Extension and Generalization of Newell's Simplified Theory of Kinematic Waves

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 was previously proposed. Simplifying elements includes translation of the problem to moving coordinate system, adoption of bi-linear 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. This research explores the limitations of the simplified theory of kinematic waves. First this research documents a relaxation of several key constraints. In the original theory, priority was given to on-ramp merging vehicles so that they can bypass any queue at the merge. This research proposes to relax this constraint using a capacity-based weighted fair queuing (CBWFQ) merge model. In the original theory, downstream queue affects upstream traffic as a whole and exiting traffic can always be able to leave as long as it gets to the diverge. This research proposes that this diverge constraint be replaced with a contribution-based weighted splitting (CBWS) diverge model. This research proposes a revised notation system, permitting the solution techniques to be extended to freeway networks with multiple freeways and their ramps. This research proposes a generalization to permit application of the revised theory to general transportation networks. A generalized CBWFQ merge model and a generalized CBWS diverge model are formulated to deal with merging and diverging traffic. Finally, this research presents computational procedure for solving the new system of equations. Comparisons of model predictions with field observations are conducted on GA 400 in Atlanta. Investigations into the performance of the proposed CBWFQ and CBWS models are conducted. Results are quite encouraging, quantitative measures suggest satisfactory accuracy with narrow confidence interval.Ph.D.Committee Chair: Leonard, John D.; Committee Member: Amekudzi, Adjo; Committee Member: Dixon, Karen; Committee Member: Goldsman, Dave; Committee Member: Hunter, Michae

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

Dynamic Trajectory Planning for Automated Lane Changing Using the Quintic Polynomial Curve

As one of the key algorithms in supporting AV (autonomous vehicle) to complete the LC (lane changing) maneuver, the LTP (LC trajectory planning) algorithm generates safe and efficient LC trajectory for the AV. This paper proposes a novel dynamic LTP algorithm based on the quintic polynomial curve. This algorithm is capable of adjusting LC trajectory according to the state changes of the surrounding driving environment. The formulation of our proposed algorithm mainly consists the underlying form of trajectory equation, the optimization objective function, the corresponding constrains, and the SQP (sequential quadratic programming) algorithm. For each planning step, the time-based quintic polynomial function is introduced to model the trajectory equation. The problem of solving the parameters of the corresponding equation is then transformed into an optimization problem, which takes driver’s safety, comfort, and efficiency into account. After that, the SQP algorithm is employed to solve this optimization problem. Finally, both numerical simulation and field-data validation are used to verify the effectiveness of our proposed algorithm. We anticipate that the research could provide certain valuable insights for developing more reliable LC algorithms for AVs

Simulation-Based Assessment of Multilane Separate Freeways at Toll Station Area: A Case Study from Huludao Toll Station on Shenshan Freeway

To support the rapid growth of demand in passengers and freight, separating trucks and passenger-cars is a potential solution to improve traffic efficiency and safety. The primary purpose of this paper is to comprehensively assess the multilane separate freeway at Huludao Toll Station in Liaoning Province, China. Based on the configuration and segmentation of the freeway near a toll station, a six-step guidance strategy is designed to adapt to the separate organization mode. Five conventional traffic scenarios are designed in the Vissim platform for comparative analysis between different guidance strategies. To investigate the vehicle-to-infrastructure (V2I) environment, a microscopic testbed is established with cooperative car-following and lane-changing models using the MATLAB platform. The numerical simulation results show that the guidance strategy significantly improves efficiency and safety, and also reduces emissions and fuel consumption. Meanwhile, pre-guidance before toll channels outperforms the scenario only applied with guidance measures after toll plaza. Compared to conventional conditions, the assessment of pollutant emissions and fuel consumption also embodies the superiority of the other five scenarios, especially in the sections of toll plaza and channels with the lowest efficiency and safety level. Generally, all indexes indicate that the cooperative V2I technology is the best alternative for multilane separate freeways

An Efficient Methodology for Calibrating Traffic Flow Models Based on Bisection Analysis

As urban planning becomes more sophisticated, the accurate detection and counting of pedestrians and cyclists become more important. Accurate counts can be used to determine the need for additional pedestrian walkways and intersection reorganization, among other planning initiatives. In this project, a camera-based approach is implemented to create a real-time pedestrian and cyclist counting system which is regularly accurate to 85% and often achieves higher accuracy. The approach retasks a state-of-the-art traffic camera, the Autoscope Solo Terra, for pedestrian and bicyclist counting. Object detection regions are sized to identify multiple pedestrians moving in either direction on an urban sidewalk and bicyclists in an adjacent bicycle lane. Collected results are processed in real time, eliminating the need for video storage and postprocessing. In this paper, results are presented for a pedestrian walkway for pedestrian flow up to 108 persons/min and the limitations of the implemented system are enumerated. Both pedestrian and cyclist counting accuracy of over 90% is achieved

A novel intelligent vehicle risk assessment method combined with multi-sensor fusion in dense traffic environment

Purpose - The purpose of this paper is to accurately capture the risks which are caused by each road user in time. Design/methodology/approach - The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area. Findings - The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance. Originality/value - This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated

Fuzzy Sets to Describe Driver Behavior in the Dilemma Zone of High-Speed Signalized Intersections

The Type II dilemma zone describes a segment of road on the approach to a signalized intersection where, if occupied by a motorist presented with the circular yellow indication, is likely to result in a motorist having difficulty deciding to stop at the stop line or proceed through the intersection. This phenomenon results in increased frequency of three failure conditions: rear-end collision at the stop line (excessive deceleration rates), the more severe right-angle crashes in the intersections, and left-turn head-on collisions (both resulting from incorrect estimates of clearance time). A more effective boundary definition for Type II dilemma zones could contribute to the safe design of signalized intersections. The prevailing approaches to dilemma zone delineation include the consideration of the vehicle’s travel time to the stop line or the driver’s likelihood of stopping at a particular distance from the stop line. The imprecision of the driver’s perception of speed and distance suggest that fuzzy logic may contribute to the identification of the Type II dilemma zone boundaries. A fuzzy logic (FL) model was constructed and validated from driver’s empirically observed behavior at high-speed signalized intersections. The research resulted in an increased understanding of the phenomenon which, when applied to the timing of signals and the placement of vehicle detection, can improve the overall safety of signalized intersections.This is the authors' peer-reviewed accepted manuscript, which is copyrighted by Elsevier. The final version of record can be found here: http://www.journals.elsevier.com/transportation-research-part-f-traffic-psychology-and-behaviour/Keywords: Dilemma zone, Safety, Driver behavior, Signalized intersection

Multiscale Modeling of Traffic Flow

This paper presents a broad perspective on traffic flow modeling at a spectrum of four scales. Modeling objective and model properties at each scale are discussed and existing efforts are reviewed. In order to ensure modeling consistency and provide a microscopic basis for macroscopic models, it is critical to address the coupling among models at different scales, i.e. how less detailed models are derived from more detailed models and, conversely, how more detailed models are aggregated to less detailed models. With this understanding, a consistent modeling approach is proposed based on field theory and modeling strategies at each of the four scales are discussed. In addition, a few special cases are formulated at both microscopic and macroscopic scales. Numerical and empirical results suggest that these special cases perform satisfactorily and aggregate to realistic macroscopic behavior. By ensuring model coupling and modeling consistency, the proposed approach is able to establish the theoretical foundation for traffic modeling and simulation at multiple scales seamlessly within a single system

A unified perspective on traffic flow theory. Part I: The field theory

Over more than half a century, traffic flow theorists have been pursuing two goals: (1) simple and efficient models to abstract vehicular traffic flow and (2) a unified framework in which existing traffic flow models fit and relate to each other. Continuing these efforts, we report our humble understanding in a trio of papers. This paper (Part I) introduces a Field Theory with an emphasis on traffic flow modeling at the microscopic level. In this theory, highways and vehicles are perceived as a field by a subject driver whose driving strategy is to navigate through the field along its valley. The second paper (Part II) uses the Field Theory as a framework to relate existing microscopic and macroscopic models to each other and such relations are represented in a Unified Diagram. The third paper (Part III) provides numerical and empirical results of the Field Theory and benchmarks some traffic flow models presented in the Unified Diagram