scholarly journals Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sara Movaghar ◽  
Mahmoud Mesbah ◽  
Meeghat Habibian
Keyword(s):  
Travel Time ◽  
Autonomous Vehicles ◽  
Transition Period ◽  
Transportation Network ◽  
Autonomous Vehicle ◽  
Planning Horizon ◽  
Bilevel Optimization ◽  
Network Design Problem ◽  
Variable Capacity ◽  
Total Travel Time

This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.

scholarly journals A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Haigen Min ◽  
Yukun Fang ◽  
Runmin Wang ◽  
Xiaochi Li ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Game Theory ◽  
Travel Time ◽  
Fuel Consumption ◽  
Transportation Network ◽  
Control Algorithms ◽  
Traffic Density ◽  
Automated Vehicles ◽  
Network Efficiency ◽  
Total Travel Time ◽  
The Cost

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.

Comparison of Light Rail Streetcar Against Shared Autonomous Vehicle Fleet for Brooklyn–Queens Connector in New York City

2017 ◽  
Vol 2650 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Lucas Mestres Mendes ◽  
Manel Rivera Bennàssar ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Light Rail ◽  
Rail System ◽  
Total Travel Time ◽  
Vehicle Fleet ◽  
Event Based

Policy makers predict that autonomous vehicles will have significant market penetration in the next decade or so. In several simulation studies shared autonomous vehicle fleets have been shown to be effective public transit alternatives. This study compared the effectiveness of a shared autonomous vehicle fleet with an upcoming transit project proposed in New York City, the Brooklyn–Queens Connector light rail project. The study developed an event-based simulation model to compare the performance of the shared autonomous vehicle system with the light rail system under the same demand patterns, route alignment, and operating speeds. The simulation experiments revealed that a shared autonomous vehicle fleet of 500 vehicles of 12-person capacity (consistent with the EZ10 vehicle) would be needed to match the 39-vehicle light rail system if operated as a fixed-route system. However, as a demand-responsive system, a fleet of only 150 vehicles would lead to the same total travel time (22 min) as the 39-vehicle fleet light rail system. Furthermore, a fleet of 450 12-person vehicles in a demand-responsive operation would have the same average wait times while reducing total travel times by 36%. The implications for system resiliency, idle vehicle allocation, and vehicle modularity are discussed.

scholarly journals Shared Autonomous Taxi System and Utilization of Collected Travel-Time Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Zhiguang Liu ◽  
Tomio Miwa ◽  
Weiliang Zeng ◽  
Michael G. H. Bell ◽  
Takayuki Morikawa
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Traffic Information ◽  
Path Finding ◽  
Fleet Size ◽  
Real Time Traffic ◽  
Total Travel Time ◽  
The Mean ◽  
Time Information

Shared autonomous taxi systems (SATS) are being regarded as a promising means of improving travel flexibility. Each shared autonomous taxi (SAT) requires very precise traffic information to independently and accurately select its route. In this study, taxis were replaced with ride-sharing autonomous vehicles, and the potential benefits of utilizing collected travel-time information for path finding in the new taxi system examined. Specifically, four categories of available SATs for every taxi request were considered: currently empty, expected-empty, currently sharable, and expected-sharable. Two simulation scenarios—one based on historical traffic information and the other based on real-time traffic information—were developed to examine the performance of information use in a SATS. Interestingly, in the historical traffic information-based scenario, the mean travel time for taxi requests and private vehicle users decreased significantly in the first several simulation days and then remained stable as the number of simulation days increased. Conversely, in the real-time information-based scenario, the mean travel time was constant. As the SAT fleet size increased, the total travel time for taxi requests significantly decreased, and convergence occurred earlier in the historical information-based scenario. The results demonstrate that historical traffic information is better than real-time traffic information for path finding in SATS.

scholarly journals Soluciones de conducción para vehículos autónomos, y propuestas adaptativas para establecer los tiempos de semáforos y elegir rutas

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
José Gerardo Carrillo González
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Lights ◽  
Linear Path ◽  
Time Cycle ◽  
Adaptive Solutions ◽  
The Times ◽  
The Right

Two objectives are pursued in this article: 1) with adaptive solutions, improve the traffic flow by setting the time cycle of traffic lights at intersections and reduce the travel time by selecting the vehicles route (treated as separated problems). 2) Avoid driving conflicts among autonomous vehicles (which have defined trajectories) and these with a non-autonomous vehicle (which follows a free path). The traffic lights times are set with formulas that continuously recalculate the times values according the number of vehicles on the intersecting streets. For selecting the vehicles route an algorithm was developed, this calculates different routes (connected streets that conform a solution from the origin to the destination) and selects a route with low density. The results of the article indicate that the adaptive solutions to set the traffic lights times and to select the vehicles path, present a greater traffic flow and a shorter travel time, respectively, than conventional solutions. To avoid collisions among autonomous vehicles which follow a linear path, an algorithm was developed, this was successfully tested in different scenarios through simulations, besides the algorithm allows the interaction of a vehicle manually controlled (circulating without restrictions) with the autonomous vehicles. The algorithm regulates the autonomous vehicles acceleration (deceleration) and assigns the right of way among these and with the human controlled vehicle.

scholarly journals Optimal Signal Design for Mixed Equilibrium Networks with Autonomous and Regular Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Nan Jiang
Keyword(s):  
Autonomous Vehicles ◽  
Design Problem ◽  
Control Strategies ◽  
Transportation Network ◽  
Bilevel Optimization ◽  
User Equilibrium ◽  
Signal Design ◽  
Level Model ◽  
Mixed Equilibrium ◽  
Mixed Networks

A signal design problem is studied for efficiently managing autonomous vehicles (AVs) and regular vehicles (RVs) simultaneously in transportation networks. AVs and RVs move on separate lanes and two types of vehicles share the green times at the same intersections. The signal design problem is formulated as a bilevel program. The lower-level model describes a mixed equilibrium where autonomous vehicles follow the Cournot-Nash (CN) principle and RVs follow the user equilibrium (UE) principle. In the upper-level model, signal timings are optimized at signalized intersections to allocate appropriate green times to both autonomous and RVs to minimize system travel cost. The sensitivity analysis based method is used to solve the bilevel optimization model. Various signal control strategies are evaluated through numerical examples and some insightful findings are obtained. It was found that the number of phases at intersections should be reduced for the optimal control of the AVs and RVs in the mixed networks. More importantly, incorporating AVs into the transportation network would improve the system performance due to the value of AV technologies in reducing random delays at intersections. Meanwhile, travelers prefer to choose AVs when the networks turn to be congested.

A heuristic algorithm based on leaf photosynthate transport

SIMULATION ◽  
2017 ◽  
Vol 94 (7) ◽  
pp. 593-607 ◽  
Author(s):  
Yu Bin ◽  
Shan Wenxuan ◽  
Guo Zhen ◽  
Wang Yunpeng
Keyword(s):  
Network Design ◽  
Objective Function ◽  
Travel Time ◽  
Ant Colony Algorithm ◽  
Programming Model ◽  
Lower Layer ◽  
Transportation Network ◽  
Biological Evolution ◽  
Genetic Transmission ◽  
Total Travel Time

Transportation network design is non-deterministic polynomial-time hard due to its attributes of multi-objects, multi-constraints, and the non-convexity objective function. In this paper, a bi-level programming model is proposed for the transportation network design. The upper layer pursues the minimum total travel time of users and the total length of the road network simultaneously, while the lower layer is an equilibrium assignment model. A new algorithm for the network optimization based on the principle of leaf photosynthate transport in nature is proposed. The proposed algorithm simulates the natural selection of biological evolution and genetic transmission. It can retain the genetic idea of the evolutionary algorithm, together with the heuristic information update mechanism of swarm intelligence. Finally, empirical research is carried out with the Sioux Falls network to validate the performance of the proposed algorithm. The results show that although the total network length obtained by the proposed algorithm increases slightly compared with the ant colony algorithm and the genetic algorithm, the total travel time and objective function value reduce obviously. This indicates that the proposed algorithm has good performance on topology and efficiency.

scholarly journals A Multi-Objective Meta-Heuristic Approach to Improve the Bus Transit Network: A Case Study of Fargo-Moorhead Area

2021 ◽  
Vol 13 (19) ◽  
pp. 10885
Author(s):  
Mohsen Momenitabar ◽  
Jeremy Mattson
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Transportation Systems ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Nsga Ii ◽  
Transit Network ◽  
Multi Objective ◽  
Total Travel Time ◽  
Initial Route

In this study, the Transit Network Design Problem (TNDP) is studied to determine the set of routes and frequency on each route for public transportation systems. To ensure the important concerns of planners like route length, route configuration, demand satisfaction, and attractiveness of the transit routes, the TNDP is solved to generate a set of routes by proposing an initial route set generation (IRSG) procedure embedded into the NSGA-II algorithm. The proposed IRSG algorithm aims to produce high-quality initial route set solutions to reach better optimization procedures. Moreover, the Multi-Objective Mixed-Integer Non-Linear Programming (MOMINLP) model is proposed to formulate the frequency setting problem on each route by minimizing the total travel time of passengers (user costs) and operator costs simultaneously, while maximizing the service coverage area near all the bus stops. The MOMINLP model is solved by applying the NSGA-II algorithm to produce a Pareto front between the first and the second objective functions. The model was applied to the Fargo-Moorhead Area (FMA), a small urban area. Results were compared with the existing transit network to measure the efficiency of the NSGA-II solution methodology. The proposed algorithm was found to considerably decrease the total travel time of passengers.

Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

2017 ◽  
Vol 2665 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Patrícia S. Lavieri ◽  
Venu M. Garikapati ◽  
Chandra R. Bhat ◽  
Ram M. Pendyala ◽  
Sebastian Astroza ◽  
...  
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Network Performance ◽  
Transportation Network ◽  
Autonomous Vehicle ◽  
Puget Sound ◽  
Heterogeneous Data ◽  
Model System ◽  
Vehicle Technologies ◽  
The One

Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.

scholarly journals A Nondominated Genetic Algorithm Procedure for Multiobjective Discrete Network Design under Demand Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Bian Changzhi
Keyword(s):  
Standard Deviation ◽  
Network Design ◽  
Travel Time ◽  
Network Performance ◽  
Demand Uncertainty ◽  
Construction Cost ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Trade Offs ◽  
Total Travel Time

This paper addresses the multiobjective discrete network design problem under demand uncertainty. The OD travel demands are supposed to be random variables with the given probability distribution. The problem is formulated as a bilevel stochastic optimization model where the decision maker’s objective is to minimize the construction cost, the expectation, and the standard deviation of total travel time simultaneously and the user’s route choice is described using user equilibrium model on the improved network under all scenarios of uncertain demand. The proposed model generates globally near-optimal Pareto solutions for network configurations based on the Monte Carlo simulation and nondominated sorting genetic algorithms II. Numerical experiments implemented on Nguyen-Dupuis test network show trade-offs among construction cost, the expectation, and standard deviation of total travel time under uncertainty are obvious. Investment on transportation facilities is an efficient method to improve the network performance and reduce risk under demand uncertainty, but it has an obvious marginal decreasing effect.

scholarly journals Maneuver-Based Objectification of User Comfort Affecting Aspects of Driving Style of Autonomous Vehicle Concepts

2020 ◽  
Vol 10 (11) ◽  
pp. 3946 ◽  
Author(s):  
Ferdinand Schockenhoff ◽  
Hannes Nehse ◽  
Markus Lienkamp
Keyword(s):  
Pilot Study ◽  
Travel Time ◽  
Autonomous Vehicles ◽  
User Study ◽  
Autonomous Vehicle ◽  
User Needs ◽  
Vehicle Concept ◽  
Driving Style ◽  
The Individual ◽  
User Comfort

Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As an example, the methodology was applied to the parameters of user comfort and travel time. The driven maneuvers resulted in statistical equations to objectify the interdependencies of these two aspects. Finally, this paper provides an outlook for needed maneuvers in order to tackle the entire driving style with its multidimensional facets.

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