Application of advanced sampling for efficient probabilistic traffic modelling

In probabilistic traffic models, consideration of stochasticity in the dynamics of traffic gives a closer representation of a traffic system in comparison to that of a deterministic approach. Monte Carlo simulation is a broadly accepted method to consider variations in traffic within modelling. In this contribution, the possibility of increasing the efficiency of probabilistic traffic flow models using Monte Carlo simulation is analysed using variance reduction techniques and sequencing, for varied capacity and traffic demand values. The techniques of Importance Sampling, Latin Hypercube Sampling and Quasi-Random Sequencing are compared in a dynamic macroscopic traffic model to demonstrate the effectiveness of these techniques for reduction of the computational load when considering multiple input variations. Demonstration of their efficiency in traffic modelling is expected to lead to a wider application of the methods in practice.     

Probabilistic multi-aircraft conflict detection approach for trajectory-based operation

Conflict detection (CD) is one of the key functions used to ensure air transport safety and efficiency. In trajectory-based operation (TBO), aircraft are provided with more flexibility in en route trajectory planning and more responsibility for self-separation. The high flexibility in trajectory planning enables random changes in pilot intent, thus increasing the uncertainty in trajectory prediction and CD. This study proposes a novel probabilistic CD approach for TBO in which the uncertainty of pilot intent is taken into account by quantifying the aircraft reachable domain constrained by the flight plan. First, a probabilistic model for aircraft trajectory prediction is developed using the truncated Brownian bridge method. Based on this model, a novel conflict probability estimation method is developed. Finally, the performance of the proposed probabilistic CD approach is demonstrated through an illustrative air traffic scenario.     

Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment

The paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the stochastic cell transmission model (SCTM), to model traffic flow density on freeway segments with stochastic demand and supply. The SCTM consists of five operational modes corresponding to different congestion levels of the freeway segment. Each mode is formulated as a discrete time bilinear stochastic system. A set of probabilistic conditions is proposed to characterize the probability of occurrence of each mode. The overall effect of the five modes is estimated by the joint traffic density which is derived from the theory of finite mixture distribution. The SCTM captures not only the mean and standard deviation (SD) of density of the traffic flow, but also the propagation of SD over time and space. The SCTM is tested with a hypothetical freeway corridor simulation and an empirical study. The simulation results are compared against the means and SDs of traffic densities obtained from the Monte Carlo Simulation (MCS) of the modified cell transmission model (MCTM). An approximately two-miles freeway segment of Interstate 210 West (I-210W) in Los Ageles, Southern California, is chosen for the empirical study. Traffic data is obtained from the Performance Measurement System (PeMS). The stochastic parameters of the SCTM are calibrated against the flow-density empirical data of I-210W. Both the SCTM and the MCS of the MCTM are tested. A discussion of the computational efficiency and the accuracy issues of the two methods is provided based on the empirical results. Both the numerical simulation results and the empirical results confirm that the SCTM is capable of accurately estimating the means and SDs of the freeway densities as compared to the MCS.     

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

Stochastic adaptive control model for traffic signal systems

An adaptive control model of a network of signalized intersections is proposed based on a discrete-time, stationary, Markov decision process. The model incorporates probabilistic forecasts of individual vehicle actuations at downstream inductance loop detectors that are derived from a macroscopic link transfer function. The model is tested both on a typical isolated traffic intersection and a simple network comprised of five four-legged signalized intersections, and compared to full-actuated control. Analyses of simulation results using this approach show significant improvement over traditional full-actuated control, especially for the case of high volume, but not saturated, traffic demand.     

The changing face of parking-related data collection and analysis: The role of new technologies

Current trends in requirements for parking related information and in the availability of data are reviewed. Important influences include the increased need for data to assist in the efficient operation and management of parking stock and to assess the impact of parking on the local network and economy. New sources of data are described, particular attention being given to the availability of data as a byproduct of parking management systems and computerised enforcement systems. The use and performance of audio, video and data loggers in parking surveys is discussed as is the role of computers in questionnaire surveys. New methods of analysis involving spreadsheets, graphics and analysis software, links with databases and simulation models are outlined.     

Real-time automatic rescheduling strategy for an urban rail line by integrating the information of fault handling

In the operation of urban rails, faults are inevitable, which leads to deviation between the actual timetable and the planned timetable. In nowadays, timetable rescheduling strategies rarely integrate the information of fault handling. In this paper, we develop a real-time automatic rescheduling strategy, which integrates the dynamic information of fault handling. The rescheduled timetable is obtained by a mathematical optimization model, the constraints set of which is automatically generated and adjusted as more information of fault handling is feedback. Compared with the experience-based rescheduling methods, the automatic rescheduling strategy reacts more quickly, and uses the information of fault handling more efficiently. A simulation system for testing the automatic rescheduling strategy is built, which uses the data of the Beijing Yizhuang metro line. Via testing on the simulation system, the effectiveness and efficiency of the automatic rescheduling strategy are validated.     

A Bayesian network approach for population synthesis

Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.     

Gantry crane operations to transfer containers between trains: A simulation study of a Spanish terminal

Freight transfer operations are critical in combined transport networks. In this paper a simulation model and modelling approach to the transfer of cargo between trains at rail terminals is presented. The model is used to study the Port-Bou terminal, the main intermodal terminal at the Spanish-French frontier. Four different gantry crane operation modes to interchange containers between trains are evaluated. These operation rules are tested in several scenarios to examine the critical factors of the system and the best operation rule for each situation. Latest generation software is used to develop the model that incorporates modular programming and enhanced graphic systems for output representation. It allows a dynamic display of the simulated system and, likewise, the possibility of developing modules that can be reused in other studies. The research shows how simulation can be a useful planning tool in the rail transportation context.     

Some developments in equilibrium traffic assignment

A network optimization problem is formulated which yields a probabilistic equilibrated traffic assignment incorporating congestion effects and which as a special case, reduces to a user optimized equilibrium solution. In the resulting model, path choice is determined by a logit formula in which path costs are functions of the assigned flows. The article also demonstrates the similarity between some fixed demand incremental methods of traffic assignment and the minimization problem associated with computing the user equilibrium assignment.     

Predicting queue variability to enable analysis of overload risk

ABSTRACT

Predicting the risk of traffic demands and delays exceeding critical limits at road junctions, airports, hospitals, etc., requires knowing how both mean and variance of queue size vary over time. Microscopic simulation can explore variability but is computationally demanding and gives only sample results. A computationally efficient approximation to the mean is used in many modelling tools, but only empirical extensions for variance in particular situations have been available. The paper derives theoretical formulae for time-dependent and equilibrium variance, believed to be novel and to apply generally to queues covered by the Pollaczek–Khinchin mean formula, and offering possible structural insights. These are applied in an extended approximation giving mutually consistent mean and variance estimates with improved accuracy. Tests on oversaturated peak demand cases are compared with Markov probabilistic simulation, demonstrating accuracy (R^2?>?0.99) for typical random, priority-like (M/M/1) and traffic-signal-like (M/D/1) queues. Implications for risk analysis, planning and policy are considered.     

A tensor-based Bayesian probabilistic model for citywide personalized travel time estimation

Urban travel time information is of great importance for many levels of traffic management and operation. This paper develops a tensor-based Bayesian probabilistic model for citywide and personalized travel time estimation, using the large-scale and sparse GPS trajectories generated by taxicabs. Combined with the knowledge learned from historical trajectories, travel times of different drivers on all road segments in some time slots are modeled with a 3-order tensor. This tensor-based modeling approach incorporates both the spatial correlation between different road segments and the person-specific variation between different drivers, as well as the coarse-grain temporal correlation between recent and historical traffic conditions and the fine-grain temporal correlation between different time slots. To account for the variability caused by the intrinsic uncertainties in urban road network, each travel time entry in the built tensor is treated as a variable following a log-normal distribution. With the help of the fully Bayesian treatment, the model achieves automatic hyper-parameter tuning and model complexity controlling, and therefore the problem of over-fitting is prevented even when the used data is large-scale and sparse. The proposed model is applied to a real case study on the citywide road network of Beijing, China, using the large-scale and sparse GPS trajectories collected from over 32,670 taxicabs for a period of two months. Empirical results of extensive experiments demonstrate that the proposed model provides an effective and robust approach for urban travel time estimation and outperforms the considered competing methods.     

输油管道仿真技术研究

输油管道仿真是一项综合利用油气储运、流体力学、计算机、网络技术、控制工程等多学科知识的前沿技术。该技术的成熟与发展,对实现管道的经济运行、智能管理有着重要的意义。结合数学模型及模型求解方法,系统阐述了管道仿真技术的基本理论。通过分析仿真技术在管道检漏及顺序输送两方面的应用现状,指出相应的不足之处,并就该技术的进一步发展提出了几点建议。     

基于微观仿真模型的立交方案研究

采用微观交通仿真模型进行立交方案运行分析,可得到行程时间、速度、延误、排队长度等定量指标,为从交通运行角度研究立交方案提供了一种方法。文章阐述了采用微观交通仿真模型的立交方案分析流程,并结合南宁市某立交方案仿真分析实例,论证了采用该仿真模型进行立交方案交通运行分析的可行性。     

A capacity related reliability for transportation networks

A capacity related reliability for transportation networks with random link capacity is introduced. It is defined as the probability that the road network can accommodate a certain level of traffic demand, and is built on the concept of network reserve capacity. Network reserve capacity is defined as the largest multiplier applied to an existing origin-destination demand matrix that can be allocated to a transportation network in a user-optimal way without violating the link capacities. Due to large variability associated with link capacities, a probabilistic approach is adopted to model the different physical and operational factors that often degrade the capacity of roadways. A Monte Carlo simulation procedure is developed to estimate the capacity related reliability measure. Numerical results are provided to demonstrate the feasibility of the approach.     

Stated choice: a study in predictive validity using an aggregate truth set

Stated Choice models expand the ability of transportation planners to forecast future trends. The Stated Choice approach can forecast demand for new services or policies. However, Stated Choice models are subject to a range of experimental error not found within Revealed Preference (RP) designs. Primary among the concerns facing researchers is the ability of respondents to understand and operate upon hypothetical choice scenarios in a manner that will reproduce choices made under actual situations. These concerns are specified in the magnitude of a scaling factor. Efforts to estimate the scaling factor has proceeded by linking real decisions taken from a revealed preference survey with comparable decisions made under hypothetical conditions. However, where the alternative is new, actual decision data is not available. This study examines the level of error incorporated in a study where no RP data is available. The test of predictive validity focuses on the switching behavior of commuters at a single employment site. The actual data used to test the forecast is limited to company wide or aggregate ridership levels on the public transit mode taken two years after estimation of the SC model. The Fowkes and Preston hypothesis is examined and shown to bound the future actual value between forecasts derived from probabilistic and deterministic methods. The results show that with the passage of time, the probabilistic method approaches the reported ridership levels within 15 percent error.     

A probabilistic stationary speed–density relation based on Newell’s simplified car-following model

Probabilistic models describing macroscopic traffic flow have proven useful both in practice and in theory. In theoretical investigations of wide-scatter in flow–density data, the statistical features of flow density relations have played a central role. In real-time estimation and traffic forecasting applications, probabilistic extensions of macroscopic relations are widely used. However, how to obtain such relations, in a manner that results in physically reasonable behavior has not been addressed. This paper presents the derivation of probabilistic macroscopic traffic flow relations from Newell’s simplified car-following model. The probabilistic nature of the model allows for investigating the impact of driver heterogeneity on macroscopic relations of traffic flow. The physical features of the model are verified analytically and shown to produce behavior which is consistent with well-established traffic flow principles. An empirical investigation is carried out using trajectory data from the New Generation SIMulation (NGSIM) program and the model’s ability to reproduce real-world traffic data is validated.     

Visualisation of uncertainty in probabilistic traffic models for policy and operations

This study investigates different methods to visualise uncertainty in static representations of probabilistic traffic models predictions on road-networks. Although various graphical cues may be used to represent uncertainty it is not a priori clear which of them are most suited for this purpose, since their legibility, intelligibility and the degree to which they interfere with other graphical elements in a representation differ widely. Several graphical uncertainty representations were therefore developed and analysed in expert sessions. A selection of the initial set of uncertainty visualisations was further evaluated in a cognitive alternative task-switching experiment. The results show that graphical representations are able to convey uncertainty information relatively accurately, while some uncertainty visualisations outperform others. It depends on the model and scenario which representation is most suited for a given application. This paper presents an overview of possible graphic uncertainty representations and the considerations involved when applying them to uncertainty in traffic model visualisations.     

A stochastic model of traffic flow: Theoretical foundations

In a variety of applications of traffic flow, including traffic simulation, real-time estimation and prediction, one requires a probabilistic model of traffic flow. The usual approach to constructing such models involves the addition of random noise terms to deterministic equations, which could lead to negative traffic densities and mean dynamics that are inconsistent with the original deterministic dynamics. This paper offers a new stochastic model of traffic flow that addresses these issues. The source of randomness in the proposed model is the uncertainty inherent in driver gap choice, which is represented by random state dependent vehicle time headways. A wide range of time headway distributions is allowed. From the random time headways, counting processes are defined, which represent cumulative flows across cell boundaries in a discrete space and continuous time conservation framework. We show that our construction implicitly ensures non-negativity of traffic densities and that the fluid limit of the stochastic model is consistent with cell transmission model (CTM) based deterministic dynamics.     

Effect of checkpoint control strategies in a simulated transit operation

Reliability of a public transport operation is known to be an important measure of level-of-service for its patrons. It has been suggested that the reliability of a transit operation may be increased by regulating transit vehicles at various checkpoints to improve the schedule adherence of vehicles. It is possible to analyse changes in vehicle performance when checkpoint control strategies are introduced, as well as measure changes in level-of-service from the patrons' point of view, by using the TRAMS package to simulate the operation of a transit network. The simulation observations can also be used to determine optimum checkpoint control strategies.