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

This paper proposes a multilane traffic flow model based on the notions of conservation laws in Lagrangian coordinates. Both the mathematical formulation and the graphical representation are provided. A logit choice model is applied to describe drivers' lane choice probability. The lane changing rate is estimated by employing the Incremental Transfer (IT) principle and the lane choice probability model. The numerical implementation of the model in the case of two lanes is discussed. The simulation results reveal that the proposed Lagrangian model is able to describe lane changing dynamics of vehicle platoons; while the lane changing equilibrium curve at the macroscopic level is consistent with that from the multilane Eulerian model as well as the observed data on the highway.

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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

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 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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