Research Publications

@article{ABOUELELA2023103602,

title = {Understanding the landscape of shared-e-scooters in North America; Spatiotemporal analysis and policy insights},

author = {Mohamed Abouelela and Emmanouil Chaniotakis and Constantinos Antoniou},

url = {https://www.sciencedirect.com/science/article/pii/S0965856423000228},

doi = {https://doi.org/10.1016/j.tra.2023.103602},

issn = {0965-8564},

year  = {2023},

date = {2023-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {169},

pages = {103602},

abstract = {Shared-e-scooters are being introduced in cities worldwide, with their introduction often being distant from the actual service characteristics understanding, potential benefits, and threats realization. This research explores scooter use by examining approximately nine million scooter trips from five North American cities (Austin; TX, Calgary; AB, Chicago; IL, Louisville; KY, Minneapolis; MN). By investigating the spatiotemporal hourly and daily use, we found that demand patterns tend to be similar in the different cities. Trip characteristics (speed, duration, and distance) are almost empirically consistent across the five cities; however, there is evidence that trip characteristics change over time in the same city. We also examined the impact of exogenous factors on scooter demand, and found that weather (temperature, wind speed, precipitation, and snow), day of the week, infrastructure (bike lanes, sidewalks, and shared bike stations), sociodemographics (gender, age, and income), land use, and accessibility to transit significantly impact demand. Findings highlight the need for evidence-based examination of shared-e-scooters and regulatory processes to guide policy decisions by the different stakeholders.},

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pubstate = {published},

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}

@article{SIL2022100005,

title = {Exploring satisfaction for transfers at intermodal interchanges: A comparison of Germany and India},

author = {Ankita Sil and Emmanouil Chaniotakis and Uttam Kumar Roy and Constantinos Antoniou},

url = {https://www.sciencedirect.com/science/article/pii/S1077291X22000054},

doi = {https://doi.org/10.1016/j.jpubtr.2022.100005},

issn = {1077-291X},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Public Transportation},

volume = {24},

pages = {100005},

abstract = {abstract 

Multimodality in Public Transport has been proven to be one of the main drivers of sustainability and economic feasibility for the last few decades. Consequently, user satisfaction for transfers is the key to adequately serving demand. This research studies on commuters’ perception of comfort at interchanges, focusing on the connection between metro systems and other modes. Satisfaction analysis and modelling is conducted using weighted regression, factor analysis and ordered logit models for nine transfers at major interchanges in two Indian cities (New Delhi and Kolkata) and one German city (Munich); aiming at revealing the differences in user satisfaction in developing and developed economy, and for different Public Transport quality and interchanges. The results indicate that factors of transfer quality, accessibility and physical hindrances are significant in Indian case and the human factor, and transfer quality are significant in the case of Munich, Germany. Additionally, it is found that perceived comfort differs on commuters’ experiences with transfer distance and time.},

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pubstate = {published},

tppubtype = {article}

}

@article{KAMARGIANNI2022103873,

title = {Changing transport planning objectives during the Covid-19 lockdowns: Actions taken and lessons learned for enhancing sustainable urban mobility planning},

author = {Maria Kamargianni and Christina Georgouli and Luciano Pana Tronca and Manos Chaniotakis},

url = {https://www.sciencedirect.com/science/article/pii/S0264275122003122},

doi = {https://doi.org/10.1016/j.cities.2022.103873},

issn = {0264-2751},

year  = {2022},

date = {2022-01-01},

journal = {Cities},

volume = {131},

pages = {103873},

abstract = {Whilst there is research on how Covid-19 impacted travel demand and transport business, little attention has been paid on how Covid-19 has affected authorities' transport planning priorities and the actions taken to protect the public while travelling. This paper attempts to shed light on: a) how the transport planning priorities changed during the Covid-19 lockdowns in 2020/2021, and b) how the planning phases of the Sustainable Urban Mobility Plan (SUMP) framework can be strengthened to support a more resilient emergency planning environment. To address these questions, an online questionnaire was designed followed up by personal interviews from selected European cities. Data collection took place in November and December 2020, when most European countries were in lockdowns. Thirteen public authorities participated in the online questionnaire, while nine out of them were further interviewed. A mixed methods approach is used to analyse the quantitative and qualitative data and bring the results together to assess how SUMP priorities have changed. The results showed that the priority planning objectives were different in the period during the 2020/2021 lockdowns compared to the period before that. Public transport system planning was a priority in both periods, while planning for shared mobility and Mobility as a Service was further prioritised in the 2020/21 lockdowns. The main reasons for prioritising specific planning objectives were to secure public health, minimise environmental impact, support economic recovery and address social equity. The changes in the priority of planning objectives were also diverse between smaller and larger urban areas. Most of the actions adopted to accommodate the prioritised planning objectives were already defined before Covid-19, indicating that the lockdowns have acted as an accelerator of specific existing planning objectives.},

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pubstate = {published},

tppubtype = {article}

}

@article{NARAYANAN2022103651,

title = {Modelling reservation-based shared autonomous vehicle services: A dynamic user equilibrium approach},

author = {Santhanakrishnan Narayanan and Emmanouil Chaniotakis and Constantinos Antoniou},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X22000948},

doi = {https://doi.org/10.1016/j.trc.2022.103651},

issn = {0968-090X},

year  = {2022},

date = {2022-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {140},

pages = {103651},

abstract = {Shared Autonomous Vehicles (SAVs) are expected to be used for regular and pre-planned trips. Such trips are suitable for reservation-based services, wherein the customer needs to book for a trip in advance. Systems enabling reservation of trips can allow for better planning of routes and schedules, and if optimally designed, enable higher efficiency. The primary objective of this research is to model the effects of such a system, by formulating and solving the combined Dynamic User Equilibrium and Shared autonomous vehicle Chain Formation (DUESCF) problem. The problem is formulated as a bilevel model based on game theory, involving road users and SAV service operator. Given a situation where conventional private and shared autonomous vehicles co-exist, road users select paths and departure times to maximize a perceived utility (commonly treated as minimizing a disutility) by forming a DUE (fixed point problem), and the SAV service operator tries to maximize the performance by forming appropriate SAV chains (combinatorial problem). The final objective of this bilevel model is a traffic assignment that includes SAV chain formation, such that both road users and SAV service operator obtain optimal solutions by reaching a Nash equilibrium, where no player is better off by unilaterally changing their decisions. A solution approach, based on Iterative Optimization and Assignment (IOA) method, is proposed with path flow and SAV performance changes as convergence criteria. Furthermore, the solution approach is tested for its robustness, and a scenario analysis is carried out to evaluate the impacts of reservation-based SAV services. The results show that a ridesharing SAV system is better compared to a carsharing and a mixed system consisting of both, in terms of total system travel time, congestion levels, total vehicle kilometres travelled and vehicle requirements.},

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}

@article{ABOUELELA2022103632,

title = {Characterizing the adoption and frequency of use of a pooled rides service},

author = {Mohamed Abouelela and Alejandro Tirachini and Emmanouil Chaniotakis and Constantinos Antoniou},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X22000754},

doi = {https://doi.org/10.1016/j.trc.2022.103632},

issn = {0968-090X},

year  = {2022},

date = {2022-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {138},

pages = {103632},

abstract = {Pooled-ride services have a significant potential for reducing traffic externalities and enhancing transportation systems in the urban environment. These services and their users’ characteristics still need further inspection and exploration. We investigated factors encouraging the shift from the currently used modes to pooled-ride-services, the choice between different pooled services vehicles types, and the frequency of use of pooled-rides, using data collected via a large-scale online survey conducted in Mexico City, Mexico (CDMX) for a start-up that organizes pooled rides, Jetty. We modeled the pooled-ride-service adoption process as a function of the users’ sociodemographics, latent travel attitudes, accessibility to public transportation, trip characteristics, reasons to use the service, and users’ activities during the trips. We estimated hybrid choice models and binary logit models, which show that users’ sociodemographic and travel attitudes are the main factors impacting the shift from different modes to pooled rides. Service-related characteristics such as multi-tasking, trip fare, and avoiding parking problems also impact the shift decision. On the other hand, the frequency of service use is mainly impacted by trip characteristics such as total trip distance, and the headway at the user’s home location nearest Metro stations. Income, employment status, number of cars in the household, and gender were the only sociodemographic factors impacting the service use frequency directly and indirectly.},

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pubstate = {published},

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}

@article{CHANIOTAKIS2022100081,

title = {Investigating social media spatiotemporal transferability for transport},

author = {Emmanouil Chaniotakis and Mohamed Abouelela and Constantinos Antoniou and Konstadinos Goulias},

url = {https://www.sciencedirect.com/science/article/pii/S2772424722000312},

doi = {https://doi.org/10.1016/j.commtr.2022.100081},

issn = {2772-4247},

year  = {2022},

date = {2022-01-01},

journal = {Communications in Transportation Research},

volume = {2},

pages = {100081},

abstract = {Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies. Particularly useful for travel analysis is when Social Media users allow (voluntarily or not) tracing their movement using geotagged information of their communication with these online platforms. In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns, study differences and commonalities among these cities, and explore the nature of user location recurrence. The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models. Identification of repeated rates of location (recurrence) can be used to define activity spaces. Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here. The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces.},

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pubstate = {published},

tppubtype = {article}

}

@article{Tsiamasiotis_smartcities4040066,

title = {Identifying and Quantifying Factors Determining Dynamic Vanpooling Use},

author = {Konstantinos Tsiamasiotis and Emmanouil Chaniotakis and Moeid Qurashi and Hai Jiang and Constantinos Antoniou},

url = {https://www.mdpi.com/2624-6511/4/4/66},

doi = {10.3390/smartcities4040066},

issn = {2624-6511},

year  = {2021},

date = {2021-01-01},

journal = {Smart Cities},

volume = {4},

number = {4},

pages = {1243–1258},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IET_ITS_Shrithi,

title = {An estimation framework to quantify railway disruption parameters},

author = {Bhagya Shrithi Grandhi and Emmanouil Chaniotakis and Stephan Thomann and Felix Laube and Constantinos Antoniou},

url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/itr2.12095},

doi = {https://doi.org/10.1049/itr2.12095},

year  = {2021},

date = {2021-01-01},

journal = {IET Intelligent Transport Systems},

volume = {15},

number = {10},

pages = {1256-1268},

abstract = {Abstract 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.},

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}

@article{Antoniou_Chaniotakis_Katrakazas_Tirachini_2020,

title = {Editorial: A better tomorrow: towards human-oriented, sustainable transportation systems},

author = {Constantinos Antoniou and Emmanouil Chaniotakis and Christos Katrakazas and Alejandro Tirachini},

url = {https://journals.open.tudelft.nl/ejtir/article/view/5468},

doi = {10.18757/ejtir.2020.20.4.5468},

year  = {2020},

date = {2020-10-01},

journal = {European Journal of Transport and Infrastructure Research},

volume = {20},

number = {4},

pages = {354–361},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1080/19427867.2020.1824309,

title = {Clustered tabu search optimization for reservation-based shared autonomous vehicles},

author = {Shun Su and Emmanouil Chaniotakis and Santhanakrishnan Narayanan and Hai Jiang and Constantinos Antoniou},

url = {https://doi.org/10.1080/19427867.2020.1824309},

doi = {10.1080/19427867.2020.1824309},

year  = {2020},

date = {2020-01-01},

journal = {Transportation Letters},

volume = {0},

number = {0},

pages = {1-5},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TIRACHINI2020102707,

title = {The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities?},

author = {Alejandro Tirachini and Emmanouil Chaniotakis and Mohamed Abouelela and Constantinos Antoniou},

url = {http://www.sciencedirect.com/science/article/pii/S0968090X20306227},

doi = {https://doi.org/10.1016/j.trc.2020.102707},

issn = {0968-090X},

year  = {2020},

date = {2020-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {117},

pages = {102707},

abstract = {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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NARAYANAN2020255,

title = {Shared autonomous vehicle services: A comprehensive review},

author = {Santhanakrishnan Narayanan and Emmanouil Chaniotakis and Constantinos Antoniou},

url = {http://www.sciencedirect.com/science/article/pii/S0968090X19303493},

doi = {https://doi.org/10.1016/j.trc.2019.12.008},

issn = {0968-090X},

year  = {2020},

date = {2020-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {111},

pages = {255 - 293},

abstract = {The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ALHADDAD2020696,

title = {Factors affecting the adoption and use of urban air mobility},

author = {Christelle Al Haddad and Emmanouil Chaniotakis and Anna Straubinger and Kay Plötner and Constantinos Antoniou},

url = {http://www.sciencedirect.com/science/article/pii/S0965856419303830},

doi = {https://doi.org/10.1016/j.tra.2019.12.020},

issn = {0965-8564},

year  = {2020},

date = {2020-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {132},

pages = {696 - 712},

abstract = {Technological advances have recently led to the development of urban air mobility (UAM), an alternative transportation mode with several concepts including vehicles operated by on-demand fully-automated vertical take-off and landing aircraft (VTOL) for intra-city passenger transportation. However, despite a growing interest in UAM, understanding users’ perceptions to it remains limited. This research aims to identify and quantify the factors affecting the adoption and use of UAM, based on relevant tools from the literature, such as recurring factors in studies on aerial vehicle concepts, ground autonomous vehicles, but also acceptance models, such as the Technology Acceptance Model by Davis et al. (1989). A stated-preference survey was developed to assess the perception of users in terms of adoption time horizon, including options such as the first six years of the service’s implementation, “unsure”, and “never”. The obtained results were evaluated using exploratory factor analyses, and the specification and estimation of suitable discrete choice models, multinomial logit models (MNLs) and ordered logit models (OLMs), with adoption time horizon as dependent variable. Findings revealed the importance of safety and trust, affinity to automation, data concerns, social attitude, and socio-demographics for adoption. Factors, such as the value of time savings, the perception of automation costs, and service reliability, were also found to be highly influential. There was also an indication that skeptical respondents, i.e. answering “unsure”, had a behavior similar to late and non-adopters, i.e. adoption time horizon higher than six years or answering “never”. The summarized results were represented in an extended Technology Acceptance Model for urban air mobility, and provided insights for policymakers and industrial stakeholders.},

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pubstate = {published},

tppubtype = {article}

}

@article{Qurashi_2019,

title = {PC-SPSA: Employing Dimensionality Reduction to Limit SPSA Search Noise in DTA Model Calibration},

author = {M. Qurashi and T. Ma and E. Chaniotakis and C. Antoniou},

doi = {10.1109/TITS.2019.2915273},

issn = {1524-9050},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Transactions on Intelligent Transportation Systems},

pages = {1-11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/0361198119849908,

title = {Built Environment Factors Affecting Bike Sharing Ridership: Data-Driven Approach for Multiple Cities},

author = {David Duran-Rodas and Emmanouil Chaniotakis and Constantinos Antoniou},

url = {https://doi.org/10.1177/0361198119849908},

doi = {10.1177/0361198119849908},

year  = {2019},

date = {2019-01-01},

journal = {Transportation Research Record},

volume = {2673},

number = {12},

pages = {55-68},

abstract = {Identification of factors influencing ridership is necessary for policy-making, as well as, when examining transferability and aspects of performance and reliability. In this work, a data-driven method is formulated to correlate arrivals and departures of station-based bike sharing systems with built environment factors in multiple cities. Ridership data from stations of multiple cities are pooled in one data set regardless of their geographic boundaries. The method bundles the collection, analysis, and processing of data, as well as, the model’s estimation using statistical and machine learning techniques. The method was applied on a national level in six cities in Germany, and also on an international level in three cities in Europe and North America. The results suggest that the model’s performance did not depend on clustering cities by size but by the relative daily distribution of the rentals. Selected statistically significant factors were identified to vary temporally (e.g., nightclubs were significant during the night). The most influencing variables were related to the city population, distance to city center, leisure-related establishments, and transport-related infrastructure. This data-driven method can help as a support decision-making tool to implement or expand bike sharing systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kotoula_2018,

title = {Exploring the Effects of University Campus Decentralization to Students Mode Choice},

author = {Kornilia Kotoula and Alexandros Sialdas and George Botzoris and Emmanouil Chaniotakis and Josep Maria Salanova Grau},

url = {https://pp.bme.hu/tr/article/view/11641},

doi = {https://doi.org/10.3311/PPtr.11641},

year  = {2018},

date = {2018-01-01},

journal = {Periodica Polytechnica Transportation Engineering},

volume = {46},

number = {4},

pages = {207-214},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aifado_2018DSS,

title = {An intelligent decision support system for managing natural and man-made disasters},

author = {Georgia Aifadopoulou and Emmanouil Chaniotakis and Iraklis Stamos and Sokratis Mamarikas and Evangelos Mitsakis},

url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJDSS.2018.094263},

doi = {10.1504/IJDSS.2018.094263},

year  = {2018},

date = {2018-01-01},

journal = {International Journal of Decision Support Systems},

volume = {3},

number = {1-2},

pages = {91-105},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salanova2018_JLBS,

title = {Correlation between digital and physical world, case study in Thessaloniki},

author = {Josep Maria Salanova Grau and Ioannis Toumpalidis and Emmanouil Chaniotakis and Nicolaos Karanikolas and Georgia Aifadopoulou},

url = {https://doi.org/10.1080/17489725.2017.1420257},

doi = {10.1080/17489725.2017.1420257},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Location Based Services},

volume = {11},

number = {2},

pages = {118-132},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PEL2017123,

title = {Stochastic user equilibrium traffic assignment with equilibrated parking search routes},

author = {Adam J. Pel and Emmanouil Chaniotakis},

url = {http://www.sciencedirect.com/science/article/pii/S0191261516305525},

doi = {https://doi.org/10.1016/j.trb.2017.03.015},

issn = {0191-2615},

year  = {2017},

date = {2017-01-01},

journal = {Transportation Research Part B: Methodological},

volume = {101},

number = {Supplement C},

pages = {123 - 139},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TRB2016_Activities,

title = {Inferring Activities from Social Media Data},

author = {Emmanouil Chaniotakis and Constantinos Antoniou and Georgia Aifadopoulou and Loukas Dimitriou},

url = {https://doi.org/10.3141/2666-04},

doi = {10.3141/2666-04},

year  = {2017},

date = {2017-01-01},

journal = {Transportation Research Record: Journal of the Transportation Research Board},

volume = {2666},

pages = {29-37},

abstract = {Social media produce an unprecedented amount of information that can be extracted and used in transportation research, with one of the most promising areas being the inference of individuals’ activities. Whereas most studies in the literature focus on the direct use of social media data, this study presents an efficient framework that follows a user-centric approach for the inference of users’ activities from social media data. The framework was applied to data from Twitter, combined with inferred data from Foursquare that contains information about the type of location visited. The users’ data were then classified with a density-based spatial classification algorithm that allows for the definition of commonly visited locations, and the individual-based data were augmented with the known activity definition from Foursquare. On the basis of the known activities and the Twitter text, a set of classification algorithms was applied for the inference of activities. The results are discussed according to the types of activities recognized and the classification performance. The classification results allow for a wide application of the framework in the exploration of the activity space of individuals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IEEEIS2016_SM,

title = {Mapping Social Media for Transportation Studies},

author = {E. Chaniotakis and C. Antoniou and F. Pereira},

doi = {10.1109/MIS.2016.98},

issn = {1541-1672},

year  = {2016},

date = {2016-11-01},

journal = {IEEE Intelligent Systems},

volume = {31},

number = {6},

pages = {64-70},

abstract = {Social media has dominated the online lives of individuals and has created a research stream with potential applications in many scientific fields, including transportation. This article attempts to map the landscape of social media, focusing on transportation, by identifying the main streams of research in the pertinent literature and analyzing the taxonomy of the dominant social media in terms of functionalities fulfilled and data availability. Additionally, the authors present a strengths, weaknesses, opportunities, and threats (SWOT) analysis and examine the applicability of data collected from social media sources in a case study.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chaniotakis2015228,

title = {Drivers' parking location choice under uncertain parking availability and search times: A stated preference experiment},

author = {E. Chaniotakis and A. J. Pel},

url = {http://goo.gl/VawI5h},

doi = {http://dx.doi.org/10.1016/j.tra.2015.10.004},

issn = {0965-8564},

year  = {2015},

date = {2015-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {82},

pages = {228 - 239},

abstract = {To assess parking pricing policies and parking information and reservation systems, it is essential to understand how drivers choose their parking location. A key aspect is how drivers' behave towards uncertainties towards associated search times and finding a vacant parking spot. This study presents the results from a stated preference experiment on the choice behaviour of drivers, in light of these uncertainties. The attribute set was selected based on a literature review, and appended with the probabilities of finding a vacant parking spot upon arrival and after 8 min (and initially also after 4 min, but later dropped to reduce the survey complexity). Efficient Designs were used to create the survey design, where two rounds of pilot studies were conducted to estimate prior coefficients. Data was successfully collected from 397 respondents. Various random utility maximisation (RUM) choice models were estimated, including multinomial logit, nested logit, and mixed logit, as well as models accounting for panel effects. These model analyses show how drivers appear to accept spending time on searching for a vacant parking spot, where parking availability after 8 min ranks second most important factor in determining drivers' parking decisions, whilst parking availability upon arrival ranks fourth. Furthermore, the inclusion of heterogeneity in preferences and inter-driver differences is found to increase the predictive power of the parking location choice model. The study concludes with an outlook of how these insights into drivers' parking behaviour can be incorporated into traffic assignment models and used to support parking systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}