Algebraic Methods for Traffic Flow Densities Estimation

Multiple model stochastic filtering for traffic density estimation on urban arterials

The road traffic network in the urban interlaced zone is a collection of road sections and nodes. The difficulty in road traffic monitoring lies in the processing of traffic at the node locations. In this paper, the cell transmission model is used to monitor the extended geographical information of the urban interlaced zone. Using the cell transmission model, the channelisation and cell outflow capacity at the intersection are calculated. According to the timing of traffic signals in the interlaced zone, calculations are made on the road traffic volume and accurately monitoring the vehicle density. The simulation experiment results show that the model in this paper can accurately monitor the congestion information of the gradation road network and the uniform road network in the urban interlaced zone traffic network, and the monitoring results are close to the actual results, indicating that the research method in this paper has stronger applicability.

Analyzing and Modeling of Expressway Traffic Characteristics near Ramps in Beijing 3rd Ring Road

Robust variable speed limit control with respect to uncertainties

The variable message signs (VMS) have been widely used in guiding and managing the dynamic traffic with development of intelligent transportation technologies. It is known that cell transmission model (CTM) can well reproduce such traffic dynamics as shock waves and jams. This paper is novel in simulating the traffic propagations based on CTM and VMS route recommendations. The impacts of VMS on congestion are numerically investigated. Simulation results show that the real-time traffic conditions can well be reproduced. The VMS can positively influence travelers' decision making so as to alleviate the grave degree of traffic congestion. And further, the higher traffic demands are, the better effect of VMS route recommendation is.

Recently, Daganzo introduced the cell transmission model—a simple approach for modeling highway traffic flow consistent with the hydrodynamic model. In this paper, we use the cell transmission model to formulate the single destination System Optimum Dynamic Traffic Assignment (SO DTA) problem as a Linear Program (LP). We demonstrate that the model can obtain insights into the DTA problem, and we address various related issues, such as the concept of marginal travel time in a dynamic network and system optimum necessary and sufficient conditions. The model is limited to one destination and, although it can account for traffic realities as they are captured by the cell transmission model, it is not presented as an operational model for actual applications. The main objective of the paper is to demonstrate that the DTA problem can be modeled as an LP, which allows the vast existing literature on LP to be used to better understand and compute DTA. A numerical example illustrates the simplicity and applicability of the proposed approach.

This paper presents a cell transmission model with a lane-changing and vehicle-tracking (CTM-LV) framework for simulation of international land ports of entry (POEs). The development of the modeling framework is motivated by the need to simulate lane-specific queuing yet permit lane changing of vehicles between queues–-a behavior that was not accounted for or tested by the earlier cell transmission models and that cannot be easily replicated in microscopic traffic simulation models. The CTM-LV's network representation, traffic flow logic, and lane-changing rules are discussed. A simulation experiment is then presented to illustrate the importance of modeling the lane changing between queues that will affect the estimation of average queue length. The concept of CTM-LV has been applied to simulate the northbound traffic at the Bridge of the Americas POE, for passenger cars entering El Paso, Texas, from Ciudad Juarez, Mexico. Finally, a method is proposed to track selected vehicles within the CTM-LV framework so that an individual vehicle's travel time can be calculated and vehicle trajectories compared against data collected by probe vehicles with Global Positioning System receivers.

Dynamic traffic assignment is a component of the basic theories in Intelligent Transportation System (ITS). This paper presents an environmental constraint with an Operational Street Pollution Model (OSPM), which considers roads as a link pollution source. The environmental constraint is added to the traditional dynamic user optimization (DUO) model, in which the traffic capacity constraint is changed. To construct an equivalent variational inequality (VI) model that is solved by a modified projection algorithm, the environmental constraint is first used to construct a modified ideal DUO model under an environmental traffic capacity (ETC) constraint. A simple method is given to calculate actual travel time. This model is simulated with a Cell Transmission Model (CTM), which considers different vehicle types. An example study indicates that this model can represent the effects of ETC on traffic assignment.

In this paper we propose a CA movement model based on the “Cell Transmission Model” developed by Daganzo. The cell transmission model was developed as a discrete approximation to the hydrodynamic theory of vehicular traffic flow. The proposed movement model refers instead to the motion of crowd and is required to simulate the dynamic of an egress process in tall buildings taking into account not only queue and spillback phenomena but also the capacity drop phenomenon. In fact we performed experiments that show important evidence that, for a given cross section, in presence of jam upstream the section, the pedestrian flow through the section is not always equal to the section capacity but suddenly it can drop (dropped capacity). This finding is coherent with recent empirical studies of pedestrian behavior at an exit and in contrast with many previous works where it is assumed that in presence of jam upstream a cross section, the flow through the section equals its capacity. The movement model has been used for simulating evacuation processes in high rise buildings. The target is to assess to which extend the capacity drop phenomenon affects the building evacuation time.KeywordsCellular AutomatonMovement ModelHigh Rise BuildingEvacuation TimePrevious Time StepThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the development of intelligent transportation technologies, the variable message signs (VMS) have been widely used in guiding and managing the dynamic traffic. It is known that cell transmission model (CTM) can well reproduce such traffic dynamics as shock waves and jams. This paper employs the CTM to study the location problem of VMS. Simulation results show that there exists an optimal VMS location to minimize the total travel time of the traffic system. How changes of route choice probabilities before and after a traffic accident affect the optimal VMS locations is also investigated and it is concluded that the VMS should be placed far from the accident site if the anticipated accident is very serious.

Connected automated vehicles (CAVs) can significantly shorten the headway of car following, thereby effectively improving the traffic capacity and injecting new power to alleviate traffic congestion. To investigate the congestion characteristics of mixed traffic flow with CAVs and human-driven vehicles (HDVs), this paper proposes a cell transmission model to capture and simulate traffic congestion for mixed traffic flow. Firstly, the Newell, adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC) models are adopted to capture the car-following behavior of different vehicles. Secondly, the fundamental diagram under different penetration rates of CAVs is derived based on car-following models. Then, the cell transmission model (CTM) of mixed traffic flow is developed based on the classical CTM and fundamental diagram of mixed traffic flow. Finally, two simulation methods, mixed traffic flow CTM and micro-simulation, are designed to verify the effectiveness of the proposed model. Moreover, taking the moving bottleneck on the expressway as an example, the congestion characteristics of mixed traffic flow are analyzed using multiple indexes, such as average travel speed, congestion delay, and congestion scale. The results show the following: (i) CAVs can significantly alleviate traffic congestion, (ii) the duration of the bottleneck is positively correlated with the degree of traffic congestion, and (iii) The traffic congestion assessment results under different model parameters slightly differ, but the impact is negligible.

This paper considers the solution of the adaptive network traffic signal control problem under a fully distributed architecture. To achieve a system-wide optimal solution, the problem is modeled as a large-scale mixed integer linear program with the traffic dynamics being captured by the cell transmission model. To achieve an online distributed solution to the considered problem, the loosely connected structure of transportation networks is exploited to decompose the problem in both space and time. The proposed solution methodology involves two main phases. In the first phase, binary decision variables are relaxed and the resulting linear program is distributedly solved via the alternating direction method of multipliers. The second phase involves distributed rounding of the obtained relaxed solution. Simulation results demonstrate the effectiveness of the proposed approach in providing close to optimal, online solutions.

This paper takes the urban road network as the research object to study the temporal and spatial characteristics and evolution mechanism of incidental traffic congestion. Firstly, the improved cell transmission model (CTM) is proposed considering the traffic flow delay phenomenon and the boundary conditions of the cell at the intersection. Secondly, considering the delay time of vehicles at the intersection, the A * path-finding algorithm is proposed, and a dynamic path selection model based on real-time road impedance is established. Finally, combined with the improved CTM and dynamic path selection model, starting from the severity of the incident and the frequency of information induction and update, the traffic flow is simulated by MATLAB software to simulate the traffic flow in the road network under different conditions and analyze the diffusion characteristics of congestion under sudden events. The simulation results show that during the simulation time period, when the traffic capacity caused by the severity of the incident is only 1/2, 1/4 and 1/8 of the original, the maximum number of congestion cells in the network is 9, 26 and 35, and the maximum delay per unit time stepping is 290, 1.31*103 and 2.41*103 vehicles, and the total delay in the road network is 2.73*105, 7.19*105, and 1.50*106 vehicles, after 320δ, 578δ and 588δ the congestion cells in the road network completely disappear. At the information induced update time of 30s, 120s and 600s, the maximum number of congestion cells in the road network is 5, 10 and 23, the maximum number of delayed vehicles is 162, 254 and 985 vehicles - time stepping, the total delay is 1.25*105, 1.68*105 and 5.84*105 vehicles. The findings of the study can provide reference for urban road network design and traffic management.

With the increasing penetration of electric vehicles in the transportation system, more and more interactions appear between the transportation and power systems. This requires considering the impact of disturbances in the electrified transportation system onto the stability of the power system. This paper addresses this issue by proposing a method based on a cell transmission model (CTM) of the electrified transportation system and an alternative current (AC) model of the power system. Specifically, CTM is used to simulate the dynamic realtime traffic under congestion disturbances and evaluate the charging demands in the areas of the electrified transportation system. The charging demands are input to the AC model of the power system to calculate the fluctuations in power flow distributions. The proposed method can simulate the dynamic interactions between the electrified transportation system and the power system, and quantitatively measure the impacts of traffic disturbances on the stability of the power system. A numerical example is used to illustrate the proposed method.

Offset Optimizing with CTM and Genetic Algorithms: Results from Field Studies in Hannover