Real-time Traffic Information Provision Research Articles

Active traffic management in urban areas: Is it effective for professional drivers? The case of variable message signs

Active traffic management is the ability to dynamically manage congestion based on prevailing and predicted traffic conditions. Several technologies and algorithms have been employed to develop a set of tools utilised by Traffic Control/Traffic Management Centres with the aim to mitigate disruptions and improve traffic flow in the road networks. One such tool, belonging to the Advanced Traveller Information Systems category is Variable Message Signs. Variable Message Signs are located in major roads and provide real-time traffic information to the drivers so as to modify their behaviour considering the prevailing traffic conditions. To this end, the question arises. Is active traffic management in urban road networks – as currently provided by Variable Message Signs – effective for professional drivers? In plain words, do professional drivers modify their route choice, i.e. divert under the provision of real-time traffic information? A stated preference survey was performed to truck and taxi drivers to identify their response to information provided by Variable Message Signs. Results, in the form of ordered probit models indicated that the provided information is not that relevant to the investigated driver categories, as it is for passenger car drivers.

A comparative study of centralised and decentralised architectures for network traffic control

ABSTRACTThis paper presents a comparative study of centralised and decentralised architectures for managing urban road network efficiency with consideration of users’ responses and uncertainties with respect to prevailing traffic conditions. The control systems are applied to different network topologies with different levels and spatial distributions of traffic demand. The study reveals that the computationally effective decentralised systems could perform almost as well as the conventional centralised one when users’ responses are taken into account with provision of real-time traffic information. This generates new insight on cooperative transport management with applications of information and communication technology.

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

Real-Time Traffic Information Provision Using Individual Probe and Five-Minute Aggregated Data

Simulation study of the impact of local real-time traffic information provision strategy in connected vehicle systems

Abstract Current vehicle navigation systems usually rely on global real-time traffic information. They suggest the best paths to travelers through an independent and selfish-routing mechanism. Existing studies show that such systems have a great potential to cause traffic congestions oscillating among several alternative corridors, i.e., iteratively leading exceeding traffic into light traffic corridors and making congestion on one and then another within a short time period. To address this issue, this research proposes a local information provision strategy, given travelers are still guided by an independent and selfish-routing mechanism. The coverage of the real-time information provision is identified by the minimum bounding rectangle, and the ellipse method respectively. The benefits of the proposed strategy in system level are investigated by the simulation studies built upon the Borman expressway network. The simulation results indicate that as the local travel time information provision strategy is used, it may save 10–25% of the system travel time resulting from global information provision strategy under medium or high network traffic loads. Moreover, the sensitivity analyses indicate that the merits of the local travel time information provision strategy are more significant as the penetration of smart vehicles increases over the network. Thus, the proposed local information provision strategy outperforms global information provision strategy in system level network performance.

A dynamic taxi traffic assignment model: A two-level continuum transportation system approach

This paper proposes a two-level continuum transportation system approach to modeling a dynamic taxi traffic assignment (DTTA) problem in a dense network with real-time traffic information provision and three types of vehicles, including private cars, occupied taxis, and vacant taxis. The proposed approach treats the dense network as a continuum in the first level, in which private car and occupied taxi drivers are free to choose their paths in a two-dimensional continuous space. The proposed approach also divides the modeling region into many identical squares to form a cell-based network in the second level, in which the cells are classified into two categories: target cell with an acceptable expected rate of return (EROR) to vacant taxi drivers and non-target cell with an unacceptable EROR. The EROR associated with a cell is the ratio of the cumulative expected profit of a taxi driver who successfully picks up a customer during the customer search that starts from that cell to the sum of expected search time for this customer and expected occupied travel time to serve this customer. Based on the cell-based network, we develop a cell-based intervening opportunity model to capture the fact that vacant taxi drivers can meet a customer on the way to their destination zones and estimate the EROR. Each vacant taxi driver has a mixed strategy to determine his/her customer-search direction according to the EROR: Each vacant taxi driver in a target cell selects its neighbor cells with maximum EROR, and each vacant taxi driver in a non-target cell selects the travel time-based shortest path to his/her target cell. Meanwhile, each private car driver chooses the path that minimizes his/her own generalized travel cost, and each occupied taxi driver chooses the path that minimizes his/her customer's generalized in-vehicle travel cost. In our model, traffic density in the system is governed by the conservation law (CL), and the flow directions of different vehicles are determined by the path-choice strategies of their drivers, which are captured by Hamilton–Jacobi (HJ) equations. Both the proposed CL and HJ equations can be solved by the Lax–Friedrichs scheme, which forms the backbone of the developed solution algorithm. Finally, numerical examples and a case study are used to demonstrate the properties of the model, the performance of the solution algorithm, and the value of using our methodology for estimating network performance.

Personalized real-time traffic information provision: Agent-based optimization model and solution framework

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time congested traffic management in a congested network. This paper proposes an agent-based optimization modeling framework to provide personalized traffic information for heterogeneous travelers. Based on a space–time network, a time-dependent link flow based integer programming model is first formulated to optimize various information strategies, including elements of where and when to provide the information, to whom the information is given, and what alternative route information should be suggested. The analytical model can be solved efficiently using off-the-shelf commercial solvers for small-scale network. A Lagrangian Relaxation-based heuristic solution approach is developed for medium to large networks via the use of a mesoscopic dynamic traffic simulator.

Route Choice Behaviour of Freeway Travellers Under Real-time Traffic Information Provision – Application of the Best Route and the Habitual Route Choice Mechanisms

This paper investigates route choice behaviour on freeways between Taipei and Taichung in Taiwan under the provision of real-time traffic information. Two types of route choice selection rules (the best-route and habitual-route) are analysed using ordered probit models to identify the major influences on freeway travellers’ route choice behaviour. The level of service associated with each route is defined as a generalised cost saving (GCS) and specified non-linearly with a threshold inherent to travellers. The marginal (dis)utility thresholds in the ‘best’ and ‘habitual’ behaviour models are identified through a goodness-of-fit grid. The results confirm that the thresholds for changing the inertia behaviour of drivers should be larger than the ones for choosing the best routes. In addition, the drivers are more likely to choose either the best or the habitual routes once the GCS are greater than the identified threshold values.

MODELLING THE IMPACTS OF TRANSPORT POLICIES TO REDUCE GREENHOUSE GAS EMISSIONS FROM URBAN FREIGHT TRANSPORT IN SYDNEY

Freight transport in urban areas has few opportunities for modal shift away from road. What policy options might there be to reduce greenhouse gas emissions from road freight vehicles? A combined travel demand, traffic network and emissions model was established to test the impacts of the generic policy initiatives 'best practice' truck fleet fuel efficiency, general reductions in peak period traffic congestion, improved traffic management, provision of real-time traffic information, infrastructure improvements, changes to industrial land use distribution, and improved vehicle load factors. Sydney was used as a case study. Whilst most policy options showed some potential for improved emissions outcomes, the best option was to reduce vehicle load factors. Policies aimed at changing the distribution of industrial land use provided mixed results, suggesting the need for complementary measures. The model is also able to analyse emissions from private vehicles.

Highway control

The world grows evermore congested but the demand for personal mobility is undiminished. The author describes how technology can keep the traveller on the move. Two obvious corrective measures would be to restrict the use of private cars and to build new roads. Both approaches are political minefields. Car owners do not welcome limitations on their personal mobility, and there is little public support for road building on the scale needed to expand capacity in line with demand. Confronted by such difficulties, government and traffic authorities are increasingly turning to modern information systems as a means of developing alternative approaches to the control of traffic and the management of demand. The author describes the SCOOT system, a real time traffic light control system, and the variable speed limit signs on the M25. The further development of such systems and concepts is the provision of real-time traffic information, which is discussed by the author.

Neural network models for automated detection of lane-blocking incidents on freeways: Cheu, Ruey Long, Ph.D. University of California, Irvine, 1994. 197 pp. Chair: Stephen G. Ritchie

A major source of urban freeway delay in the United States is non-recurring congestion caused by incidents such as accidents, disabled vehicles, spilled loads, temporary maintenance and construction activities, signal and detector malfunctions, and other special and unusual events that disrupt the normal flow of traffic. The automated detection of freeway incidents is an important function of a freeway traffic management center. Early detection of incidents is vital for formulating effective response strategies such as timely dispatch of emergency services and incident removal crews, control and routing of traffic around the incident location, and provision of real-time traffic information to motorists. A number of incident detection algorithms, based on conventional approaches, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These conventional algorithms have met with varying degree of success in their detection performance. In this research, a new incident detection technique based on an artificial neural network approach has been proposed. The objective of this research was to demonstrate the use of artificial neural network models for automated detection of lane-blocking incidents on urban freeways. The study focused on the application of neural network models in classifying traffic surveillance data obtained from inductive loop detectors, and the use of the classified output to detect an incident. Three types of neural network models were developed to detect lane-blocking incidents: the multi-layer feed-forward neural network, self-organizing feature map and adaptive resonance theory 2. The models were developed with simulation data from a study site and tested with both simulation and field data at the study site and other locations. The multi-layer feed-forward neural network was found to have the highest potential among the four models to achieve a better incident detection performance. This network consistently detected most of the lane-blocking incidents and gave a false alarm rate lower than the conventional algorithms currently in use. The results have demonstrated the potential of artificial neural network models in improving incident detection performance over currently available techniques.