Cell Transmission Model

This paper presents a simple representation of traffic on a highway with a single entrance and exit. The representation can be used to predict traffic's evolution over time and space, including transient phenomena such as the building, propagation and dissipation of queues. The easy-to-solve difference equations used to predict traffic's evolution are shown to be the discrete analog of the differential equations arising from a special case of the hydrodynamic model of traffic flow. The proposed method automatically generates appropriate changes in density at locations where the hydrodynamic theory would call for a shockwave; i.e. a jump in density such as those typically seen at the end of every queue. The complex side calculations required by classical methods to keep track of shockwaves are thus eliminated. The paper also shows how the equations can mimic the real-life development of stop-and-go traffic within moving queues. The model predicts that the oscillation patter...

This paper reveals and explores the flow characteristics of airport surface network on both mesoscopic and macro-scopic levels. We propose an efficient modeling approach based on the cell transmission model for simulating the spatio-temporal evolution of flow and congestion on taxiway and apron networks. The existence of link-based fundamental diagram that expresses the functional relationship between link density and flow is demonstrated using empirical data collected in Guangzhou Baiyun airport. The proposed CTM-based network model is shown to be an efficient and accurate method capable of supporting air traffic prediction and decision support. In addition, using both CTM-based computation and empirical data, we further reveal the existence of an aggregate relationship between traffic density and runway throughput, which is referred to as macroscopic fundamental diagram (MFD) in the literature of road traffic. The MFD on the airport surface is analyzed in depth, and utilized to devise robust off-block control strategies under uncertainties, which is shown to significantly outperform existing off-block control methods.

This paper discusses a dynamic evacuation network optimization problem that incorporates lane reversal and crossing elimination strategies. These two lane-based planning strategies complement one another by increasing capacity in specific directions through the evacuation network. A bi-level network optimization model is formulated, in which the upper level aims at optimizing the network evacuation performance subject to the lane-reversal and crossing-elimination constraints and the lower level conveys a cell transmission-based dynamic traffic assignment problem. An integrated Lagrangian relaxation and tabu search method is devised for approximating optimal problem solutions through an iterative optimization-evaluation process. The numerical results of implementing the developed modeling and solution approach to a synthetic network and a real-world example application justify its theoretical and practical value.

This paper formulates the problem of real-time estimation of tra-c state in freeway networks by means of the particle flltering framework. A particle fllter (PF) is developed based on a recently proposed speed-extended cell-transmission model of freeway tra-c. The freeway is considered as a network of components representing difierent freeway stretches called segments. The evolution of the tra-c in a segment is modeled as a dynamic stochastic system, in∞uenced by states of neighbor segments. Measurements are received only at boundaries between some segments and averaged within possibly irregular time intervals. This limits the measurement update in the PF to only these time instants when a new measurement arrives, while in between measurement updates any simulation model can be used to describe the evolution of the particles. The PF performance is validated and evaluated using synthetic and real tra-c data from a Belgian freeway. An Unscented Kalman fllter is also presented. A compa...

The main objectives of Task Order 4136 are (1) the design of improved freeway on-ramp metering strategies that make use of recent developments in traffic data collection, traffic simulation, and control theory, and (2) the testing of these methods on a 14-mile segment of Interstate 210 Westbound in southern California. To date, the major accomplishments of this project include (i)

There have been different approaches which have been proposed to understand the mechanism of traffic congestion propagation. In this paper, we use the cell transmission model and apply it to simulate the formation and dissipation of traffic jams at the microscopic level. In particular, our model focuses on jam propagation and dissipation in two-way rectangular grid networks. In the model, the downstream exit of the link is channelized to represent the interactions of vehicles in different directions. We have used traffic jam size and congestion delay to measure jam growth and dispersal. Numerical examples exploring the impact of model parameters on jam growth and congestion delay are provided. The simulation results show that there are two strategies to minimize jam size and reduce time for jam dissipation: (1) reduce the length of channelized area and, (2) allocate the stopline widths for all directions in the same ratio as the demands. Furthermore, we obtain some new results about gridlock and discuss the effect of incident position and link length on jam propagation.

Intelligent Transportation Systems (ITS) arethe majorsolutions torelieving traffic congestion. Traffic signal control androuteguidance, twokeysubsystems of ITS,jointly influence traffic flowintimeandspace. Over thelast threedecades, combined traffic signal control and routeguidance (CTSCRG)hasbeenresearch emphasis. Therearetwokindsofmodelfordescribing CTSCRG: Multiple UserClass Traffic Assignment ModelandDiscrete ChoiceModel.Theintent ofthepaperistocomparethe traffic equilibrium results ofthesetwo models.For describing routeguidance systemwhichiscomposedof guideddriverand non-guided driver, theconceptof multiple userclass (MUC)isintroduced. InMUC traffic assignment model, guided drivers aremodeled ascomplete information userequilibrium traffic assignment, andnon- guided drivers aremodeledasstochastic userequilibrium traffic assignment. Indiscrete choice model, guided drivers andnon-guided drivers aremodeled asstochastic dynamic useroptimumandroutechoice modelislogit model.The percep...

ABSTRACT The primary objective of this paper was to develop a control strategy of variable speed limits (VSLs) to reduce rear-end collision risks near freeway recurrent bottlenecks. The risks of rear-end collisions were estimated using a crash risk prediction model that is specifically developed for rear-end collisions in freeway bottleneck areas. The effects of the VSL control strategy were evaluated using a cell transmission model. Several control factors were tested, including the start-up threshold of the collision likelihood, the target speed limit, the speed change rate, and the speed difference between adjacent links. A genetic algorithm was used to optimize critical control factors. For the high demand scenario, the proposed control strategy used 25% of the maximum collision likelihood for the start-up threshold, 35 mi/h for the target speed limit, 10 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. For the moderate demand scenario, the strategy used 20% of the maximum collision likelihood for the start-up threshold, 40 mi/h for the target speed limit, 15 mi/h per 30 s for the speed change rate, and 10 mi/h for the speed difference between different links. The results of comparative analyses suggested that the proposed control strategy outperformed other strategies in reducing the rear-end collision risks near freeway recurrent bottlenecks. With the proposed control strategy, the VSL control reduced the rear-end crash potential by 69.84% for the high demand scenario and by 81.81% for the moderate demand scenario.