Logarithmic Mean Divisia Index Decomposition of CO2 Emissions from Urban Passenger Transport: An Empirical Study of Global Cities from 1960–2001

The urban transport sector has become one of the major contributors to global CO2 emissions. This paper investigates the driving forces of changes in CO2 emissions from the passenger transport sectors in different cities, which is helpful for formulating effective carbon-reduction policies and strategies. The logarithmic mean Divisia index (LMDI) method is used to decompose the CO2 emissions changes into five driving determinants: Urbanization level, motorization level, mode structure, energy intensity, and energy mix. First, the urban transport CO2 emissions between 1960 and 2001 from 46 global cities are calculated. Then, the multiplicative decomposition results for megacities (London, New York, Paris, and Tokyo) are compared with those of other cities. Moreover, additive decomposition analyses of the 4 megacities are conducted to explore the driving forces of changes in CO2 emissions from the passenger transport sectors in these megacities between 1960 and 2001. Based on the decomposition results, some effective carbon-reduction strategies can be formulated for developing cities experiencing rapid urbanization and motorization. The main suggestions are as follows: (i) Rational land use, such as transit-oriented development, is a feasible way to control the trip distance per capita (ii) fuel economy policies and standards formulated when there are oil crisis are effective ways to suppress the increase of CO2 emissions, and these changes should not be abandoned when oil prices fall and (iii) cities with high population densities should focus on the development of public and non-motorized transport. Document type: Articl

Optimisation des services de "ridesourcing" pour le déploiement futur des véhicules autonomes et connectés en milieu urbain

On-demand ridesourcing services have become increasingly popular due to their convenience. There are some debates claiming that ridesourcing services could increase congestion and pollution. Ridesplitting, a new shared mobility service, is a more sustainable travel mode for improving traffic efficiency and reducing air pollution. Therefore, the motivation of this study is to propose an optimization framework for the shared mobility system (SMS). The originality and innovative aspects of this dissertation could be summarized according to 2 perspectives. For the value of theory and methodology, the proposed framework for the SMS could provide a systematic methodology for the modelling and simulation. The proposed artificial intelligent algorithms could provide a better understanding for the researches of travel behaviors analysis and spatiotemporal modelling. For the value of practical application, the proposed shared mobility system could help improve ridesplitting service to build a low carbon transport, which could incorporate CAVs for the future mobility.Les services de transport à la demande sont de plus en plus populaires en raison de leur commodité. Cependant, certaines études font apparaitre que ces services pourraient augmenter les congestions et le niveau de pollution. Le ridesplitting, un nouveau service de mobilité partagée, est un moyen plus durable de se déplacer pour améliorer l'efficacité des transports et réduire les émissions de polluants. Dans ce contexte, ce travail propose un cadre d'optimisation pour un Système de Mobilité Partagée (SMP). L'originalité et les aspects innovants développés dans cette thèse sont aussi bien théoriques et méthodologiques, qu’appliqués. Du point de vue théorique et méthodologique, le cadre proposé pour le SMP fournit une méthodologie systématique et générique pour la modélisation et la simulation. Les algorithmes d’IA proposés permettent d’analyser et de mieux comprendre les comportements de déplacement des usagers et leur modélisation spatio-temporelle. Pour ce qui est de l’application pratique de ces travaux, le SMP proposé peut améliorer significativement les services de ridesplitting tout en réduisant l’empreinte carbone. De plus, ce SMP est facilement extrapolable aux CAV et aux futurs systèmes de mobilités

Optimization of ridesourcing services for the future deployment of connected and autonomous vehicles in urban areas

Les services de transport à la demande sont de plus en plus populaires en raison de leur commodité. Cependant, certaines études font apparaitre que ces services pourraient augmenter les congestions et le niveau de pollution. Le ridesplitting, un nouveau service de mobilité partagée, est un moyen plus durable de se déplacer pour améliorer l'efficacité des transports et réduire les émissions de polluants. Dans ce contexte, ce travail propose un cadre d'optimisation pour un Système de Mobilité Partagée (SMP). L'originalité et les aspects innovants développés dans cette thèse sont aussi bien théoriques et méthodologiques, qu’appliqués. Du point de vue théorique et méthodologique, le cadre proposé pour le SMP fournit une méthodologie systématique et générique pour la modélisation et la simulation. Les algorithmes d’IA proposés permettent d’analyser et de mieux comprendre les comportements de déplacement des usagers et leur modélisation spatio-temporelle. Pour ce qui est de l’application pratique de ces travaux, le SMP proposé peut améliorer significativement les services de ridesplitting tout en réduisant l’empreinte carbone. De plus, ce SMP est facilement extrapolable aux CAV et aux futurs systèmes de mobilités.On-demand ridesourcing services have become increasingly popular due to their convenience. There are some debates claiming that ridesourcing services could increase congestion and pollution. Ridesplitting, a new shared mobility service, is a more sustainable travel mode for improving traffic efficiency and reducing air pollution. Therefore, the motivation of this study is to propose an optimization framework for the shared mobility system (SMS). The originality and innovative aspects of this dissertation could be summarized according to 2 perspectives. For the value of theory and methodology, the proposed framework for the SMS could provide a systematic methodology for the modelling and simulation. The proposed artificial intelligent algorithms could provide a better understanding for the researches of travel behaviors analysis and spatiotemporal modelling. For the value of practical application, the proposed shared mobility system could help improve ridesplitting service to build a low carbon transport, which could incorporate CAVs for the future mobility

Improving Ridesplitting Service Using Optimization Procedures on Shareability Network: A Case Study of Chengdu, China

ITSC 2019, IEEE Intelligent Transportation Systems Conference, Auckland, NOUVELLE-ZELANDE, 27-/10/2019 - 30/10/2019; Ridesourcing services play a crucial role in metropolitan transportation systems and aggravate urban traffic congestion and air pollution. Ridesplitting is one possible way to reduce these adverse effects and improve transport efficiency. This paper aims to explore the potential of ridesplitting in peak hours using empirical ridesourcing data of Chengdu, China provided by DiDi Chuxing. A ridesplitting trip identification algorithm based on a shareability network is developed to quantify the potential of ridesplitting. Then, we evaluate the gap between the potential and actual scales of ridesplitting, which the literature has not yet reported. We compare the potential of ridesplitting under three different objectives. The results show that the objective of minimizing the total travel cost produces better performance than the objectives of maximizing shared trips and time savings. Under the objective of maximizing cost savings, the percentage of potential cost savings is 18.47% with an average delay of 4.76 minutes, whereas the actual percentage is 1.22% with an average delay of 9.86 minutes. The potential percentage of shared trips is 90.69%, while the actual percentage is 7.85%. Furthermore, the potential time savings can reach 25.75%, while the actual time savings are 2.38% in the real world. The findings of this study can help transportation management agencies and ridesourcing companies develop sensible policies to improve ridesplitting services

Impact Evaluation of Cyberattacks on Connected and Automated Vehicles in Mixed Traffic Flow and Its Resilient and Robust Control Strategy

Connected and automated vehicles (CAVs) present significant potential for improving road safety and mitigating traffic congestion for the future mobility system. However, cooperative driving vehicles are more vulnerable to cyberattacks when communicating with each other, which will introduce a new threat to the transportation system. In order to guarantee safety aspects, it is also necessary to ensure a high level of information quality for CAV. To the best of our knowledge, this is the first investigation on the impacts of cyberattacks on CAV in mixed traffic (large vehicles, medium vehicles, and small vehicles) from the perspective of vehicle dynamics. The paper aims to explore the influence of cyberattacks on the evolution of CAV mixed traffic flow and propose a resilient and robust control strategy (RRCS) to alleviate the threat of cyberattacks. First, we propose a CAV mixed traffic car-following model considering cyberattacks based on the Intelligent Driver Model (IDM). Furthermore, a RRCS for cyberattacks is developed by setting the acceleration control switch and its impacts on the mixed traffic flow are explored in different cyberattack types. Finally, sensitivity analyses are conducted in different platoon compositions, vehicle distributions, and cyberattack intensities. The results show that the proposed RRCS of cyberattacks is robust and can resist the negative threats of cyberattacks on the CAV platoon, thereby providing a theoretical basis for restoring the stability and improving the safety of the CAV

Atomic cerium modulated palladium nanoclusters exsolved ferrite catalysts for lean methane conversion

Abstract The active and stable palladium (Pd) based catalysts for CH4 conversion are of great environmental and industrial significance. Herein, we employed N2 as an optimal activation agent to develop a Pd nanocluster exsolved Ce‐incorporated perovskite ferrite catalyst toward lean methane oxidation. Replacing the traditional initiator of H2, the N2 was found as an effective driving force to selectively touch off the surface exsolution of Pd nanocluster from perovskite framework without deteriorating the overall material robustness. The catalyst showed an outstanding T50 (temperature of 50% conversion) plummeting down to 350°C, outperforming the pristine and H2‐activated counterparts. Further, the combined theoretical and experimental results also deciphered the crucial role that the atomically dispersed Ce ions played in both construction of active sites and CH4 conversion. The isolated Ce located at the A‐site of perovskite framework facilitated the thermodynamic and kinetics of the Pd exsolution process, lowering its formation temperature and promoting its quantity. Moreover, the incorporation of Ce lowered the energy barrier for cleavage of C─H bond, and was dedicated to the preservation of highly reactive PdOx moieties during stability measurement. This work successfully ventures uncharted territory of in situ exsolution to provide a new design thinking for a highly performed catalytic interface