OD flows provide important information for traffic management and planning. The prediction of dynamic OD matrices gives the possibility to apply anticipatory traffic management measures. In this paper, we propose an OD prediction approach based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make predictions. We further propose three K-Nearest Neighbour (K-NN) based long-term pattern recognition approaches. The proposed approaches are validated with field ANPR data from Changsha city, P.R. China. The results show that the observed OD flows can be accurately predicted by our proposed approaches. Which prediction method performs best depends on the quality of the available data: for regular, periodic OD matrices the Kalman filter is better, for irregular OD matrices the pattern recognition that looks at different time periods in the historical data, gives better results.@en

Data recorded by Automated Number Plate Recognition (ANPR) cameras can be used to determine several important traffic characteristics, such as real time travel time, travel time statistics, travel time reliability and OD matrices. In this paper ANPR data collected in Chinese city Changsha have been validated. Travel time extracted from ANPR data includes some outliers which are often caused by drivers who have an intermediate stop between two observation points or deviate from the straight route. Exceptional travel time reduces the validity of the estimation of the travel time and reliability. Firstly, the Rapid-Moving Window method is introduced to identify outliers. Afterwards, another method based on wavelet analysis is put forward to identify and remove the outliers in the travel time series. The wavelet analysis method is compared with the Rapid-Moving Window method and shows to be more accurate in outlier identification. The method for eliminating outliers in travel times can be implemented in real time to enhance the data quality for traffic network monitoring and management. After the removal of the outliers, the resulting travel times are used for the analysis of the relation between average travel time and standard deviation/skewness.

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The dynamic origin-destination (OD) information is crucial for traffic operations and control. This paper presents a dynamic traffic demand prediction framework based on an anomaly detection algorithm. The Principal Component Analysis (PCA) method is applied to extract main demand patterns which are used to detect the abnormal conditions. The proposed approach can select prediction methods (parametric or nonparametric) automatically based on the pattern detection results. Both simulation and field observed Automatic Number Plate Recognition (ANPR) data are used to verify the proposed approach where the Kalman filter model and the K-nearest neighbor model are chosen as the basic prediction methods. The results show that the prediction framework can effectively reduce the noise of a single prediction model particularly in the abnormal conditions and provide more accurate and reliable prediction results.@en

All drivers have individual ways of driving. Still, there are groups of drivers with more or less similar characteristics. In this research, 28 drivers from Chengdu city (P.R. China) participated in an experiment where car following behaviour was measured with GPS devices. In every measured trip there was a leading and following vehicle both equipped with a GPS device. Drivers are classified based on a Driver Behaviour Questionnaire and observed acceleration and deceleration behaviour. The result shows four distinct classes of drivers: macho drivers, careful/inexperienced drivers, smooth going/ professional drivers, and experienced/fast drivers. Drivers in the different classes give different emission of air pollution and fuel consumption. Saturation flows are determined from the trajectories and vary between different driver types. The measured trajectories have been analysed in detail to determine some parameters for the Wiedemann 74 model. Most default parameters in the VISSIM program appear to be unsuited for the simulation of driving behaviour measured in the experiment. The emissions and fuel consumption calculated by a simulation model with default parameters are not consistent with the empirical data. The calibration done for different driver types shows that several model parameters are significantly different for the different driver classes.@en

The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.

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Urban travel times are rather variable as a result of a lot of stochastic factors both in traffic flows, signals, and other conditions on the infrastructure. However, the most common way both in literature and practice is to estimate or predict only expected travel times, not travel time distributions. By doing so, it fails to provide full insight into the travel time dynamics and variability on urban roads. Another limitation of this common approach is that the effect of traffic measures on travel time reliability cannot be evaluated. In this paper, an analytical travel time distribution model is presented especially for urban roads with fixed-time controlled intersections by investigating the underlying mechanisms of urban travel times. Different from mean travel time models or deterministic travel time models, the proposed model takes stochastic properties of traffic flow, stochastic arrivals and departures at intersections, and traffic signal coordination between adjacent intersections into account, and therefore, is able to capture the delay dynamics and uncertainty at intersections. The queue spillback phenomenon is explicitly taken into account by applying shockwave theory in a probabilistic way. The proposed model was further validated with both VISSIM simulation data and field GPS data collected in a Chinese city. The results demonstrate that the travel time distributions derived from the analytical model can well represent those from VISSIM simulation. The comparison with field GPS data shows that the model estimated link and trip travel time distributions can also represent the field travel time distributions, though a small discrepancy can be observed in both middle range travel times and higher travel times.

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The importance of travel time reliability in traffic management, control, and network design has received a lot of attention in the past decade. In this paper, a network travel time distribution model based on the Johnson curve system is proposed. The model is applied to field travel time data collected by Automated Number Plate Recognition (ANPR) cameras. We further investigate the network-level travel time reliability by connecting the network reliability measures such as the weighted standard deviation of travel time rate and the weighted skewness of travel time rate distributions with network traffic characteristics (e.g., the network density). The weighting is done with respect to the number of signalized intersections on a trip. A clear linear relation between the weighted average travel time rate and the weighted standard deviation of travel time rate can be observed for different time periods with time-varying demand. Furthermore, both the weighted average travel time rate and the weighted standard deviation of travel time rate increase monotonically with network density. The empirical findings of the relation between network travel time reliability and network traffic characteristics can be possibly applied to assess traffic management and control measures to improve network travel time reliability.

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The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.

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Fuel consumption and atmospheric pollution emissions of vehicles depend on driving conditions, the characteristics of the driver and the car. The influence of driving style on the environmental aspects of a car journey has been investigated. Driver characteristics were determined by a Driver Behaviour Questionnaire and observed acceleration and deceleration behaviour. That results in four types of drivers with similar characteristics within a type group. We measured 56 trajectories of 28 drivers using GPS devices. The measurements were done on a route of 8.4 km in an urban environment in Chengdu (PR China). From the trajectories, the emissions and fuel consumption were determined with the Comprehensive Modal Emissions Model. The results were related to the traffic control along the journey resulting in fuel consumption and emissions per stop and per second idling. There are significant differences in saturation flow, emissions and fuel consumption between different driver types. Cautious, novice drivers have the lowest emission and fuel consumption and give the lowest saturation flow and have the lowest cruise speed; experienced smooth driving drivers give a high saturation flow while keeping fuel consumption and emissions also low. Aggressive experienced drivers have a high saturation flow and fuel consumption / emissions. Therefore, microscopic traffic models that simulate emissions and fuel consumption should take the differences between driver types into account.

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An Origin Destination matrix for urban trips is more difficult to develop than for interurban long and medium distance trips. The socio-economic characteristics are valuable parameters to estimate trip attractions and destinations, but often the distance does not have a significant effect on the distribution of urban trips. Since the 1980s methods are developed to estimate the trip matrix from traffic volumes. The problem is underdetermined: the information in the OD matrix is more than the information contained in the traffic volumes. Nowadays there are more information sources like probe vehicles, Automated Number Plate Recognition cameras, mobile phone data etc. This article discusses the possibilities and limitations of these additional information sources. Use is made of traffic data collected in Changsha, a town in middle-south China.

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It is widely accepted that travelers value both the reliability of travel time and its mean or expected value. Strategies for traffic signal control typically seek to optimize average travel times, although reliability is in general not explicitly taken into account. In this paper, we propose a new framework for evaluating the consequences of signal-control tactics on both reliability and expected values of travel time, based on an analytic model of travel time distribution. A genetic-algorithm-based approach is then employed to identify optimal multicriteria signal control strategies, including sensitivity analysis, to the relative weighting between reliability and expected value. We expose the properties of the proposed framework via an empirical case study of four alternative optimization approaches (the signal setting optimized with the traditional Webster's method, TRANSYT model, and the newly proposed model) under various traffic conditions. Results indicate that the newly proposed framework outperforms the alternative signal control strategies in terms of both travel-time variability and expected travel time.@en