Threshold queueing describes the fundamental diagram of uninterrupted traffic

Queueing due to congestion is an important aspect of road traffic. This paper provides a brief overview of queueing models for traffic and a novel threshold queue that captures the main aspects of the empirical shape of the fundamental diagram. Our numerical results characterises the sources of variation that influence the shape of the fundamental diagram

Planning and Scheduling Transportation Vehicle Fleet in a Congested Traffic Environment

Transportation is a main component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routing is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic nature of travel times in this problem. In this paper, a vehicle routing problem with time windows and stochastic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture the stochastic behavior of travel times. A case study is used both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment which is often the case on the european road networkstransportation; vehicle fleet; planning; scheduling; congested traffic

Threshold Queueing to Describe the Fundamental Diagram of Uninterrupted Traffic

Queueing because of congestion is an important aspect of road traffic. This paper provides a novel threshold queue that models the empirical shape of the fundamental diagram. In particular, we show that our threshold queue with two service phases captures the capacity drop that is eminent in the fundamental diagram of modern traffic. We use measurements on a Danish highway to illustrate that our threshold queue is indeed capable of capturing the fundamental diagram of real-world traffic systems. We furthermore indicate the modelling power of our threshold queue via a sensitivity study showing that our model is able to capture a wide range of shapes for the fundamental diagram

Stochastic vehicle routing with random time dependent travel times subject to perturbations

Assigning and scheduling vehicle routes in a stochastic time dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic, resulting in a planning gap (i.e. difference in performance between planned route and actual route). Our methodology introduces the traffic congestion component based on queueing theory, thereby introducing an analytical expression for the expected travel. In real life travel times are subject to uncertainty, we solve a time dependent vehicle routing problem to find robust solutions, that can potentially absorb such uncertainties. We model uncertainty as perturbations that are randomly inserted on the routes, we optimize the perturbed solutions via Tabu Search. We conduct experiments on a set of 32 cities, and found that the perturbed solutions generally cope better with the uncertainty than the non-perturbed solutions, with a small increase in expected travel times

How incidents impact congestion on roadways: A queuing network approach

Motivated by the need for transportation infrastructure and incident management planning, we study traffic density under non-recurrent congestion. This paper provides an analytical solution approximating the stationary distribution of traffic density in roadways where deterioration of service occurs unpredictably. The proposed solution generalizes a queuing model discussed in the literature to long segments that are not space-homogeneous. We compare single and tandem queuing approaches to segments of different lengths and verify whether each model is appropriate. A single-queue approach works sufficiently well in segments with similar traffic behavior across space. In contrast, a tandem-queue approach more appropriately describes the density behavior for long segments with sections having distinct traffic characteristics. These models have a comparable fit to the ones generated using a lognormal distribution. However, they also have interpretable parameters, directly connecting the distribution of congestion to the dynamics of roadway behavior. The proposed models are general, adaptable, and tractable, thus being instrumental in infrastructure and incident management

Vehicle routing with stochastic time-dependent travel times

Abstract Assigning and scheduling vehicle routes in a stochastic timedependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the traffic congestion is captured based on queueing theory in an analytical way and applied to the V RP problem. In this paper, we introduce the variability in the traffic flows into the model. This allows for an evaluation of the routes based on the uncertainty involved. Different experiments show that the risk taking/avoiding behaviour of the planner can be taken into account during optimization. As more weight is contributed to the variability component, the resulting optimal route will take a slightly longer travel time, but be more reliable. We propose to evaluate the solution quality in terms of the 95 th -percentile of the travel time distribution (assumed lognormal) as this measure captures well the trade-off between the average travel time and its variance

프로브 차량 자료를 이용한 도시교통 네트워크의 속도 추정 순환형 신경망 모형

학위논문(박사)--서울대학교 대학원 :공과대학 건설환경공학부,2020. 2. 고승영.Urban traffic flows are characterized by complexity. Due to this complexity, limitations arise when using models that have commonly been using to estimate the speed of arterial road networks. This study analyzes the characteristics of the speed data collected by the probe vehicle method in links on the urban traffic flow, presents the limitations of existing models, and develops a modified recurrent neural network model as a solution to these limitations. In order to complement the limitations of existing models, this study focused on the interrupted flow characteristics of urban traffic. Through data analysis, we verified the separation of platoons and high-frequency transitions as phenomena in interrupted flow. Using these phenomena, this study presents a two-step model using the characteristics of each platoon and the selected dropout method that applies traffic conditions separately. In addition, we have developed an active imputation method to deal with frequent missing data in data collection effectively. The developed model not only showed high accuracy on average, but it also improved the accuracy of certain states, which is the limitation of the existing models, increased the correlation between the estimated value and the estimated target value, and properly learned the periodicity of the data.도시교통류는 복잡성을 내재하고 있다. 이 복잡성으로 인해, 일반적으로 지역간 간선 도로 네트워크의 속도를 추정하던 모형들을 사용할 경우 여러가지 한계점이 발생하게 된다. 본 연구는 도시교통류 상의 링크에서 프로브 차량 방식으로 수집된 속도자료의 특성을 분석하고, 기존 모형의 한계점을 제시하고, 이러한 한계점에 대한 해법으로서 변형된 순환형 신경망 모형을 개발하였다. 모형 개발에 있어, 기존 모형의 한계점을 보완하기 위해, 본 연구에서는 도시교통류의 단속류적 특징에 주목하였다. 자료 분석을 통해, 본 연구에서는 단속류에서 나타나는 현상으로서 차량군의 분리와 높은 빈도의 전이상태 발생을 확인하였다. 해당 현상들을 이용하여, 본 연구에서는 각 차량군의 특징을 이용한 이용한 2단계 모형과, 교통 상태를 분리하여 적용하는 선택적 드롭아웃 방식을 제시하였다. 추가적으로, 자료의 수집에 있어 빈발하는 결측 데이터를 효과적으로 다루기 위한 능동적 대체 방식을 개발하였다. 개발 모형은 평균적으로 높은 정확도를 보일 뿐 아니라, 기존 모형들의 한계점인 특정 상황에 대한 정확도를 제고하고 추정값과 추정 대상값의 상관관계를 높이며, 자료의 주기성을 적절하게 학습할 수 있었다.Chapter 1. Introduction 1 1.1. Study Background and Purpose 1 1.2. Research Scope and Procedure 8 Chapter 2. Literature Review 11 2.1. Data Estimation 11 2.2. Traffic State Handling 17 2.3. Originality of This Study 20 Chapter 3. Data Collection and Analysis 22 3.1. Terminology 22 3.2. Data Collection 23 3.3. Data Analysis 26 Chapter 4. Model Development 54 4.1. Basic Concept of the Model 54 4.2. Model Development 58 Chapter 5. Result and Findings 72 5.1. Estimation Accuracy of Developed Models 72 5.2. Correlation Analysis of Developed Model 77 5.3. Periodicity Analysis for Developed Models 81 5.4. Accuracy Analysis by Traffic State 86 5.5. Summary of the Result 92 Chapter 6. Conclusion 94 6.1. Summary 94 6.2. Limitation of the Study 95 6.3. Applications and Future Research 96 Appendix 98 Bibliography 119Docto

Vehicle routing with stochastic time-dependent travel times

Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the traffic congestion is captured in an analytical way using queueing theory. The congestion is then applied to the VRP problem. In this paper, we introduce the variability in traffic flows into the model. This allows for an evaluation of the routes based on the uncertainty involved. Different experiments show that the risk taking behavior of the planner can be taken into account during optimization. As more weight is given to the variability component, the resulting optimal route will take a slightly longer travel time, but will be more reliable. We propose a powerful objective function that is easily implemented and that captures the trade-off between the average travel time and its variance. The evaluation of the solution is done in terms of the 95th-percentile of the travel time distribution (assumed to be lognormal), which reflects well the quality of the solution in this stochastic time-dependent environment