Risk assessment:method and case study for traffic projects

A comprehensive project evaluation and decision-making method considering multiple objectives,stakeholders,and attributes of proposed traffic treatments is inherently complicated.Although individual techniques in evaluating operations,safety,economic,and stakeholder objectives are available,a practical method that integrates all these risk factors and their uncertainties into a multiattribute decision-making tool is absent.A three-level project decision-making process was developed to model and assess multiple-attribute risk in a proposed traffic treatment from the perspective of multiple stakeholders.The direct benefits from reducing delay and safety risk(basic objectives of traffic treatments) are computed in Level 1 with established methods.Feasibility and performance analysis in Level 2 examine site-specific constraints and conduct detailed performance analysis using advanced analysis tools.In Level 3,this paper introduces an innovative and integrated multiple attributes evaluation process under fuzziness and uncertainty(MAFU) process for evaluation and decision-making.The MAFU is a comprehensive and systematic assessment and decision-making procedure that can assess the magnitudes of project performance and to integrate conflicting interests and tradeoffs among stakeholders.A case study illustrates theapplication of MAFU for the selection of a traffic alternative involving several evaluation attributes and stakeholders.Results show that the MAFU produced the smallest variance for each alternative.With traditional cost–benefit evaluation methods,the uncertainty associated with performance of a traffic project in terms of operation,safety,environmental impacts,etc.,is unrestricted and cumulative.Therefore,a reliable multi-attribute evaluation of complex traffic projects should not be made with conventional cost–benefit analysis alone but with a process like MAFU.

State-of-the-art review on multi-criteria decision-making in the transport sector

Consistent decision-making requires a structured and systematic evaluation of advantages and disadvantages of different choice possibilities.For transport projects,policies or policy measures,and transport options evaluation,various multi-criteria methods have been developed and effectively applied to complement conventional cost effectiveness and cost benefit analysis.The present paper aims to present a state-of-the-art review of pertinent literature regarding multi-criteria decision-making(MCD M)in the transport sector,focusing on the basic concepts and procedure for multi-criteria decision-making in the transport sector,along with its role and evaluation parameters.A large selection of over 50 papers and publications between 1982 and 2019 have been reviewed,in order to provide an insight into the uses of MCDM methods in transport applications.Most commonly used MCDM metho ds techniques are identified and discussed through a wide review of pertinent literature,research and case studies,leading to interesting conclusions that provide a valuable insight into the use of multicriteria analysis techniques in transport sector related decision-making.Based on the wide range of reviewed literature,it is concluded that MCDM methods are being applied mostly to evaluate transport options rather than transport policies or projects and the most commonly used MCDM method in transport sector problems are analytic hierarchy process(AHP).

基于Vague集的最优路径选择方法（英文）

Optimal route selection is an important function of vehicle traffic flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

Research of the traffic characteristics for the real time online traffic classification

Aiming at the hysteretic characteristics of classification problem existed in current intemet traffic identification field, this paper investigates the traffic characteristic suitable for the on-line traffic classification, such as quality of service （QoS）. By the theoretical analysis and the experimental observation, two characteristics （the ACK-Len ab and ACK-Len ha） were obtained. They are the data volume which first be sent by the communication parties continuously. For these two characteristics only depend on data＇s total length of the first few packets on the flow, network traffic can be classified in the early time when the flow arrived. The experiment based on decision tree C4.5 algorithm, with above 97% accuracy. The result indicated that the characteristics proposed can commendably reflect behavior patterns of the network application, although they are simple.

Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning

Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling timeand promote intersection capacity. However, the existing RLTSC methods do not consider the driver’s response time requirement, sothe systems often face efficiency limitations and implementation difficulties.We propose the advance decision-making reinforcementlearning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment.First, the relationship between the intersection perception range and the signal control period is established and the trust region state(TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will bedisplayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automatedvehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speedbased on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcementlearning training;a standardized reward is proposed to enhance the performance of intersection control and prioritized experiencereplay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiencyshowed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.