Daiheng Ni

Trajectory Reconstruction for Travel Time Estimation

In this article, we propose a Trajectory Reconstruction Model as an improvement to existing speed-based travel time estimation models. The proposed model utilizes point-based speed data collected by existing Intelligent Transportation Systems (ITS). Using the smoothing scheme proposed, it is possible to construct a speed surface as a function of space and time. Then, one can reconstruct the trajectory of an imaginary vehicle by allowing it to adopt the local speed determined by the speed surface wherever the vehicle travels. Therefore, the travel time of this vehicle can be readily determined from its trajectory. This article develops an analytical formulation of the model. Meanwhile, a discrete version of the formulation is also provided as a computational algorithm to facilitate real world implementation. In comparison with existing models, the proposed model accounts for continuous speed variation in both time and space. This ensures that the model preserves vehicle trajectories and provides sound estimates of travel time. Empirical studies were conducted based on comparison of the reconstructed travel time (estimated by the proposed model) against the Ground Truth travel time and the Instantaneous and Linear Model travel time. The empirical results showed that (1) the reconstructed travel time agrees well with the Ground Truth travel time; (2) the reconstructed travel time is more smooth than the Ground Truth, Instantaneous, and Linear Model travel times; (3) the Instantaneous and Linear Model travel time does not exhibit much difference from the other two when traffic condition is good (e.g., low travel time for the same stretch of road); (4) the difference is noticeable when traffic condition deteriorates; and (5) the difference reaches its peak under severe congestion. Quantitatively, the reconstructed travel time is not statistically different from the Ground Truth travel time and the corresponding mean absolute percentage error (MAPE) is 6.3%. In contrast, the Linear and Instantaneous travel time is statistically different from the Ground Truth travel time and the corresponding MAPE is 11.7% and 14.0%, respectively.

Analysis of LWR model with fundamental diagram subject to uncertainties

The LWR model is of interest since it is simple and can successfully reproduce some essential features of traffic flow, such as the formation and propagation of traffic disturbances. In this article, we investigate the LWR model from an uncertainty perspective. We attempt to analyse how reliable the LWR model prediction will be if the fundamental diagram (FD) in use is not accurately specified. To fulfil this end, we postulate a flux function (equivalently a FD) driven by a random free flow speed, which accommodates the uncertain feature observed in the speed–density data. We provide essential mathematical properties and solution schemes of the LWR model with the probabilistic FD. In case studies, the approach to evaluate the uncertainty of traffic disturbance propagation with this model is presented. We find that if FD in a LWR model cannot be perfectly specified, the uncertainty associated with the location of a traffic disturbance would increase over time. In contrast, the magnitude of the traffic disturbance can still be accurately predicted.

Systematic Approach for Validating Traffic Simulation Models

Modeling processes and model testing processes are discussed as parts of the model life cycle, and the tasks of these processes and their relations are highlighted. Of particular interest is the model validation process, which ensures that the model closely simulates what the real system does. A collection of validation techniques is presented to facilitate a systematic check of model performance from various perspectives. Under the qualitative category, a few graphical techniques are presented to help a visual examination of the differences between the simulation and the observation. Under the quantitative category, several statistical measures are discussed to quantify the goodness of fit; to achieve a higher level of confidence about model performance, a simultaneous statistical inference technique is proposed that tests both model accuracy and precision. As an illustrative example, these validation techniques are comprehensively applied to test an enhanced macroscopic simulation model, KWaves, in a systematic manner.

RFID-based vehicle positioning and its applications in connected vehicles.

This paper proposed an RFID-based vehicle positioning approach to facilitate connected vehicles applications. When a vehicle passes over an RFID tag, the vehicle position is given by the accurate position stored in the tag. At locations without RFID coverage, the vehicle position is estimated from the most recent tag location using a kinematics integration algorithm till updates from the next tag. The accuracy of RFID positioning is verified empirically in two independent ways with one using radar and the other a photoelectric switch. The former is designed to verify whether the dynamic position obtained from RFID tags matches the position measured by radar that is regarded as accurate. The latter aims to verify whether the position estimated from the kinematics integration matches the position obtained from RFID tags. Both means supports the accuracy of RFID-based positioning. As a supplement to GPS which suffers from issues such as inaccuracy and loss of signal, RFID positioning is promising in facilitating connected vehicles applications. Two conceptual applications are provided here with one in vehicle operational control and the other in Level IV intersection control.

A Sampling Theorem Approach to Traffic Sensor Optimization

With the objective of minimizing the total cost, which includes both sensor and congestion costs, the authors adopted a novel sampling theorem approach to address the problem of sensor spacing optimization. This paper presents the analysis and modeling of the power spectral density of traffic information as a 2-D stochastic signal using highly detailed field data. The field data were captured by the next-generation simulation (NGSIM) program in 2005. To the best knowledge of the authors, field data with such a level of detail were previously unavailable. The resulting model enables the derivation of a characterization curve that relates sensor error to sensor spacing. The characterization curve, concurring in general with observations of a previous work, provides much more detail to facilitate sensor deployment. Based on the characterization curve and a formulation relating sensor error to congestion cost, the optimal sensor spacing that minimizes the total cost can be determined.

Simple Engine Models for VII-Enabled In-Vehicle Applications

The rapid development of intervehicular communication technology, coupled with the United States department of transportation (USDOT)s vehicle infrastructure integration (VII) initiative, will soon enable a new class of in-vehicle applications such as cooperative driving assistance systems (CDASs). An engine model with reasonable accuracy and excellent computational efficiency is called for to facilitate the development of such systems. This paper presents and formulates three candidate models (Models I, II, and III), among which Model I is an existing one, and the other two are new. Their performances are evaluated based on a system of metrics including accuracy, accessibility, computational efficiency, formulation, and the need for calibration. Overall, Model II outperforms the other two and appears promising to fit the need of the particular application.

Markov Chain Monte Carlo Multiple Imputation Using Bayesian Networks for Incomplete Intelligent Transportation Systems Data

The rich data on intelligent transportation systems (ITS) are a precious resource for transportation researchers and practitioners. However, the usability of this resource is greatly limited by missing data. Many imputation methods have been proposed in the past decade. However, some issues are still not addressed or are not sufficiently addressed, for example, the missing of entire records, temporal correlation in observations, natural characteristics in raw data, and unbiased estimates for missing values. This paper proposes an advanced imputation method based on recent development in other disciplines, especially applied statistics. The method uses a Bayesian network to learn from the raw data and a Markov chain Monte Carlo technique to sample from the probability distributions learned by the Bayesian network. It imputes the missing data multiple times and makes statistical inferences about the result. In addition, the method incorporates a time series model so that it allows data missing in entire row...

The Network Kinematic Waves Model: A Simplified Approach to Network Traffic

Flow of traffic on freeways and limited access highways can be represented as a series of kinemetic waves. Solutions to these systems of equations become problematic under congested traffic flow conditions, and under complicated (real-world) networks. A simplified theory of kinematics waves (KWaves) was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of triangular speed-density relationships, and adoption of restrictive constraints at the on- and off-ramps. However, these simplifying assumptions preclude application of this technique to most practical situations. By directly addressing the limitations of the original theory, this article proposes a simplified Kwaves model for network traffic (N-KWaves). Several key constraints of the original theory are relaxed. For example, the original merge model, which gives full priority to on-ramp traffic, is relaxed and replaced with a capacity-based weighted queuing (CBWFQ) merge model. The original diverge model, which blocks upstream traffic as a whole when a downstream queue exceeds the diverge, is also relaxed and replaced with a contribution-based weighted splitting (CBWS) diverge model. Based on the above, the original theory is reformulated and extended to address network traffic. Central to the N-KWaves model is a five-step computational procedure based on a generic building block. It is assumed that a freeway network can be represented by the combination of some special cases of the generic building block. An empirical field study showed satisfactory results. The N-KWaves model is best suited for modeling traffic operation in a regional freeway network and has a strong connection to Intelligent Transportation Systems (ITS).

A framework for new generation transportation simulation

This paper discussed the evolution and future trend of simulation in general domain and in transportation. Some challenges facing transportation modeling and simulation were identified. As an effort to address these challenges, a framework of new generation transportation simulation was developed. The framework is envisioned to be multi-scale in resolution, parallel in execution, and driven by objects. The paper further discussed strategies of transportation simulation at a nanoscopic level which offers a level of modeling detail beyond the state-of-the-art

Composite Nearest Neighbor Nonparametric Regression to Improve Traffic Prediction

The ability to predict traffic conditions accurately is of paramount importance in effective management of a highway network. A more accurate prediction will allow for better allocation of resources, which may reduce experienced travel times. This paper introduces a composite approach to the already popular nonparametric regression used in predicting traffic conditions. The composite approach performs a nearest neighbor search for each loop detector station using only data that are in proximity to the detectors position on the roadway. This method accommodates every detector station individually to minimize the forecast error on the entire roadway. A case study using data from the Next Generation Simulation program recorded on US Highway 101 demonstrates that the composite approach significantly mitigates forecast error and performs the forecast in a reasonable amount of computational time. The case study also shows the ability of the composite approach to predict the onset and propagation of traffic shock waves.