Stochastic Multi-class Traffic Assignment for Autonomous and Regular Vehicles in a Transportation Network

A transition period from regular vehicles (RVs) to autonomous vehicles (AVs) is imperative. This article explores both types of vehicles using a route choice model, formulated as a stochastic multi-class traffic assignment (SMTA) problem. In RVs, cross-nested logit (CNL) models are used since drivers do not have complete information and the unique characteristics of CNL. AVs, however, are considered to behave in a user equilibrium (UE) due to complete information about the network. The main innovation of this article includes the introduction of three solution methods for SMTA. Depending on the size of the network, each method can be used. These methods include solving the nonlinear complementary problem (NCP) with GAMS software, the decomposition-assignment algorithm, and the modified Wang's algorithm. Through the modification of Wang's algorithm, we have increased the convergence speed of Wang's algorithm and shown its numerical results for the Nguyen and Sioux Falls networks. As it is not possible to consider all paths in the traffic assignment, we proposed a creative path generation-assignment (PGA) algorithm. This algorithm generates several attractive paths for each origin-destination (OD), and the modified Wang's algorithm assigns traffic flow. Keywords: Autonomous Vehicles, Stochastic Multi-class Traffic Assignment, Cross-Nested Logit Mode

Transportation Systems with Autonomous Vehicles: Models and algorithms for equilibrium assignment

Abstract Technologies for connected, automated or autonomous vehicles (AVs) are fast developing, so that they seem ready for substituting in the near future privately owned non-autonomous traditional vehicles (TVs) and further supporting the spread of shared vehicles both for person and good transportation. On the other hand, it may easily be anticipated that the time needed to turn the existing stock of TVs into AVs will last several years during which mixed traffic is expected. A change so great may be not technology-driven only, but also requires a carefully analysis of its several impact through well designed enhancements of tools already available to the transportation systems modelers and planners. Such enhanced tools may be casted in the general framework of multi-user class assignment to transportation networks, concerning: (i) transportation network analysis, through level-of-service models distinguishing between non-autonomous vs. autonomous vehicles, presumably sharing same infrastructure; (ii) travel demand analysis, through behavioral choice modeling paradigms, including choice between AVs vs. TVs, owned vs. shared, as well as route choice behavior; (iii) steady-state equilibrium assignment. This paper describes models and algorithms to deal with steady-state equilibrium assignment; they are used to show to which extent existing methods can still be applied as well as which issues remain still open and worth of further research efforts

Traffic Congestion Aware Route Assignment

Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation

A Novel Efficient Task-Assign Route Planning Method for AUV Guidance in a Dynamic Cluttered Environment

Promoting the levels of autonomy facilitates the vehicle in performing long-range operations with minimum supervision. The capability of Autonomous Underwater Vehicles (AUVs) to fulfill the mission objectives is directly influenced by route planning and task assignment system performance. The system fives the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstractComment: 7 pages, 8 figures, conference paper, IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada. July 201

A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases

A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation