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In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects.

The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic.

In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision.

The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.

As the world continues to urbanise, cities face increasing pressure to become more sustainable, efficient and livable. Sustainable smart cities are emerging as a promising solution to this challenge, leveraging technology and data to improve urban systems and services while reducing environmental impact. This chapter provides an overview of the concept of sustainable smart cities and its implications for urban development. It explores the key features of sustainable smart cities, including their focus on technology, data and citizen engagement and the challenges they are facing in terms of infrastructure, data management, social equity, environmental sustainability, governance and regulations. The chapter also highlights the implications of sustainable smart cities for urban planners, policymakers and other stakeholders, emphasising the need for collaborative approaches that engage citizens and stakeholders in the design and implementation of smart city initiatives.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

While the adversarial nature of precast concrete (PC) building construction is frequently cited in the PC building construction press, only a few researchers have investigated construction supply chain management within the construction industry. Due to the interdisciplinary transportation environment, which inevitably results in disruption, the uses of construction supply chain and recovery from construction supply chain risk must be a subject of real interest, yet transportation management research in this area is scarce.

The purpose of this study is to discuss the weakness in system approaches and their application for managing precast concrete building in the context of construction supply chain practice and how to overcome it. As a precursor to this paper, the paper reviews current construction supply chain management occurrence on PC building construction and explores the hybrid intelligent vehicle tools and techniques currently being used on such management. This paper also presents the new hybrid intelligent vehicle-based approach to manage construction supply chain risk and reduce associated tension on PC building construction schemes.

The findings reveal the need for more sophisticated construction supply chain management solutions which accord with the needs of PC building construction schemes.

The paper concludes by presenting a research framework for developing such a system in the future.

In this study, a mode choice model explicitly considering travel time reliability is developed. This model quantifies travelers' attitudes towards travel time variability as well as average travel time. Data were collected from the morning commuters who have two or three alternative modes including some public transportation and private vehicles. The survey period includes both a normal period where all the transportation modes were available and an abnormal period where the main major public transportation service was closed. The model is applied to practical commuters' decision making, and one of the findings in the mode choice model is that they pay relatively large attention to the travel time variability. In this model, travel time variability is dealt with as the possibilities that the commuters arrive before or after their job starting time separately. The best-fit model indicates that the commuters pay more attention to early arrival and less to late arrival in the normal period. In the abnormal period, however, their attention shifts drastically to late arrival. This suggests that the commuters behave optimistically in the normal period and pessimistically in the abnormal period.

The application of lean thinking in the transportation industry provides opportunities to streamline operations with a value-added orientation. Prior literature shows evidence of limited application of lean thinking in the transportation operations of the pharmaceutical industry. This study aims to close this research gap by investigating the application of lean thinking for improving pharmaceutical transportation performance.

This study conducted an action research approach at an Indonesian pharmaceutical distribution company. One cycle in one year was analyzed; empirical data were collected and analyzed through direct observations, interviews and the study of company data and documentation.

The application of lean thinking in waste elimination allowed the delivered project to achieve a remarkable 40% reduction in overall transportation costs, 75% reduction in total lead time, 200% improvement in truck productivity and 100% improvement in truckload capacity utilization.

This study can guide the pharmaceutical industry toward achieving excellence in transportation operations through lean thinking implementation.

There has been limited research on this topic, and this study is the first attempt to generate new and significant evidence of a real-life application of lean thinking within the field of pharmaceutical transportation.

The purpose of this paper is to present the state-of-the-art E-commerce logistics in supply chain management by investigating worldwide implementations and corresponding models together with supporting techniques via furniture industry.

Typical E-commerce logistics companies from North America, Europe, and Asia Pacific are comprehensively investigated so as to get the lessons and insights from these practices.

Future technologies like Internet of Things, Big Data Analytics, and Cloud Computing would be possibly adopted to enhance the E-commerce logistics in terms of system level, operational level, and decision-making level that may be real time and intelligent in the next decade.

This paper takes the furniture industry for example to illustrate the E-commerce logistics and supply chain management (LSCM). Other industries like electronic appliance industry are not considered.

Opportunities and future perspectives are summarized from practical implementations so that interested parties like E-commerce and logistics companies are able to get some guidance when they are contemplating the business.

E-commerce is booming with the development of new business models and will be continuously boosted in the near future. With large number of enterprises carrying out E-commerce, logistics has been largely influenced.

Insights and lessons from this paper are significant for academia and practitioners for considering E-commerce LSCM.

Since the emergence of physical distribution/logistics management as an applied discipline in the early 1960s, the search for requisite skills has dominated the concerns of professionals in this new field. While there had been some earlier attempts to organise educational approaches in this field, the first real national and cohesive attempts were launched with the founding of the Annual Transportation and Logistics Educators' Conference. The enthusiasm of the participants and the heavy focus of the first conference proceedings on this issue marked a really strong inertia towards the search for, and development of, status in this field of management concern.