137 research outputs found

    The use of real-time connected vehicles and HERE data in developing an automated freeway incident detection algorithm

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    Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson’s correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm’s detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data

    ComfRide: A smartphone based system for comfortable public transport recommendation

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    Passenger comfort is a major factor influencing a commuter's decision to avail public transport. Existing studies suggest that factors like overcrowding, jerkiness, traffic congestion etc. correlate well to passenger's (dis)comfort. An online survey conducted with more than 300 participants from 12 different countries reveals that different personalized and context dependent factors influence passenger comfort during a travel by public transport. Leveraging on these findings, we identify correlations between comfort level and these dynamic parameters, and implement a smartphone based application, ComfRide, which recommends t

    Road traffic open data in Sweden: Availability and commercial exploitation - A research study on the state of open transportation data in Sweden

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    This chapter includes a description of how the study was conducted. In order to explore the possibilities for private companies to use open data, an extensive literature review was conducted. Furthermore, this helped to get familiarized with the subject of open data and understand how it is utilized today by public companies. While researching different methods for data analytics that are being used in transportation, it was found that predictive analytics was one of the most prominent methods as it can be used in numerous ways in order to improve predictions and planning within organizations. The use of predictive analytics in transportation includes predicting delays and traffic conditions which were found to be appropriate areas of analytics with regards to the types of open data that are commonly available. Hence, these will be the areas of transport analytics that will be focused on in this study. In order to analyze the full potential of open transport data, both as a means of improving existing businesses as well as to allow for new business opportunities to originate, the methodology had to be considered accordingly. To scope out opportunities for improvement of business activities, research projects were reviewed where a number of types of open transport-related data were used to predict future outcomes of traffic conditions and events in public transportation that could have potential impacts on how daily activities within transportation organizations are performed. The projects were chosen based on the potential accessibility that the data used for the analysis has in Swedish open data sources, in order to make sure that corresponding solutions to the problems are feasible to perform in Sweden. Furthermore, in order to analyze the potential for new businesses to arise from available open data, several existing companies that have gained their success through the use of such data were studied to gain an insight into how value can be extracted from it. To analyze the accessibility of relevant open data in Sweden, Trafiklab, and Trafikverket, two open data sources for transportation-related data have been used. These were chosen in a screening method of the biggest open data sources that offer a large amount of data publicly in Sweden.Incomin

    Road traffic open data in Sweden: Availability and commercial exploitation - A research study on the state of open transportation data in Sweden

    Get PDF
    This chapter includes a description of how the study was conducted. In order to explore the possibilities for private companies to use open data, an extensive literature review was conducted. Furthermore, this helped to get familiarized with the subject of open data and understand how it is utilized today by public companies. While researching different methods for data analytics that are being used in transportation, it was found that predictive analytics was one of the most prominent methods as it can be used in numerous ways in order to improve predictions and planning within organizations. The use of predictive analytics in transportation includes predicting delays and traffic conditions which were found to be appropriate areas of analytics with regards to the types of open data that are commonly available. Hence, these will be the areas of transport analytics that will be focused on in this study. In order to analyze the full potential of open transport data, both as a means of improving existing businesses as well as to allow for new business opportunities to originate, the methodology had to be considered accordingly. To scope out opportunities for improvement of business activities, research projects were reviewed where a number of types of open transport-related data were used to predict future outcomes of traffic conditions and events in public transportation that could have potential impacts on how daily activities within transportation organizations are performed. The projects were chosen based on the potential accessibility that the data used for the analysis has in Swedish open data sources, in order to make sure that corresponding solutions to the problems are feasible to perform in Sweden. Furthermore, in order to analyze the potential for new businesses to arise from available open data, several existing companies that have gained their success through the use of such data were studied to gain an insight into how value can be extracted from it. To analyze the accessibility of relevant open data in Sweden, Trafiklab, and Trafikverket, two open data sources for transportation-related data have been used. These were chosen in a screening method of the biggest open data sources that offer a large amount of data publicly in Sweden.Incomin

    Evaluating Crowdsourcing as a VMT Reduction Tool to Support Smart Cities Initiatives

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    Vehicle miles traveled (VMT) is an indicator of vehicular emissions, which impacts climate change. Various stakeholders aim at reducing VMT to cause reductions in emissions. This research explores the use of crowdsourcing in supporting the efforts of the stakeholders in reducing VMT among college students at California State University Long Beach. Crowdsourcing is emerging as a very promising tool in finding solutions to problems otherwise impossible to solve without a collective human intelligence. A smartphone application is developed to collect travel data and behavior of 55 college students as participants. The behavior is tracked after providing advance information on parking availability on the university campus. It is observed that VMT reductions occur from Monday, Wednesday, and Thursday with car users and Monday through Thursday with transit bus users. The largest reduction of 4% occurs with car usage on Thursday. On the same day, the highest reduction in VMT of 5% occurs with bus usage. Thus, crowdsourcing information on campus parking showed that VMT reduction is effective with the small number of participants involved in this pilot study

    Fuzz sensoring

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    Treball desenvolupat en el marc del programa "European Project Semester".Traffic congestion is a significant problem which affects smoothness in transportation in many cities around the world. It is unavoidable due to increasing numbers of vehicles and overuse of roads in large and growing metropolises. Although, there are several policies that are implemented to reduce traffic congestion, such as improvement of public transport, car and motorcycle restriction on several roads, and an even-odd license plate policy, the major problem involves getting data in order to predict and avoid traffic. Information can be collected from many sources such as: city sensors, GPS, as well as, from many application programming interfaces (API) provided by different companies. The project involves gathering sources and information about traffic congestion in order to create guidelines which can be essential in creating a traffic map of Vilanova i la Geltrú in the future. Eventually, the guidelines to the city of Vilanova i la Geltrú are provided, consisting of analysis of traffic inside the city, IoT management, choices of APIs, effective selection of sensors, and cost analysis to vastly improve traffic flow.Incomin

    iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

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    Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other

    Modeling User Transportation Patterns Using Mobile Devices

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    Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data. Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data. This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance. Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality
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