Elaboration d'un système de navigation auto-alimenté (SNA) et évaluation de ses performances

This research deals with self-supplied navigation systems (SNS), a combination of the technology of gathering travel time using floating car data (FCD) and dynamic route guidance (DRG). Using similar on-board equipment, these two systems can easily be combined in the same vehicle. The main goal is to prove the relationship between the percentage of SNS-equipped vehicles and the performance that can be expected from the use of this technology. After the description of the SNS architecture, an evaluation method based on microscopic traffic simulation results is proposed. The different components of a SNS are introduced, particularly the statistics and treatment module, which estimates and predicts travel times from the data transmitted by equipped vehicles. An innovative approach, resulting from the disaggregated observation of these travel times, is proposed in order to improve existing estimation techniques. The description of the parameters influencing this estimation's performance is followed by an analysis of their combined impact. It stresses the necessity of adopting a combination of these parameters, depending on the equipment rate of the vehicles, in order to maximise the estimation's precision. Before the comparison between the performances of equipped and non-equipped vehicles, based on the Lausanne city centre road network, the realism of existing traffic assignment models is analysed. As a consequence, a new alternative for traffic assignment is proposed, consisting of an iterative approach based on the use of historic knowledge of a "typical" day and on a differentiation of three driver categories: standard, expert and tourist. The SNS performance evaluation, mainly in terms of travel time, shows that an equipment rate of only 1 to 2 0/00 is sufficient in order for equipped vehicles to show similar performances to standard drivers, this category representing the majority of drivers. An equipment rate of 5 to 50 0/00 is needed in order to pass above the expert category, which has a perfect knowledge of the road network. For higher equipment rates the benefit compared with the other drivers is less noticeable, but the overall performance of all vehicles is highly improved. Finally, a behaviour study of SNS-equipped vehicles in the case of an incident on the network shows certain limits implied by the fact that the guided vehicles and the ones providing traffic data are the same ones

Simulating fuel consumption and vehicle emissions in an Australian context

Road transport is a major source of air pollution and greenhouse gas emissions around the world. There is an increasing interest in accurate information on local vehicle emission levels for policy development and sustainable traffic management. Previous studies have shown that emission predictions for the Australian situation need to reflect both the Australian fleet and driving behaviour to avoid unreliable outcomes. This paper discusses a new Australian vehicle emission software (PΔP) and a case-study where traffic simulation software (Aimsun) is combined with PΔP to demonstrate how consistent results can be achieved for the Australian situation. The case-study is an Australian city modelled using the microscopic simulator to generate the required trajectory data of each individual vehicle for the emission model. The simulation results are used in a number of ways: to assess the impacts of urban driving behaviour on fuel consumption, to create maps showing where and when elevated emission levels occur and to compare results with another program (COPERT Australia). The paper will also discuss where further research is required

Framework for Traffic Pattern Identification: Required Step for Short-term Forecasting

In the world of transport management, the term ‘anticipation’ is gradually replacing ‘reaction’. Indeed, the ability to forecast traffic evolution in a network should ideally form the basis for many traffic management strategies and multiple ITS applications. Real-time prediction capabilities are therefore becoming a concrete need for the management of networks, both for urban and interurban environments, and today’s road operator has increasingly complex and exacting requirements. Recognising temporal patterns in traffic or the manner in which sequential traffic events evolve over time have been important considerations in short-term traffic forecasting. However, little work has been conducted in the area of identifying or associating traffic pattern occurrence with prevailing traffic conditions. This paper presents a framework for detection pattern identification based on finite mixture models using the EM algorithm for parameter estimation. The computation results have been conducted taking into account the traffic data available in an urban network

Safety indicators for microsimulation-based assessments

In the field of ITS applications evaluation, micro-simulation is becoming more and more a useful and powerful tool. In the evaluation process, one of the most important steps is the safety analysis. For that purpose, classical micro-simulation outputs give some helpful information, but which aren’t sufficient for an accurate analysis in many cases. Nevertheless, the microscopic level of traffic description offers the possibility of tracking the simulated vehicles getting at each time step their relative position, speed and deceleration. This paper explains how a safety indicator can be calculated with these different parameters. This safety indicator is used in a ramp metering case study to illustrate the utility of such output for a safety analysis. However, this indicator is limited to the linear collision probability and gives therefore no information on crossing trajectories conflicts like in junctions. On the other hand the likelihood of an incident to happen depends not only on traffic conditions but on the influence of many other factors as for example the geometry of the road, the visibility or the pavement conditions (wet, dry, etc.). When significant statistical information is available an estimation of the probability of an incident to happen can be computed, and used in microsimulation analysis. The paper is completed with the development and testing of hierarchical logit based model to estimate this probability.Peer ReviewedPostprint (published version

Present and future methodology for the implementation of decision support systems for traffic management

Real-time predictions are an indispensable requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. The combination of predicting the state of the network and the evaluation of different traffic management strategies in the short term future allows system managers to anticipate the effects of traffic control strategies ahead of time in order to mitigate the effect of congestion. This paper presents the current framework of decision support systems for traffic management based on short and medium-term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies

Combining mesoscopic and microscopic simulation in an integrated environment as a hybrid solution

The evaluation of advanced Intelligent Transportation Systems, and particularly those which involve real–time traffic management, requires a network-wide assessment of their impact as opposed to an isolated analysis of key intersections. To support such assessments, an integrated simulation environment that allows the use of different modeling levels (e.g., macro-meso-micro) offers undeniable advantages. One of the advantages is that traffic assignment results produced by any type of network loading modeling can be stored and reused for another simulation run. But even in an integrated environment with separate models, deciding between microscopic or mesoscopic was until recently a necessary and difficult choice. On the one hand, microscopic traffic simulation models emulate the dynamics of individual vehicles in a detailed network representation based on car-following, lane changing, and gap acceptance models. They also account explicitly for traffic control. As such, they are very appropriate for operational analysis due to the detail of information provided by the simulator. However, they have a significant calibration and computational cost. On the other hand, mesoscopic models combine simplified flow dynamics with explicit treatment of interrupted flows at intersections and allow modeling of large networks with high computational efficiency. However, the loss of realism implied by a mesoscopic model makes it necessary to emulate detailed outputs; for instance, de-tector measurements or instantaneous emissions. Some outputs, such as the number of start-stops or the exact location of con-gestion within a section elude even the most detailed mesoscopic simulators. This analysis gives rise to the need to combine meso and micro approaches into new concurrent hybrid traffic simulators where very large-scale networks are modeled mesoscopically and areas of complex interactions benefit from the finer detail of microscopic simulation. Combining an event-based mesoscopic model with a more detailed, time-sliced microsimulator raises consistency problems within the network rep-resentation and the meso-micro-meso transitions