scholarly journals Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhibo Gao ◽  
Zhizhou Wu ◽  
Wei Hao ◽  
Kejun Long
Keyword(s):  
Decision Making ◽  
Traffic Safety ◽  
Programming Model ◽  
Travel Cost ◽  
Heterogeneous Traffic ◽  
Traffic Network ◽  
Traffic Demand ◽  
Automated Vehicle ◽  
Traffic Efficiency ◽  
Upper Level

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.

scholarly journals Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks

2020 ◽  
Vol 10 (2) ◽  
pp. 498
Author(s):  
Xinhua Mao ◽  
Xiandong Jiang ◽  
Changwei Yuan ◽  
Jibiao Zhou
Keyword(s):  
Programming Model ◽  
User Equilibrium ◽  
Maintenance Scheduling ◽  
Traffic Demand ◽  
Scheduling Strategy ◽  
Bridge Networks ◽  
Traffic Delays ◽  
Optimal Maintenance ◽  
Level Model ◽  
Upper Level

An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.

Estimation of Origin-Destination Demand Matrix for the Urban Multimodal Traffic Network

2014 ◽  
Vol 511-512 ◽  
pp. 963-970
Author(s):  
Ya Nan Wang ◽  
Bing Feng Si
Keyword(s):  
Transport System ◽  
Heuristic Algorithm ◽  
Programming Model ◽  
User Equilibrium ◽  
Traffic Network ◽  
Transit Network ◽  
Solution Approach ◽  
Multimodal Transport ◽  
Level Problem ◽  
Upper Level

In this paper we consider a procedure for the estimation of origin-destination (O-D) matrices for a multimodal transit network. The structure of urban multimodal transport system is fully analyzed and then a bi-level programming model is established for O-D demand matrices estimation, where the upper-level problem seeks to minimize the sum of error measurements in traffic counts and O-D matrices, while the lower-level problem is to assign a target O-D matrix onto the network according to the user equilibrium principle. And a heuristic algorithm is proposed, finally a numeral example is given which indicates that the solution approach can be applied in practice.

scholarly journals Evaluation of Transportation Network Reliability under Emergency Based on Reserve Capacity

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xiongfei Zhang ◽  
Qi Zhong ◽  
Qin Luo
Keyword(s):  
Evaluation Method ◽  
Programming Model ◽  
Transportation Network ◽  
Emergency Evacuation ◽  
Reserve Capacity ◽  
Capacity Limits ◽  
Traffic Demand ◽  
Solution Algorithms ◽  
Level Model ◽  
Upper Level

There are differences between the requirements for traffic network for traffic demand in daily and emergency situations. In order to evaluate how the network designed for daily needs can meet the surging demand for emergency evacuation, the concept of emergency reliability and corresponding evaluation method is proposed. This paper constructs a bilevel programming model to describe the proposed problem. The upper level problem takes the maximum reserve capacity multiplier as the optimization objective and considers the influence of reversible lane measures taken under emergency conditions. The lower level model adopts the combined traffic distribution/assignment model with capacity limits, to describe evacuees’ path and shelter choice behavior under emergency conditions and take into account the traits of crowded traffic. An iterative optimization method is proposed to solve the upper level model, and the lower level model is transformed into a UE assignment problem with capacity limits over a network of multiple origins and single destination, by adding a dummy node and several dummy links in the network. Then a dynamic penalty function algorithm is used to solve the problem. In the end, numerical studies and results are provided to demonstrate the rationality of the proposed model and feasibility of the proposed solution algorithms.

scholarly journals Managing Rush Hour Congestion with Lane Reversal and Tradable Credits

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Qing Li ◽  
Ziyou Gao
Keyword(s):  
Programming Model ◽  
Transportation Network ◽  
Urban Transportation ◽  
Total System ◽  
Traffic Demand ◽  
The Government ◽  
Upper Level ◽  
Tradable Credits ◽  
Bilevel Programming Model ◽  
Tradable Credit

Within the morning and evening rush hour, the two-way road flows are always unbalanced in opposite directions. In order to make full advantage of the existing lanes, the two-way road lane has to be reallocated to play the best role in managing congestion. On the other hand, an effective tradable credit scheme can help to reduce the traffic demand and improve fairness for all travelers. So as to alleviate the commute congestion in urban transportation network, a discrete bilevel programming model is established in this paper. In the bilevel model, the government at the upper level reallocates lanes on the two-way road to minimize the total system cost. The traveler at the lower level chooses the optimal route on the basis of both travel time and credit charging for the lanes involved. A numerical experiment is conducted to examine the efficiency of the proposed method.

Collaborative Action Research In Academic Administration

2019 ◽  
Vol 8 (3) ◽  
pp. 227-252
Author(s):  
Bradley C. Thompson
Keyword(s):  
Decision Making ◽  
Action Research ◽  
Collaborative Action Research ◽  
Team Members ◽  
Academic Administration ◽  
Leadership Culture ◽  
Collaborative Action ◽  
Working Together ◽  
Upper Level ◽  
Action Process

This research involved a study exploring the changes in an academic institution expressed through decision-making in a shifting leadership culture. Prior to the study, the school was heavily entrenched in authoritarian and centralized decision-making, but as upper-level administrators were exposed to the concept of collaborative action research, they began making decisions through a reflection and action process. Changing assumptions and attitudes were observed and recorded through interviews at the end of the research period. The research team engaged in sixteen weekly cycles of reflection and action based on an agenda they mutually agreed to and through an analysis of post-research interviews, weekly planning meetings, discussions, and reflection and action cycles. Findings revealed experiences centering around the issues of:  The nature of collaboration- it created discomfort, it created a sense of teamwork, it created difficulty.  The change of environment in the process- team members began to respect each other more, and the process became more enjoyable.  The freedom and change in the process- freedom to voice opinions and to actively listen, the use of experience to lead elsewhere in the school.  How issues of power are better understood by working together- the former process was less collaborative, politics will always be part of the process. As a result of this study, members have started using this decision-making methodology in other areas of administration.

scholarly journals Evaluation and Analysis of CFI Schemes with Different Length of Displaced Left-Turn Lanes with Entropy Method

2021 ◽  
Vol 13 (12) ◽  
pp. 6917
Author(s):  
Binghong Pan ◽  
Shasha Luo ◽  
Jinfeng Ying ◽  
Yang Shao ◽  
Shangru Liu ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Heavy Traffic ◽  
Efficiency Index ◽  
Entropy Method ◽  
Sustainable Transportation ◽  
Assessment Model ◽  
Left Turn ◽  
Traffic Demand ◽  
Traffic Efficiency ◽  
Multi Attribute Decision Making

As an unconventional design to alleviate the conflict between left-turn and through vehicles, Continuous Flow Intersection (CFI) has obvious advantages in improving the sustainability of roadway. So far, the design manuals and guidelines for CFI are not enough sufficient, especially for the displaced left-turn lane length of CFI. And the results of existing research studies are not operational, making it difficult to put CFI into application. To address this issue, this paper presents a methodological procedure for determination and evaluation of displaced left-turn lane length based on the entropy method considering multiple performance measures for sustainable transportation, including traffic efficiency index, environment effect index and fuel consumption. VISSIM and the surrogate safety assessment model (SSAM) were used to simulate the operational and safety performance of CFI. The multi-attribute decision-making method (MADM) based on an entropy method was adopted to determine the suitability of the CFI schemes under different traffic demand patterns. Finally, the procedure was applied to a typical congested intersection of the arterial road with heavy traffic volume and high left-turn ratio in Xi’an, China, the results showed the methodological procedure is reasonable and practical. According to the results, for the studied intersection, when the Volume-to-Capacity ratio (V/C) in the westbound and eastbound lanes is less than 0.5, the length of the displaced left-turn lanes can be selected in the range of 80 to 170 m. Otherwise, other solutions should be considered to improve the traffic efficiency. The simulation results of the case showed CFI can significantly improve the traffic efficiency. In the best case, compared with the conventional intersection, the number of vehicles increases by 13%, delay, travel time, number of stops, CO emission, and fuel consumption decrease by 41%, 29%, 25%, 17%, and 17%, respectively.

scholarly journals Drivers’ Age and Automated Vehicle Explanations

2021 ◽  
Vol 13 (4) ◽  
pp. 1948
Author(s):  
Qiaoning Zhang ◽  
Xi Jessie Yang ◽  
Lionel P. Robert
Keyword(s):  
Decision Making ◽  
Middle Age ◽  
Age Groups ◽  
Design Experiment ◽  
Mixed Design ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Automated Decision Making

Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.

scholarly journals Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method

2021 ◽  
Author(s):  
Jian Li ◽  
Li-li Niu ◽  
Qiongxia Chen ◽  
Zhong-xing Wang
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Programming Model ◽  
Multicriteria Decision Making ◽  
Decision Makers ◽  
Multicriteria Decision ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Decision Making Models ◽  
Best Worst Method

AbstractHesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

scholarly journals Multiobjective Optimization Model for Collaborative En-Route and Slot Allocation

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Shangwen Yang ◽  
Jingting Zhang ◽  
Ping Chen ◽  
Yongjie Yan
Keyword(s):  
Decision Making ◽  
Traffic Control ◽  
Programming Model ◽  
Numerical Test ◽  
Simulated Data ◽  
Slot Allocation ◽  
Collaborative Decision Making ◽  
Average Delay ◽  
Flight Delay ◽  
Collaborative Decision

To allocate the en-routes and slots resource to the flights with collaborative decision-making, a multiobjective 0-1 integer programming model was proposed. According to different demands from air traffic control departments, airlines, and passengers, efficiency, equity, and effectiveness principles of collaborative decision-making were considered. With the aim to minimize the total flight delay costs, the total number of turning points, and average delay time of passengers, the effectiveness constraints were achieved. The algorithm was designed to solve the model on the basis of the objective method, and Lingo11 and MatlabR2007b were applied in numerical tests. To test how well the model works in real world, a numerical test was performed based on the simulated data of a civil en-route. Test results show that, compared with the traditional strategy of first come first served, the model gains better effect. The superiority of the model was verified.

scholarly journals Interval-Valued Probabilistic Hesitant Fuzzy Set Based Muirhead Mean for Multi-Attribute Group Decision-Making

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 342 ◽  
Author(s):  
Krishankumar ◽  
Ravichandran ◽  
Ahmed ◽  
Kar ◽  
Peng
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Set ◽  
Group Decision ◽  
Partial Information ◽  
Programming Model ◽  
Hesitant Fuzzy Set ◽  
Occurrence Probability ◽  
Source Selection ◽  
Interval Valued

As a powerful generalization to fuzzy set, hesitant fuzzy set (HFS) was introduced, which provided multiple possible membership values to be associated with a specific instance. But HFS did not consider occurrence probability values, and to circumvent the issue, probabilistic HFS (PHFS) was introduced, which associates an occurrence probability value with each hesitant fuzzy element (HFE). Providing such a precise probability value is an open challenge and as a generalization to PHFS, interval-valued PHFS (IVPHFS) was proposed. IVPHFS provided flexibility to decision makers (DMs) by associating a range of values as an occurrence probability for each HFE. To enrich the usefulness of IVPHFS in multi-attribute group decision-making (MAGDM), in this paper, we extend the Muirhead mean (MM) operator to IVPHFS for aggregating preferences. The MM operator is a generalized operator that can effectively capture the interrelationship between multiple attributes. Some properties of the proposed operator are also discussed. Then, a new programming model is proposed for calculating the weights of attributes using DMs’ partial information. Later, a systematic procedure is presented for MAGDM with the proposed operator and the practical use of the operator is demonstrated by using a renewable energy source selection problem. Finally, the strengths and weaknesses of the proposal are discussed in comparison with other methods.

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