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Article Dans Une Revue Transportation Research Record Année : 2023

Evaluation of Low-Traffic Neighborhoods and Scale Effects: The Paris Case Study

Résumé

This paper investigates the socio-economic impacts of the driving restriction zone policy for the Paris region, using the multi-agent transport simulation MATSim. Based on a previous work which has calibrated a first instantiation of the mobility model to the Paris region, we extend it by modeling and simulating two forms of intermodality: Park-and-Walk and Park-and-Ride. After re-calibrating the model, we evaluate two policy scenarios featuring different scales of the restriction zone, using indicators related to mobility, traffic emissions, and social cost. The analysis puts special attention on how modal shift and emissions vary across space, that is, inside and outside the policy zone. We find that the small-scale scenario with a restriction zone limited to the Paris center leads to very limited modal shift, whereas the larger-scale policy scenario encompassing the whole of Paris shifts significantly more trips (about 1.4% of total trips) from cars to public transport and other modes. In both scenarios, traffic emissions decline in the restriction zone and its near surroundings. Far from the restriction zone, however, emissions increase in the small-scale scenario but decrease in the large-scale scenario. These differences are caused by the traffic detour, modal shift, and rebound effects, the magnitudes of which vary depending on the scale of the restriction zone. The driving restrictions also cause the social cost to increase by around 10% in both scenarios. This ultimately implies a trade-off between reducing emissions and increasing user costs, with a substantially greater efficiency (larger reduction for similar cost) in the large-scale policy scenario.
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Dates et versions

hal-04099038 , version 1 (16-05-2023)

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Biao Yin, Azise Oumar Diallo, Tatiana Seregina, Nicolas Coulombel, Liu Liu. Evaluation of Low-Traffic Neighborhoods and Scale Effects: The Paris Case Study. Transportation Research Record, 2023, ⟨10.1177/03611981231170130⟩. ⟨hal-04099038⟩
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