Derivation of Continuum Traffic Model for Weaving Sections on Freeways.

This paper presents a new continuum model describing the dynamics of multiclass traffic flow on multilane freeways including weaving sections. In this paper, we consider a specific freeway weaving type, which is formed when an on ramp is near to an off ramp and these two ramps are joined by an auxiliary lane. Traffic interactions in this weaving zone are very complex due to the involvement of weaving flows and non-weaving flows in the so-called mandatory lane-changing process. To handle this complexity, it is essential to have a good understanding of the (microscopic) driving behavior within the weaving zones. These behaviors are modeled based on a gap-acceptance model. The methodology to obtain a weaving continuum traffic model is thus twofold. On the one hand, we develop a (macroscopic) model to determine the mandatory lane-changing probability based on a renewal process. On the other hand, we implement the lane-changing model into a current gas-kinetic traffic flow model for heterogeneous traffic flow on multilane roadways. From this, corresponding macroscopic model is obtained based on the method of moments

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Recent development of information and communication technologies (ICT) has enabled vehicles to timely communicate with others through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. In the vast literature of traffic flow theory, there are continuum models considering multiple forward anticipative strategy, where the driver reacts to many leaders. Few study effort has been undertaken to include bidirectional driving strategy, where the driver reacts to both direct leader and direct follower, in the continuum traffic flow models. This paper aims to derive a continuum traffic model considering both multiple forward and backward driving strategy. It is shown that the derived model is a generalised version of a current continuum model for ITS and can improve important properties of such bidirectional (continuum) model

Optimal queue placement in dynamic system optimum solutions for single origin-destination traffic networks

The Dynamic System Optimum (DSO) traffic assignment problem aims to determine a time-dependent routing pattern of travellers in a network such that the given time-dependent origin-destination demands are satisfied and the total travel time is at a minimum, assuming some model for dynamic network loading. The network kinematic wave model is now widely accepted as such a model, given its realism in reproducing phenomena such as transient queues and spillback to upstream links. An attractive solution strategy for DSO based on such a model is to reformulate as a set of side constraints apply a standard solver, and to this end two methods have been previously proposed, one based on the discretisation scheme known as the Cell Transmission Model (CTM), and the other based on the Link Transmission Model (LTM) derived from variational theory. In the present paper we aim to combine the advantages of CTM (in tracking time-dependent congestion formation within a link) with those of LTM (avoiding cell discretisation, providing a more computationally attractive with much fewer constraints). The motivation for our work is the previously-reported possibility for DSO to have multiple solutions, which differ in where queues are formed and dissipated in the network. Our aim is to find DSO solutions that optimally distribute the congestion over links inside the network which essentially eliminate avoidable queue spillbacks. In order to do so, we require more information than the LTM can offer, but wish to avoid the computational burden of CTM for DSO. We thus adopt an extension of the LTM called the Two-regime Transmission Model (TTM), which is consistent with LTM at link entries and exits but which is additionally able to accurately track the spatial and temporal formation of the congestion boundary within a link (which we later show to be a critical element, relative to LTM). We set out the theoretical background necessary for the formulation of the network-level TTM as a set of linear side constraints. Numerical experiments are used to illustrate the application of the method to determine DSO solutions avoiding spillbacks, reduce/eliminate the congestion and to show the distinctive elements of adopting TTM over LTM. Furthermore, in comparison to a fine-level CTM-based DSO method, our formulation is seen to significantly reduce the number of linear constraints while maintaining a reasonable accuracy

A bilevel programming model for autonomous intersection control and trajectory planning

Advances in autonomous and connected vehicles bring new opportunities for intelligent intersection control strategies. In this paper, we propose a centralised way to jointly integrate an intersection control problem with vehicle trajectory planning. It is formulated as a bilevel optimisation problem in which the upper level is designed to minimise the total travel time by a mixed integer linear programming (MILP) model. In contrast, the lower level is a linear programming (LP) model with an objective function to maximise the total speed entering the intersection. The two levels are coupled by the arrival time and terminal speed. By using the relationship between the safe time headway and the process time, a novel platoon-based method is developed to reduce the computational burden. Finally, simulation tests are carried out to investigate the control performance under different demands, intersection lengths, communication ranges and traffic compositions

A stochastic schedule-following simulation model of bus routes

Microsimulation models of bus routes allow transit operators to both better understand the dynamics of bus routes and facilitate better policy making. Several simulation models of bus routes have been proposed in the literature, including cellular-automata, bus-following and traffic-following models. The majority of these approaches aim to simulate the interactions of a bus with other buses (the bus-following model), with passengers or the surrounding traffic (the traffic-following model), but they all fail to consider the important interactions between buses and their schedules. In a conventional schedule-based public transport system, bus drivers aim to arrive at each stop on time. This means that they will either speed up or slow down if their vehicles are not meeting the schedule. The research within this paper is a novel contribution to the literature of bus route simulation. We introduce the first schedule-following model where buses try to adhere to their schedule in a conventional schedule-based public transport system. A simulated numerical analysis shows the characteristics of the proposed schedule-following model and compares it to existing models. Finally, the model is calibrated using Automatic Vehicle Location and Smart Card data from Brisbane, Australia. The results show good model performance against the observed data. The model is relatively simple, yet the fundamental mechanisms that drive the model are novel and it has the potential to be applied in any city with well-defined bus schedules

A Joint Control-Communication Design for Reliable Vehicle Platooning in Hybrid Traffic

Recent studies have shown that traffic safety and efficiency can be substantially improved by vehicle platooning, in which vehicles periodically broadcast their kinetic status to neighbors, known as beacon message dissemination. As a networked control system, vehicle platoon has attracted significant attention from both the control and networking areas. However, few studies consider the practical traffic scenario with both platoons and individual vehicles, and the proposed beaconing schemes lack the deep understanding of relationship between the beaconing performance and the requirements of the control mechanism. To address these challenging issues, we propose a joint controlcommunication design to achieve reliable vehicle platooning in a more realistic traffic scenario, wherein the traffic consists of both platoons and individual vehicles, and both periodic beacon messages and event-based safety messages shall be delivered together. Specifically, we first develop a comprehensive control-theoretical analysis to understand how the vehicular communication can affect features of platoon driving; based on the understanding, we then propose and analyze an adaptive platoon-based message dissemination scheme; finally, we conduct extensive numerical experiments to validate the effectiveness of the protocol and to confirm the accuracy of the our theoretical analysis

Real-Time Dynamic Traffic Control Based on Traffic-State Estimation

The accurate depiction of the existing traffic state on a road network is essential in reducing congestion and delays at signalized intersections. The existing literature in the optimization of signal timings either utilizes prediction of traffic state from traffic flow models or limited real-time measurements available from sensors. Prediction of traffic state based on historic data cannot represent the dynamics of change in traffic demand or network capacity. Similarly, data obtained from limited point sensors in a network provides estimates which contain errors. A reliable estimate of existing traffic state is, therefore, necessary to obtain signal timings which are based on the existing condition of traffic on the network. This research proposes a framework which utilizes estimates of traffic flows and travel times based on real-time estimated traffic state for obtaining optimal signal timings. The prediction of traffic state from the Cell Transmission Model (CTM) and measurements from traffic sensors are combined in the recursive algorithm of Extended Kalman Filter (EKF) to obtain a reliable estimate of existing traffic state. The estimate of traffic state obtained from the CTM-EKF model is utilized in the optimization of signal timings using Genetic Algorithm (GA) in the proposed CTM-EKF-GA framework. The proposed framework is applied to a synthetic signalized intersection and the results are compared with a model-based optimal solution and simulated reality. The optimal delay estimated by CTM-EKF-GA framework is only 0.6% higher than the perfect solution, whereas the delay estimated by CTM-GA model is 12.9% higher than the perfect solution

A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks

Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost

A new multi-anticipative car-following model with consideration of the desired following distance

We propose in this paper an extension of the multi-anticipative optimal velocity car-following model to consider explicitly the desired following distance. The model on the following vehicle’s acceleration is formulated as a linear function of the optimal velocity and the desired distance, with reaction-time delay in elements. The linear stability condition of the model is derived. The results demonstrate that the stability of traffic flow is improved by introducing the desired following distance, increasing the time gap in the desired following distance or decreasing the reaction-time delay. The simulation results show that by taking into account the desired following distance as well as the optimal velocity, the multi-anticipative model allows longer reaction-time delay in achieving stable traffic flows

A Lane-based Predictive Model of Downstream Arrival Rates in a Queue Estimation Model Using a Long Short-Term Memory Network

In this study, we develop a mathematical framework to predict cycle-based queued vehicles at each individual lane using a deep learning method - the long short-term memory (LSTM) network. The key challenges are to decide the existence of residual queued vehicles at the end of each cycle, and to predict the lane-based downstream arrivals to calculate vertical queue lengths at individual lanes using an integrated deep learning method. The primary contribution of the proposed method is to enhance the predictive accuracy of lane-based queue lengths in the future cycles using the historical queuing patterns. A major advantage of implementing an integrated deep learning process compared to the previously Kalman-filter-based queue estimation approach (Lee et al., 2015) is that there is no need to calibrate the co-variance matrix and tune the gain values (parameters) of the estimator. In the simulation results, the proposed method perform better in only straight movements and a shared lane with left turning movements