25 results found
Li J, Guo F, Sivakumar A, et al., 2021, Transferability Improvement in Short-term Traffic Prediction using Stacked LSTM Network, Transportation Research Part C: Emerging Technologies, Vol: 124, ISSN: 0968-090X
Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to provide proactive traffic state information to road network operators. A variety of methods to predict traffic variables in the short-term can be found in the literature, ranging from time-series algorithms, machine learning tools and deep learning methods to a selective hybrid of these approaches. Despite the advances in prediction techniques, a challenging problem that affects the application of such methods in the real world is the prevalence of insufficient data across an entire network. It is rare that extensive historical training data required for model training are available for all the links in a city. In order to address this data insufficiency problem, this paper applies transfer learning techniques to machine learning methods in short-term traffic prediction. All the traffic data used in this paper were collected from Highways England road networks in the UK. The results show that through improving the transferability of machine learning-based models, the computational burden due to the model training process can be significantly reduced and the prediction accuracy under data deficient scenarios can be improved for one-step ahead prediction. However, the prediction accuracy gradually decreases in multi-step ahead prediction. It is also found that the accuracy of the proposed hybrid method is highly dependent upon consistency between datasets but less dependent on geographical attributes of links.
Li T, Guo F, Krishnan R, et al., 2020, Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles, Transportation Research Part C: Emerging Technologies, Vol: 115, ISSN: 0968-090X
Autonomous Vehicles (AVs) are bringing challenges and opportunities to urban traffic systems. One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate, and to efficiently reallocate the Right-of-Way (RoW) for the mixed flow of AVs and Human Driven Vehicles (HDVs). Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all-AV scenario, when AVs are able to drive with smaller headways to the leading vehicle. However, this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs. In order to investigate the impacts of mixed flow conditions (AVs and HDVs), this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation. Secondly, four different RoW reallocation strategies are compared using a SUMO simulation to cross-validate the results in a numerical analysis. A range of scenarios with different AV penetration rates and traffic demands are used. The results show that road capacity on a two-lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates, compared with the do-nothing RoW strategy.
Zhu L, Krishnan R, Guo F, et al., 2019, Early identification of recurrent congestion in heterogeneous urban traffic, IEEE Intelligent Transportation Systems Conference - ITSC 2019, Publisher: IEEE, Pages: 1-6
Urban traffic congestion has become a criticalissue that not only affects the quality of daily lives but alsoharms the environment and economy. Traffic patterns arerecurrent in nature, so is congestion. However, little attentionhas been paid to the development of methods that wouldenable early warning of the formation of congestion and itspropagation. This paper proposes a method for automatedearly congestion detection operating over time horizons rangingfrom half an hour to three hours. The method uses a deeplearning technique, Convolutional Neural Networks (CNN), andadapts it to the specific context of urban roads. Empiricalresults are reported from a busy traffic corridor in the city ofBath. Comprehensive evaluation metrics, including DetectionRate, False Positive Rate and Mean Time to Detection, areused to evaluate the performance of the proposed methodcompared to more conventional machine learning methodsincluding Feed-forward Neural Network and Random Forest.The results indicate that recurrent congestion can indeed bepredicted before it occurs and demonstrates that CNN basedmethod offers superior detection accuracy compared to theconventional machine learning methods in this context.
Zhu L, Krishnan R, Sivakumar A, et al., 2019, Traffic monitoring and anomaly detection based on simulation of Luxembourg Road network, IEEE Intelligent Transportation Systems Conference - ITSC 2019, Publisher: IEEE, Pages: 1-6
Traffic incidents which commonly result fromtraffic accidents, anomalous construction events and inclementweather can cause a wide range of negative impacts on urbanroad networks. Developing a high efficiency and transferabletraffic incident detection system plays an important role insolving the imbalance caused by traffic incidents betweentraffic demand and capacity. However, the existing literatureon transferability of traffic incident detection is rather limited.The objective of this paper is to provide an accurateand transferable incident detection approach based on therelationship between traffic variables and observed trafficincidents, in particular at a network level. We propose a deeplearning based method which has been calibrated using partof the collected traffic variables and the pre-assigned trafficincidents and then tested against the rest of the dataset. Theproposed method is compared to other benchmarks commonlyused in traffic incident detection, in terms of detection rate, falsepositive rate, f-measurement and detection time. The resultsindicate that the proposed method is significantly promising fortraffic incident detection with high accuracy and transferabilitycompared to the more widely used techniques in the literature.
Luan J, Polak J, Krishnan R, 2019, The structure of public-private sector collaboration in travel information markets: A game theoretic analysis, TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, Vol: 129, Pages: 19-38, ISSN: 0965-8564
Zhu L, Guo F, Polak J, et al., 2018, Urban link travel time estimation using traffic states based data fusion, IET Intelligent Transport Systems, Vol: 12, Pages: 651-663, ISSN: 1751-956X
Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the `ground truth'. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.
Guo F, Polak JW, Krishnamoorthy R, 2018, Predictor fusion for short-term traffic forecasting, Transportation Research Part C: Emerging Technologies, Vol: 92, Pages: 90-100, ISSN: 0968-090X
Zhu L, Guo F, Krishnan R, et al., 2018, A Deep Learning Approach for Traffic Incident Detection in Urban Networks, 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 1011-1016, ISSN: 2153-0009
Guo F, Krishnan R, Polak JW, 2017, The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol: 21, Pages: 214-226, ISSN: 1547-2450
This article investigates the impact of alternative data smoothing and traffic prediction methods on the accuracy of the performance of a two-stage short-term urban travel time prediction framework. Using this framework, we test the influence of the combination of two different data smoothing and four different prediction methods using travel time data from two substantially different urban traffic environments and under both normal and abnormal conditions. This constitutes the most comprehensive empirical evaluation of the joint influence of smoothing and predictor choice to date. The results indicate that the use of data smoothing improves prediction accuracy regardless of the prediction method used and that this is true in different traffic environments and during both normal and abnormal (incident) conditions. Moreover, the use of data smoothing in general has a much greater influence on prediction performance than the choice of specific prediction method, and this is independent of the specific smoothing method used. In normal traffic conditions, the different prediction methods produce broadly similar results but under abnormal conditions, lazy learning methods emerge as superior.
Araghi BN, Pedersen KS, Christensen LT, et al., 2015, Accuracy of Travel Time Estimation Using Bluetooth Technology: Case Study Limfjord Tunnel Aalborg, INTERNATIONAL JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS RESEARCH, Vol: 13, Pages: 166-191, ISSN: 1348-8503
Araghi BN, Krishnan R, Lahrmann H, 2015, Mode-Specific Travel Time Estimation Using Bluetooth Technology, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol: 20, Pages: 219-228, ISSN: 1547-2450
The problem of mode-specific travel time estimation is mostly relevant to arterials with different travel modes, including cars, buses, cyclists, and pedestrians. Traditional travel time measurement systems such as automated number plate recognition (ANPR) cameras detect only motor vehicles and provide an estimate of their travel times. Bluetooth technology has been used as an alternative to more expensive ANPR for travel time measurements in the recent past. However, Bluetooth-sensors detect discoverable electronic devices used by all travel modes. Bluetooth-based systems currently use the time stamp of device detection events by two sensors to estimate the travel time, and there is no direct way to estimate mode-specific travel times using this approach. Hence, estimating travel time using Bluetooth technology on urban arterials without classifying the modes of detected devices could provide a biased estimate. A novel method to estimate mode-specific travel times using Bluetooth technology that is capable of estimating mode-specific travel times, specifically distinguishing between the travel time of motor vehicles and bicycles, is presented in this article. The proposed method uses information about type of detected device (class of device, CoD) and radio signal strength indication (RSSI). The proposed method also uses the travel time of the detected device and its detection pattern across the road network by multiple Bluetooth sensors to estimate the travel mode of each detected device. The accuracy of the proposed method was evaluated against the ground truth obtained by manual transcription of traffic video recordings, and was compared against travel times obtained from ANPR, a commercially deployed Bluetooth-based method, and a clustering method. The results show that the proposed method provides travel time estimates using Bluetooth with almost the same level of accuracy as ANPR under mixed traffic conditions.
Hodge VJ, Krishnan R, Austin J, et al., 2014, Short-term prediction of traffic flow using a binary neural network, NEURAL COMPUTING & APPLICATIONS, Vol: 25, Pages: 1639-1655, ISSN: 0941-0643
Araghi BN, Olesen JH, Krishnan R, et al., 2014, Reliability of Bluetooth Technology for Travel Time Estimation, Journal of Intelligent Transportation Systems, Vol: 0, Pages: null-null
Abstract A unique Bluetooth-enabled device may be detected several times or not at all when it passes a sensor location. This depends mainly on the strength and speed of a transmitting device, discovery procedure, location of the device relative to the Bluetooth sensor, the Bluetooth sensor’s ping cycle (0.1 seconds), the size and shape of the sensor’s detection zone, and the time span that the Bluetooth-enabled device is within the detection zone. The influences of size of Bluetooth sensor detection zones and Bluetooth discovery procedure on multiple detection events have been mentioned in previous research. However, their corresponding impacts on accuracy and reliability of estimated travel time have not been evaluated. In this study, a controlled field experiment is conducted to collect both Bluetooth and GPS data for 1000 trips to be used as the basis for evaluation. Data obtained by GPS logger is used to calculate actual travel time, referred to as ground truth, and to geo-code the Bluetooth detection events. In this setting, reliability is defined as the percentage of devices captured per trip during the experiment. It is found that, on average, Bluetooth-enabled devices will be detected 80% of the time while passing a sensor location. The impact of location ambiguity caused by size of detection zone is evaluated using geo-coded Bluetooth data. Results show that more than 80% of the detection events are recorded within the range of 100 meters from the sensor centre line. It is also shown that short-range antennae detect Bluetooth-enabled devices in a closer location to the sensor, thus providing a more accurate travel time estimate. However, the smaller the size of the detection zone, the lower the penetration rate, which could itself influence the accuracy of estimates. Therefore, there has to be a trade-off between acceptable level of location ambiguity and penetration rate for configuration and coverage of the antennae.
Guo F, Krishnan R, Polak JW, 2014, Novel Three-Stage Framework for Short-Term Travel Time Prediction Under Normal and Abnormal Traffic Conditions, 93rd Annual Meeting of the Transportation Research Board
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
Guo F, Krishnan R, Polak J, 2013, A computationally efficient two-stage method for short-term traffic prediction on urban roads, TRANSPORTATION PLANNING AND TECHNOLOGY, Vol: 36, Pages: 62-75, ISSN: 0308-1060
Araghi BN, Christensen LT, Krishnan R, et al., 2013, Use of Low-Level Sensor Data to Improve the Accuracy of Bluetooth-Based Travel Time Estimation, TRANSPORTATION RESEARCH RECORD, Pages: 29-34, ISSN: 0361-1981
Guo F, Krishnan R, Polak JW, 2012, Short-term traffic prediction under normal and abnormal traffi c conditions on urban roads, Transportation Research Board 91st Annual Meeting
Short-term traffic prediction can support proactive traffic control in Intelligent Transportation Systems (ITS) to help traffic network managers anticipate and mitigate network problem in advance. In previous work on this topic, three models with increasing information in explanatory variables were developed and tested for 15-min ahead traffic prediction concerned with normal and abnormal traffic conditions using a dataset from Inductive Loop Detectors (ILDs) in central London. In this paper, the k-Nearest Neighbour (kNN) and Support Vector Regression (SVR) algorithms were used as machine learning tools for implementing the models with an objective to compare these machine learning tools using the model structures used earlier. The prediction accuracy of models implemented using the kNN and SVR methods is evaluated for normal traffic conditions and incident conditions using data from central London. This study shows that the kNN and SVR methods have the similar prediction accuracy in normal, non-incident traffic conditions. However, the kNN method outperforms the SVR approach under abnormal, incident traffic conditions. In addition, of the three different model mechanisms used, the structure of error feedback improved the prediction accuracy of the kNN based model under non-recurring abnormal traffic conditions, supporting the results from earlier studies.
Han J, Polak JW, Barria J, et al., 2010, On the estimation of space-mean-speed from inductive loop detector data, TRANSPORTATION PLANNING AND TECHNOLOGY, Vol: 33, Pages: 91-104, ISSN: 0308-1060
Esposito M-C, Polak JW, Krishnan R, et al., 2010, A global comparison of ramp-metering algorithms optimising traffic distribution on motorways and arterials, Pages: 1-6-1-6
Guo F, Polak JW, Krishnan R, 2010, Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions, Pages: 1209-1214-1209-1214, ISSN: 2153-0009
Krishnan R, 2008, Travel time estimation and forecasting on urban roads
Travel time estimation is an important function in ITS applications such as Advanced Traveller Information Systems (ATIS), Dynamic Route Guidance (DRG) and Network Performance Monitoring. These applications, when deployed for an urban area, require models that estimate travel times on urban arterial corridors with traffic signals. Most of the travel time estimation models in the literature are inductive in nature, which require vehicle travel time observations for calibration. Obtaining vehicle travel time data is both a time consuming and costly process for a single link and it is impractical to collect this information for all the links across the road network. Hence, there is a need for travel time estimation models that do not require reference vehicle travel time data for calibration. A deductive travel time estimation method based on the cumulative counts approach is presented in this thesis.The cumulative counts method can use flow counts obtained from single Inductive Loop Detectors (ILD) that are commonly deployed on urban links for the purpose of traffic control. However, the accuracy of cumulative counts method depends on the accuracy of flow counts. While flow counts from ILDs are generally considered to be reliable, they are not error free. Moreover, many of the ILDs on urban links are installed across two lanes leading to under-estimation of flow counts. An analytical model to estimate unbiased flows from cross-lane ILDs is proposed in this thesis to address this problem. The proposed analytical model is applicable to time periods when the flow rate is uniform. However, the flow rates on urban links switch between periods of high and low flow rate due to signalisation. A calibration free-platoon identification algorithm is proposed in this thesis to distinguish periods of high flow rate from periods of low flow rate.The flow counts estimated from ILDs after the application of cross-lane flow estimation model is subject to a number of other error source
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.
Krishnan R, Polak JW, 2007, A platoon identification algorithm for ubran arterial links, Transportation Research Board 86th Annual Meeting
Identification of vehicle platoons in traffic flow is an important problem in traffic operations, as vehicle arrival patterns can be used to cooordinate traffic signal offsets and minimize delays. Information about platoons can also be used as an input to urban link travel time estimation models. While there are existing methods in the literature to identify platoons using second-by-second flow data from a point on a link, they are generally rule-based algorithms having internal parameters that need to be fine tuned or involve complex statistical approaches. Some of these algorithms also tend to be less robust when presented with traffic flow on signals urban links. A simple platoon identification method is proposed in this paper where platoons are identified using changes in temporal flow density. The difference in total flow counts between a forward and backward looking window in time will assume a maximum value at the point in time when a platoon begins, and a minimum value when a platoon ends. These local maxima and minima points are identified using second-by-second flow data obtained from an inductive loop detector (ILD) to determine platoon beginning and end points in time. The accuracy of the proposed approach is tested using ILD data obtained from a Paramics micro-simulation model and is compared against established existing platoon identification methods. The results show that the proposed method matches or exceeds the accuracy of existing platoon identification algorithms despite being simple and easy to implement, and is suitable for use on signals urban links.
Krishnan R, Polak JW, 2006, Estimating traffic flow from cross-lane Inductive Loop Detectors
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