System Level Impacts of V2X Enabled Vehicle Control Strategies

With an increasing number of vehicles on road the quantity of CO2 emissions and the amount of fuel wasted because of traffic congestion have been rising. Use of alternate means of transport that generate fewer emissions does not resolve the problem of congestions and vehicle wait time at traffic signal whereas further expansion of existing network of roads is not only constrained by finite space, but any network can get saturated as the number of vehicles increase. V2X technology allows vehicles and traffic infrastructure to communicate with each other, and could facilitate better use of existing resources by providing vehicles information about their surroundings and traffic signals. The information regarding the phase of traffic signal, vehicles’ position and vehicles’ speed can be used by drivers and autonomous vehicle control algorithms to make informed decisions as they approach traffic signals. This research proposes and analyzes system level impacts of implementing a coordination heuristic over single-vehicle optimization to realize the true potential of V2X technology. The results of this research can help policymakers choose the most suitable control strategy depending on the traffic conditions and the penetration rate of V2X technology. The analysis indicates that at 900 vehicles per hour for either of the two driving strategies: coordination heuristic or single-vehicle optimization, to be more preferred over baseline driver behavior, at least 50% of the vehicles should be V2X capable. Once a threshold penetration rate of V2X vehicles is achieved, vehicles following coordination heuristic generate nearly 10% fewer CO2 emissions than vehicles following baseline driver behavior, a 30% improvement over the reduction in CO2 emissions obtained using single-vehicle optimization. The vehicles following the coordination heuristic also have less travel time than vehicles following single-vehicle optimization, and less wait times than vehicles following baseline driver behavior

Integrating Feedback into the Transportation Planning Mode

This research develops and applies a new structure for the transportation planning model that includes feedback between demand, assignment, and traffic control. New methods, combined with a renewed interest in transportation planning models prompted by the Clean Air Act of 1990 and the Intermodal Surface Transportation Efficiency Act of 1991, warrant a reconsideration of the traditional "four-step" transportation planning model. This paper presents an algorithm for feedback which results in consistent travel times as input to travel demand and output from route assignment. The model, including six stages of Trip Generation, Destination Choice, Mode Choice, Departure Time Choice, Route Assignment and Intersection Control is briefly outlined. This is followed by an application comparing a base year 1990 application with a forecast year of 2010. The 2010 forecast is solved both with and without feedback for comparison purposes. Incorporation of feedback gives significantly different results than the standard model. l.

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Development and evaluation of advanced traveler information system (ATIS) using vehicle-to-vehicle (V2V) communication system

This research develops and evaluates an Advanced Traveler Information System (ATIS) model using a Vehicle-to-Vehicle (V2V) communication system (referred to as the GATIS-V2V model) with the off-the-shelf microscopic simulation model, VISSIM. The GATIS-V2V model is tested on notional small traffic networks (non-signalized and signalized) and a 6X6 typical urban grid network (signalized traffic network). The GATIS-V2V model consists of three key modules: vehicle communication, on-board travel time database management, and a Dynamic Route Guidance System (DRGS). In addition, the system performance has been enhanced by applying three complementary functions: Autonomous Automatic Incident Detection (AAID), a minimum sample size algorithm, and a simple driver behavior model. To select appropriate parameter ranges for the complementary functions a sensitivity analysis has been conducted. The GATIS-V2V performance has been investigated relative to three underlying system parameters: traffic flow, communication radio range, and penetration ratio of participating vehicles. Lastly, the enhanced GATIS-V2V model is compared with the centralized traffic information system. This research found that the enhanced GATIS-V2V model outperforms the basic model in terms of travel time savings and produces more consistent and robust system output under non-recurrent traffic states (i.e., traffic incident) in the simple traffic network. This research also identified that the traffic incident detection time and driver's route choice rule are the most crucial factors influencing the system performance. As expected, as traffic flow and penetration ratio increase, the system becomes more efficient, with non-participating vehicles also benefiting from the re-routing of participating vehicles. The communication radio ranges considered were found not to significantly influence system operations in the studied traffic network. Finally, it is found that the decentralized GATIS-V2V model has similar performance to the centralized model even under low flow, short radio range, and low penetration ratio cases. This implies that a dynamic infrastructure-based traffic information system could replace a fixed infrastructure-based traffic information system, allowing for considerable savings in fixed costs and ready expansion of the system off of the main network corridors.Ph.D.Committee Chair: Hunter, Michael; Committee Member: Fujimoto, Richard; Committee Member: Guensler, Randall; Committee Member: Leonard, John; Committee Member: Meyer, Michae

Optimization of urban traffic control strategies by a network design model

The efficiency of congested urban transportation networks can be improved by implementing appropriate traffic control strategies, such as signal control timing, turning movement control; implementation of one-way traffic policies, lane distribution controls etc.. In this dissertation, the following strategies are addressed: 1) Intersection left turn addition/deletion, 2) Lane designation,. and 3) Signal optimization. The analogy between the network design problem (NDP) and the optimization of traffic control strategies motivated the formulation of an urban transportation network design problem (UTNDP) to optimize traffic control strategies. An UTNDP is a typical bi-level programming program, where the lower level problem is a User Equilibrium (UE) traffic assignment problem, while the upper level problem is a 0-1 integer programming problem. The upper level of an UTNDP model is used to represent the choices of the transportation authority. The lower level problem captures the travelers\u27 behavior. The objective function of the UTNDP is to minimize the total UE travel time. In this dissertation, a realistic travel time estimation procedure based on the 1997 HCM which takes account the effects of the above factors is proposed. The UTNDP is solved through a hybrid simulated annealing-TABU heuristic search strategy that was developed specifically for this problem. TABU lists are used to avoid cycling, and the Simulated Annealing step is used to select moves such that an annealing equilibrium state is achieved so that a reasonably good solution is guaranteed. The computational experiments are conducted on four test networks to demonstrate the feasibility and effectiveness of the UTNDP search strategy. Sensitivity analyses are also conducted on TABU list length, Markov chain increasing rate and control parameter dropping rate, and the weight coefficients of the HEF, which is composed of the current link v/c ratio, the historical contribution factor, and the random factor

What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?

This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data