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  • 1. Allström, Andreas
    et al.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Gundlegård, David
    Archer, Jeffery
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Mobile Millennium Stockholm2011In: Proceedings of the 2nd International Conference on Models and Technologies for ITS, 2011Conference paper (Refereed)
  • 2. Anzanpour, A.
    et al.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Liljeberg, P.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Context-aware early warning system for in-home healthcare using internet-of-things2016In: 2nd International Summit on Internet of Things, IoT 360° 2015, Springer, 2016, p. 517-522Conference paper (Refereed)
    Abstract [en]

    Early warning score (EWS) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing EWS systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.

  • 3. Biem, Alain
    et al.
    Bouillet, Eric
    Ranganathan, Anand
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Traffic and Logistics (closed 20110301).
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Traffic and Logistics (closed 20110301).
    Real-Time Traffic Information Management using Stream Computing2010Report (Other academic)
  • 4. Ding, Jing
    et al.
    Gao, Song
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Huang, He
    Ma, Long
    Pereira, Francisco
    Ben-Akiva, Moshe
    Routing policy choice set generation in stochastic time-dependent networks: Case studies for Stockholm and Singapore2014Conference paper (Refereed)
    Abstract [en]

    Transportation systems are inherently uncertain due to the occurrence of random disruptions; meanwhile, real-time traveler information offers the potential to help travelers make better route choices under such disruptions. This paper presents the first revealed preference (RP) study of routing policy choice where travelers opt for routing policies instead of fixed paths. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler's ability to look ahead in order to incorporate real-time information not yet available at the time of decision. An efficient algorithm to find the optimal routing policy (ORP) in large-scale networks is presented, as the algorithm is a building block of any routing policy choice set generation method. Two case studies are conducted in Stockholm, Sweden and Singapore, respectively. Data for the underlying stochastic time-dependent network are generated from taxi Global Positioning System (GPS) traces through the methods of map-matching and non-parametric link travel time estimation. The routing policy choice sets are then generated by link elimination and simulation, in which the ORP algorithm is repetitively executed. The generated choice sets are first evaluated based on whether or not they include the observed GPS traces on a specific day, which is defined as coverage. They are then evaluated on the basis of adaptiveness, defined as the capability of a routing policy to be realized as different paths over different days. It is shown that using a combination of link elimination and simulation methods yield satisfactory coverage. The comparison to a path choice set benchmark suggests that a routing policy choice set could potentially provide better coverage and capture the adaptive nature of route choice. The routing policy choice set generation enables the development of a discrete choice model of routing policy choice, which will be explored in the second stage of the study.

  • 5. Ding, Jing
    et al.
    Gao, Song
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Huang, He
    Ma, Long
    Pereira, Francisco
    Ben-Akiva, Moshe
    Routing Policy Choice Set Generation in Stochastic Time-Dependent Networks Case Studies for Stockholm, Sweden, and Singapore2014In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, no 2466, p. 76-86Article in journal (Refereed)
    Abstract [en]

    Transportation systems are inherently uncertain because of random disruptions; nevertheless, real-time information can help travelers make better route choices under such disruptions. The first revealed-preference study of routing policy choice is presented. A "routing policy" is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions to be made on the link next. The policy represents a traveler's ability to incorporate real-time information not yet available at the time of decision. Two case studies are conducted in Stockholm, Sweden, and in Singapore. Data for the underlying stochastic time-dependent network are generated from taxi GPS traces through map-matching and nonparametric link travel time estimation. An efficient algorithm to find the optimal touting policy in large-scale networks is first presented, which is a building block of any routing policy choice set generation method. The routing policy choice sets are then generated by link elimination and simulation. The generated choice sets are first evaluated on the basis of whether they include the observed traces on a specific day, or coverage. The sets are then evaluated on the basis of "adaptiveness," defined as the capability of a routing policy to be realized as different paths over different days. A combination of link elimination and simulation methods yields satisfactory coverage. The comparison with a path choice set benchmark also suggests that a routing policy choice set could potentially provide better coverage and capture the adaptive nature of route choice.

  • 6. Ding, Jing
    et al.
    Gao, Song
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Rahmani, Mahmood
    KTH.
    Pereira, Francisco
    Ben-Akiva, Moshe
    Latent-class routing policy choice model with revealed-preference data2015Conference paper (Refereed)
  • 7. Ding-Mastera, Jing
    et al.
    Gao, Song
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE).
    Ben-Akiva, Moshe
    A latent-class adaptive routing choice model in stochastic time-dependent networks2019In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 124, p. 1-17Article in journal (Refereed)
    Abstract [en]

    Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler's ability to look ahead in order to incorporate real-time information not yet available at the time of decision. A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. A latent-class Policy Size Logit model is specified, with routing policy users who follow routing policies and path users who follow fixed paths. Two additional layers of latency in the measurement equation are accounted for: 1) the choice of a routing policy is latent and only its realized path on a given day can be observed; and 2) when GPS readings have relatively long gaps, the realized path cannot be uniquely identified, and the likelihood of observing vehicle traces with non-consecutive links is instead maximized. Routing policy choice set generation is based on the generalization of path choice set generation methods. The generated choice sets achieve 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold. Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and/or willingness to plan ahead and utilize real-time information, and an appropriate route choice model for uncertain networks should take into account the underlying stochastic travel times and structured traveler heterogeneity in terms of real-time information utilization.

  • 8.
    Haghbayan, Mohammad-Hashem
    et al.
    University of Turku, Finland.
    Kanduri, Anil
    University of Turku, Finland.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Liljeberg, Pasi
    University of Turku, Finland.
    Jantsch, Axel
    TU Wien, Austria.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    MapPro: Proactive Runtime Mapping for Dynamic Workloads by Quantifying Ripple Effect of Applications on Networks-on-Chip2015In: NOCS '15 Proceedings of the 9th International Symposium on Networks-on-Chip, ACM Digital Library, 2015Conference paper (Refereed)
    Abstract [en]

    Increasing dynamic workloads running on NoC-based many-core systems necessitates efficient runtime mapping strategies. With an unpredictable nature of application profiles, selecting a rational region to map an incoming application is an NP-hard problem in view of minimizing congestion and maximizing performance. In this paper, we propose a proactive region selection strategy which prioritizes nodes that offer lower congestion and dispersion. Our proposed strategy, MapPro, quantitatively represents the propagated impact of spatial availability and dispersion on the network with every new mapped application. This allows us to identify a suitable region to accommodate an incoming application that results in minimal congestion and dispersion. We cluster the network into squares of different radii to suit applications of different sizes and proactively select a suitable square for a new application, eliminating the overhead caused with typical reactive mapping approaches. We evaluated our proposed strategy over different traffic patterns and observed gains of up to 41% in energy efficiency, 28% in congestion and 21% dispersion when compared to the state-of-the-art region selection methods.

  • 9. Haghbayan, Mohammad-Hashem
    et al.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Miele, Antonio
    Fattah, Mohammad
    Plosila, Juha
    Liljeberg, Pasi
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    A Power-Aware Approach for Online Test Scheduling in Many-Core Architectures2016In: I.E.E.E. transactions on computers (Print), ISSN 0018-9340, E-ISSN 1557-9956, Vol. 65, no 3, p. 730-743Article in journal (Refereed)
    Abstract [en]

    Aggressive technology scaling triggers novel challenges to the design of multi-/many-core systems, such as limited power budget and increased reliability issues. Today's many-core systems employ dynamic power management and runtime mapping strategies trying to offer optimal performance while fulfilling power constraints. On the other hand, due to the reliability challenges, online testing techniques are becoming a necessity in current and near future technologies. However, state-of-the-art techniques are not aware of the other power/performance requirements. This paper proposes a power-aware non-intrusive online testing approach for many-core systems. The approach schedules software based self-test routines on the various cores during their idle periods, while honoring the power budget and limiting delays in the workload execution. A test criticality metric, based on a device aging model, is used to select cores to be tested at a time. Moreover, power and reliability issues related to the testing at different voltage and frequency levels are also handled. Extensive experimental results reveal that the proposed approach can i) efficiently test the cores within the available power budget causing a negligible performance penalty, ii) adapt the test frequency to the current cores' aging status, and iii) cover available voltage and frequency levels during the testing.

  • 10.
    Jenelius, Erik
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Travel time estimation for urban road networks using low frequency GPS probes2012Conference paper (Refereed)
  • 11.
    Negash, Behailu
    et al.
    University of Turku, Finland.
    Rahmani, Amir-Mohammad
    University of Turku.
    Westerlund, Tomi
    University of Turku, Finland.
    Liljeberg, Pasi
    University of Turku, Finland.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    LISA: Lightweight Internet of Things Service Bus Architecture2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 52, p. 436-443Article in journal (Refereed)
    Abstract [en]

    A critical challenge faced in Internet of Things (IoT) is the heterogeneous nature of its nodes from the network protocol and platform point of view. To tackle the heterogeneous nature, we introduce a distributed and lightweight service bus, LISA, which fits into network stack of a real-time operating system for constrained nodes in IoT. LISA provides an application programming interface for developers of IoT on tiny devices. It hides platform and protocol variations underneath it, thus facilitating interoperability challenges in IoT implementations. LISA is inspired by the Network on Terminal Architecture (NoTA), a service centric open architecture by Nokia Research Center. Unlike many other interoperability frameworks, LISA is designed specifically for resource constrained nodes and it provides essential features of a service bus for easy service oriented architecture implementation.

  • 12.
    Nguyen Gia, Tuan
    et al.
    University of Turku, Finland.
    Jiang, Mingzhe
    University of Turku, Finland.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Westerlund, Tomi
    University of Turku, Finland.
    Mankodiya, Kunal
    University of Rhode Island, USA.
    Liljeberg, Pasi
    University of Turku, Finland.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Fog Computing in Body Sensor Networks: An Energy Efficient Approach2015Conference paper (Refereed)
    Abstract [en]

    Internet of Things based systems provides a viable and organized approach to improve health and wellbeing of mankind. Particularly, health monitoring systems based on wireless body sensor networks become attainable due to increasing number of elderly people that needs healthcare services frequently. In such system, power consumption of a sensor node is an important issue. In order to handle the issue, a smart gateway with fog computing capabilities is presented. Fog computing includes several services such as distributed database management, Electrocardiography (ECG) feature extraction, user graphical interface with access management and push notations. With fog computing, the burden of a cloud server can be reduced and more than 50% of power consumption can be saved at a sensor node. Additionally, through fog computing, the system ensures that the obtained health data can be visualized and diagnosed in real-time even though there is a disconnection between the gateway and cloud server.

  • 13. Odunmbaku, A.
    et al.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Liljeberg, P.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Elderly monitoring system with sleep and fall detector2016In: 2nd International Summit on Internet of Things, IoT 360° 2015, Springer, 2016, p. 473-480Conference paper (Refereed)
    Abstract [en]

    Monitoring of elderly people has drawn attention of healthcare and medical professionals. Various health problems have been attributed to either fall or lack of sleep in the context of elderly people. Falling and sleep problems on a long term basis could eventually lead to sharp deteriorate in health, poor state of health and high cost for covering their health care. In this paper a new accurate and convenient while cost-efficient implementation of a monitoring system is presented. The use of an accelerometer based system was utilized in this work. The targeted device fit for this implementation is a smart watch. The algorithm of both the fall detector and sleep monitor presented in this work have been implemented and tested on multiple subjects. It also includes a database backend which is used to save the information collected from the system for further analysis and can provide healthcare professional with more insight of the person’s life and can help more on further health medication being given to the person.

  • 14.
    Rahimi Moosavi, Sanaz
    et al.
    University of Turku, Finland.
    Nguyen Gia, Tuan
    University of Turku, Finland.
    Rahmani, Amir-Mohammad
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Nigussie, Ethiopia
    University of Turku, Finland.
    Virtanen, Seppo
    University of Turku, Finland.
    Isoaho, Jouni
    University of Turku, Finland.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    SEA: A Secure and Efficient Authentication and Authorization Architecture for IoT-Based Healthcare Using Smart Gateways2015In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 52, p. 452-459Article in journal (Refereed)
    Abstract [en]

    In this paper, a secure and efficient authentication and authorization architecture for IoT-based healthcare is developed. Security and privacy of patients’ medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. Secure authentication and authorization of a remote healthcare professional is the main focus of this work. Due to resource constraints of medical sensors, it is infeasible to utilize conventional cryptography in IoT-based healthcare. In addition, gateways in existing IoTs focus only on trivial tasks without alleviating the authentication and authorization challenges. In the presented architecture, authentication and authorization of a remote end-user is done by distributed smart e-health gateways to unburden the medical sensors from performing these tasks. The proposed architecture relies on the certificate-based DTLS handshake protocol as it is the main IP security solution for IoT. The proposed authentication and authorization architecture is tested by developing a prototype IoT-based healthcare system. The prototype is built of a Pandaboard, a TI SmartRF06 board and WiSMotes. The CC2538 module integrated into the TI board acts as a smart gateway and the WisMotes act as medical sensor nodes. The proposed architecture is more secure than a state-of-the-art centralized delegation-based architecture because it uses a more secure key management scheme between sensor nodes and the smart gateway. Furthermore, the impact of DoS attacks is reduced due to the distributed nature of the architecture. Our performance evaluation results show that compared to the delegation-based architecture, the proposed architecture reduces communication overhead by 26% and communication latency from the smart gateway to the end-user by 16%.

  • 15.
    Rahmani, Amir-Mohammad
    et al.
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Haghbayan, Mohammad-Hashem
    University of Turku, Finland.
    Kanduri, Anil
    University of Turku, Finland.
    Yemane Weldezion, Awet
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics.
    Liljeberg, Pasi
    University of Turku, Finland.
    Plosila, Juha
    University of Turku, Finland.
    Jantsch, Axel
    Vienna University of Technology, Austria.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics. University of Turku, Finland.
    Dynamic Power Management for Many-Core Platforms in the Dark Silicon Era: A Multi-Objective Control Approach2015In: Low Power Electronics and Design (ISLPED), 2015 IEEE/ACM International Symposium on, IEEE conference proceedings, 2015, p. 219-224Conference paper (Refereed)
    Abstract [en]

    Power management of NoC-based many-core systems with runtime application mapping becomes more challenging in the dark silicon era. It necessitates a multi-objective control approach to consider an upper limit on total power consumption, dynamic behaviour of workloads, processing elements utilization, per-core power consumption, and load on network-on-chip. In this paper, we propose a multi-objective dynamic power management method that simultaneously considers all of these parameters. Fine-grained voltage and frequency scaling, including near-threshold operation, and per-core power gating are utilized to optimize the performance. In addition, a disturbance rejecter is designed that proactively scales down activity in running applications when a new application commences execution, to prevent sharp power budget violations. Simulations of dynamic workloads and mixed time-critical application profiles show that our method is effective in honoring the power budget while considerably boosting the system throughput and reducing power budget violation, compared to the state-of-the-art power management policies.

  • 16.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Path Inference of Sparse GPS Probes for Urban Networks: Methods and Applications2012Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, systems that collect floating car data are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging.

    This thesis introduces a map-matching and path inference algorithm for sparse GPS probes in urban networks. The method is utilized in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature. The method is used to process floating car data from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that the data can be processed and transformed into information that is suitable for traffic studies.

    The thesis implemented the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control.

  • 17.
    Rahmani, Mahmood
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Urban Travel Time Estimation from Sparse GPS Data: An Efficient and Scalable Approach2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS.

    Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to individual travelers. They all make decisions based on average travel time or variability of travel time among other factors.

    AVI (Automatic Vehicle Identification) systems have been commonly used for collecting point-to-point travel time data. Floating car data (FCD) -timestamped locations of moving vehicles- have shown potential for travel time estimation. Some advantages of FCD compared to stationary AVI systems are that they have no single point of failure and they have better network coverage. Furthermore, the availability of opportunistic sensors, such as GPS, makes the data collection infrastructure relatively convenient to deploy.

    Currently, systems that collect FCD are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space, for example with 2 minutes gaps. For sparse FCD to be useful for transport applications, it is required that the corresponding probes be matched to the underlying digital road network. Matching such data to the network is challenging.

    This thesis makes the following contributions: (i) a map-matching and path inference algorithm, (ii) a method for route travel time estimation, (iii) a fixed point approach for joint path inference and travel time estimation, and (iv) a method for fusion of FCD with data from automatic number plate recognition. In all methods, scalability and overall computational efficiency are considered among design requirements.

    Throughout the thesis, the methods are used to process FCD from 1500 taxis in Stockholm City. Prior to this work, the data had been ignored because of its low frequency and minimal information. The proposed methods proved that the data can be processed and transformed into useful traffic information. Finally, the thesis implements the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging data sources for traffic monitoring and control. Processes are developed to be computationally efficient, scalable, and to support real time applications with large data sets through a proposed distributed implementation.

  • 18.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Floating Car and Camera Data Fusion for Non-Parametric Route Travel Time Estimation2014Conference paper (Refereed)
    Abstract [en]

    The paper proposes a non-parametric route travel time estimation method based on fusion of floating car data (FCD) and automated number plate recognition (ANPR) data. Today’s traffic management utilizes heterogeneous data collection systems which can be stationary or mobile. Each data collection system has its own advantages and disadvantages. Stationary sensors usually have less measurement noise than mobile sensors but their network coverage is limited. On the other hand, mobile sensors, commonly installed in fleet vehicles, cover relatively wider areas of the network but they suffer from low penetration rate and low sampling frequency. Traffic state estimations can benefit from fusion of data collected by various sources as they complement each other. The proposed estimation method is implemented using FCD from taxis and the ANPR data from Stockholm, Sweden. The results suggest that the fusion increases the robustness of the estimation, meaning that the fused estimates are always better than the worst of the two (FCD or ANPR), and it sometimes outperforms the two single sources.

  • 19.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Northeastern University, United States.
    Non-parametric estimation of route travel time distributions from low-frequency floating car data2015In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 58B, p. 343-362Article in journal (Refereed)
    Abstract [en]

    The paper develops a non-parametric method for route travel time distribution estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations for estimating the travel time distribution on a route. Potential biases associated with the use of sparse FCD are identified. The method involves a number of steps to reduce the impact of these biases. For evaluation purposes, a case study is used to estimate route travel times from taxi FCD in Stockholm, Sweden. Estimates are compared to observed travel times for routes equipped with Automatic Number Plate Recognition (ANPR) cameras with promising results. As vehicles collecting FCD (in this case, taxis) may not be a representative sample of the overall vehicle fleet and driver population, the ANPR data along several routes are also used to assess and correct for this bias. The method is computationally efficient, scalable, and supports real time applications with large data sets through a proposed distributed implementation.

  • 20.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Route travel time estimation using low-frequency floating car data2013Conference paper (Refereed)
  • 21.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport and Location Analysis.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Route travel time estimation using low-frequency floating car data2013In: 2013 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC): Intelligent Transportation Systems for All Modes, IEEE conference proceedings, 2013, p. 2292-2297Conference paper (Refereed)
    Abstract [en]

    The paper develops a non-parametric method for route travel time estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations directly for estimating the travel time distribution on a defined route. A list of potential biases associated with FCD is presented and discussed. For each source of bias, a correction method for the observations is proposed. The estimation method is implemented using FCD data from taxis in Stockholm, Sweden. Estimates are compared to observed travel times for two routes equipped with automatic number plate recognition (ANPR) cameras. The mean travel time estimates incorporating all bias corrections perform equally well or better than the link-based approach in terms of RMSE, and estimated percentiles show a good match to ANPR.

  • 22.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Koutsopoulos, Hans N.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Path inference from sparse floating car data for urban networks2013In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 30, p. 41-54Article in journal (Refereed)
    Abstract [en]

    The use of probe vehicles in traffic management is growing rapidly. The reason is that the required data collection infrastructure is increasingly in place in urban areas with a significant number of mobile sensors constantly moving and covering expansive areas of the road network. In many cases, the data is sparse in time and location and includes only geo-location and timestamp. Extracting paths taken by the vehicles from such sparse data is an important step towards travel time estimation and is referred to as the map-matching and path inference problem. This paper introduces a path inference method for low-frequency floating car data, assesses its performance, and compares it to recent methods using a set of ground truth data.

  • 23.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301).
    Koutsopoulos, Harilaos
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301).
    Ranganathan, Anand
    IBM.
    Requirements and Potential of GPS-based Floating Car Data for Traffic Management: Stockholm Case Study2010In: 2010 13th International IEEE Conference on Intelligent Transportation Systems, 2010, p. 730-735Conference paper (Refereed)
    Abstract [en]

    The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. The paper presents the development of a laboratory designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. It also presents results to illustrate the scope of traffic information that can be provided by GPS-based data, using the city of Stockholm as a case study. The preliminary analysis shows that network coverage, especially during peak weekday hours, is adequate. Further investigation is needed to validate the data, and increase its value through fusion with complementary data from other sources.

  • 24.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Path inference of low-frequency GPS probes for urban networks2012In: Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, IEEE , 2012, p. 1698-1701Conference paper (Refereed)
    Abstract [en]

    The use of probe vehicles in traffic management is growing rapidly. The reason is that the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. The data is usually sparse in time and location. It usually includes only geo-location and timestamp. Extracting the paths taken by the vehicles is an important step in using this data. Such methods are referred to as map-matching or path inference. This paper introduces a path inference method for low-frequency probes and evaluates its accuracy in comparison to a recent method.

  • 25.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Jenelius, Erik
    Travel Time Estimation from Sparse Floating Car Data with Consistent Path Inference: A Fixed Point ApproachManuscript (preprint) (Other academic)
    Abstract [en]

    An important application of sparse floating car data (FCD) is the estimation of network link travel times, which requires pre-processing by map-matching and path inference filters. Path inference, in general, requires some a priori assumption about link travel times to infer paths that are reasonable and temporally consistent with observations. Path inference and travel time estimation is thus a joint problem. This paper proposes a fixed point approach to the travel time estimation problem with consistent path inference.The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. In this case study, existing methods for path inference and travel time estimation, without any particular assumptions about path choice models or travel time distributions, are used. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times and data sizes. Using historical initial travel times reduces bias. The results highlight the value of the fixed point estimation process, in particular for accurate path finding and route optimization.

  • 26.
    Rahmani, Mahmood
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Koutsopoulos, Haris N.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Northeastern University, United States.
    Jenelius, Erik
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach2017In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 85, p. 628-643Article in journal (Refereed)
    Abstract [en]

    Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.

1 - 26 of 26
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