Piecewise-Linearized Cell Transmission Model and Parameter Calibration Methodology

This paper studies the development of macroscopic freeway traffic models and parameter calibration methodologies that are computationally efficient and suitable for use in real-time traffic monitoring and control applications. Toward the fulfillment of these objectives, a macroscopic traffic model, the switching-mode model (SMM), is presented; it is a piecewise-linearized version of Daganzo's cell transmission model (CTM). The observability and controllability properties of the SMM modes are reviewed because these properties are of fundamental importance in the design of traffic estimators and on-ramp metering controllers. A semiautomated method has been developed for calibrating the CTM and SMM parameters. In this method, a least-squares data-fitting approach is applied to loop detector data to determine free-flow speeds, congestion-wave speeds, and jam densities for specified subsections of a freeway. Bottleneck capacities are estimated from measured mainline and on-ramp flows. The calibration method was tested with loop detector data from Interstate 210 westbound (I-210W) in Southern California. The main traffic data source was the performance measurement system. Parameters were calibrated for a 2-mi (3-km) subsection of I-210W and were tested on both the SMM and CTM, which were shown to perform similarly and to reproduce the general behavior of traffic congestion.

An integrated macroscopic traffic model is proposed, which integrates a macroscopic urban traffic flow model with a microscopic traffic emission model for individual vehicles. As a macroscopic model, the integrated model is fast enough for on-line control purposes. Nevertheless, the model can still capture the traffic emissions for vehicles in different states compared with using a macroscopic traffic emission model, because of the accuracy of the microscopic traffic emission model. Model Predictive Control is applied to control urban traffic areas based on the integrated traffic model, aiming at reducing both travel delays and traffic emissions.

Intelligent Vehicle/Highway Systems (IVHS) is a multi-year research/demonstration program which aims at improving the vehicle/highway system operation and management techniques for the post-interstate construction era. The authors first describe the framework of IVHS, which as four major components: (1) advanced traffic management system (ATMS); (2) advanced driver information system (ADIS); (3) commercial vehicle operations (CVO); and (4) advanced vehicle control systems (AVCS). The ATMS related research and development plans to support the real-time traffic management systems and control strategies are considered. The state-of-the-art traffic engineering modeling software is reviewed. The discussion centers around the TRAF system, a family of microscopic and macroscopic traffic simulation software models for surface streets, freeways, and corridor networks. The gap between the capabilities provided by the current traffic modeling software and the functional requirement of the IVHS traffic models is also addressed. >

A cell-based dynamic traffic assignment model: Formulation and properties

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.

This paper discusses a new individual anode current measurement scheme and its applications in real-time monitoring and control of the Hall-Héroult process. While anode current can be directly measured from the anode rod, this approach takes measurements from the anode beam allowing the sensors to remain intact through various cell operations, including anode change. This instrumentation scheme employs smart sensors that are daisy-chained on a common bus for digital data transfer. This approach limits electromagnetic interferences and offers system self-configuration and self-diagnosis, thus allowing for easy maintenance. The system can be configured to work across a broad range of cell technologies. Monitoring anode current distributions helps improve process operation and allows early detection of process faults such as perfluorocarbon co-evolution and blocked feeders. This also offers the ability to monitor process states such as local alumina concentration and bath temperature, along with potential improvements to cell operation and current efficiency.KeywordsIndividual anode current measurementOnline monitoringCurrent distribution

This research proposes an integrated framework of capacitated vehicle routing problems (CVRP) and traffic flow model (cell transmission model in this research) to assess the effect of time-varying congestion. We develop a framework consisting sequence of mixed integer programs solving the CVRP with updated cost obtained from the traffic flow model. A real-world network with 15 cities and towns is tested with the framework and results show significant travel time reduction from the case where time-varying congestion is not considered. In addition, we consider system optimal type of route choice behavior within the traffic flow model.

This paper presents an acyclic real-time traffic signal control model with transit priority based on a rolling horizon process for isolated intersections. The developed model consists of two components, including: an Improved Genetic Algorithm (IGA)-based signal optimization module and a microscopic traffic simulation module. The acyclic real-time traffic signal control model optimizes the phase sequence and the phase length with the aim to minimize the total delay of both transit vehicles and general vehicles for the next decision horizon. Numerical results show that the proposed IGA signal optimization module could provide a more efficient search for optimal solutions. The results also show that the acyclic real-time traffic signal control model outperforms the fixed-time control model. It prioritizes transit vehicles while minimizing the impact on the general vehicles.

With the continuous improvement of national economic strength, the transportation field has also been developed at an unprecedented speed, and the transportation system simulation is an important technology and method in the field of transportation engineering. Establishing rele-vant models through traffic system simulation can not only improve the efficiency of urban traf-fic utilization in urban planning, but also optimize some existing traffic problems and promote the sustainable development of the traffic field. This paper first describes the specific concept of the traffic system simulation model, and discusses the development status of the traffic system simulation model from the microscopic traffic simulation model, the mesoscopic traffic simula-tion model, and the macroscopic traffic simulation model, as well as the characteristics and dif-ferences of the three types of models. Finally, it selects examples of each type of simulation model for specific analysis. The overview of the traffic system simulation model can improve the public's awareness of the traffic system simulation and help the specific development of urban traffic.

Route swapping in dynamic traffic networks

This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of advanced traffic management strategies-including adaptive signal control and dynamic rerouting-under varied traffic scenarios. Unlike conventional models that rely on static or reactive approaches, this framework integrates real-time data from IoT-enabled sensors with predictive analytics to enable proactive adjustments to traffic flows. Distinctively, the study couples this integration with a multiagent simulation environment that models the traffic actors-private vehicles, buses, cyclists, and emergency services-as autonomous, behaviourally dynamic agents responding to real-time conditions. This enables a more nuanced, realistic, and scalable evaluation of urban mobility strategies. The simulation results indicate substantial performance gains, including a 30% reduction in average travel times, a 50% decrease in congestion at major intersections, and a 28% decline in CO2 emissions. These findings underscore the transformative potential of sensor-driven adaptive systems for advancing sustainable urban mobility. The study addresses critical gaps in the existing literature by focusing on scalability, equity, and multimodal inclusivity, particularly through the prioritisation of high-occupancy and essential traffic. Furthermore, it highlights the pivotal role of IoT sensor networks in real-time traffic monitoring, control, and optimisation. By demonstrating a novel and practical application of sensor technologies to traffic systems, the proposed framework makes a significant and timely contribution to the field and offers actionable insights for smart city planning and transportation policy.

We present a coupled macroscopic traffic and emission modelling system tailored to the Barcelona metropolitan area that allows estimating hourly road transport emissions at road link level. We use the developed system to perform an emission sensitivity analysis of typically high uncertainty emission features and assess their impact. We also explore the uncertainties of our system compared to a microscopic approach in a representative area of Barcelona. The developed macroscopic system shows a high sensitivity to spatially-resolved vehicle fleet composition inputs, meteorological effects on diesel engines (+19% in NOx) and non-exhaust sources (80% of total PM emissions). The comparison with the microscopic system shows that discrepancies grow as a function of the congestion level, up to +65% in NOx. The resulting coupled system will be used in further steps of the research to evaluate the impact of traffic management strategies upon urban emissions and air quality levels in Barcelona.

Macroscopic traffic flow models play a significant role in the Intelligent Transportation Systems (ITS), especially for the urban traffic control and the dynamic traffic assignment. Two main problems limit the application of the existing traffic models. One is the traffic dynamics described by the complex nonlinear equations which are difficult to resolve in real time, and the other is that some model parameters need to be calibrated in prior. This paper presents a hybrid macroscopic and microscopic (macro-microscopic) traffic flow model of freeway traffic by extending the cell transmission model (CTM) with the real time speed dynamics. Simple supplying and demanding functions are defined to represent the traffic propagation between the upstream and downstream cell boundaries. A microscopic car-following model is integrated to specify the dynamics of the average speed in each cell. Simulation results with specific traffic data set collected from a 4-kilometre long urban freeway show the performance of the model compared to the well-known METANET model.

Ramp metering has emerged as a freeway traffic management control strategy that facilitates efficient freeway operations. It has been recognized as an effective freeway management strategy that can significantly improve freeway capacity utilisation as well as increase overall network performance. However, despite these potential benefits, a number of limitations and challenges in developing ramp metering systems have been revealed from the literature. These include equity between road users, performance during over-saturated condition, real-time responsiveness and the challenges of a decentralised control approach. This thesis aims to develop and evaluate the proposed multi-agent based ramp metering algorithm and investigate some important aspects arising from the implementation of ramp metering systems. Agent technologies were introduced to model an individual ramp controller that would react dynamically under real-time traffic information. The agent concept of cooperation and collaboration was applied to develop a decentralised ramp metering system to balance system-wide efficiency and equity between road users. The proposed multi-agent ramp metering control was modelled as a hierarchical multi-agent control system comprising three operational control levels - local, group, and region. Each controller was modelled as an individual agent. At each control level, different goals and principles in metering rate calculation were applied. The developed multi-agent ramp metering algorithm (AGENT) was comparatively evaluated with other control algorithms including no control, time-of-day plan, ALINEA, FLOW and Stratified Zone under recurring and non-recurring traffic congestion. The developed Pacific Motorway traffic simulation model was applied as the study test-bed for evaluation of the algorithm performances. Under recurring traffic congestion, AGENT was found to perform very well under both normal (100 percent) and heavy (120 percent) traffic demand. Under normal traffic demand, it was found that AGENT was obviously superior to FLOW and Stratified Zone, but slightly worse than ALINEA (1.6 percent less on average flow) in terms of overall network performance. However, for on-ramp performance, AGENT was found to increase on-ramp flow by 20 percent compared to ALINEA. It was also found that AGENT generated the lowest average travel time, lowest travel delay and least on-ramp waiting time, all of which are the most important indices in term of network equity. The benefit of AGENT was found when traffic demand increased (heavy demand). It was found from the experiment that AGENT was considerably superior to ALINEA (1.7 percent higher on average flow) and other algorithms. In term of equity, AGENT was found to have the ability to balance network efficiency and equality by producing high network efficiency and excellent equity to the road users. According to the experiments under non-recurring traffic congestion regardless of severity and duration of incident, AGENT was found to have superior performance compared to other control strategies, including ALINEA. In addition, it can significantly recover the impact from incident faster than other algorithms. Its network-wide benefits over those of ALINEA, FLOW and Stratified Zone are up to 1.3, 11 and 16.1 percent on the average flow, up to 2.7, 38.7 and 67.9 percent on average speed and up to 7.4, 20.9 and 17.1 percent on average delay, respectively. For the on-ramp performance, the benefits of AGENT over ALINEA and FLOW can be found from the improvement on average on-ramp flow by 20.3, 2.4 percent and the reduction on average on-ramp delay by 1.8, 33.9 percent, respectively. Compared with Stratified Zone, AGENT was found to increase average on-ramp speed by up to 16 percent. These combined findings clearly demonstrate the potential of a multi-agent ramp metering algorithm in enhancing freeway performance and reducing congestion. This thesis has successfully achieved its stated objectives by demonstrating the feasibility of applying a decentralised approach to a ramp metering control system using a multi-agent concept and evaluating its performance against other well-known ramp metering algorithms. The study also successfully achieved its secondary objectives which included advancing the state of knowledge in the calibration and validation of traffic simulation model for freeway network; enhancing the procedures for ramp metering rate determination; formulating methodologies and frameworks for determining and calibrating parameters for ramp metering algorithm, and formulating general methodologies and frameworks that can be used to evaluate the performance of Intelligent Transport Systems applications.

This paper is concerned with the macroscopic modeling, simulation of traffic flow on junctions and application of traffic control on intersections. More precisely, we deal with a generic class of second order models, known in the literature as the GSOM family. While classical approaches focus on the Eulerian point-of-view, here we recast the model using its Lagrangian coordinates and we treat the junction as a specific discontinuity in Lagrangian framework. We propose a complete numerical methodology based on a finite difference scheme for solving such a model and we provide an application of traffic control on intersections and some numerical examples. Notice that in our scheme vehicles are discretized into packets of N particles. Hence, our scheme can be seen as a microscopic car-following model for the particular choice of N = 1.