Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks

This article presents two new models of Dynamic User Equilibrium that are particularly suited for ITS applications, where the evolution of vehicle flows and travel times must be simulated on large road networks, possibly in real-time. The key feature of the proposed models is the detail representation of the main congestion phenomena occurring at nodes of urban networks, such as vehicle queues and their spillback, as well as flow conflicts in mergins and diversions. Compared to the simple word of static assignment, where only the congestion along the arc is typically reproduced through a separable relation between vehicle flow and travel time, this type of DTA models are much more complex, as the above relation becomes non-separable, both in time and space. Traffic simulation is here attained through a macroscopic flow model, that extends the theory of kinematic waves to urban networks and non-linear fundamental diagrams: the General Link Transmission Model. The sub-models of the GLTM, namely the Node Intersection Model, the Forward Propagation Model of vehicles and the Backward Propagation Model of spaces, can be combined in two different ways to produce arc travel times starting from turn flows. The first approach is to consider short time intervals of a few seconds and process all nodes for each temporal layer in chronological order. The second approach allows to consider long time intervals of a few minutes and for each sub-model requires to process the whole temporal profile of involved variables. The two resulting DTA models are here analyzed and compared with the aim of identifying their possible use cases. A rigorous mathematical formulation is out of the scope of this paper, as well as a detailed explanation of the solution algorithm. The dynamic equilibrium is anyhow sought through a new method based on Gradient Projection, which is capable to solve both proposed models with any desired precision in a reasonable number of iterations. Its fast convergence is essential to show that the two proposed models for network congestion actually converge at equilibrium to nearly identical solutions in terms of arc flows and travel times, despite their two diametrical approaches wrt the dynamic nature of the problem, as shown in the numerical tests presented here

A state of the art of sensor location, flow observability, estimation, and prediction problems in traffic networks

A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations

Strategy-based dynamic assignment in transit networks with passenger queues

This thesis develops a mathematical framework to solve the problem of dynamic assignment in densely connected public transport (or transit – the two words are interchangeably used) networks where users do not time their arrival at a stop with the lines’ timetable (if any is published). In the literature there is a fairly broad agreement that, in such transport systems, passengers would not select the single best itinerary available, but would choose a travel strategy, namely a bundle of partially overlapping itineraries diverging at stops along different lines. Then, they would follow a specific path depending on what line arrives first at the stop. From a graph-theory point of view, this route-choice behaviour is modelled as the search for the shortest hyperpath (namely an acyclic sub-graph which includes partially overlapping single paths) to the destination in the hypergraph that describes the transit network. In this thesis, the hyperpath paradigm is extended to model route choice in a dynamic context, where users might be prevented from boarding the lines of their choice because of capacity constraints. More specifically, if the supplied capacity is insufficient to accommodate the travel demand, it is assumed that passenger congestion leads to the formation of passenger First In, First Out (FIFO) queues at stops and that, in the context of commuting trips, passengers have a good estimate of the expected number of vehicle passages of the same line that they must let go before being able to board. By embedding the proposed demand model in a fully dynamic assignment model for transit networks, this thesis also fills in the gap currently existing in the realm of strategy-based transit assignment, where – so far – models that employ the FIFO queuing mechanism have proved to be very complex, and a theoretical framework for reproducing the dynamic build-up and dissipation of queues is still missing.Open Acces

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented

A Review of Models of Urban Traffic Networks (With Particular reference to the Requirements for Modelling Dynamic Route Guidance Systems)

This paper reviews a number of existing models of urban traffic networks developed in Europe and North America. The primary intention is to evaluate the various models with regard to their suitability to simulate traffic conditions and driver behavior when a dynamic route guidance system is in operation

Models for dynamic network loading and algorithms for traffic signal synchronization

The effectiveness of optimization strongly relies on the underlying model of the phenomenon. According to this, a considerable effort has been spent in improving the General Link Transmission Model (Gentile, 2008) to address urban networks, intersection and lane modelling and multimodal simulation. A genetic algorithm with a formulation tailored on the signal coordination problem has been integrated with the simulation engine. So, a practical and effective multi-objective optimization tool for traffic signal coordination is here presented

A simulation model for truck-shovel operation

A truck-shovel mining system is a flexible mining method commonly used in surface mines. Both simulation and queuing models are commonly used to model the truckshovel mining operation. One fundamental problem associated with these types of models is that most of the models handle the truck haulage system as macroscopic simulation models, which ignore the fact that a truck as an individual vehicle unit dynamically interacts not merely with other trucks in the system but also with other elements of the traffic network. Some important operational factors, such as the bunching effect and the influence of the traffic intersections, are either over simplified or ignored in such a macroscopic model. This thesis presents a developed discrete-event truck-shovel simulation model, referred to as TSJSim (Truck and Shovel JaamSim Simulator), based on a microscopic traffic and truck-allocation approach. The TSJSim simulation model may be used to evaluate the Key Performance Indicators (KPIs) of the truck-shovel mining system in an open pit mine. TSJSim considers a truck as an individual traffic vehicle unit that dynamically interacts with other trucks in the system as well as other elements of the traffic network. TSJSim accounts for the bunching of trucks on the haul routes, practical rules at intersections, multiple decision points along the haul routes as well as the influence of the truck allocation on the estimated queuing time. TSJSim also offers four truck-allocation modules: Fixed Truck Assignment (FTA), Minimising Shovel Production Requirement (MSPR), Minimising Truck Waiting Time (MTWT) and Minimising Truck Semi-cycle Time (MTSCT) including Genetic Algorithm (GA) and Frozen Dispatching Algorithm (FDA)