Modeling the effects of traveler information on freeway origin–destination demand prediction

The primary focus of this research is to develop an approach to capture the effect of travel time information on travelers’ route switching behavior in real-time, based on on-line traffic surveillance data. It also presents a freeway Origin–Destination demand prediction algorithm using an adaptive Kalman Filtering technique, where the effect of travel time information on users’ route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traffic flow characteristics is captured using a Kalman Filter modeling framework. Changes in drivers’ perceptions, as well as other randomness in the route diversion behavior, have been modeled using an adaptive, aggregate, dynamic linear model where the model parameters are updated on-line using a Bayesian updating approach. The impact of route diversion on freeway Origin–Destination demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained from a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway corridor in northwest Indiana establish that significant improvement in Origin–Destination demand prediction can be achieved by explicitly accounting for route diversion behavior.     

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L_p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L_p-PFE models. In addition, the proposed L_p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.     

A new approach for modeling of Fundamental Diagrams

According to the intra-vehicle interaction, a traffic flow can generally be divided into three homogeneous states (1) that of free driving, (2) that of bunched driving, and (3) that of standing. The parameter describing the state of free driving is the desired speed, for the state of bunching it is the intra-vehicle gaps (time headway) within the convoy and the mean speed of the convoy, and for the state of standing it is the maximum jam density. These are the most essential parameters which do not depend on the actual traffic situation.This paper introduces a new model which considers the Fundamental Diagram (equilibrium speed–flow–density relationship) as a function of the homogeneous states. All traffic situations in reality can be considered as combinations of the homogeneous states and therefore can be described by the essential parameters mentioned above. The non-congested (fluid) traffic is a combination (superposition) of the states of free driving and bunched driving, the congested (jam, stop, and go) traffic is a combination of the states of bunched driving (go) and standing (stop). The contribution of the traffic states within the differently congested traffic situations can then be easily obtained from the queuing and probability theory. As a result, Fundamental Diagram in all equilibrium traffic situations is derived as simple functions of the essential parameters.According to the new model the capacity of freeways and rural highways can be determined by measuring the essential parameters. This is much easier than measuring the capacity directly.Furthermore, the probabilities of the various traffic states can be obtained from the new model. This leads to new possibilities in real-time controlling and telematics.The new model is verified by comprehensive measurements carried out on freeways and rural highways in Germany.     

c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation

The simultaneous perturbation stochastic approximation (SPSA) algorithm has been used in the literature for the solution of the dynamic origin–destination (OD) estimation problem. Its main advantage is that it allows quite general formulations of the problem that can include a wide range of sensor measurements. While SPSA is relatively simple to implement, its performance depends on a set of parameters that need to be properly determined. As a result, especially in cases where the gradient of the objective function changes quickly, SPSA may not be as stable and even diverge. A modification of the SPSA algorithm, referred to as c-SPSA, is proposed which applies the simultaneous perturbation approximation of the gradient within a small number of carefully constructed “homogeneous” clusters one at a time, as opposed to all elements at once. The paper establishes the theoretical properties of the new algorithm with an upper bound for the bias of the gradient estimate and shows that it is lower than the corresponding SPSA bias. It also proposes a systematic approach, based on the k-means algorithm, to identify appropriate clusters. The performance of c-SPSA, with alternative implementation strategies, is evaluated in the context of estimating OD flows in an actual urban network. The results demonstrate the efficiency of the proposed c-SPSA algorithm in finding better OD estimates and achieve faster convergence and more robust performance compared to SPSA with fewer overall number of function evaluations.     

A real time forecasting tool for dynamic travel time from clustered time series

This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.     

Quasi-dynamic estimation of o–d flows from traffic counts: Formulation,statistical validation and performance analysis on real data

The paper proposes a “quasi-dynamic” framework for estimation of origin–destination (o–d) flow from traffic counts, under the assumption that o–d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o–d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o–d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4–A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o–d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o–d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o–d flows. In addition, it is shown that the aggregation of quasi-dynamic o–d estimates across subsequent time slices represents also the most effective way to obtain o–d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o–d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators.     

Application of intervention analysis for assessing the effectiveness of CO pollution control legislation in India

An Intervention Analysis Model (IAM) (Box and Tiao, 1975) was developed to study the impact of the ‘intervention' brought in by the Government of India (GoI), to control the CO pollution caused by the vehicular exhaust emissions, by the enforcement of the emission standards for the vehicles, on the mean level of the time-series of CO concentration. The study was conducted for an Air Quality Control Region (AQCR) comprising of an urban road intersection in Delhi, India, where almost 100% CO is contributed by vehicular traffic. Application of the model suggests that the ‘intervention' has not been effective in bringing down the desired change; some likely causes of which have also been mentioned.     

Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System

Due to their complementary characteristics, Global Positioning System (GPS) is integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low-cost. In this study, a low cost reduced inertial sensor system (RISS) is considered. It consists of a MEMS-grade gyroscope and the vehicle built-in odometer. The system works together with GPS to provide 2D navigation for land vehicles. With adequate accuracy, Kalman filter (KF) is the commonly used estimation technique to achieve the data fusion of GPS and inertial sensors in case of high-end IMUs. However, due to the inherent error characteristics of MEMS grade devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be handled by KF and its linear error models. To overcome the problem, Fast Orthogonal Search (FOS), a nonlinear system identification technique, is suggested for modeling the higher order RISS errors. As a general-purpose numerical method, FOS algorithm has the ability to figure out the system nonlinearity efficiently with a tolerance of arbitrary stochastic system noise. Even using online short-term training data, this method is still able to build an accurate nonlinear model that predicts the system dynamics. Motivated by the above merits, an augmented KF/FOS module is proposed by cascading FOS algorithm to a traditional KF structure. By estimating and reducing both linear and nonlinear RISS errors, the proposed method is supposed to offer substantial enhancement on the positioning accuracy of MEMS-based RISS during GPS outages. In order to examine the effectiveness of the proposed technique, the KF/FOS module is applied on the low cost RISS together with GPS in a land vehicle for several road test trajectories. The performance of the proposed method is compared to KF-only solution, both assessed with respect to a reference offered by a high-end solution. The experimental results confirm that KF/FOS module outperforms KF-only method. The results also show the applicability of the proposed method for real-time vehicle applications.     

The treatment of uncertainty in the dynamic origin–destination estimation problem using a fuzzy approach

Regardless of existing types of transportation and traffic model and their applications, the essential input to these models is travel demand, which is usually described using origin–destination (OD) matrices. Due to the high cost and time required for the direct development of such matrices, they are sometimes estimated indirectly from traffic measurements recorded from the transportation network. Based on an assumed demand profile, OD estimation problems can be categorized into static or dynamic groups. Dynamic OD demand provides valuable information on the within-day fluctuation of traffic, which can be employed to analyse congestion dissipation. In addition, OD estimates are essential inputs to dynamic traffic assignment (DTA) models. This study presents a fuzzy approach to dynamic OD estimation problems. The problems are approached using a two-level model in which demand is estimated in the upper level and the lower level performs DTA via traffic simulation. Using fuzzy rules and the fuzzy C-Mean clustering approach, the proposed method treats uncertainty in historical OD demand and observed link counts. The approach employs expert knowledge to model fitted link counts and to set boundaries for the optimization problem by defining functions in the fuzzification process. The same operation is performed on the simulation outputs, and the entire process enables different types of optimization algorithm to be employed. The Box-complex method is utilized as an optimization algorithm in the implementation of the approach. Empirical case studies are performed on two networks to evaluate the validity and accuracy of the approach. The study results for a synthetic network and a real network demonstrate the robust performance of the proposed method even when using low-quality historical demand data.