43 results found
Chen C, Hu S, Ochieng WY, et al., 2021, Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach, JOURNAL OF ADVANCED TRANSPORTATION, Vol: 2021, ISSN: 0197-6729
Liu Q, Hu S, Angeloudis P, et al., 2021, Dynamic wireless power transfer system for electric-powered connected and autonomous vehicle on urban road network, IET INTELLIGENT TRANSPORT SYSTEMS, Vol: 15, Pages: 1153-1166, ISSN: 1751-956X
Tang C, Hu W, Hu S, et al., 2021, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 2956-2968, ISSN: 1524-9050
With the rapid development of urbanization, the problem of urban traffic congestion has become increasingly prominent. Dynamic route guidance promises to improve the capacity of urban traffic management and mitigate traffic congestion in big cities. In the design of simulation-based experiments for most dynamic route guidance methods, the simulation data is generally estimated from a specific traffic scenario in the real-world. However, highly dynamic traffic in the city implies that traffic scenarios in real systems are diverse. Therefore, if a route guidance method cannot adjust its strategy according to the spatial and temporal characteristics of different traffic scenarios, then it cannot guarantee optimal results under all traffic scenarios. Thus, ideal dynamic route guidance methods should have a highly adaptive learning ability under diverse traffic scenarios so as to have extensive improvement capabilities for different traffic scenarios. In this study, an A* trajectory rejection method based on multi-agent reinforcement learning (A*R²) is proposed; the method integrates both system and user perspectives to mitigate traffic congestion and reduce travel time (TT) and travel distance (TD). First, owing to its adaptive learning ability, the A*R² can comprehensively analyze the traffic conditions for different traffic scenarios and intelligently evaluate the road congestion index from a system perspective. Then, the A*R² determines the routes for all vehicles from user perspective according to the road network congestion index. An extensive set of simulation experiments reveal that, under various traffic scenarios, the A*R² can rely on its adaptive learning ability to achieve better traffic efficiency. Moreover, even in cases where many drivers are not fully compliant with the route guidance, the traffic efficiency can still be improved significantly by A*R².
Wang Y, Yu X, Zhang S, et al., 2021, Freeway Traffic Control in Presence of Capacity Drop, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 22, Pages: 1497-1516, ISSN: 1524-9050
Taleongpong P, Hu S, Jiang Z, et al., 2020, Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network, Journal of Intelligent Transportation Systems: technology, planning, and operations, Vol: 2020, Pages: 1-19, ISSN: 1547-2450
Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques alongside data mining processes to create a framework that predicts key performance indicators (KPIs), reactionary arrival delay, reactionary departure delay, dwell time and travel time. The frameworks in this paper provide greater accuracy in predicting KPIs through state-of-the-art ML models compared to existing delay prediction systems. Further discussion on the improvements, applicability and scalability of this framework are also provided in this paper.
Wu C, Hu S, Lee C-H, et al., 2020, Multi-platform data collection for public service with Pay-by-Data, MULTIMEDIA TOOLS AND APPLICATIONS, Vol: 79, Pages: 33503-33518, ISSN: 1380-7501
Liu W, van Hooff T, An Y, et al., 2020, Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method, Building and Environment, Vol: 186, Pages: 1-11, ISSN: 0360-1323
It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model.
Wu C, Zheng P, Xu X, et al., 2020, Discovery of the Environmental Factors Affecting Urban Dwellers' Mental Health: A Data-Driven Approach, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 17
Wu C, Wang Z, Hu S, et al., 2020, An automated machine-learning approach for road pothole detection using smartphone sensor data, Sensors, Vol: 20, Pages: 1-23, ISSN: 1424-8220
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
Song J, Hu S, Han K, et al., 2020, Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation, NETWORKS & SPATIAL ECONOMICS, Vol: 20, Pages: 675-702, ISSN: 1566-113X
Yu J, Stettler MEJ, Angeloudis P, et al., 2020, Urban network-wide traffic speed estimation with massive ride-sourcing GPS traces, Transportation Research Part C: Emerging Technologies, Vol: 112, Pages: 136-152, ISSN: 0968-090X
The ability to obtain accurate estimates of city-wide urban traffic patterns is essential for the development of effective intelligent transportation systems and the efficient operation of smart mobility platforms. This paper focuses on the network-wide traffic speed estimation, using trajectory data generated by a city-wide fleet of ride-sourcing vehicles equipped with GPS-capable smartphones. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. Data limitations are addressed using the Schatten p-norm matrix completion algorithm, which can minimize speed estimation errors even with high rates of data unavailability. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.
Li Y, Zhong Z, Song Y, et al., 2020, Longitudinal Platoon Control of Connected Vehicles: Analysis and Verification, IEEE Transactions on Intelligent Transportation Systems, ISSN: 1524-9050
This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles.
Tian Y, Hu W, Du B, et al., 2019, IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment, WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, Vol: 22, Pages: 2129-2151, ISSN: 1386-145X
Yan L, Hu W, Hu S, 2019, SALA: A Self-Adaptive Learning Algorithm-Towards Efficient Dynamic Route Guidance in Urban Traffic Networks, NEURAL PROCESSING LETTERS, Vol: 50, Pages: 77-101, ISSN: 1370-4621
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
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.
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.
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