The present situation in transport policy is that many authorities have a responsibility for a part of the transport system and that each authority has its own goals and priorities. Central steering is only on main policy goals. Public transport and municipal, regional, provincial and national road authorities have their own policies, targets and instruments. The interaction between different policies themselves exists indirectly, but policies are 'granular'. In this paper the granular transport policies are described as a game with many players. Integrated traffic control and traffic assignment problem are studied for a situation with two road authorities. The road authorities try to optimize their own objectives and the same is done by the travelers. This leads to a two-level three-player, multi-stage optimization problem with complete information. Game theory gives a suitable framework to analyze the problem and to find solutions for different situations, e.g. no cooperation, cooperation between the two authorities and a system optimum where all actors cooperate to minimize the total costs for all travellers. In this paper two approached are used: an analytical one and an approach based on a simulation and assignment framework. Both approaches are described and used to study a simple example, for which the results are given and discussed. The results show that separate or integrated anticipatory control gives better results than iterative reacting to the current situation. If one road authority takes the lead and anticipates the reactions of both the road users and the other road authority a sub-optimum is reached. The model calculations give evidence that cooperation of road authorities improves the utilization of the infrastructure and that a global optimization does not necessarily give a worse situation for one road authority. @en

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.@en

The integrated traffic control and traffic assignment problem is studied for a situation with two road authorities. Road authorities try to optimize their own objectives, and the same is done by road users. The result is a two-level, three-player multistage optimization problem with complete information. Game theory gives a suitable framework to analyze the problem and to find solutions for different situations such as no cooperation, cooperation between the two authorities, and a system optimum in which all actors cooperate to minimize the total costs for all travelers. Two approaches are used: an analytical approach and one based on a simulation and assignment framework. Both approaches are described and used to study a simple example, for which the results are given and discussed. The results show that separate or integrated anticipatory control gives better results than iterative reaction to the current situation. If one road authority takes the lead and anticipates the reactions of both the road users and the other road authority, a suboptimum is reached. The model calculations provide evidence that cooperation of road authorities improves utilization of the infrastructure and that global optimization does not necessarily result in a worse situation for one road authority.@en

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.@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.@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.@en

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.@en

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

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.@en

Insight into traffic flow characteristics is often gained using local measurements. To determine macroscopic flow characteristics, time aggregation of microscopic information is required. Usually, a data collection system stores values averaged over time. However, it is well known that a time mean average overestimates the influence of faster vehicles, and consequently overestimates the mean speed. As a direct result, densities, computed from flow and speed, are underestimated. This paper compares the time mean speed and space mean speed, using data of individual car passages on a motorway road stretch. We show that the differences between time mean and space mean averages are substantial, up to a factor four. In particular in the lower speed regions the error is big. We indicate the considerable consequences for the jam density and shock wave speed. Finally, a fundamental diagram based on correctly averaged microscopic data can be fitted much better.@en

Much effort has been spent in developing more efficient control systems for signalized intersections to adapt the capacity of the network to the variability of the demand. This variability is partly due to time-dependent factors but also to the stochastic nature of the demand itself. The available formulae can still be successfully adopted in undersaturated conditions, where the stochastic effects fade into one green phase, or in highly oversaturated cases, where the queues aren't comparable with their portions caused by these uncertainties. In cases where the value of the degree of saturation is close to one the stochastic effect plays a relevant role. This paper provides a novel heuristic formula able to model the transition phase when the study involves a variable demand. It particularly solves the queuing behavior of an oversaturated period on the successive undersaturated ones. The delay experienced by the users is modeled using a Markov Chain process. Based on this data the novel model is calibrated. The new model for queues is further applied in two case studies. The first involves a two arms intersection where users of the two OD pairs can only choose between different times of departure. The second involves one OD pair and two parallel routes, one faster but ending with a traffic light while the other is unsignalised. The paper discusses the change in users' decisions with respect to the travel times and the delays they perceive at signalized intersections both from the route choice and the departure time choice point of view. In this sense a Dynamic Traffic Assignment problem in an extended network can be applied and solved. From this research we gained a deeper knowledge of the relevant role played by the random nature of the queue evolution in time for the delay experienced by the users. By acquiring this knowledge a time-dependent queue function has been provided. It is now possible to model the queue as a continuous function with more accurate results than the ones provided by the simple deterministic method.@en

In contemporary transportation planning route travel times are considered key indicators on the reliability of a road network. Mean and variance of travel times do not provide much insight in the travel time reliability since these metrics tend to obscure rare but relevant high travel times under specific circumstances. We therefore introduce two metrics, based on just three characteristic percentiles (10th, 50th and 90th), which indicate both congestion as well as possible onset (dissolve) of congestion. High values of either metrics indicate high travel time unreliability. We subsequently propose a simple neural network model to predict these percentile values and hence unreliability based on time-of-day (TOD), and day-of-week (DOW). The conclusion for traffic managers is twofold. The metrics can be used to identify not only the unreliability of travel times for a given DOW/TOD period, but also identify DOW/TOD periods in which it is likely that congestion sets in (or dissolves. Practically, this means identifying the uncertainty of start, end and hence length of morning and afternoon peak hours. Secondly, the neural network based model can serve as a long-term travel time prediction tool, but also as a travel time unreliability prediction tool, using the two metrics presented above on the model outcomes.@en

De Nota Mobiliteit die de regering in 2004 aan de tweede kamer aanbood stelt een uitdagend doel, 40% meer verkeer verwerken op een kwaliteitsniveau in termen van reistijdbetrouwbaarheid dat we nu nog niet eens overal op zondag gerealiseerd krijgen. Het doel om de situatie van `altijd zondag¿ te realiseren betekent dat er in de maatschappij en politiek het een en ander veranderd moeten worden. Bijvoorbeeld met betrekking tot de weerstand om het onderliggend wegennet te gebruiken om pieken op te vangen, om nieuwe infrastructuur aan te leggen, en om lagere snelheden en verkeersregulering op de snelweg te accepteren. Nog fundamenteler zouden we onze manier van werken (en leven) kunnen veranderen, d.w.z. op andere tijden naar het werk gaan, meer thuis te werken en ons vervoer anders organiseren ¿ aflevering op andere tijden en met andere modi. Vooralsnog vraagt het doel dat het kabinet stelt een enorme inspanning. `Altijd zondag¿ op de autosnelwegen zal bepaald niet gelden voor de verkeersingenieurs en logistieke managers. Die krijgen een vrijwel onmogelijke taak om uit te voeren.@en

The aim of this paper is threefold. For one, we present a new computation scheme for the numerical approximation of solutions of Payne-type models. This scheme is proven to satisfy the positive constraints of traffic variables and thus prevents traffic from moving upstream. Secondly, we propose an automated model calibration approach, which is applied to determine the parameters of the considered macroscopic model for different numerical schemes (Steger-Warming, MacCormack, and the proposed numerical scheme). Thirdly, we will cross-compare the different calibrated numerical approximation models by comparing model predictions with observations from the validation dataset. Based on this cross-comparison, we conclude that the proposed numerical scheme is both more accurate and more robust than the other schemes considered. It yields a small mean squared error, fast computation due to the ability to use a larger time step, and also provides accurate results in regions where the gradients of the speeds and densities are high.@en

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

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.@en

Accuracy and robustness with respect to missing or corrupt input data are two key characteristics for any travel time prediction model that is to be applied in a real-time environment (e.g. for display on variable message signs on freeways). This article proposes a freeway travel time prediction framework that exhibits both qualities. The framework exploits a recurrent neural network topology, the so-called state-space neural network (SSNN), with preprocessing strategies based on imputation. Although the SSNN model is a neural network, its design (in terms of input- and model selection) is not ¿black box¿ nor location-specific. Instead, it is based on the lay-out of the freeway stretch of interest. In this sense, the SSNN model combines the generality of neural network approaches, with traffic related (¿white-box¿) design. Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation. Although there are clear theoretical shortcomings to ¿simple¿ imputation schemes to remedy input failure, our results indicate that their use is justified in this particular application. The SSNN model appears to be robust to the ¿damage¿ done by these imputation schemes. This is true for both incidental (random) and structural input failure. We demonstrate that the SSNN travel time prediction framework yields good accurate and robust travel time predictions on both synthetic and real data.@en

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

Traditional traffic control is in most cases reactive: it responds to certain past and/or current traffic conditions. In the design of traffic control plans, past conditions determine the off-line or on-line implementation of traffic control plans. Also, most current on-line traffic control systems (local or area wide) only take the current traffic conditions into account or, at the best, make predictions for the arrival pattern at the stop-line. This paper investigates some of the possibilities of anticipatory control. Anticipatory control takes not only the current, but also the future traffic conditions into account. These future traffic conditions are related to the behaviour of road users. With behaviour not the short term driving behaviour is mend, such as choice of lane or speed level, but the long term behaviour such as route choice and choice of departure time. Traffic control should anticipate this behaviour. In most cases motorways and urban roads are operated and maintained by different road managers. These road managers are responsible for their own piece of the network and only try to optimise their part. In the paper integrated control is studied, which means that the network is considered to be one network. It shows the advantages of working together to decrease the congestion problem. In the paper the interaction between traffic control and route choice for integrated networks is studied. This is called the integrated traffic control and assignment problem. The problem formulation will be given and the algorithms to solve it. Several control strategies are described and compared with each other. One of these strategies is anticipatory control, which takes the route choice behaviour into account. The results are given for two networks combining a motorway and urban roads. The paper gives the results of the calculations and draws some conclusions.@en

In this paper the integrated traffic control and traffic assignment problem is studied. This problem can be considered as a non-cooperative game in which the traffic authority, that controls the traffic signals, and the road users are the players, who use their own strategy and seek their own optimum. The game theoretical formulation leads to several different control strategies in which users' reactions to traffic control decisions are taken into account. Users' reactions can be the choice of route, departure time or even mode. In this paper some of these control strategies for traffic signal control are described: Webster control, Smith's P0, local control using genetic algorithms, anticipatory control and system optimum control. The anticipatory control strategy can be formulated as a bi-level optimisation problem and this problem is solved using genetic algorithms. Also the system optimum solution can be found using genetic algorithms, simultaneously optimising route flows and green times. In the paper the assignment method, together with the traffic model are formulated. For several simple example networks the traffic control methods are tested and it is shown that taking route choice into account is beneficial to the network performance. One of the examples includes ramp metering in its simplest form and can be considered as an example of integrated control. Further research will focus on larger networks, the use of microscopic simulation and the problem of departure time choice.@en