The indirect environmental impacts of transportation disruptions in urban mobility are frequently overlooked due to a lack of appropriate assessment methods. Consequential Life Cycle Assessment (CLCA) is a method to capture the environmental consequences of the entire cause and effect chain of these disruptions but has never been adapted to transportation disruption at the city scale. This paper proposes a mathematical formalization of CLCA applied to a territorial mobility change. The method is applied to quantify the impact on climate change of the breakthrough of free-floating e-scooters (FFES) in Paris. An FFES user survey is conducted to estimate the modal shifts due to FFES. Trip substitutions from all the Parisian modes concerned are considered -personal or shared bicycles and motor scooters, private car, taxi and ride-hailing, bus, streetcar, metro and RER (the Paris metropolitan area mass rapid transit system). All these Parisian modes are assessed for the first time using LCA.Final results estimate that over one year, the FFES generated an extra thirteen thousand tons of CO2eq under an assumption of one million users, mainly due to major shifts coming from lower-emitting modes (60% from the metro and the RER, 22% from active modes). Recommendations are given to enhance their carbon footprint. A scenario analysis shows that increasing the lifetime mileage is insufficient to get a positive balance: reducing drastically servicing emissions is also required. A sensitivity analysis switching the French electricity mix for eleven other country mixes suggests a better climate change effect of the FFES in similar metropolitan areas with higher electricity carbon intensity, such as in Germany and China. Finally, the novelty and the limits of the method are discussed, as well as the results and the role of e-scooters, micromobility, and shared vehicles towards sustainable mobility.
This paper presents a mathematical model for the train dynamics in a mass-transit metro line system with one symmetrically operated junction. We distinguish three parts: a central part and two branches. The tracks are spatially discretized into segments (or blocks) and the train dynamics are described by a discrete event system where the variables are the k th departure times from each segment. The train dynamics are based on two main constraints: a travel time constraint modeling theoretic run and dwell times, and a safe separation constraint modeling the signaling system in case where the traffic gets very dense. The Max-plus algebra model allows to analytically derive the asymptotic average train frequency as a function of many parameters, including train travel times, minimum safety intervals, the total number of trains on the line and the number of trains on each branch. This derivation permits to understand the physics of traffic. In a further step, the results will be used for traffic control.
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