Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Bayesian dynamic graphical models for high-dimensional flow forecasting in road traffic networks

Congestion on roads is a crucial problem which affects our lives in many ways: As a consequence, there is a strong effort to improve road networks in order to keep the traffic flowing. Flow forecasting models based on the large amount of traffic data, which are now available, can be a very useful tool to support decisions and actions when managing traffic networks. Although many forecasting models have been developed to this end, very few of them capture important features of high dimensional traffic data and, moreover, operating most of these models is a hard task when considering on-line traffic management environments. Dynamic graphical models can be a suitable choice to address the challenge of forecasting high-dimensional traffic flows in real-time. These models represent network flows by a graph, which not only is a useful pictorial representation of multivariate time series of traffic flow data, but it also ensures that model computation is always simple, even for very complex road networks. One example of such a model is the multiregression dynamic model (MDM). This thesis focuses on the development of two classes of dynamic graphical models to forecast traffic flows . Firstly, the linear multiregression dynamic model (LMDM), which is an MDM particular case, is extended to allow important traffic characteristics in its structure, such as the heterocedasticity of daily traffic flows, measurement errors due to malfunctions in data collection devices, and the use of extra traffic variables as predictors to forecast flows. Due to its graphical structure, the MDM assumes independence of flows at the entrances of a road network. This thesis therefore introduces a new class of dynamic graphical models where the correlation across road network entrances is accommodated, resulting in better forecasts when compared to the LMDM. All the methodology proposed in this thesis is illustrated using data collected at the intersection of three busy motorways near Manchester, UK

AN APPROACH OF TRAFFIC FLOW PREDICTION USING ARIMA MODEL WITH FUZZY WAVELET TRANSFORM

It is essential for intelligent transportation systems to be capable of producing an accurate forecast of traffic flow in both the short and long terms. However, the counting datasets of traffic volume are non-stationary time series, which are integrally noisy. As a result, the accuracy of traffic prediction carried out on such unrefined data is reduced by the arbitrary components. A prior study shows that Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models convey demand of noise-free dataset for model construction. Therefore, this study proposes to overcome the noise issue by using a hybrid approach that combines the ARIMA model with fuzzy wavelet transform. In this approach, fuzzy rules are developed to categorize traffic datasets according to influencing factors such as the time of a day, the season of a year, and weather conditions. As the input of linear data series for ARIMA model needs to be converted into linear time series for traffic flow prediction, the discrete wavelet transform is applied to help separating the nonlinear and linear part of the time series along with denoised time series traffic data

Redefine time series models for transportation planning use

Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types

Effect of Time Intervals on K-nearest Neighbors Model for Short-term Traffic Flow Prediction

The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval

Some statistical techniques for analysing Bluetooth tracking data in traffic modelling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand

The economy and the environment are both affected by traffic congestion. People spend time stuck in traffic, which limits their free time. Every city's road infrastructure is under increased pressure, particularly in large cities, due to population growth and vehicle ownership patterns. Therefore, traffic control and management are crucial to reducing traffic congestion problems and effectively using existing road infrastructure. Bluetooth is a commonly used wireless technology for short-distance data exchange. This technology allows all mobile phones, GPS systems, and in-vehicle applications such as navigation systems to connect with the personal devices of drivers and passengers. A Media Access Control (MAC) address is a unique electronic identifier used by each Bluetooth device. The concept is that, while a Bluetooth-equipped device travels along a road, its MAC address, detection time, and location can be detected anonymously at different locations. Bluetooth technology can be integrated into Intelligent Transportation Systems (ITS) to enable better and more effective traffic monitoring and management, hence reducing traffic congestion. This thesis aims to develop some statistical methods for analysing Bluetooth tracking data in traffic modelling. One of the challenges of using Bluetooth data, particularly for travel time estimation, is multiple Bluetooth detections, which occur when a Bluetooth sensor records a Bluetooth device several times while it passes through the detection zone. We employ cluster analysis to look at the possibility of extracting meaningful traffic information from multiple detections, and the observed gap distribution, which is the time difference between records when multiple detections occur. We also develop a novel regression method to investigate the relationship between data from Bluetooth and Automatic Traffic Counts (ATCs) through weighted regression analysis, in order to explore potential causes of bias in the representativeness of Bluetooth detections. Finally, we seek the practical objective of recovering ATC from Bluetooth data as a statistical calibration problem, following the development of a new time-varying coefficients Poisson regression model

DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA

The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments

Traffic assignment: methods and simulations for an alternative formulation of the fixed demand problem

Motorists often face the dilemma of choosing the route enabling them to realise the fastest (i.e., shortest) journey time. In this paper we examine discrete and continuous optimisation and equilibrium-type problems for a simplified parallel link traffic model using a variance based approach. Various methodologies used for solving these problems (brute force, dynamic programming, tabu search, steepest descent) are explored and comparison is made with the Beckmann cost function traditionally employed in transport modelling.Ministry of Research and Innovation, CNCS-UEFISCDI, project number PN-III- P2-2.1-BG-2016-0333, within PNCDI III