Publications
92 results found
Yang H, Zhang X, Zhong L, et al., 2019, Short-term demand forecasting for bike sharing system based on machine learning, 5th International Conference on Transportation Information and Safety (ICTIS), Publisher: IEEE, Pages: 1295-1300
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- Citations: 5
Wu C, Wang G, Zhu J, et al., 2019, Exploratory Analysis for Big Social Data Using Deep Network, IEEE ACCESS, Vol: 7, Pages: 21446-21453, ISSN: 2169-3536
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- Citations: 1
Wang L, Ye F, Wang Y, et al., 2019, A Q-learning Foresighted Approach to Ego-efficient Lane Changes of Connected and Automated Vehicles on Freeways, IEEE Intelligent Transportation Systems Conference (IEEE-ITSC), Publisher: IEEE, Pages: 1385-1392, ISSN: 2153-0009
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- Citations: 12
Liu Q, Hu S, Angeloudis P, et al., 2019, Simulation and Evaluation of CAVs Behavior in an Isolated Signalized Intersection Equipped with Dynamic Wireless Power Transfer System, IEEE Intelligent Transportation Systems Conference (IEEE-ITSC), Publisher: IEEE, Pages: 2207-2212, ISSN: 2153-0009
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- Citations: 6
Koudis GS, Hu SJ, Majumdar A, et al., 2018, The impact of single engine taxiing on aircraft fuel consumption and pollutant emissions, Aeronautical Journal, Vol: 122, Pages: 1967-1984, ISSN: 0001-9240
Optimisation of aircraft ground operations to reduce airport emissions can reduce resultant local air quality impacts. Single engine taxiing (SET), where only half of the installed number of engines are used for the majority of the taxi duration, offers the opportunity to reduce fuel consumption, and emissions of NOX, CO and HC. Using 3510 flight data records, this paper develops a model for SET operations and presents a case study of London Heathrow, where we show that SET is regularly implemented during taxi-in. The model predicts fuel consumption and pollutant emissions with greater accuracy than previous studies that used simplistic assumptions. Without SET during taxi-in, fuel consumption and pollutant emissions would increase by up to 50%. Reducing the time before SET is initiated to the 25th percentile of recorded values would reduce fuel consumption and pollutant emissions by 7–14%, respectively, relative to current operations. Future research should investigate the practicalities of reducing the time before SET initialisation so that additional benefits of reduced fuel loadings, which would decrease fuel consumption across the whole flight, can be achieved.
Koudis GS, Hu SJ, North RJ, et al., 2017, The impact of aircraft takeoff thrust setting on NO<inf>X</inf> emissions, Journal of Air Transport Management, Vol: 65, Pages: 191-197, ISSN: 0969-6997
Reduced thrust takeoff has the potential to reduce aircraft-related NO X emissions at airports, however this remains to be investigated using flight data. This paper analyses the effect of takeoff roll thrust setting variability on the magnitude and spatial distribution of NO X emissions using high-resolution data records for 497 Airbus A319 activities at London Heathrow. Thrust setting varies between 67 and 97% of maximum, and aircraft operating in the bottom 10th percentile emit on average 514 g less NO X per takeoff roll (32% reduction) than the top 10th percentile, however this is dependent on takeoff roll duration. Spatial analysis suggests that peak NO X emissions, corresponding to the start of the takeoff roll, can be reduced by up to 25% by adopting reduced thrust takeoff activities. Furthermore, the length of the emission source also decreases. Consequently, the use of reduced thrust takeoff may enable improved local air quality at airports.
Koudis GS, Hu J, Majumdar A, et al., 2017, Airport emissions reductions from reduced thrust takeoff operations, Transportation Research Part D: Transport and Environment, Vol: 52, Pages: 15-28, ISSN: 1361-9209
Given forecast aviation growth, many airports are predicted to reach capacity and require expansion. However, pressure to meet air quality regulations emphasises the importance of efficient ground-level aircraft activities to facilitate growth. Operational strategies such as reducing engine thrust setting at takeoff can reduce fuel consumption and pollutant emissions; however, quantification of the benefits and consistency of its use have been limited by data restrictions. Using 3,336 high-resolution flight data records, this paper analyses the impact of reduced thrust takeoff at London Heathrow. Results indicate that using reduced thrust takeoff reduces fuel consumption, nitrogen oxides (NOX) and black carbon (BC) emissions by 1.0-23.2%, 10.7-47.7%, and 49.0-71.7% respectively, depending on aircraft-engine combinations relative to 100% thrust takeoff. Variability in thrust settings for the same aircraft-engine combination and dependence on takeoff weight (TOW) is quantified. Consequently, aircraft-engine specific optimum takeoff thrust settings that minimise fuel consumption and pollutant emissions for different aircraft TOWs are presented. Further reductions of 1.9%, 5.8% and 6.5% for fuel consumption, NOX and BC emissions could be achieved, equating to reductions of approximately 0.4%, 3.5% and 3.3% in total ground level fuel consumption, NOX and BC emissions. These results quantify the contribution that reduced thrust operations offer towards achieving industry environmental targets and air quality compliance, and imply that the current implementation of reduced thrust takeoff at Heathrow is near optimal, considering operational and safety constraints.
Sun R, Han K, Hu J, et al., 2016, An integrated algorithm based on BeiDou/GPS/IMU and its application for anomalous driving detection., ION GNSS+
Recent years have seen a booming of safety-related Intelligent Transportation System (ITS) applications, which have placed increasingly stringent requirements on the performance of Global Navigation Satellite Systems (GNSS). Examples include lane control, collision avoidance, and intelligent speed assistance. Detecting the lane level anomalous driving behavior is crucial for these safety critical ITS applications. The two major issues associated with the lane-level irregular driving identification are (1) accessibility to high accuracy positioning and vehicle dynamic parameters, and (2) extraction of anomalous driving behavior from these parameters. This paper introduces an integrated algorithm for detecting lane-level anomalous driving. Lane-level high accuracy vehicle positioning is achieved by fusing GPS and Beidou feeds with Inertial Measurement Unit (IMU) using Unscented Particle Filter (UPF). Anomalous driving detection is achieved based on the application of a newly designed Fuzzy Inference System. Computer simulation and real-world field test demonstrate the advantage of the proposed approach over existing ones from previous studies.
Song J, Hu J, Han K, 2016, Real-time adaptive traffic signal control: Trade-off between traffic and environmental objectives, Transportation Research Board 96th Annual Meeting
Sun R, Han K, Hu J, et al., 2016, Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements, Transportation Research Part C: Emerging Technologies, Vol: 69, Pages: 193-207, ISSN: 1879-2359
There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimetre/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.
Mascia M, Hu J, Han K, et al., 2016, Impact of traffic management on black carbon emissions: a microsimulation study, Networks & Spatial Economics, Vol: 17, Pages: 269-291, ISSN: 1572-9427
This paper investigates the effectiveness of traffic management tools, includ- ing traffic signal control and en-route navigation provided by variable message signs (VMS), in reducing traffic congestion and associated emissions of CO2, NOx, and black carbon. The latter is among the most significant contributors of climate change, and is associated with many serious health problems. This study combines traffic microsimulation (S-Paramics) with emission modeling (AIRE) to simulate and predict the impacts of different traffic management measures on a number traffic and environmental Key Performance Indicators (KPIs) assessed at different spatial levels. Simulation results for a real road network located in West Glasgow suggest that these traffic management tools can bring a reduction in travel delay and BC emission respectively by up to 6 % and 3 % network wide. The improvement at local levels such as junctions or corridors can be more significant. However, our results also show that the potential benefits of such interventions are strongly dependent on a number of factors, including dynamic demand profile, VMS compliance rate, and fleet composition. Extensive discussion based on the simulation results as well as managerial insights are provided to support traffic network operation and control with environmental goals. The study described by this paper was conducted under the support of the FP7-funded CARBOTRAF project.
Mascia M, Hu J, Han K, et al., 2016, A holistic approach for performance assessment of personal rapid transit, Research in Transportation Business & Management, ISSN: 2210-5395
Personal Rapid Transit (PRT) has received increased attention in recent years due to technological innovation and the need for safer, more efficient, and more sustainable transport systems in dense urban areas. PRT service is on demand, and provides a good level of service due to short waiting time with no intermediate stops. The cost to run the system is lower compared to traditional transport systems due to utilizing autonomous pods.While a number of studies have focused on specific aspects of the performance of PRT, there is still a lack of comprehensive assessment of PRT's performance from the perspectives of both operators and users. This paper addresses this gap by proposing a set of PRT-specific Key Performance Indicators (KPI) relevant to its operational characteristics (e.g. pod utilization, total distance travelled) and user experience (e.g. average waiting time, delay). The proposed KPIs are demonstrated through a simulation study. The findings made in this paper constitute the first step towards comprehensive benchmarking for PRT systems, and facilitate comparative analyses of different PRT systems to help operators identify and implement best practise.
Vranckx S, Lefebvre W, van Poppel M, et al., 2015, Air quality impact of intelligent transportation system actions used in a decision support system for adaptive traffic management, International Journal of Environment and Pollution, Vol: 57, Pages: 133-145, ISSN: 1741-5101
The presented traffic control system (CARBOTRAF) combines real-time monitoring of traffic and air pollution with simulation models for traffic, emission and local air quality predictions to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimising the traffic flows. A chain of models combines microscopic traffic simulations, emission models and air quality simulations for a range of traffic demand levels and intelligent transport system (ITS) actions. These ITS scenarios simulate combinations of traffic signal optimisation plans and variable messaging systems. The real-time decision support system uses these simulations to select the best traffic management in terms of traffic and air quality. In this paper the modelled effects of ITS measures on air quality are analysed with a focus on BC for urban areas in two European cities, Graz and Glasgow.
Hu J, Kobl R, Heilmann B, et al., 2015, An assessment of VMS-rerouting and traffic signal planning with emission objectives in an urban network — A case study for the city of Graz, Models and Technologies for Intelligent Transportation Systems, Publisher: IEEE
This paper discusses a case study evaluating the potential impact of ITS traffic management on CO 2 and Black carbon tailpipe emissions. Results are based on extensive microsimulations performed using a calibrated VISSIM model in combination with the AIRE model for calculating the tailpipe emissions from simulated vehicle trajectories. The ITS traffic management options hereby consist of easily implementable actions such as the usage of a variable message sign (VMS) or the setting of fixed time signal plans. Our simulations show that in the current case shifting 5% of vehicles from one route to another one leads to an improvement in terms of emissions only if the VMS is complemented with an adaptation of the signal programs, while the VMS sign or the change of the signal plans alone do not yield benefits. This shows that it is not sufficient to evaluate single actions in a ceteris paribus analysis, but their joint network effects need to be taken into account.
Han K, Mascia M, Hu SJ, et al., 2015, Day-to-day dynamic traffic assignment model with variable message signs and endogenous user compliance, The 94th Transportation Research Board Annual Meeting, Publisher: Transportation Research Board
This paper proposes a dual-time-scale, day-to-day dynamic traffic assignment model that takes into account variable message signs (VMS) and its interactions with drivers’ travel choices and adaptive learning processes. The within-day dynamic is captured by a dynamic network loading problem with en route update of path choices influenced by the VMS; the day-to-day dynamic is captured by a simultaneous route-and-departure-time adjustment process that employs bounded user rationality. Moreover, we describe the evolution of the VMS compliance rate by modeling drivers’ learning processes. We endogenize traffic dynamics, route and departure time choices, travel delays, and VMS compliance, and thereby captur their interactions and interdependencies in a holistic manner. A case study in the west end of Glasgow is carried out to understand the impact of VMS has on road congestion and route choices in both the short and long run. Our main find- ings include an adverse effect of the VMS on the network performance in the long run (the “rebound” effect), and existence of an equilibrium state where both traffic and VMS compliance are stabilized.
Mascia M, Hu SJ, Han K, et al., 2015, Reducing Environmental Impact By Adaptive Traffic Control And Management For Urban Road Networks, The 94th Transportation Research Board Annual Meeting, Publisher: Transportation Research Board
This paper investigates the effectiveness of traffic signal control and variable message sign (VMS) as environmental traffic management tool. The focus is on black carbon and CO2, which are among the highest contributors to climate change. The modelling tool chain adopted to support this study includes traffic microsimulation, emission modelling and dispersion modelling. A number of scenarios have been simulated with different levels of demand and VMS compliance rates. The results demonstrate the potential of these interventions in reducing black carbon and CO2 emissions and improving air quality, as well as reducing traffic congestion and travel delays.
Hu J, Mascia M, Han K, et al., 2015, Assessment of different urban traffic control strategy impacts on vehicle emissions, The 47th Annual UTSG Conference
This paper investigates the influence of traffic signal control strategy on vehicle emissions, vehicle journey time and total throughput flow within a single isolated four-armed junction. Two pre-timed signal plans are considered, one with two-stages involving permissive-only opposing turns and the other with four-stages which has no conflicting traffic. Additionally, the increase in efficiency by utilising actuated signal timing where green time is re-optimised as flow values vary is investigated. A microscopic traffic simulation model is used to model flows and AIRE (Analysis of Instantaneous Road Emissions) microscopic emissions model is utilised to out- put emission levels from the flow data. A simple junction model shows that the two-stage signal plan is more efficient in both emis- sions and journey time. However, as the level of opposed turning vehicles and conflicting movement increases, the two-stage model moves to being the inferior signal plan choice and the four-stage plan outputs fewer emissions than the two-stage plan. A real-world example of a four-armed junction has been used in this study and from the traffic survey data and existing junction layout; it is rec- ommended that a two-stage plan is used as it produces lower amounts of emissions and shorter journey times compared to a four-stage plan. The results also show that nitrogen oxides (NOx) are the most sensitive to changes in flow followed by carbon dioxide (CO2), Black Carbon and then particulate matter (PM10).
Hu J, Mascia M, Litzenberger M, et al., 2014, Field investigation of vehicle acceleration at the stop line with a dynamic vision sensor, Journal of Traffic and Transportation Engineering, Vol: 2, Pages: 116-124, ISSN: 2328-2142
This article presents a study of vehicle acceleration distribution at a traffic signal stop line in an urban environment. Accurate representation of vehicle acceleration behavior provides important inputs to traffic simulation models especially when traffic related emissions need to be estimated. A smart eye traffic data sensor (TDS) system was used to record vehicle trajectories, which were extracted to calculate vehicle acceleration profiles. This paper presents the acceleration distributions obtained from over 300 passenger-car acceleration cycles observed on site from the stop line up to a maximum speed of 40 km/h. These distributions are compared to the outputs from a traffic micro simulation tool modeling a similar stop line scenario. The comparison shows that measured accelerations present wider distribution and lower values than the micro simulation. This result highlights the importance of using acceleration distribution calibrated with real-world measured data rather than default values in order to estimate accurate emission levels.
Mascia M, Hu SJ, Han K, et al., 2014, Environmental impact of combined ITS traffic management strategies, The 20th International Transport and Air Pollution Conference 2014
Transport was responsible for 20% of the total greenhouse gas emissions in Europe during 2011(European Environmental Agency 2013) with road transport being the key contributor. To tacklethis, targets have been established in Europe and worldwide to curb transport emissions. Thisposes a significant challenge on Local Government and transport operators who need to identifya set of effective measures to reduce the environmental impact of road transport and at the sametime keep the traffic smooth. Of the road transport pollutants, this paper considers NOx, CO2 andblack carbon (BC). A particular focus is put on black carbon, which is formed through incompletecombustion of carboneous materials, as it has a significant impact on the Earth’s climate system.It absorbs solar radiation, influences cloud processes, and alters the melting of snow and icecover (Bond et al. 2013). BC also causes serious health concerns: black carbon is associatedwith asthma and other respiratory problems, heart attacks and lung cancer (Sharma 2010; UnitedStates Environmental Protection Agency 2012).Since BC emissions are mainly produced during the decelerating and accelerating phases(Zhang et al. 2009), ITS actions able to reduce stop&go phases have the potential to reduce BCemissions. This paper investigates the effectiveness of combined ITS actions in urban context inreducing CO2 and BC emissions and improving traffic conditions.
Vranckx S, Lefebvre W, van Poppel M, et al., 2014, Air Quality Impact of a Decision Support System for Reducing Pollutant Emissions: CARBOTRAF, International transport and air pollution conference
Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions.Black carbon (BC) is a good indicator of combustion-related air pollution and results in negativehealth effects. Both BC and CO2 emissions are also known to contribute significantly to globalwarming. Current traffic control systems are designed to improve traffic flow and reducecongestion. The CARBOTRAF system combines real-time monitoring of traffic and air pollutionwith simulation models for emission and local air quality prediction in order to deliver on-linerecommendations for alternative adaptive traffic management. The aim of introducing aCARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimizingthe traffic flows. The system is implemented and evaluated in two pilot cities, Graz andGlasgow.Model simulations link traffic states to emission and air quality levels. A chain of modelscombines micro-scale traffic simulations, traffic volumes, emission models and air qualitysimulations. This process is completed for several ITS scenarios and a range of traffic boundaryconditions. The real-time DSS system uses these off-line model simulations to select optimaltraffic and air quality scenarios. Traffic and BC concentrations are simultaneously monitored. Inthis paper the effects of ITS measures on air quality are analysed with a focus on BC.
Vranckx S, van Poppel M, Theunis J, et al., 2014, CARBOTRAF: A decision Support system for reducing pollutant emissions by adaptive traffic management, 16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes
Traffic congestion with frequent “stop & go” situations causes substantial pollutant emissions. Blackcarbon (BC) is a good indicator of combustion-related air pollution and results in negative health effects. Both BCand CO2 emissions are also known to contribute significantly to global warming. Current traffic control systems aredesigned to improve traffic flow and reduce congestion. The CARBOTRAF system combines real-time monitoring oftraffic and air pollution with simulation models for emission and local air quality prediction in order to deliver on-linerecommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is toreduce BC and CO2 emissions and improve air quality by optimizing the traffic flows. The system is implementedand evaluated in two pilot cities, Graz and Glasgow.Model simulations link traffic states to emission and air quality levels. A chain of models combines micro-scaletraffic simulations, traffic volumes, emission models and air quality simulations. This process is completed forseveral ITS scenarios and a range of traffic boundary conditions. The real-time DSS system uses all these modelsimulations to select optimal traffic and air quality scenarios. Traffic and BC concentrations are simultaneouslymonitored. In this paper the effects of ITS measures on air quality are analysed with a focus on BC.
Koudis G, North R, Majumdar A, et al., 2014, Method for the improvement of aircraft take-off trajectory simulation using variability analysis, 6th International Conference on Research in Air Transport
Angeloudis P, Hu J, Bell MGH, 2014, A strategic repositioning algorithm for bicycle-sharing schemes, Transportmetrica A: Transport Science, Vol: 10, Pages: 759-774
Araghi BN, Hu S, Krishnan R, et al., 2014, A comparative study of k-NN and hazard-based models for incident duration prediction, Pages: 1608-1613
Angeloudis P, Hu J, Bell MGH, 2012, A strategic repositioning algorithm for Bicycle-sharing schemes, TRB
Hu J, Kaparias I, Bell MGH, 2012, Current state and future outlook of traffic data fusion in London, Pages: 1483-1488
Hu J, Krishnan R, Bell M, 2011, Incident duration prediction for in-vehicle navigation systems, 90th Annual Meeting of the Transportation Research Record
Hu J, Bell M, 2009, Spatial Analysis for Congestion Prediction on Road Networks, 49th European Congress of the Regional Science Association International
Current vehicle navigation systems receive congestion information via RDS-TMC, to theextent that congestion is detected. A better quality of guidance would be possible, however,if short-term prediction of future congestion were available beside information on currentcongestion.Congestion is thought to be spatially and temporally correlated on the road network. Anotable phenomenon is that the congestion on a link is affected by its immediate past trafficcondition and the number of its neighbouring links. A time component defines how trafficconditions are related in the temporal dimension, and a spatial component defines how thetraffic conditions on neighbouring links have affected each other in the spatial dimension.This paper explores the possibility of incorporating spatial and temporal effects in congestionprediction using a zero-inflated Poisson model. A year‟s worth of TMC data from aparticular area of London have been collected and input into the model for this purpose, andthe results are presented in this paper.
Hu J, Kaparias I, Bell MGH, 2009, Spatial econometrics models for congestion prediction with in-vehicle route guidance, IET INTELLIGENT TRANSPORT SYSTEMS, Vol: 3, Pages: 159-167, ISSN: 1751-956X
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- Citations: 11
Hu J, Krishnan R, Bell MGH, 2008, TPEG feed from the BBC: A potential source of ITS data?, Road Transport Information and Control - RTIC 2008 and ITS United Kingdom Members' Conference, IET, Pages: 1-11, ISSN: 0537-9989
The provision of traffic and travel information has long been at the centre of development of Intelligent Transport System (ITS). TPEG (Transport Protocol Expert Group) is a new standard format for delivering real-time traffic information to drivers over digital radio channels. TPEG is considered to be a replacement to the current RDS-TMC standard in the future, which is currently used by in-car navigation systems. TPEG standard also specifies an XML format (tpegML) for delivery over the Internet. The BBC has launched a pilot service that delivers a live feed of incident, congestion and roadwork information in tpegML format through their website. This makes it a potential data source for ITS applications deployed on a wide range of platforms.
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