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Achieving sustainability is a major goal of many urban transport systems. To attain an efficient, safe, and sustainable transport system, many innovative policies have been attempted in the past. Those policies often require smart technologies to assist in the implementation process and to enhance effectiveness. This paper discusses how sustainability can be promoted by embedding smart technologies in a modern transport system. In particular, this paper studies the transport system of Singapore to see how it is addressing sustainability through the use of smart technologies. Various technological initiatives in managing traffic flow, monitoring and enforcement, sharing real-time information, and managing revenues are discussed in light of their potential to address sustainability issues. The Singapore experience provides a useful reference for cities that intend to develop and promote a sustainable transport system.

Mobility is a subject of increasing importance in a time when cities have gained prominence, as they are home to over 56% of the world’s population and generate over 80% of global GDP. Urban planning principles have traditionally been developed to promote urban efficiency and enhance productivity. The emergence of ‘Smart Mobility’ has provided researchers and policy practitioners new ways to understand and plan cities. With rapid urbanization growth and the sustained mobility challenges faced in most global cities, this paper sets forth to understand and map the evolution of the concept of ‘Smart Urban Mobility’ through a bibliometric analysis and science mapping techniques using VOSviewer. In total, 6079 articles were retrieved from the Web of Science database over 5 decades, from 1968 to 2021, and divided into four sub-periods, namely 1968 to 2010, 2011 to 2015, 2016 to 2019, and 2020 to 2021. The paper provides a better understanding of the thematic focus and associated trends of smart mobility beyond technical issues related to Intelligent Transport Systems (ITS), where due to diverse dynamics, such as unprecedented growth and advancement in technologies, attention has extended to incorporating the impacts of the application of different technologies in urban mobility as well as associated fields. This paper further identifies major sources, authors, publications, and countries that have made more contributions to the development of this field. The findings of this study can help researchers better understand the evolution of the subject, and help policymakers make better-informed decisions on investable infrastructures for better mobility outcomes in urban regeneration pursuits and future cities.

With a goal of achieving net-zero emissions by developing Smart Cities (SCs) and industrial decarbonization, there is a growing desire to decarbonize the renewable energy sector by accelerating green buildings (GBs) construction, electric vehicles (EVs), and ensuring long-term stability, with the expectation that emissions will need to be reduced by at least two thirds by 2035 and by at least 90% by 2050. Implementing GBs in urban areas and encouraging the use of EVs are cornerstones of transition towards SCs, and practical actions that governments can consider to help with improving the environment and develop SCs. This paper investigates different aspects of smart cities development and introduces new feasible indicators related to GBs and EVs in designing SCs, presenting existing barriers to smart cities development, and solutions to overcome them. The results demonstrate that feasible and achievable policies such as the development of the zero-energy, attention to design parameters, implementation of effective indicators for GBs and EVs, implementing strategies to reduce the cost of production of EVs whilst maintaining good quality standards, load management, and integrating EVs successfully into the electricity system, are important in smart cities development. Therefore, strategies to governments should consider the full dynamics and potential of socio-economic and climate change by implementing new energy policies on increasing investment in EVs, and GBs development by considering energy, energy, techno-economic, and environmental benefits.

Le développement d'un réseau de transport intelligent tenant compte des questions environnementales a donné la priorité aux solutions basées sur l'industrie 4.0. Dans cette direction, les systèmes de bus électriques alimentés par batterie ont été largement considérés pour assurer la flexibilité, les coûts d'exploitation et des émissions de polluants moindres. L'industrie 4.0 fournit l'automatisation grâce à un système cyber-physique (CPS), l'interconnexion des entités du système de bus avec l'Internet des objets industriel (IIoT), la disponibilité de l'information à distance grâce au cloud computing et à l'intégration de disciplines scientifiques (interaction homme-machine, intelligence artificielle, apprentissage automatique, etc.). Dans ce travail, une approche de simulation-optimisation basée sur des événements discrets est intégrée qui prend en charge la consommation d'énergie des bus en fonction des besoins des passagers de la ville en temps réel et des niveaux de friction sur route. La méthodologie d'optimisation de simulation proposée utilise un multi-objectif avec des variables dépendantes et indépendantes pour optimiser la performance globale du système. Dans l'optimisation de la simulation, les fonctions objectives sont conçues pour s'attaquer à la consommation de la batterie, aux performances du réseau Internet des objets (IoT), à l'efficacité des opérations dans le cloud et à l'intégration intelligente de la discipline scientifique. Les paramètres de simulation sont basés sur un système de bus en temps réel qui est analysé, filtré et adapté en fonction des besoins du système. Dans une autre analyse, les capacités du compresseur sont variées pour évaluer les performances du système proposé et identifier le système de transport intelligent à faible coût et efficace. Les résultats de la simulation montrent différents scénarios pour les variations du nombre de bus, de stations de charge, de dépôts de bus, d'installations de charge mobiles et d'horaires de bus. Les résultats de la simulation montrent que le temps d'attente moyen du passager dans l'attente (après la réservation du billet) varie entre 0,2 minute et 0,7 minute dans des conditions de circulation en temps réel. Dans des conditions de circulation similaires, le temps total passé par les passagers dans le système (réservation de billets pour voyager) varie entre 41,6 minutes (pour 24 heures) et 45,5 minutes (pour 1 an). Dans la simulation, les priorités sont données aux variables dépendantes et indépendantes qui économisent la consommation de la batterie et allongent l'utilisation des bus. Enfin, il est également observé que le système proposé est adapté aux dispositifs à contraintes de ressources car le calcul de Gate Equivalent (GE) montre que le système proposé peut être mis en œuvre entre 1986 GE (coût de communication sans confidentialité et authentification) et 7939 GE (coût de calcul avec HMAC pour l'authentification dans le stockage de données). Cela garantit des primitivités de sécurité variables telles que la confidentialité, la disponibilité et l'authentification. El desarrollo inteligente de la red de transporte teniendo en cuenta los problemas ambientales ha dado prioridad a las soluciones basadas en la Industria 4.0. En este sentido, los sistemas de autobuses eléctricos alimentados por baterías se han considerado ampliamente para garantizar la flexibilidad, el costo de operación y la menor emisión de contaminantes. La Industria 4.0 proporciona automatización a través de un sistema ciberfísico (CPS), la interconexión de entidades del sistema de bus con el internet de las cosas industrial (IIoT), la disponibilidad de información remota a través de la computación en la nube y la integración de disciplinas científicas (interacción hombre-máquina, inteligencia artificial, aprendizaje automático, etc.). En este trabajo, se integra un enfoque discreto de simulación-optimización basado en eventos que se ocupa del consumo de energía del autobús de acuerdo con las necesidades de los pasajeros de la ciudad en tiempo real y los niveles de fricción en carretera. La metodología de optimización de simulación propuesta utiliza objetivos múltiples con variables dependientes e independientes para optimizar el rendimiento general del sistema. En la optimización de la simulación, las funciones objetivas están diseñadas para abordar el consumo de batería, el rendimiento de la red de Internet de las Cosas (IoT), la eficiencia de las operaciones en la nube y la integración de disciplinas científicas inteligentes. Los parámetros de simulación se basan en un sistema de bus en tiempo real que se analiza, filtra y adapta según las necesidades del sistema. En otro análisis, las capacidades del sobrealimentador son variadas para evaluar el rendimiento del sistema propuesto e identificar el sistema de transporte inteligente de bajo costo y eficiente. Los resultados de la simulación muestran diferentes escenarios para las variaciones en el número de autobuses, estaciones de carga, depósitos de autobuses, instalaciones de carga móvil y horarios de autobuses. Los resultados de la simulación muestran que el tiempo medio de espera del pasajero en la espera (después de la reserva del billete) varía entre 0,2 minutos y 0,7 minutos en condiciones de tráfico en tiempo real. En condiciones de tráfico similares, el tiempo total de los pasajeros en el sistema (reserva de billetes para viajar) varía entre 41,6 minutos (durante 24 horas) y 45,5 minutos (durante 1 año). En la simulación se priorizan aquellas variables dependientes e independientes que ahorran el consumo de batería y alargan la utilización de buses. Por último, también se observa que el sistema propuesto es adecuado para dispositivos de restricción de recursos porque el cálculo de Equivalente de Puerta (GE) muestra que el sistema propuesto puede implementarse entre los GE de 1986 (costo de comunicación sin confidencialidad y autenticación) y los GE de 7939 (costo computacional con HMAC para autenticación en almacenamiento de datos). Esto garantiza primitividades de seguridad variables, como la confidencialidad, la disponibilidad y la autenticación. Smart transportation network development with environmental issues into consideration has brought Industry 4.0 based solutions on priority. In this direction, battery-powered electric bus systems have been considered widely for ensuring flexibility, operation cost, and lesser pollutants emission. Industry 4.0 provides automation through a cyber-physical system (CPS), the interconnection of bus system entities with industrial internet-of-things (IIoT), remote information availability through cloud computing and scientific disciplines (human-computer interaction, artificial intelligence, machine learning etc.) integration. In this work, a discrete event-based simulation-optimization approach is integrated that take care of bus energy consumption according to real-time city's passenger needs and on-road friction levels. The proposed simulation optimization methodology utilizes multi-objective with dependent and independent variables for optimizing the overall system performance. In simulation optimization, objective functions are designed to tackle battery consumption, Internet-of-Thing (IoT) network performance, cloud operations efficiency and smart scientific discipline integration. Simulation parameters are based on a real-time bus system which is further analyzed, filtered and adapted as per the needs of the system. In another analysis, supercharger's capacities are varied to evaluate the performance of the proposed system and identify the low cost and efficient smart transportation system. Simulation results show different scenarios for variations in the number of buses, charging stations, bus-depots, mobile charging facilities, and bus-schedules. Simulation results show that the average passenger's waiting time in the waiting is (after ticket booking) varies between 0.2 minutes to 0.7 minutes in real-time traffic conditions. In similar traffic conditions, total passenger's time in system (ticket booking to travel) varies between 41.6 minutes (for 24 hours) to 45.5 minutes (for 1 year). In the simulation, priorities are given to those dependent and independent variables which save the battery consumption and elongate the utilization of buses. Lastly, it is also observed that the proposed system is suitable for resource-constraint devices because Gate Equivalent (GE) calculation shows that the proposed system can be implemented between 1986 GEs (communicational cost without confidentiality and authentication) and 7939 GEs (computational cost with HMAC for authentication in data storage). This ensures varies security primitivs such as confidentiality, availability and authentication. أدى تطوير شبكة النقل الذكية مع مراعاة القضايا البيئية إلى إعطاء الأولوية للحلول القائمة على الصناعة 4.0. في هذا الاتجاه، تم النظر في أنظمة الناقلات الكهربائية التي تعمل بالبطاريات على نطاق واسع لضمان المرونة وتكلفة التشغيل وانبعاثات أقل للملوثات. توفر الصناعة 4.0 الأتمتة من خلال نظام فيزيائي إلكتروني (CPS)، والربط البيني بين كيانات نظام الحافلات مع إنترنت الأشياء الصناعية (IIoT)، وتوافر المعلومات عن بُعد من خلال الحوسبة السحابية والتخصصات العلمية (التفاعل بين الإنسان والحاسوب، والذكاء الاصطناعي، والتعلم الآلي، وما إلى ذلك) التكامل. في هذا العمل، تم دمج نهج محاكاة منفصل قائم على الأحداث يعتني باستهلاك طاقة الحافلات وفقًا لاحتياجات الركاب في المدينة في الوقت الفعلي ومستويات الاحتكاك على الطريق. تستخدم منهجية تحسين المحاكاة المقترحة أهدافًا متعددة مع متغيرات تابعة ومستقلة لتحسين الأداء العام للنظام. في تحسين المحاكاة، تم تصميم الوظائف الموضوعية لمعالجة استهلاك البطارية، وأداء شبكة إنترنت الأشياء (IoT)، وكفاءة العمليات السحابية، وتكامل الانضباط العلمي الذكي. تعتمد معلمات المحاكاة على نظام ناقل في الوقت الفعلي يتم تحليله وتصفيته وتكييفه وفقًا لاحتياجات النظام. في تحليل آخر، تتنوع قدرات الشاحن الفائق لتقييم أداء النظام المقترح وتحديد نظام النقل الذكي منخفض التكلفة والفعال. تُظهر نتائج المحاكاة سيناريوهات مختلفة للاختلافات في عدد الحافلات ومحطات الشحن ومستودعات الحافلات ومرافق الشحن المتنقلة وجداول الحافلات. تظهر نتائج المحاكاة أن متوسط وقت انتظار الراكب في الانتظار (بعد حجز التذكرة) يتراوح بين 0.2 دقيقة إلى 0.7 دقيقة في ظروف حركة المرور في الوقت الفعلي. في ظروف المرور المماثلة، يتراوح إجمالي وقت الركاب في النظام (حجز التذاكر للسفر) بين 41.6 دقيقة (لمدة 24 ساعة) إلى 45.5 دقيقة (لمدة عام واحد). في المحاكاة، يتم إعطاء الأولويات للمتغيرات التابعة والمستقلة التي توفر استهلاك البطارية وتطيل من استخدام الحافلات. أخيرًا، لوحظ أيضًا أن النظام المقترح مناسب لأجهزة تقييد الموارد لأن حساب البوابة المكافئة (GE) يوضح أنه يمكن تنفيذ النظام المقترح بين 1986 GEs (تكلفة الاتصال دون السرية والمصادقة) و 7939 GEs (التكلفة الحسابية مع HMAC للمصادقة في تخزين البيانات). وهذا يضمن تنوع أساسيات الأمان مثل السرية والتوافر والمصادقة.

One-way car-sharing systems are becoming increasingly popular, and the introduction of autonomous vehicles could make these systems even more widespread. Shared Autonomous Electric Vehicles could also allow for more controllable charging compared to private electric vehicles, allowing large scale demand response and providing essential ancillary services to the electric grid. In this work, we develop a simulation methodology for evaluating a Shared Autonomous Electric Vehicle system interacting with passengers and charging at designated charging stations using a heuristic-based charging strategy. The influence of fleet size is studied in terms of transport service quality and break-even prices for the system. We test the potential of the system to supply operating reserve by formulating an optimization problem for the optimal deployment of vehicles during a grid operator request. The results of the simulations for the case study of Tokyo show that a fleet of Shared Autonomous Electric Vehicles would only need to be about 10%–14% of a fleet of private cars providing a comparable level of transport service, with low break-even prices. Moreover, we show that the system can provide operating reserve under several operational conditions even at peak transport demand without significant disruption to transport service.

Since the Belt and Road Initiative (BRI) has been put in practice by the Chinese government, several High-Speed Railways (HSR) have been built by Chinese Engineering Procurement and Construction (EPC) firms. However, many delays have created severe detrimental consequences on the progress of most HSR projects. This study sought to explore the essence of the recurring triggers of delays in international EPC HSR projects under the BRI, and a structured questionnaire survey approach was applied to compile the first-hand dataset from Chinese EPC firms working for BRI infrastructure projects. The data were evaluated, and the Relative Importance Index (RII) was adopted to assess the magnitude of the important delay triggers. The findings suggest that HSR projects are still susceptible to unavoidable delays in global construction infrastructure projects. In the engineering phase, improper management of the design, unsustainable land acquisition, and insufficient use of EPC joint venture are the salient trigger of delays. In the procurement phase, the leading causes of unsuitable procurement, undervalued procurement cost, inefficient logistics in labor and materials, improper planning, unqualified site supervisors, inefficient technical standard management, and inefficient constant payment terms are likely to trigger delays in the construction phase HSR projects. Five critical groups of delay factors are identified by this study, which has an essential primary contribution to the body of knowledge and is helpful to EPC contractors working for HSR projects under BRI.

There is an ongoing debate worldwide about the indicators that policy makers should use to evaluate progress toward sustainable transportation systems. In general, studies of indicators lack an overall normative framework that allows decision makers or the public to make sense of the many overlapping and partial measures. A statewide urban growth model for California was run iteratively with the California statewide travel model to evaluate major transportation scenarios, such as freeway widening and high-speed rail. In addition, transportation and land use policies intended to provide more affordable housing accessible to jobs, widespread habitat protection, and strong reductions in greenhouse gas emissions were evaluated. This model provides many performance measures for travel, economic welfare and equity, rents paid, energy use, greenhouse gas emissions, vehicular air pollution, and habitat loss. A framework for interpreting these data on the basis of recent advances in the theories of well-being for individuals and nations is proposed. This theoretical framework for evaluating the model outputs used in planning also applies to the analysis of the empirical indicators used to track actual outcomes.