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Palma, André de; Marchal, Fabrice

doi:10.1023/a:1020847511499

Cited by 93 publications

(6 citation statements)

References 8 publications

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“…( 2) to determine the evolution of congestion over time and the cost of each user. In order to provide some stability, the commonly used (Peeta and Mahmassani, 1995;de Palma and Marchal, 2002) method of successive averages (MSA) is applied on the departure time decisions. At each iteration, the departure time decisions of a fraction of the population (e.g.…”

Section: Description Of the Simulationmentioning

confidence: 99%

The morning commute in urban areas with heterogeneous trip lengths

Lamotte

Geroliminis

2018

Transportation Research Part B: Methodological

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Section: Description Of the Simulationmentioning

confidence: 99%

The morning commute in urban areas with heterogeneous trip lengths

Lamotte

Geroliminis

2018

Transportation Research Part B: Methodological

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“…This model is one of the first transport simulations in France to be conducted at the regional scale. Earlier applications remain limited to the Paris and Lyon regions [7][8][9][10]. The study region covers the north of France with a population of four million inhabitants distributed over 1546 administrative jurisdictions.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Transport Model to Evaluate Transport Policies in the North of France

Kilani¹,

Diop

Wolf

2022

Sustainability

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We developed a passenger transport model for the North of France and used it to discuss the impacts of some policies focusing on the limitations of polluting gas emissions and congestion. The model is calibrated for the North of France and includes both urban and intercity trips. Four transport modes are considered: walking, biking, public transport and private cars. To some extent, the combination of these modes is possible. The model is calibrated to match mode shares and the dynamic of congestion along a full day. The simulations are conducted within the MATSim framework. We evaluate the impacts, on traffic flows and polluting gas emissions, of two pricing reforms: free public transport and road pricing in city center of Lille (the main metropolitan area in the study region). Free public transport yields a significant modal shift towards public transport, resulting in a reduction in the usage of private cars. The road pricing scheme we have considered results in similar impacts but with limited magnitude. Overall, a significant reduction in congestion and emissions of pollutant gases can be obtained by applying convenient pricing reforms. Since we use an agent-based model, we are able to identify the specific location of the main impacts on the network.

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“…Some previously mentioned models have explored this research direction. For example, METROPOLIS assumed that the perception of network conditions follows Bayes' Law [31,32] and a Markov process of order one has been introduced to combine historical and instantaneous information. An information center is introduced to process travel knowledge and provides travelers with the expected journey travel time, which represents the collective history of all travelers.…”

Section: Introductionmentioning

confidence: 99%

“…The potential for new individual-based route choice models is driven not only by policy applications, but also due to advances in understanding of the mechanisms of travel behavior, fast-growing computational power, and increasing data availability. Many route choice models have been developed and integrated into travel demand models applicable on regional networks [2,[27][28][29][30][31][32][33][34]. The most frequently cited models in the literature are summarized and compared in Table 1.…”

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confidence: 99%

Agent-Based Route Choice with Learning and Exchange of Information

Zhu

Levinson

2018

Urban Science

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Planning models require consideration of travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g., willingness to switch routes with potential savings) in a differentiated market (where routes have varying tolls and levels of service). This paper proposes to explicitly model the formation and spreading of spatial knowledge among travelers, following cognitive map theory. An agent-based route choice (ARC) model was developed to track choices of each individual decision-maker in a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied to both the Sioux Falls and Chicago sketch networks. Comparisons between ARC and existing models (user equilibrium (UE) and stochastic user equilibrium (SUE)) on both networks show ARC is valid and computationally tractable. In brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of transportation planning under an integrated framework.Urban Sci. 2018, 2, 58 2 of 17 accurate description of this complex mechanism, so they are incapable of addressing important issues such as equity. Many researchers [9][10][11][12] have emphasized the importance of equity as a consequence of road pricing and pointed out "equity is an individual, not a group, problem" [13]. To account for this complexity, transport economists and policy makers have long advanced their focus from first-best prices with homogeneous network assumptions to second-best prices under heterogeneity for both network users and service providers [14][15][16][17][18][19][20], which requires explicitly modeling individual travelers' route choice behavior. Although some researchers have studied these problems on small networks, a behaviorally based model, which is not only sufficiently accurate but also applicable on a large network, does not exist [8].It is crucial to recognize that travel decisions are based on travelers' knowledge about the network. Travelers, limited in their capability of acquiring, processing and storing spatial knowledge, can only consider the routes they know. This is the very reason that engineers try to assist travelers to make decisions by providing additional information about the network through technologies like ATIS. As indicated by [2], ATIS cannot be well evaluated without explicitly accounting for the "heterogeneity in behavior" and "the presence of dynamic learning and adjustment processes in user behavior". Other researchers [21,22] also emphasized the role of information and learning in traveler behavior such as route choice decisions. However, limited work has been done to systematically model the mechanism of acquiring, processing, storing of spatial knowledge and its communication among travelers.The route choice model maps travel demand (traditionally defined by an origin-destination (OD) trip table, or in more recent research and practice as a list of travelers with particular attributes going betwee...

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Cited by 93 publications

References 8 publications

The morning commute in urban areas with heterogeneous trip lengths

The morning commute in urban areas with heterogeneous trip lengths

A Multimodal Transport Model to Evaluate Transport Policies in the North of France

Agent-Based Route Choice with Learning and Exchange of Information

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