Now showing 1 - 10 of 10
  • Publication
    Evaluating Citywide Bus Service Reliability Using Noisy GPS Data
    (IEEE, 2017-09-17) ;
    AbstractAn increasing number of people use smartphoneapplications to plan their trips. Unfortunately, for variousreasons, bus trips suggested by such applications are not asreliable as other trip types (e.g. by car, on foot, or by bicycle),which can result in excessive waiting time, or even the needto revise a planned trip. Traditional punctuality-based busservice reliability metrics do not capture route deviations, whichare especially frequent in rapid changing urban environmentsdue to rapidly changing road conditions caused by trafficcongestion, road maintenance, etc. The prevalence of GPS dataallows buses to be tracked and route deviations to be captured.We use such data to propose and calculate a novel reliabilityscore for bus trips. This score is a linear weighted combinationof distance, time, and speed deviations from an expected, predefinedbus trip. GPS trajectory data is large and noisy whichmakes it challenging to process. This paper also presents anefficient framework that can de-noise and semantically splitraw GPS data by pre-defined bus trips in citywide. Finally,the paper presents a comparative case study that applies theproposed reliability score to publicly available open bus datafrom Rio de Janeiro in Brazil and Dublin in Ireland.
      650Scopus© Citations 2
  • Publication
    A Multi-Agent based vehicles re-routing system for unexpected traffic congestion avoidance
    As urbanization has been spreading across the world for decades, the traffic congestion problem becomes increasingly serious in most of the major cities. Among the root causes of urban traffic congestion, en route events are the main source of the sudden increase of the road traffic load, especially during peak hours. The current solutions, such as on-board navigation systems for individual vehicles, can only provide optimal routes using current traffic data without considering any traffic changes in the future. Those solutions are thus unable to provide a better alternative route quickly enough if an unexpected congestion occurs. Moreover, using the same alternative routes may lead to new bottlenecks that cannot be avoided. Thus a global traffic load balance cannot be achieved. To deal with these problems, we propose a Multi Agent System (MAS) that can achieve a trade-off between the individual and global benefits by giving the vehicles optimal turn suggestions to bypass a blocked road ahead. The simulation results show that our strategy achieves a substantial gain in average trip time reduction under realistic scenarios. Moreover, the negative impact of selfish re-routing is investigated to show the importance of altruistic re-routing applied in our strategy.
    Scopus© Citations 47  453
  • Publication
    Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion
    During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.
      686Scopus© Citations 107
  • Publication
    A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
    The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ Unmanned Aerial Vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This paper provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue operations, acting as aerial wireless BSs and drone light shows). As guidance for researchers and practitioners, the paper also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.
      194Scopus© Citations 101
  • Publication
    ROGER: An On-Line Flight Efficiency Monitoring System using ADS-B Data
    Flight efficiency indicators reported monthly in the European area by the Performance Review Unit (PRU) help the air traffic management (ATM) community determine if excessive distances are being flown (compared with the ideal lengths of flight routes). Recent research, however, provides more indicators that comprehensively capture flight efficiencies in terms of other factors including fuel consumption, time adherence, and route charges. The efficacy of all of these indicators, however, is diminished as they are currently only available almost a month after flights take place. This is not sufficiently timely to use these indicators for the allevi- ation of unpredictable hotspots (i.e. sectors with congested air traffic), which often leads to unexpected ground delays. This paper proposes a methodology to calculate general flight efficiency indicators on-line in near real-time using nearest point search. A prototype system called ROGER (compRe- hensive On-line fliGht Efficiency monitoRing) is implemented using Apache Kafka and Spark. ROGER can digest large- scale heterogeneous datasets (i.e. mainly ADS-B data, the next generation aircraft surveillance technology) to compute indi- cators every 5 seconds. Our experiments on realistic datasets demonstrate that the proposed on-line indicator calculation method can achieve high accuracy compared with existing off-line approaches, and that ROGER can achieve desirable system performance in throughput and latency. A use case is also described showing how ROGER can assist in alleviating hotspots more effectively.
      487Scopus© Citations 1
  • Publication
    Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach
    Nowadays, the unprecedented increase in road traffic congestion has led to severe consequences on individuals, economy and environment, especially in urban areas in most of big cities worldwide. The most critical among the above consequences is the delay of emergency vehicles, such as ambulances and police cars, leading to increased deaths on roads and substantial financial losses. To alleviate the impact of this problem, we design an advanced adaptive traffic control system that enables faster emergency services response in smart cities while maintaining a minimal increase in congestion level around the route of the emergency vehicle. This can be achieved with a Traffic Management System (TMS) capable of implementing changes to the road network's control and driving policies following an appropriate and well-tuned adaptation strategy. This latter is determined based on the severity of the emergency situation and current traffic conditions estimated using a fuzzy logic-based scheme. The obtained simulation results, using a set of typical road networks, have demonstrated the effectiveness of our approach in terms of the significant reduction of emergency vehicles' response time and the negligible disruption caused to the non-emergency vehicles travelling on the same road network.
      636Scopus© Citations 41
  • Publication
    An Adaptive and VANETs-based Next Road Re-routing System for Unexpected Urban Traffic Congestion Avoidance
    Unexpected road traffic congestion caused by en-route events, such as car crashes, road works, unplanned parades etc., is a real challenge in today's urban road networks as it considerably increases the drivers' travel time and decreases travel time reliability. To face this challenge, this paper extends our previous work named Next Road Rerouting (NRR) by designing a novel vehicle rerouting strategy that can adapt itself to the sudden change of urban road traffic conditions. This is achieved through a smart calibration of the algorithmic and operational parameters of NRR without any intervention from traffic managers. Specifically, a coefficient of variation based method is used to assign weight values to three factors in the routing cost function of NRR, and the k-means algorithm is applied periodically to choose the number of NRR enabled agents needed. This adaptive-NRR (a-NRR) strategy is supported by vehicular ad-hoc networks (VANETs) technology as this latter can provide rich traffic information at much higher update frequency and much larger coverage than induction loops used in the previously proposed static NRR. Simulation results show that in the city center area of TAPASCologne scenario, compared to the existing vehicle navigation system (VNS) and static NRR, our adaptive-NRR can achieve considerable gain in terms of trip time reduction and travel time reliability improvement.
      400Scopus© Citations 21
  • Publication
    Comprehensive performance analysis and comparison of vehicles routing algorithms in smart cities
    Due to the severe impact of road traffic congestion on both economy and environment, several vehicles routing algorithms have been proposed to optimize travelers itinerary based on real-time traffic feeds or historical data. However, their evaluation methodologies are not as compelling as their key design idea because none of them had been tested under both real transportation map and real traffic data. In this paper, we conduct a deep performance analysis and comparison of four typical vehicles routing algorithms under various scalability levels (i.e. trip length and traffic load) based on realistic transportation simulation. The ultimate goal of this work is to suggest the most suitable routing algorithm to use in different transportation scenarios, so that it can provide a valuable reference for both traffic managers and researchers when they deploy or optimize a large scale centralized Traffic Management System (TMS). The obtained simulation results reveal that dynamic A* is the best routing algorithm if the TMS has sufficient memory or storage capacities, otherwise static A* is also a great alternative.
      326Scopus© Citations 16
  • Publication
    Advanced Flight Efficiency Key Performance Indicators to support Air Traffic Analytics: Assessment of European flight efficiency using ADS-B data
    Flight efficiency is of great concern in the Air Traffic Management (ATM) community since today’s ATM inefficiencies affect both airspace users (AUs) and Air Navigation Service Providers (ANSPs). Each actor has their own vision of flight efficiency: whereas airlines are concerned mainly with aspects that impact their business strategy (fuel consumption, schedule adherence and cost), ANSPs consider other aspects such as sector capacity, Air Traffic Controller (ATC) interventions, emissions and noise. Capturing both visions in new Key Performance Indicators (KPIs) is important to take new steps towards more sustainable air traffic operations. The current standard KPI used to measure flight efficiency is the “horizontal flight efficiency”, which measures the horizontal excess enroute distance compared to the orthodromic distance. This view of efficiency is very limited since it doesn’t take into account other sources of inefficiencies, namely meteorological conditions or the vertical profile of the flight, that have a big impact on the AUs operational objectives. Therefore, advanced metrics are being developed to include these objectives in the assessment of efficiency and to analyse how the inefficiencies are distributed among them, as well as new methodologies to calculate these advanced KPIs in real time. This paper presents a consolidated set of advanced user-centric cost-based efficiency and equity indicators which address different aspects of efficiency such as the horizontal and vertical component, fuel consumption or cost of the flight, thus introducing the airspace user’s viewpoint into consideration. Also, the methodology followed for the calculation of the indicators, based on historical data and in real time, is demonstrated. For the evaluation of the indicators, Automatic Dependent Surveillance-Broadcast (ADS-B) data and a set of user-preferred trajectories (including flight plan, optimal cost and optimal distance) as reference are used. Finally, a flight efficiency and equity assessment of the European traffic flow for three different scenarios is presented, where two whole days of air traffic in the European Civil Aviation Conference (ECAC) area were used for the efficiency indicators, and one month of traffic for specific city pairs was used for the equity indicators. This proves the added value of these newly introduced indicators, showing that different indicators account for different sources of inefficiencies, and that the use of ADS-B data could serve as a reliable source for performance monitoring
    Scopus© Citations 4  650
  • Publication
    EDAS– Energy-Efficient Device-based Adaptive Cross-Layer Scheme for Wireless Multimedia Transmission
    Next generation wireless networks are poised to support real-time video-on-demand and high quality multimedia streaming applications. In order to support them, several adaptation strategies have been proposed at different layers of the OSI network stack. Also, certain cross-layer strategies across physical/data link and transport/application layers have been proposed over the years. However, given the battery constraints of a smartphone, it is imperative that new cross-layer design be developed that would bring into account an energyoriented mechanism from a device perspective. In this paper, a new adaptive solution, EDAS – energy-efficient device oriented adaptive cross-layer scheme for multimedia transmission in wireless network environments is proposed. Importantly, EDAS provides a novel framework spanning across several OSI layers that would dynamically vary the energy consumption in mobile devices while maintaining a high peak signal to noise ratio (PSNR) in order to ensure a good video quality. It has been observed that EDAS significantly outperforms other schemes in terms of PSNR, loss-rate and importantly, the energy consumption in device.