Cell Transmission Model Research Articles

An Improved CTM Model for Urban Signalized Intersections and Exploration of Traffic Evolution

In this paper is going to be proposed a Cell Transmission Model (CTM), its analysis and evaluation with a case study, which addresses in a detailed way the aspect of merging and diverging operations on urban arterials. All those few CTM models that have been developed so far, to model intersections, have some limitations and drawbacks. First, unlike the simple composition road networks, such as highways, urban arterials must include some complex parts called merge sand diverges, due to the fact of vibrational values of reduced capacity, reduced saturation flow rate, etc. In order to simulate an urban network/arterial it is not possible to neglect the traffic signal indication on the respective time step. The objective of this paper is to highlight the difference between the results of the original CTM and our proposed CTM and to provide evidence that the later one is better than the old one. The proposed and formulated model will be employed through an algorithm of CTM to model a segment- arterial road of Pristina (compound from signalized intersections). For the functionalization and testing of the proposed model is build the experimental setup that is compatible with the algorithm created on C# environment. Results show that the proposed model can describe light and congested traffic condition. In light traffic conditions, in great mass traffic flow is dictated by the traffic signal status, while in medium congestion is obtained a rapid increase of the density to each cell. Fluctuations of the density from the lowest to the highest values are obvious during the first three cycles to all cells of the artery in a congested traffic state. Doi: 10.28991/cej-2021-03091659 Full Text: PDF

Dynamic evacuation optimization model based on conflict-eliminating cell transmission and split delivery vehicle routing

This paper studies dynamic evacuation optimization in the context of a new cell transmission with the split delivery vehicle routing problem (SDVRP) of multiple modes of buses and cars. A bi-directional multilane conflict-eliminating cell transmission model (BCECTM) is proposed to simulate the mixed traffic dynamic evacuation process on links and at intersections. The method of SDVRP is applied to manage dynamic evacuation demands and optimize the routing of multiple modes. The mechanisms of vehicle transmission, traveling trajectory, and residual capacity updating are depicted based on the models of BCECTM and SDVRP; thus, a mathematical programming model aiming to minimize the total evacuation time is established by integrating the BCECTM and SDVRP. A genetic algorithm with a chromosome position-coding scheme is designed to solve the evacuation optimization model. Case studies show that the proposed model can reasonably assign evacuation demands that gather at some sites around the event to, and effectively evacuate people by, buses and cars so that the capacities of buses are fully utilized, and can generate optimal vehicle traveling trajectories to improve the evacuation performance. Furthermore, the evacuation and congestion degree under different demand levels and different arrival distributions are analyzed with meaningful conclusions.

A Link Queue Model of Network Traffic Flow

Fundamental to many transportation network studies, traffic flow models can be used to describe traffic dynamics determined by drivers’ car-following, lane-changing, merging, and diverging behaviors. In this study, we develop a deterministic queueing model of network traffic flow, in which traffic on each link is considered as a queue. In the link queue model (LQM), the demand and supply of a link queue are defined in the queue size (number of vehicles), and its in- and out-flows are computed from junction flux functions corresponding to macroscopic merging and diverging rules. The new model is a system of ordinary differential equations that is mathematically tractable and computationally efficient and can capture queue spillbacks and interactions among links. We further demonstrate that the LQM is fundamentally different from the cell transmission model (CTM) and link transmission model (LTM) for a road segment, a signalized ring road, and a diverge-merge network, with respect to the shock and rarefaction waves, network fundamental diagram, and stability property. In a sense, the new model is a space-continuous approximation of the kinematic wave model and can be a useful addition to the multiscale modeling framework of network traffic flow. The model has been applied to formulate and solve network traffic control and observation problems.

ANALYSIS OF CELL TRANSMISSION MODEL FOR TRAFFIC FLOW SIMULATION WITH APPLICATION TO NETWORK TRAFFIC

AbstractThe cell transmission model (CTM) is a macroscopic model that describes the dynamics of traffic flow over time and space. The effectiveness and accuracy of the CTM are discussed in this paper. First, the CTM formula is recognized as a finite-volume discretization of the kinematic traffic model with a trapezoidal flux function. To validate the constructed scheme, the simulation of shock waves and rarefaction waves as two important elements of traffic dynamics was performed. Adaptation of the CTM for intersecting and splitting cells is discussed. Its implementation on the road segment with traffic influx produces results that are consistent with the analytical solution of the kinematic model. Furthermore, a simulation on a simple road network shows the back and forth propagation of shock waves and rarefaction waves. Our numerical result agrees well with the existing result of Godunov’s finite-volume scheme. In addition, from this accurately proven scheme, we can extract information for the average travel time on a certain route, which is the most important information a traveller needs. It appears from simulations of different scenarios that, depending on the circumstances, a longer route may have a shorter travel time. Finally, there is a discussion on the possible application for traffic management in Indonesia during the Eid al-Fitr exodus.

Extended geographic information monitoring of urban ecotone based on cell transmission model

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.

Performance Assessment of Different Cell-Transmission Models for Ramp-Metered Highway Networks

Throughout the past three decades, various versions of the the cell-transmission model (CTM) has been proposed. In spite of the increased attention to freeway traffic modelling via the CTM, an analysis of the currently available versions of the CTM has been missing. This study has the aim of filling this gap by comparing the performance of the popular versions of this model. To achieve this goal, four finite horizon optimal control problems with different underlying CTMs and cost functions are proposed. The traffic management control in all problems is the ramp metering control. The performance assessment is performed via simulation for a hypothetical network of highways, with different demand profiles along with the analysis of the equilibrium state in each case. The simulation results will provide a thorough comparison of the performance of these problems on different performance measures of the network and will highlight the advantages and disadvantages of each problem.

Human Behavior During Emergency Evacuation: Cell Transmission Model

This paper presents an extended cell transmission model to simulate an emergency evacuation of crowd in a supermarket scenario. The supermarket scenario with a daily shopping, an emergency exit and multiple parallel shelves is discretized into a set of regular hexagonal cells. First, we verify the validity of the model by comparing the results of the model with actual data obtained from a reported pedestrian evacuation experiment. To improve the realism of the simulation, we then include three characteristics of human behavior, the unadventurous effect, inertial effect, and panic psychology, in the model as parameters and apply the extended model to the supermarket scenario. By analyzing the pedestrian density distribution, fundamental diagrams, route choice, and total evacuation time, we determine that the parameters have a significant influence on the evacuation process. By establishing a minimization model, we confirm that the total evacuation time is the least when the pedestrians’ familiarity with the emergency exit is close to the ratio of the width of the emergency exit to the total width of the two exits. Moreover, by comparing two different methods to choose exits, we can conclude that the approach, by which all pedestrians choose the closer exit, is not always the most efficient evacuation strategy. This study is conducive to devise evacuation plans and design exit layouts in facilities similar to supermarkets.

Extended geographic information monitoring of urban ecotone based on cell transmission model

Extended geographic information monitoring of urban ecotone based on cell transmission model

Reinforcement Learning-Based Variable Speed Limits Control to Reduce Crash Risks Near Traffic Oscillations on Freeways

Traffic oscillation on freeways results in abrupt and frequent vehicle deceleration, which remarkably increases crash risks. Artificial Intelligence based traffic control provides a new opportunity for addressing the safety issues. This study aims at proposing a Reinforcement Learning (RL)-based variable speed limits (VSL) control algorithm to reduce crash risks associated with oscillations. The state, action and reward, which are the critical components in the RL, were designed carefully for safety improvement. The RL was trained to learn the optimal speed limit for various traffic states to achieve a goal of safety optimization. A rear-end crash risk model was applied to assess crash risks associated with oscillations near freeway bottlenecks. The cell transmission model was modified as the simulation platform for evaluating the control effects. The results showed that after the training process, the proposed RL-based VSL control successfully reduced the crash risks by 19.4%. A continuous online learning function was developed in RL to enhance the robustness of our strategy. The results showed that with continuous learning, the RL-based VSL control performed reasonably well under lower driver compliance situations.

Evaluation of Integrated Variable Speed Limit and Lane Change Control for Highway Traffic Flow

A sudden lane drop on a freeway with high volume of vehicles can cause severe traffic congestion and safety issues. Forced lane changes near the bottleneck reduce the traffic mobility on all lanes and trigger the capacity drop phenomenon. Numerous studies have explored the effect of Variable Speed Limit (VSL) and Lane Change (LC) control on solving the bottleneck congestion, but only a few have taken the measurement or model uncertainties into considerations. In this paper, we design an integrated VSL and LC controller based on a modified multi-section Cell Transmission Model (CTM) to alleviate the freeway bottleneck congestion and reject uncertainties. The location of the most upstream speed sign is treated as a parameter and optimized using simulation data. We first evaluate the proposed controller without uncertainty to demonstrate its effectiveness in improving traffic flow. Then we incorporate the uncertainties in measurements and model parameters to examine the robustness of the control. The proposed combined VSL and LC controllers are demonstrated to be robust with respect to uncertainties. The bounds of allowable uncertainties are more sensitive when uncertainties lead to a reduction of speed limit commands than an increase.

Dynamic Control Cycle Speed Limit Strategy for Improving Traffic Operation at Freeway Bottlenecks

In recent years, variable speed limit (VSL) has been used to decrease the total travel time and alleviate the traffic congestion on freeway. To improve traffic efficiency, we proposed a novel VSL strategy considering dynamic control cycle in this study. An extension of the cell transmission model was used to depict the traffic characteristics under VSL control, and the VSL strategy was designed to provide multiple control cycles. A probability formula was developed and used to determine the optimal quantity of control cycles in the VSL strategy. An objective optimization function was formulated with the aim of minimizing total travel time. A sensitivity analysis was applied to compare different control strategies under a variety of road bottleneck structures by both the numerical analysis and simulation experiments. The results show that dynamic control cycle VSL strategy outperformed other control scenarios and effectively reduced traffic congestion duration and enhanced the service level of freeway network.

Modelling Heterogeneous and Undisciplined Traffic Flow using Cell Transmission Model

This research calibrates Cell Transmission Model (CTM) for heterogeneous and non-lane disciplined traffic, as observed in Pakistan and some other developing countries by constructing a flow-density fundamental traffic flow diagram. Currently, most of the traffic simulation packages used for such heterogonous and non-lane-disciplined traffic are not calibrated for local traffic conditions and most of the traffic flow models are developed for comparatively less heterogeneous and lane-disciplined traffic. The flow-density fundamental traffic flow diagram is developed based on extensive field data collected from Karachi, Pakistan. The calibrated CTM model is validated by using actual data from another road and it was concluded that CTM is capable of modelling heterogeneous and non-lane disciplined traffic and performed very reasonably. The calibrated CTM will be a useful input for the application of traffic simulation and optimization packages such as TRANSYT, SIGMIX, DISCO, and CTMSIM.

Analysis and Design of Vehicle Platooning Operations on Mixed-Traffic Highways

Platooning of connected and autonomous vehicles (CAVs) has a significant potential for throughput improvement. However, the interaction between CAVs and non-CAVs may limit the practically attainable improvement due to platooning. To better understand and address this limitation, we introduce a new fluid model of mixed-autonomy traffic flow and use this model to analyze and design platoon coordination strategies. We propose a tandem-link fluid model that considers randomly arriving platoons sharing highway capacity with non-CAVs. We derive verifiable conditions for stability of the fluid model by analyzing an underlying M/D/1 queuing process and establishing a Foster–Lyapunov drift condition for the fluid model. These stability conditions enable a quantitative analysis of highway throughput under various scenarios. The model is useful for designing platoon coordination strategies that maximize throughput and minimize delay. Such coordination strategies are provably optimal in the fluid model and are practically relevant. We also validate our results using standard macroscopic (cell transmission model) and microscopic (simulation for urban mobility) simulation models.

Robust variable speed limit control with respect to uncertainties

The Cell Transmission Model (CTM) has been widely used to design variable speed limit (VSL) controllers. Since the CTM is an idealization of the actual traffic flow, the issue of robustness of VSL with respect to uncertainties such as disturbances due to modeling and measurement errors is an important problem. In this paper, we modify the CTM to include a constant disturbance term that is unknown and could change over time. The stability properties of the modified open-loop CTM are analyzed and used to motivate the design of a robust VSL controller. The VSL controller is designed to reject external disturbances, which are constant over large intervals of time, while guaranteeing that the density converges to the desired equilibrium state corresponding to high flow and speed. Microscopic simulations based on the commercial software VISSIM are used to demonstrate the effectiveness of the proposed robust VSL controller and associated benefits for traffic scenarios along a large segment of I-710 in Southern California.

Developing a dynamic utilisation scheme for exclusive bus lanes on urban expressways: an enhanced CTM‐based approach versus a microsimulation‐based approach

In the current practice of bus rapid transit (BRT) system, under-utilized exclusive bus lanes (EBL) could negatively impact the system efficiency, particularly when traffic congestion occurs on regular vehicle lanes during peak period. In this paper, we propose an EBL sharing scheme to dynamically control the usage of the EBL by regular vehicles based on connected vehicle technologies without disturbing normal operation of the BRT system. An enhanced cell transmission model (CTM)-based approach and a simulation-based approach are proposed to model the traffic dynamics of a BRT section currently running in Chengdu, China. The optimal entry/exit proportion of regular vehicles are derived by minimizing total car delay on both the EBL and regular lanes given fixed bus service. The performance of the proposed dynamic sharing control scheme is evaluated under-saturated and over-saturated conditions. The sensitivity of the BRT service frequency and the average bus waiting time on the performance of the control scheme is also analysed. The results show that when traffic becomes over-saturated, delays on regular lanes can be significantly reduced by allowing optimized proportion of regular vehicles to use the EBL. However, it is unnecessary to use the EBL where traffic demand on regular lanes is low.

Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

An extended dynamic model for pedestrian traffic considering individual preference

Complexity, dynamics and mutability are basic characteristics of pedestrian evacuation. However, most models for pedestrian traffic still lack an explanatory mechanism of the individual choice process. This study concerns a cell transmission model (CTM) for pedestrian traffic. But we focus on a new issue, namely, how to illustrate the decision-making process of each individual pedestrian. The decision field theory (DFT), a cognitive model to capture the deliberation process in decision making, is introduced to the cell transmission model and an extended dynamic pedestrian flow assignment model based on DFT and CTM is proposed. Pedestrian attitudes towards distance and crowdedness are considered and the individual preferences of pedestrians in the dynamic decision-making for the evacuation process are explored. Numerical studies show that the initial pedestrian distributions can have an effect on the evacuation process and different individual preferences of pedestrians will lead to different pedestrian choices. Even in the same cell, pedestrians’ route and exit choices may be different. Furthermore, some interesting phenomena can be observed, which illustrate that the preference reversals can be depicted in our model.

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

This paper introduces two traffic light strategies to control traffic and avoid traffic jam in urban networks. One strategy is a new traffic light scheduling system, which controls traffic light using local variables (waiting time and number of vehicle on links) but has a global impact on the traffic, using shared variables between neighbour intersections. The proposed traffic light scheduling system is designed based on eigenvector centrality of intersection relation matrix. The intersection relation matrix is a new representation of a junction which indicates the traffic relation between intersection’s links and adjacent intersections. The second contribution is expanding a new dual mode traffic light strategy (namely, Exit Status Traffic Light (ETL)), which notifies the drivers whether they are allowed to exit a street or not. In other words, vehicles are allowed to enter a street in both red and green ETL, but they are not allowed to exit the street for a long time in red ETL (while traffic is heavy in the subnetwork). The ETL gives a chance to relax traffic in a subnetwork and avoid traffic jam. The effectiveness of the proposed strategy is analysed and evaluated by a number of simulations on three-way grid networks. Two-way rectangular grid networks are modelled via a cell transmission model (CTM). The macroscopic fundamental diagram (MFD) and the number of jammed cells are compared with two state-of-the-art methods.

Optimal guidance strategy for crowd evacuation with multiple exits: A hybrid multiscale modeling approach

Guidance is an efficient crowd management measure used to save lives in emergencies because it can reduce the disorientation of pedestrians. This study investigates an optimal guidance strategy for large-scale crowd evacuations. To increase computational efficiency, a pedestrian cell transmission model is extended to create a rapid simulation of guided crowd dynamics. To solve the conflicts between the limited guidance capacity and the desire to improve evacuation efficiency, a strategic guidance model is proposed, which generates a leader location and exit selection plan. A simulation algorithm is proposed to integrate a pedestrian following model and strategic guidance model based on the follower-leader interaction. Finally, a hybrid multiscale approach for modeling guided crowd evacuations is established to evaluate the performance of the guidance strategy. The experimental results show that the required CPU time of the proposed model is much less than that of the microscopic models. Because the number of leaders is minimized and the exit is selected by taking both risk and congestion into account, the obtained guidance strategy can realize the full use of guidance capacity, information confusion reduction and uniform exit usage, all of which contribute to a reduction in evacuation time.

Lane-Based Traffic Signal Simulation and Optimization for Preventing Overflow

In the lane-based signal optimization model, permitted turn directions in the form of lane markings that guide road users to turn at an intersection are optimized with traffic signal settings. The spatial queue requirements of approach lanes should be considered to avoid the overdesigning of the cycle, effective red, and effective green durations. The point-queue system employed in the conventional modeling approach is unrealistic in many practical situations. Overflow conditions cannot be modeled accurately, while vehicle queues are accumulated that block back upstream intersections. In a previous study, a method was developed to manually refine the traffic signal settings by using the results of lane-based optimization. However, the method was inefficient. In the present study, new design constraint sets are proposed to control the effective red and effective green durations, such that traffic enters the road lanes without overflow. The reduced cycle times discharge the accumulated vehicles more frequently. Moreover, queue spillback and residual queues can be avoided. One of the most complicated four-arm intersections in Hong Kong is considered as a case study for demonstration. The existing traffic signal settings are ineffective for controlling the observed traffic demand, and overflow occurs in short lanes. The optimized traffic signal settings applied to the proposed optimization algorithm effectively avoided traffic overflow. The resultant queuing dynamics are simulated using TRANSYT 15 Cell Transmission Model (CTM) to verify the proposed model. The model application is extended to handle the difficult residual queue scenario. It is found that the proposed model can optimize the traffic signal settings in cases where there are short initial residual queues.