Leanne Washbourne; Mark Whitling; Nicholas S. G. Williams; Jun Yang; Kumelachew Yeshitela; Ken P. Yocom; Martin Dallimer. A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems. Nature Ecology & Evolution 2021, 5, 219 -230.

AMA Style

Mark A. Goddard, Zoe G. Davies, Solène Guenat, Mark J. Ferguson, Jessica C. Fisher, Adeniran Akanni, Teija Ahjokoski, Pippin M. L. Anderson, Fabio Angeoletto, Constantinos Antoniou, Adam J. Bates, Andrew Barkwith, Adam Berland, Christopher J. Bouch, Christine C. Rega-Brodsky, Loren B. Byrne, David Cameron, Rory Canavan, Tim Chapman, Stuart Connop, Steve Crossland, Marie C. Dade, David A. Dawson, Cynnamon Dobbs, Colleen T. Downs, Erle C. Ellis, Francisco J. Escobedo, Paul Gobster, Natalie Marie Gulsrud, Burak Guneralp, Amy K. Hahs, James D. Hale, Christopher Hassall, Marcus Hedblom, Dieter F. Hochuli, Tommi Inkinen, Ioan-Cristian Ioja, Dave Kendal, Tom Knowland, Ingo Kowarik, Simon J. Langdale, Susannah B. Lerman, Ian MacGregor-Fors, Peter Manning, Peter Massini, Stacey McLean, David D. Mkwambisi, Alessandro Ossola, Gabriel Pérez Luque, Luis Pérez-Urrestarazu, Katia Perini, Gad Perry, Tristan J. Pett, Kate E. Plummer, Raoufou A. Radji, Uri Roll, Simon G. Potts, Heather Rumble, Jon P. Sadler, Stevienna de Saille, Sebastian Sautter, Catherine E. Scott, Assaf Shwartz, Tracy Smith, Robbert P. H. Snep, Carl D. Soulsbury, Margaret C. Stanley, Tim Van de Voorde, Stephen J. Venn, Philip H. Warren, Carla-Leanne Washbourne, Mark Whitling, Nicholas S. G. Williams, Jun Yang, Kumelachew Yeshitela, Ken P. Yocom, Martin Dallimer. A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems. Nature Ecology & Evolution. 2021; 5 (2):219-230.

Chicago/Turabian Style

Mark A. Goddard; Zoe G. Davies; Solène Guenat; Mark J. Ferguson; Jessica C. Fisher; Adeniran Akanni; Teija Ahjokoski; Pippin M. L. Anderson; Fabio Angeoletto; Constantinos Antoniou; Adam J. Bates; Andrew Barkwith; Adam Berland; Christopher J. Bouch; Christine C. Rega-Brodsky; Loren B. Byrne; David Cameron; Rory Canavan; Tim Chapman; Stuart Connop; Steve Crossland; Marie C. Dade; David A. Dawson; Cynnamon Dobbs; Colleen T. Downs; Erle C. Ellis; Francisco J. Escobedo; Paul Gobster; Natalie Marie Gulsrud; Burak Guneralp; Amy K. Hahs; James D. Hale; Christopher Hassall; Marcus Hedblom; Dieter F. Hochuli; Tommi Inkinen; Ioan-Cristian Ioja; Dave Kendal; Tom Knowland; Ingo Kowarik; Simon J. Langdale; Susannah B. Lerman; Ian MacGregor-Fors; Peter Manning; Peter Massini; Stacey McLean; David D. Mkwambisi; Alessandro Ossola; Gabriel Pérez Luque; Luis Pérez-Urrestarazu; Katia Perini; Gad Perry; Tristan J. Pett; Kate E. Plummer; Raoufou A. Radji; Uri Roll; Simon G. Potts; Heather Rumble; Jon P. Sadler; Stevienna de Saille; Sebastian Sautter; Catherine E. Scott; Assaf Shwartz; Tracy Smith; Robbert P. H. Snep; Carl D. Soulsbury; Margaret C. Stanley; Tim Van de Voorde; Stephen J. Venn; Philip H. Warren; Carla-Leanne Washbourne; Mark Whitling; Nicholas S. G. Williams; Jun Yang; Kumelachew Yeshitela; Ken P. Yocom; Martin Dallimer. 2021. "A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems." Nature Ecology & Evolution 5, no. 2: 219-230.

Journal article
Published: 29 December 2020 in IEEE Transactions on Intelligent Transportation Systems
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Road crashes are one of the critical issues in the transportation sector. Crash studies aim to establish the relationship of crash occurrences with driver, environment and traffic factors. Lack of a large disaggregate driving or accident data constrains the study of driver factors and driving maneuvers. Moreover, the usual outcome of these studies is the crash likelihood without accounting for the impact of a potential crash, thus leading to an incomplete interpretation of crash risk. New and innovative ways of data collection, such as drone videography and floating car data, are a promising candidate for a disaggregated analysis. Our study proposes a method for estimation of rear-end crash risk during a specific traffic state, using separate formulations of likelihood and severity. We quantify rear-end crash risk by weighing the likelihood by the potential severity of a collision, wherein we introduce a severity indicator for the rear-end crash. The methodology is applied to the highD dataset, a large naturalistic traffic dataset collected on German freeways. The proposed methodology allows for the analysis of risk with traffic state and lane-changing maneuver. The findings show that speed-drop is associated with increased crash risk. Also, lane changing is associated with higher rear-end crash risk, as compared to lane-keeping, during the free-flow as well as congestion traffic. The understanding of the evolution of the crash risk, due to the driving maneuvers under different traffic conditions, can be useful for real-time crash prediction and for devising traffic management strategies.

ACS Style

Vishal Mahajan; Christos Katrakazas; Constantinos Antoniou. Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -10.

AMA Style

Vishal Mahajan, Christos Katrakazas, Constantinos Antoniou. Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-10.

Chicago/Turabian Style

Vishal Mahajan; Christos Katrakazas; Constantinos Antoniou. 2020. "Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-10.

Journal article
Published: 28 December 2020 in Journal of Traffic and Transportation Engineering (English Edition)
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For the purpose of exploring the factors affecting injury severity of children and adolescents involved in traffic crashes in Greece, disaggregate crash data including 13,431 involving children and adolescents from all regions of Greece for the period 2006–2015 were utilized. In order to identify factors affecting injury severity and account for potential unobserved heterogeneity, a series of mixed logit models were utilized. To explore and address potential temporal instability of crash-related risk factors, the likelihood ratio test was applied. Results indicated that night crashes, crashes outside urban areas as well as crashes involving bicycles or powered-two-wheelers are associated with higher injury severity of children and adolescents. Interestingly, crashes involving pedestrians are associated with lower injury severity than head-on collisions and run-off-road collisions with fixed objects. Side and sideswipe crashes also result in lower injury severities. The likelihood ratio test indicated that crash-related factors are instable when comparing the models utilizing data before and after 2010 respectively. This study contributes to the current knowledge in the field, as to the best of our knowledge this is the first study that addresses unobserved heterogeneity when analyzing child and adolescent injury severity. Overall, the findings of this study provide useful insights and could assist in unveiling crash risk factors and prioritize programs and measures to promote road safety of children and adolescents.

ACS Style

Athanasios Theofilatos; Constantinos Antoniou; George Yannis. Exploring injury severity of children and adolescents involved in traffic crashes in Greece. Journal of Traffic and Transportation Engineering (English Edition) 2020, 8, 596 -604.

AMA Style

Athanasios Theofilatos, Constantinos Antoniou, George Yannis. Exploring injury severity of children and adolescents involved in traffic crashes in Greece. Journal of Traffic and Transportation Engineering (English Edition). 2020; 8 (4):596-604.

Chicago/Turabian Style

Athanasios Theofilatos; Constantinos Antoniou; George Yannis. 2020. "Exploring injury severity of children and adolescents involved in traffic crashes in Greece." Journal of Traffic and Transportation Engineering (English Edition) 8, no. 4: 596-604.

Journal article
Published: 15 October 2020 in Transportation Research Part F: Traffic Psychology and Behaviour
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Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.

ACS Style

Christos Katrakazas; Constantinos Antoniou; George Yannis. Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification. Transportation Research Part F: Traffic Psychology and Behaviour 2020, 75, 16 -25.

AMA Style

Christos Katrakazas, Constantinos Antoniou, George Yannis. Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 75 ():16-25.

Chicago/Turabian Style

Christos Katrakazas; Constantinos Antoniou; George Yannis. 2020. "Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification." Transportation Research Part F: Traffic Psychology and Behaviour 75, no. : 16-25.

Research article
Published: 18 September 2020 in Transportation Letters
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This paper investigates the optimization of Reservation-based Autonomous Car Sharing (RACS) systems, aiming at minimizing the total vehicle travel time and customer waiting time. Thus, the RACS system and its routing are formulated with a consideration for system efficiency and passengers’ concerns. A meta-heuristic Tabu search method is investigated as a solution approach, in combination with K–Means (KMN–Tabu) or K–Medoids (KMD–Tabu) clustering algorithms. The proposed solution algorithms are tested in two different networks of varying complexity, and the performance of the algorithms is evaluated. The evaluation results show that the TS method is more suitable for small-scale problems, while KMD–Tabu is suitable for large-scale problems. However, KMN-Tabu has the least computation time, although the solution quality is lower.

ACS Style

Shun Su; Emmanouil Chaniotakis; SanthanaKrishnan Narayanan; Hai Jiang; Constantinos Antoniou. Clustered tabu search optimization for reservation-based shared autonomous vehicles. Transportation Letters 2020, 1 -5.

AMA Style

Shun Su, Emmanouil Chaniotakis, SanthanaKrishnan Narayanan, Hai Jiang, Constantinos Antoniou. Clustered tabu search optimization for reservation-based shared autonomous vehicles. Transportation Letters. 2020; ():1-5.

Chicago/Turabian Style

Shun Su; Emmanouil Chaniotakis; SanthanaKrishnan Narayanan; Hai Jiang; Constantinos Antoniou. 2020. "Clustered tabu search optimization for reservation-based shared autonomous vehicles." Transportation Letters , no. : 1-5.

Journal article
Published: 11 August 2020 in Sustainability
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The rapid development of automated buses holds great potential for the development of transportation systems. As research into innovative forms of automated transportation systems gains momentum, it is important to understand the public’s perceptions of such public transport systems. Previous studies have contributed based on hypothetical scenarios, but not based on real observations. Based on an online survey in Stockholm in March 2019, the current research addresses this gap by investigating the public’s perceptions from a real, fully operational, automated public transportation service operated in a mixed traffic environment on public roads. The respondents were selected along the automated bus line in Barkabystaden, Stockholm. Our findings indicate that (1) The presence of onboard operators has a positive impact on respondents’ perceived safety, (2) People who have not taken automated buses before have a more negative perception of driving speed of the bus service than people who have taken the buses before, (3) Attitudinal factors, such as public perceptions of safety, driving speed, reliability, and convenience, have a significant influence on the acceptance of the new bus system, (4) As an emerging and innovative transportation mode, automated buses are expected to attract a high share of regular public transportation mode users and the younger generations in the future, (5) Social-demographic characteristics such as gender and income had no significant impacts on the adoption of the new technology. The results provide the characteristics of early bus adopters and their travel behavior and help to prioritize possible investments and allow the policymakers and private industries to identify the special needs of users.

ACS Style

Jia Guo; Yusak Susilo; Constantinos Antoniou; Anna Pernestål Brenden. Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services. Sustainability 2020, 12, 6484 .

AMA Style

Jia Guo, Yusak Susilo, Constantinos Antoniou, Anna Pernestål Brenden. Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services. Sustainability. 2020; 12 (16):6484.

Chicago/Turabian Style

Jia Guo; Yusak Susilo; Constantinos Antoniou; Anna Pernestål Brenden. 2020. "Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services." Sustainability 12, no. 16: 6484.

Journal article
Published: 06 August 2020 in Smart Cities
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Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.

ACS Style

Stanislav Timokhin; Mohammad Sadrani; Constantinos Antoniou. Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data. Smart Cities 2020, 3, 818 -841.

AMA Style

Stanislav Timokhin, Mohammad Sadrani, Constantinos Antoniou. Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data. Smart Cities. 2020; 3 (3):818-841.

Chicago/Turabian Style

Stanislav Timokhin; Mohammad Sadrani; Constantinos Antoniou. 2020. "Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data." Smart Cities 3, no. 3: 818-841.

Journal article
Published: 05 July 2020 in Transportation Research Part C: Emerging Technologies
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Studies in several cities indicate that ridesourcing (ride-hailing) may increase traffic and congestion, given the substitution of more sustainable modes and the addition of empty kilometers. On the other hand, there is little evidence if smartphone apps that target shared rides have any influence on reducing traffic levels. We study the effects of a shared-mobility service offered by a start-up in Mexico City, Jetty, which is used by travelers to book a shared ride in a car, van or bus. A large-scale user survey was conducted to study trip characteristics, reasons for using the platform and the general travel choices of Jetty users. We calculate travel distance per trip leg, for the current choices and for the modes that riders would have chosen if the platform was not available. We find that the effect of the platform on vehicle kilometers traveled (VKT) depends on the rate of empty kilometers introduced by the fleet of vehicles, the substitution of public versus private transport modes, the occupancy rate of Jetty vehicles and assumptions on the occupancy rate of substituted modes. Following a sensitivity analysis approach for variables with unavailable data, we estimate that shared rides in cars increase VKT (in the range of 7 to 10 km/passenger), shared vans are able to decrease VKT (around −0.2 to −1.1 km/passenger), whereas buses are estimated to increase VKT (0.4 to 1.1 km/passenger), in our preferred scenarios. These results stem from the tradeoff between the effects of the occupancy rates per vehicle (larger vehicles are shared by more people) and the attractiveness of the service for car users (shared vans attract more car drivers than buses booked through Jetty). Our findings point to the relevance of shared rides in bigger vehicles such as vans as competitors to low occupancy car services for the future of mobility in cities, and to the improvement of public transportation services through the inclusion of quality attributes as provided by new shared-mobility services.

ACS Style

Alejandro Tirachini; Emmanouil Chaniotakis; Mohamed Abouelela; Constantinos Antoniou. The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities? Transportation Research Part C: Emerging Technologies 2020, 117, 102707 .

AMA Style

Alejandro Tirachini, Emmanouil Chaniotakis, Mohamed Abouelela, Constantinos Antoniou. The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities? Transportation Research Part C: Emerging Technologies. 2020; 117 ():102707.

Chicago/Turabian Style

Alejandro Tirachini; Emmanouil Chaniotakis; Mohamed Abouelela; Constantinos Antoniou. 2020. "The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities?" Transportation Research Part C: Emerging Technologies 117, no. : 102707.

Book review
Published: 10 June 2020 in Transport Reviews
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Constantinos Antoniou. The accelerating transport innovation revolution: a global, case study-based assessment of current experience, cross-sectorial effects, and socioeconomic transformations. Transport Reviews 2020, 40, 814 -816.

AMA Style

Constantinos Antoniou. The accelerating transport innovation revolution: a global, case study-based assessment of current experience, cross-sectorial effects, and socioeconomic transformations. Transport Reviews. 2020; 40 (6):814-816.

Chicago/Turabian Style

Constantinos Antoniou. 2020. "The accelerating transport innovation revolution: a global, case study-based assessment of current experience, cross-sectorial effects, and socioeconomic transformations." Transport Reviews 40, no. 6: 814-816.

Journal article
Published: 23 December 2019 in Economics of Transportation
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It is currently unknown in which city environments, automated vehicles could be deployed at reasonable speeds, given safety concerns. We analytically and numerically assess the impact of automation for optimal vehicle size, service frequency, fare, subsidy and degree of economies of scale, by developing a model that is applied for electric vehicles, with data from Chile and Germany, taken as illustrative examples of developed and developing countries. Automation scenarios include cases with partial driving cost savings and reduced running speed for automated vehicles. We find that a potential reduction in vehicle operating cost due to automation benefits operators, through a reduction of operator costs, and also benefits public transport users, through a reduction on waiting times and on the optimal fare per trip. The optimal subsidy per trip is also reduced. The benefits of vehicle automation are greater in countries where drivers’ salaries are larger.

ACS Style

Alejandro Tirachini; Constantinos Antoniou. The economics of automated public transport: Effects on operator cost, travel time, fare and subsidy. Economics of Transportation 2019, 21, 100151 .

AMA Style

Alejandro Tirachini, Constantinos Antoniou. The economics of automated public transport: Effects on operator cost, travel time, fare and subsidy. Economics of Transportation. 2019; 21 ():100151.

Chicago/Turabian Style

Alejandro Tirachini; Constantinos Antoniou. 2019. "The economics of automated public transport: Effects on operator cost, travel time, fare and subsidy." Economics of Transportation 21, no. : 100151.

Review
Published: 27 November 2019 in Sustainability
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The interaction among pedestrians and human drivers is a complicated process, in which road users have to communicate their intentions, as well as understand and anticipate the actions of users in their vicinity. However, road users still ought to have a proper interpretation of each others’ behaviors, when approaching and crossing the road. Pedestrians, as one of the interactive agents, demonstrate different behaviors at road crossings, which do not follow a consistent pattern and may vary from one situation to another. The presented inconsistency and unpredictability of pedestrian road crossing behaviors may thus become a challenge for the design of emerging technologies in the near future, such as automated driving system (ADS). As a result, the current paper aims at understanding the effectual communication techniques, as well as the factors influencing pedestrian negotiation and decision-making process. After reviewing the state-of-the-art and identifying research gaps with regards to vehicle–pedestrian crossing encounters, a holistic approach for road crossing interaction modeling is presented and discussed. It is envisioned that the presented holistic approach will result in enhanced safety, sustainability, and effectiveness of pedestrian road crossings.

ACS Style

Roja Ezzati Amini; Christos Katrakazas; Constantinos Antoniou. Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review. Sustainability 2019, 11, 6713 .

AMA Style

Roja Ezzati Amini, Christos Katrakazas, Constantinos Antoniou. Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review. Sustainability. 2019; 11 (23):6713.

Chicago/Turabian Style

Roja Ezzati Amini; Christos Katrakazas; Constantinos Antoniou. 2019. "Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review." Sustainability 11, no. 23: 6713.

Journal article
Published: 05 November 2019 in Journal of Transport Geography
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Public transportation is a key element to vivid city life. Understanding the dynamics and driving forces of public transportation ridership can be a very rewarding task. It is, however, a highly complex construct. In this research, we focus on a spatial viewpoint, which has seen little attention: the link level. It represents the trip of a vehicle between directly connected stations. Additionally, we put emphasis on the impact of exogenous events. In order to assess their spatio–temporal influences, a temporal resolution of 30 min complements the spatial link level. Ridership data for trams and buses is provided by Stadtwerke München (SWM), which is the operator of the public transportation network in Munich, Germany, including 82 bus and 17 tram lines. About 30% of trams and 50% of buses are equipped with automatic passenger counting sensors, which capture boarding and alighting at each individual station. The equipped vehicles are strategically placed by SWM to obtain a meaningful view on the whole system. The raw sensor data is cleaned and sanitized. The data we are using spans a 4–year period (2014–2017). Following a pre–processing step, ∼59.79% of the data is considered, which equates to ∼97 million observations. There are 693 tram links and 2944 bus links, which makes 3637 links in total. We distinguish the analysis in ridership prediction and inference. For prediction, we specify one model functional form and build this model for each link, using 5–fold cross–validation to avoid overfitting. We employ decision trees, combining them with bagging and boosting. We then perform inference, i.e. attempt to understand the relationship between the variables that emerged in the predictive models. Ridership is assessed for each link separately and visualized together in order to construct network views and maps. Conclusions are drawn, and recommendations for future research are formulated.

ACS Style

Stephan Karnberger; Constantinos Antoniou. Network–wide prediction of public transportation ridership using spatio–temporal link–level information. Journal of Transport Geography 2019, 82, 102549 .

AMA Style

Stephan Karnberger, Constantinos Antoniou. Network–wide prediction of public transportation ridership using spatio–temporal link–level information. Journal of Transport Geography. 2019; 82 ():102549.

Chicago/Turabian Style

Stephan Karnberger; Constantinos Antoniou. 2019. "Network–wide prediction of public transportation ridership using spatio–temporal link–level information." Journal of Transport Geography 82, no. : 102549.