1,721,017 research outputs found
"I am dependent on others to get there": Mobility barriers and solutions for societal participation by persons with disabilities
No full textTransportation challenges are important barriers for persons with disabilities (PWD) to participate in profes-sional, social and economic life. This study builds further on previous literature about the transportation problems of PWD by not only mapping in detail the perceived mobility barriers of PWD with various types of disabilities and for different travel modes, but also investigating their own solutions to lower these barriers and examining the impact of improved mobility on participation. The experiences, transport needs and wishes of 45 PWD (with physical, visual or intellectual disabilities or Autism Spectrum Disorder) were investigated using qualitative focus groups and semi-structured interviews in Flanders, Belgium. The study provides concrete rec-ommendations for the experienced mobility barriers to increasing PWD's societal participation.We would like to thank all participants for their voluntary and enthusiastic contribution in this study
A Generic Data-driven Sequential Clustering Algorithm Determining Activity Skeletons
No full textAbstractMany activity-based models start by scheduling inflexible or mandatory activities (if present), before more flexible activities. Often work and educational activities are assumed as most stringent and recognized as the only mandatory activities. According to this definition, only 45% of all schedules contains a mandatory activity (OVG single-day travel survey in Flanders, Belgium). This means 55% of schedules does not have a traditional mandatory-flexible activity structure. This research proposes a completely data-driven approach to reveal the real basic structure of individuals’ schedules, i.e. the skeleton schedule sequence. To this end, a sequential clustering algorithm was developed. Furthermore, an in-depth analysis of the parameter settings was performed. The proposed method reveals a set of skeleton activity schedules and confirms the importance of work and education
Business Applications of Cargo Drones in the EU
No full textDrones have become ubiquitous in various industries due to their versatility and efficiency in performing various tasks, from agricultural operations to search and rescue missions. This paper explores the use of drones, particularly cargo drones, in revolutionizing logistics and transportation systems. Medium-range cargo drones offer the potential to transform freight transportation by offering independence from traditional infrastructure and potentially reducing environmental impact. However, the integration of UAVs into existing logistical operations faces several challenges, including regulatory hurdles, technological limitations, and public perception issues. Drones can become a viable form of cargo transportation given that the regulatory challenges are addressed and can be efficiently integrated into the existing logistic operations. It would ultimately result in an efficient last-mile delivery option and will revolutionize the logistics industry
Simulating Collaborative and Autonomous Persistent Surveillance by Drones for Search and Rescue Operations
No full textIn search and rescue operations, time plays a critical role in saving lives. To address this challenge, a multi-drone surveillance system has emerged as a valuable tool for first responders, enabling them to cover large areas efficiently. However, for optimal 9 effectiveness, such a system needs to be collaborative and autonomous, allowing first responders and operational rescue teams to 10 focus on crucial tasks. This paper presents a simulation framework designed to assist in the selection of optimal design 11 characteristics for a multi-drone collaboration system in a specific search and rescue environment, the objective of this simulation framework is (i) to optimize coordination and continuity in large scale missions while (ii) taking into account various factors and 13 considerations to guide the decision-making process and (iii) adapt to dynamic in the resource allocation. By leveraging this 14 simulation framework, stakeholders can evaluate and choose design features that enhance situational insight, optimize resource 15 allocation, and streamline rescue operations in their unique context
Automating Composition of Origin-Destination Flows of Intersections Based on UAV Data
No full textWith the exponential development rate of UAV and computer version technologies, vast and sophisticated data on traffic is now available. Spatial and temporal data, including speed and other parameters of trajectory data, can be captured. Likewise, there are unsupervised clustering algorithms in the domain of machine learning that can group data points into clusters based on their inherent similarities without using labeled data. Algorithms such as GMM, DBSCAN, and HDBSCAN identify patterns and structures within the dataset, allowing for the discovery of natural groupings. Leveraging data from UAVs and these clustering algorithms, this paper aims to develop an effective and efficient methodology to automate the extraction of Origin-Destination (OD) flows of different types of intersections. A new custom-made intersection OD flow automation method called IODF is introduced, along with the deployment of DBSCAN, HDBSCAN, and GMM algorithms for clustering intersection trajectories, leading to the automatic extraction of OD flows. The results demonstrate that all four methods performed effectively in extracting OD flow for various intersection types.This paper is based on the project that has received funding from the European Union’s Horizon 2020 research and
innovation programme under Grant Agreement No 101037193
Enhancing Learning About Climate Change Issues Among Secondary School Students with Citizen Science Tools
No full textCitizen science is a valuable tool to inculcate awareness among citizens about climate change issues. This is also the main goal of the I-CHANGE project. On these lines, Hasselt living lab as part of the I-CHANGE project developed a collaboration with a technical secondary school. Digital sensors such as Meteotrackers and smart citizen kits will be utilized under this collaboration where students and teachers will not only collect data, but will be using it in an innovative manner to integrate the findings obtained from its analysis to enhance students learning about climate change issues. This paper presents a structured methodological approach to achieve this goal
Activity Sequence Generation Using Universal Mobility Patterns
No full textPrevious work has established that rank ordered single-day activity sequences from various study areas exhibit a universal power law distribution called Zipf's law. By analyzing datasets from across the world, evidence was provided that it is in fact a universal distribution. This study focuses on a potential mechanism that leads to the power law distribution that was previously discovered. It makes use of 15 household travel survey (HTS) datasets from study areas all over the world to demonstrate that reasonably accurate sets of activity sequences (or ''schedules'') can be generated with extremely little information required; the model requires no input data and contains few tunable parameters. The activity sequence generation mechanism is based on sequential sampling from two universal distributions: (i) the distributions of the number of activities (trips) and (ii) the activity types (trip purposes). This paper also attempts to demonstrate the universal nature of these distributions by fitting several equations to the 15 HTS datasets. The lightweight activity sequence generation model can be implemented in any (lightweight) transportation model to create a basic set of activity sequences, saving effort and cost in data collection and in model development and calibration. Keywords Zipf 's law, activity sequences, universal distributions, trip purpose, number of trips, daily activity pattern Humanity is increasingly challenged with transportation-related issues that have economic, social and ecological consequences. Transportation models are employed to try to find solutions for such problems. They can support ex-ante management decision-making by providing information about the impacts of alternative transportation, land use investments and various other policies, as well as demographic and economic trends. The demand (and need) for such models is steadily increasing. Current models are efficacious, yet they are data-hungry and costly to deploy or transfer to other study areas, making their introduction into all policy decision-making more difficult. Moreover, such models are unattainable for smaller governmental bodies or cities, or whenever scenario results need to be produced quickly. This gives rise to the need for lightweight, easy to deploy modeling solutions. Demand generation, a large component in every modern transport model (be it a four-step model or an activity-based [AB] model), typically requires a significant amount of household travel survey (HTS) data, usually collected via various (expensive and time-consuming) surveying techniques. Other approaches attempt to use mobile phone data (e.g., call detailed records), which could aid data collection but also introduce new challenges (big data processing, inferring of non-observed properties, privacy, etc.). This paper provides insights which may considerably reduce data dependency for some applications. It discusses an activity sequence generation mechanism that is based on sequential sampling of two universal distributions. The model requires no input data and contains almost no tunable parameters. Lightweight demand generation models may be developed, making transportation models more accessible for small government bodies, city managers and so forth. Lightweight models may alsoThe author(s) received no financial support for the research, authorship, and/or publication of this article
Estimation of Value of Time for a Congested Network – A Case Study of the National Highway, Karachi
No full textAbstractTraffic congestion in mega cities is a common phenomenon for developing countries. Numerous studies on congestion cost estimation, that aim to quantify their monetary losses, have been conducted. Value of Time (VOT) assessment through utility maximizing theory and choice models are abundantly applied in transport literature. However, estimating VOT on congested network is not widely applied yet. To recognize the difference under normal and congested network, the current study focuses on VOT estimation for work trips in an extremely congested network.The focus of this research is to conduct a VOT estimation of the National Highway, Karachi. It connects Karachi city with Port Qasim Industrial area and the rest of the country. A large amount of freight transport to and from the port is also observed on this road. The National highway, being the only link to commute to this industrial area, is therefore under excessive traffic congestion.A stated preference (SP) survey was conducted at various industries located in this stretch. The respondents were asked about current travel practices and their (stated) preferences based on hypothetical -though realistic- travel attributes. A choice set of four alternative modes based on the currently used mode was presented to each individual. A Multinomial Logistics Regression (MNL) Model was developed for data analysis.As perceived, the results revealed a strong impact of travel time and travel cost on the (dis)utility of travel. These results can be utilized by policy makers to reduce congestion, monetary and time losses through efficient transport planning
Exploratory analysis of Zipf’s universal power law in activity schedules
No full textPeople’s behavior is governed by extremely complex, multidimensional processes.
This fact is well-established in the transportation research community, which has
been working on travel behavior (travel demand) models for many years. The number of
degrees of freedom in a person’s activity schedule is enormous. However, the frequency of
occurrence of day-long activity schedules obeys a remarkably simple, scale-free distribution.
This particular distribution has been observed in many natural and social processes
and is commonly referred to as Zipf’s law, a power law distribution. This research provides
evidence that activity schedules from various study areas exhibit a universal power law distribution.
To this end, an elaborate analysis using 13 household travel surveys from diverse
study areas discusses the efect of proportional outlier removal on the power law’s exponent
value. Statistical evidence is provided for the hypothesis that activity schedules in all
these datasets exhibit a power law distribution with a common exponent value. The study
proposes that a Zipf power law could be used as an additional dimension within a travel
demand model’s validation process. Contrary to other validation methods, no new data
is required. The observation of a Zipf power law distribution in the generated schedules
appears to be a necessary condition. Additionally, the universal activity schedule distribution
might enable the full integration of activity schedules in models based on universal
mobility patterns.The authors are deeply grateful to the original data creators, depositors and/or copyright holders for making the microdata available for this research. The copyright and all other intellectual property rights in the data and associated documentation are vested in the original data creators or depositors. The original data creators and analyzers bear no responsibility for the further analysis or interpretation of the data in this research. The authors would like to acknowledge the researches, works, individuals and institutions supporting the following data collections for making this research possible: US NHTS 2009 (US Department of Transportation and Federal Highway Administration 2009), NLD OViN 2013 (Centraal Bureau voor de Statistiek (CBS) and Rijkswaterstaat (RWS) 2014), BEL OVG 3.0--4.5 (Janssens et al. 2014), SVN Ljubljana 2013 (Klemencic et al. 2014) [Acknowledgements go out to the City Municipality of Ljubljana], GBR NTS 2009--2014 (Department for Transport 2015), KOR Seoul HTS 2010 (Korea Transportation Institute 2011; Metropolitan Transport Authority 2012), DEU Mobidrive 1999 (Chalasani and Axhausen 2004), CHE Thurgau 2003 (Loechl 2005), FRA ENTD 2008 (Armoogum et al. 2011), BEL Beldam 2010 (Cornelis et al. 2012) [financed by BELSPO, FOD Mobiliteit & Vervoer and others. Coordinated by GRT (Universite de Namur) in cooperation with IMOB (Universiteit Hasselt) and CES (FUSL)], IRL NTS 2009 (Central Statistics Office 2011) [Accessed via the Irish Social Science Data Archive -www.ucd.ie/issda], FIN HLT 2010--2011 (Liikennevirasto -Finnish Transport Agency) [Finnish National Travel Survey 2010--2011/Finnish Transport Agency and Wim Ectors], SWE RVU 2011--2014 (Trafik Analys 2015), AUS VISTA 2007 & 2009 (Department of Economic Development; Jobs; Transport and Resources (DEDJTR) 2009; Department of Economic Development; Jobs; Transport and Resources (DEDJTR) 2007). Part of this work was presented at the hEART 2016 conference in Delft, The Netherlands, offering valuable reflections during the preparation of this work. (Ectors et al. 2016a
Zipf’s power law in activity schedules and the effect of aggregation
No full textPeople’s behavior depends on extremely complex, multidimensional processes. This poses challengeswhen trying to model their behavior. In the transportation modeling community, great effort is spentto model the activity schedules of people. Remarkably however, the frequency of occurrence of day-longactivity schedules obeys a ubiquitous power law distribution, commonly referred to as Zipf’s law. Previousresearch established the universal nature of this distribution and proposed potential application areas.However, these application areas require additional information about the distribution’s properties. Tostress-test this universal power law, this paper discusses the role of aggregation within the phenomenonof Zipf’s law in activity schedules. Aggregation is analyzed in three dimensions: activity type encoding,aggregation over time and the aggregation of individual data. Five data sets are used: the household travelsurvey from the USA (2009) and from GBR (2009–2014), two six-week travel surveys (DEU MobiDrive1999 and CHE Thurgau 2003) and a donated 450-day data set from one individual. To analyze the effectof aggregation in the first dimension, five different activity encoding aggregation levels were created,each aggregating the activity types somewhat differently. In the second dimension, the distribution ofschedules is compared over multiple years and over the days of the week. Finally, in the third dimension,the analysis moves from study area-wide aggregated data to subsets of the data, and finally to individual (longitudinal) data.The authors would like to thank prof. dr. Kay Axhausen for providing the DEU Mobidrive 1999 [30] and CHE Thurgau 2003 [31] data sets. They are also thankful to the U.S. Department of Transportation, Federal Highway Administration, and the Department for Transport for making the NHTS 2009 [28], respectively the GBR NTS 2009–2014 [29] data freely available. The authors thank the donor of the 450-day of individual trip data
- …
