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The purpose of this study is to analyze the safety performance of state ships and commercial ships according to the requirements of Solas 1974. The requirements of Solas 1974 in the context of international shipping are mainly related to safety and security issues related to the tools and types of shipping safety. Application of the 1974 Solas Convention and the 2018 Solas Consolidation with the scope of discussion on international shipping is especially related to maritime protection. This study uses the Plan, Do, Check and Action (PDCA) evaluation model. The data was collected through the interview survey method and continued with statistical testing with the factor analysis technique. Respondents consisted of crews of commercial ships with a weight of over 500 GT and crews of pioneer ships as state ships anchored at the Port of Tanjung Priok. Research respondents totaled 57 crew members, consisting of 23 crew members of state ships and 34 crew members of commercial ships. The results of this research can be used as reference material in terms of safety and security as well as protection against environmental damage, in accordance with the transportation management system policy which includes; manuals, implementation policies, supporting implementation procedures, and work instructions for all stakeholders. The research output can be used as a basis for providing recommendations related to corrective actions to improve the marine transportation management system through the implementation of Solas 1974.

The delivery system for Base Transceiver System (BTS) to strengthen the telecommunication network throughout Indonesia maritime country is needed to speed up the process due to uneven distribution of sufficient telecommunications infrastructure. PT. Gunadhya Rajawali Logistik (GRL) is a third-party logistics company that focuses on transportation and warehouse management and delivery for telecommunication equipment tools. Unfortunately, in delivering BTS installation part throughout Indonesia, PT. GRL received several warning letters and had to pay a penalty from one of customers, for being late in reporting the delivery proof. This study aims to analyze the root causes of late submission of Soft Copy of Proof of Delivery (SCPOD) to avoid penalty given by the customer and determine suitable solutions to improve the process. The approach used in this study is lean six sigma with the help of Define, Measure, Analyze, Improve, and Control (DMAIC) roadmap. According to the interview with the employees related, the root causes of late submission of SCPOD are irresponsible drivers, indiscipline drivers in carrying out deliveries, and lack of data management in the transportation system. To be able to solve this problem, several proposed solutions can be applied. The proposed solutions to overcome the root causes that should be applied by the company are to hire a document control, retraining the Standard Operating Procedure (SOP) for drivers, utilize transportation management system application, and apply incentive systems for drivers. In the future, it is recommended for PT. GRL to have a minimum standard in recruiting the drivers for example only recruit who possess a minimum of high school education to ease the completion of administrative tasks.

Human's can't imagine machines independent. From Vacuum Tubes technology to transistors and now with Integrated chip (IC), semiconductor technology defined another industrial revolution. The modern industrial twirl is already in transpire. Artificial intelligence (AI) is ushering in the new age of technologies that are super fast, more flexible, much potential, and initiating the world more digitally connected. No doubt the potential of IoT and AI to clean up the environment is cosmic. A computational concept like fuzzy logic, neural network, and machine learning will change the perspective of machine how they are used now. Our study reveals retrospect progression in the internet of things and artificial intelligence technologies to curb ecological pollution. New generation AI-enabled intelligent control systems are optimized with a complex algorithm to identify and eliminate pollutants for environmental clean-ups. With AI automation pollution management and mitigation, developments are examined. Adopting tech-savvy IoT and AI technology, dependency on machines can be seemly employed. For example; Transportation Management System using AI and IOT can be used to determine the best shipping routes by cutting fuel and gas consumption. To some extent ICT, wireless technology adds a contribution to reducing E-Waste. ICT together with IoT acts as a mediator while renting and sharing devices among people with different industries. AI helps to solve complex data analysis that supports Quick Decision System and policymaking. With intelligent robots, we can monitor and control acidification on Soil and water. The concept of neural network and fuzzy logic are greatly used to develop intelligent machines that predict different gas rates and chemical processes. AI machines that continuously check the pH level of water, Analysis Air Quality Index can be built to monitor water resources and atmosphere. Some AI robots can identify pollutants and helps to separate them. Now we are successful in a bifurcation of paper with plastic, Iron with aluminum. Such a method contributes to the waste management system appropriately. Our study also inspects the challenges of implementing AI, How data mining and big data management feed system. Authentication/Authorization work with industry and governing authority. Inspection of a biased algorithm, poor network, and security. There is no new route to achieve our goal. Our goal is to achieve paramount measures with two concepts in mind, that are Humans assist machines and Machines assist humans to control and prevent pollution in these critical situations. Both humans and machines together find an instant solution with renting and sharing that helps to minimize ecosystem effluence

The introduction of digital solutions in the automotive industry is often accompanied by a significant gap between the expectations and needs of the leaders of transport and logistics companies and the real business results achieved through the Transportation Management System. The causes of this phenomenon largely depend on the human factor, i.e. irrational actions of personnel who inefficiently use digital technology. The prerequisites for such actions are low competencies of employees, unfair attitude to work, sabotage. Accordingly, this leads to deviations in the transportation process, which can lead to violations of the conditions for the transportation of goods. From the point of view of the concept of reliability of road freight transport, this can be defined as a failure. Failures caused by the varied actions of managers when working with the Transportation Management System were extremely difficult to diagnose. Direct monitoring of managers during the working day is often fraught with various organizational difficulties, and is difficult to implement for a long time to identify ineffective staff activities. The concept of Process Mining, which has been known among scientists and researchers of information systems for more than 10 years, is currently of great interest in business. The paper examined the issues of improving and reorganizing the activities of managers of a transport company based on the mining of the TMS Autobase event log. Based on the results of the study, recommendations were made aimed at developing the digitalization of the transportation process, suggesting an increase in transportation reliability.

This paper discusses trends, challenges, and perspectives in logistics system of Indonesia during and post (new normal) of Covid-19 pandemic. PSBB (Large-Scale Social Restriction) caused a number of supply chains to be cut and made railroad transportation activities decreased. On the other hand, WFH (Work From Home) caused sector of e-Commerce to grow during the pandemic. Challenges in logistics system of Indonesia during the pandemic are changes in distribution patterns, changes in handling processes, changes in facilities and equipment, changes in business processes, and changes in networks. Adaptations in logistics system during the pandemic are using ICT (information and communication technology) such as TMS (Transportation Management System) and EDI (Electronic Data Interchange) and considering health. WMS (Warehouse Management System) still has a great opportunity to be utilized. The adaptations raise a new discipline in Industrial Engineering, namely logistics hygienic. The use of various ICTs in logistics system together with implementation of health protocols are expected to be a solution for logistics system in Indonesia, especially for the new normal of Covid-19 pandemic.

The intelligent transportation system in big data environment is the development trend of future transportation system, which effectively integrates advanced information technology, data communication transmission technology, electronic sensor technology, control technology and computer technology and is applied to overall ground transportation management. Hence, it establishes a real-time, accurate, efficient and comprehensive transportation management system that functions in a wide range and all-round aspects. In order to meet the demands of the intelligent transportation big data processing, this paper puts forward a high performance computing architecture of large-scale transportation video data management based on cloud computing, designs a parallel computing model containing the distributed file system and distributed computing system to solve the problems such as flexible server increase or decrease, load balancing and flexible dynamic storage increase or decrease, computing power and great improvement of storage efficiency. On the basis of this technical architecture, the system adopts BP neural network-related algorithms to extract the static transportation signs in road videos, and uses interframe difference algorithm and Gaussian mixture model (GMM) fusion algorithm to extract the moving targets in road transportation videos. In this way, they are taken as important integral parts and data sources of key frames of intelligent video image recognition to improve the recognition ability of key frames and eventually utilize semantic recognition model based on CNN (Convolutional Neural Network) to complete the intelligent recognition of whole transportation videos. Through network pressure test, computing ability test, recognition ability test and other tests, it has been proved that the intelligent transportation video processing system based on big data environment is successful and the design scheme of this system has strong practical application value.

There are few studies on the violation of truck drivers, especially the hazmat truck driver, although truck driver's violation may cause serious casualties. This paper aims to investigate hazmat truck drivers' violation behavior and identify associated risk factors. Different data sources in intelligent transportation system (ITS) including hazmat transportation management system and traffic safety management system are extracted and emerged together. Three years (2016-2018) of violation data that comprised 11612 trip record in China are employed in this research. Based on Bayesian theory, this study proposes zero-inflated ordered probit (ZIOP) model and three alternative models to exploring the relationship between hazmat truck drivers' violation frequency and the key risk factors. The results show that ZIOP model can handle excessive zero observation problem of violation data properly and differentiate between `always-zero group' drivers and drivers who did not violate the traffic rules during research period but would do so in different surroundings. The results also indicate that the violation probability and the violation frequency level of hazmat truck drivers are influenced by driver characteristics, freight order attributes, and drivers' violation records. This research provides guidance for driving training and safety education of hazmat truck drivers, and will be helpful in building better driving simulation models.

Large scale anomalous circulation is one of the main causes of extreme climate in East Asia. Data mining and statistical analysis of climate reanalysis data are important research methods to understand the characteristics of atmospheric circulation and the occurrence of extreme climate. In this paper, we first use SOM to distinguish the anomalous general circulation models in East Asia from 1979 to 2020. According to the results, we establish the response relationship and monitoring system between various models and climatic factors, and analyze the trend and dynamic mechanism of temperature anomalies in China. Secondly, the atmospheric circulation anomaly model is related to the extreme temperature and precipitation events in East Asia, and studies the climate anomaly characteristics under the complex effects of atmospheric circulation anomaly and monsoon system. The following analysis follows all the basic characteristics of land logistics of marine logistics, but due to the high risk of transportation, transportation time is long, so freight management is different. The existing marine logistics transportation management system has many deficiencies in information exchange, cargo management and transportation. The proposal and application of blockchain technology can effectively supplement it. The consensus mechanism is the core of blockchain technology. The decentralized credit structure and the nonoperability of the consensus mechanism of blockchain can effectively overcome the risk of freedom of contract, optimize the maritime logistics and transportation management model, improve the rationality of the sense of responsibility, ensure the transaction efficiency and transportation safety, and comply with the flexible trend of modern maritime trade. This paper introduces the monitoring system and applies it to the management of climate abnormal circulation and marine logistics transportation, which can effectively improve the management of climate abnormal circulation and marine logistics transportation.

Efficient transportation management in a business’ logistics processes is crucial to ensure competitive advantages. Therefore, the use of tools that ensure data visibility and freight costs reduction has become increasingly common. In this sense, this study intends to analyze the effects of implementing the Transportation Management System (TMS) tool in the logistics management of a large enterprise that operates in the automotive parts industry. With the use of TMS, it was found that it was possible to reduce the execution time of the month-end process by 3 days. Moreover, the hiring of a logistics operator, supplier of the tool, has resulted in a gross revenue increase from 129 to 134 million Brazilian reais and a decrease of 3.8% in freight costs. In conclusion, by monitoring the implementation of TMS, it was possible to obtain insights into the functionality of logistics in a large company, providing evidence on the importance of using technological resources to automate logistics processes seeking competitiveness in the industry.

This chapter begins by asking how Industry 4.0 affects supply chain (SC) and what kind of roles Internet of Things (IoT) and big data play in SC industry. It presents the Industrial Revolution and its historical development and the emergence of Industry 4.0. The chapter offers the work-study and application areas of SC 4.0. It provides the literature review of IoT and SC within the scope of Industry 4.0. The chapter summarizes the finding of Bibliometric mapping and clustering analysis. IoT is used in all processes from supplier supply to material handling, transportation of materials, and production to reaching latest customers. IoT can optimize whole process of Transportation Management System. The potential use of IoT technology across the supply chain is huge. The IoT enabled supply chain can be visualized as a smart interconnected network that binds together many levels of suppliers, manufacturers, service providers, distributors, and customers physically located across different areas of the world.

Detecting traffic events and their locations is important for an effective transportation management system and better urban policy making. Traffic events are related to traffic accidents, congestion, parking issues, to name a few. Currently, traffic events are detected through static sensors e.g., CCTV camera, loop detectors. However they have limited spatial coverage and high maintenance cost, especially in developing regions. On the other hand, with Web 2.0 and ubiquitous mobile platforms, people can act as social sensors sharing different traffic events along with their locations. We investigated whether Twitter – a social media platform can be useful to understand urban traffic events from tweets in India. However, such tweets are informal and noisy and containing vernacular geographical information making the location retrieval task challenging. So far most authors have used geotagged tweets to identify traffic events which accounted for only 0.1%-3% or sometimes less than that. Recently Twitter has removed precise geotagging, further decreasing the utility of such approaches. To address these issues, this research explored how ungeotagged tweets could be used to understand traffic events in India. We developed a novel framework that does not only categorize traffic related tweets but also extracts the locations of the traffic events from the tweet content in Greater Mumbai. The results show that an SVM based model performs best detecting traffic related tweets. While extracting location information, a hybrid georeferencing model consists of a supervised learning algorithm and a number of spatial rules outperforms other models. The results suggest people in India, especially in Greater Mumbai often share traffic information along with location mentions, which can be used to complement existing physical transport infrastructure in a cost-effective manner to manage transport services in the urban environment.

With the wide variety of information systems and applications for motor transport and transport logistics control we have today, one may think we are already living in the digital era of general welfare, and digital tools would easily ensure sustainable development and prosperity of businesses. However, the experience of deployment and introduction of such solutions shows that their value for transport business is significantly lower than expected. Moreover, in some projects, business performance of transport companies had no correlation with introduction of information systems. In the best-case scenario, they provided for a slight decrease in document flow transaction costs. The change of the strategic status of a company in the transportation service market is a fairly complicated task, which, as analysis of literary sources shows, is achievable for few enterprises, primarily small and medium-sized businesses. Such situations show that information solutions were introduced without analyzing or assessing the business models of certain companies which could be used a basis for digital landscape of business as a whole. In recent years, the basic concept of forming a single information space of an enterprise has been the enterprise architecture. It provided for coordination between all the business processes in order to achieve a company&rsquo, s strategic goals. The fundamentals of the concept were developed by J. Zachman in his famous Zachman Framework, and it was later developed with numerous models of enterprise architecture (e.g., TOGAF (Department of Defense Architecture Framework), GERAM (Generalised Enterprise Reference Architecture and Methodology), DoDAF (Department of Defense Architecture Framework)). However, currently some researchers note that sustainable corporate development should stem not only from a &ldquo, correct assembly&rdquo, of all its business elements, which was the purpose of enterprise architecture, but also from the interaction of these elements when reaching the emergence effect. In this context, one should pay attention to comprehensive activity analysis of a transport and logistics business using ontological and architecture approaches.

The given literature focuses on developing a Smart Factory model based on Cloud and Edge computing used to develop Transportation Management System(TMS) using a iFogSim wrapper. Cloud computing identifies data centres for users and offer computer system services on-demand, including data storage and processing power, without direct active user management. In the smart industry, several devices are connected together across the internet, where vast volumes of data are collected during the entire process of output. Thus, to handle this data smart factory based on cloud and edge computing is used. The intelligent cloud-based factory offers some facility like large scale analysis of data. Concepts like fog and edge computing play a significant role in extending data storage and network capacities in the cloud that addresses some challenges, such as over-full bandwidth and latency. The literature also focuses on the implementation of TMS using the iFogSim Simulator. The simulator provides efficient execution of TMS by showing the amount of resources used which gives an idea regarding optimum use of resources. All types of data related to TMS is obtained at cloud by using smart factory like object location, time taken and energy consumption. To implement the TMS we have created a topology which displays various devices connected to the cloud which gives necessary information regarding the ongoing transportation simulation.

This is a pre-print for the submission to IEEE LISS 2020 Conference, Budapest, Hungary.

The paper presents the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user’s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do so, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. SO is multi-modal, in that it considers private cars, public transit, walking, car pooling, and so forth. Moreover, it is dynamic, predictive, and personalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the framework with simulation results., United States. Advanced Research Projects Agency-Energy. TRANSNET Program (Award DE-AR0000611)

Due to the increasingly popular e-commerce and rapidly growing global market of delivery services, transportation management system (TMS) has been playing a critical role in minimizing transportation delay and cost as well as improving reliability. However, how to efficiently keep track of running vehicles and judiciously store their corresponding trajectories become a key issue in the presence of scarce wireless bandwidth and limited storage space. In this paper, we propose a novel mobile trajectory reduction scheme, called T-Reduce , to reduce the size of trajectory data stored in a TMS server. We first develop a path refinement operation to adjust raw location update data transmitted from vehicles to a given road topology as close as possible to improve their location accuracy. Second, we develop a route matching operation consisting of three sub-operations, route extraction, route recognition, and trajectory generation, for the server to identify and extract a set of routes by decomposing the received location update data. Then, the server can store each trajectory as a set of corresponding route $ids$ instead of storing entire location update data. We set up a small-scale testbed, implement the proposed scheme with a real-world trace data collected from a logistic company, and conduct extensive experiments for performance evaluation and comparison. The experimental results show that the proposed scheme can significantly reduce the average trajectory error and route information up to 26.4% and 88%, respectively, compared to that of two prior trajectory-based and corner-based approaches. The proposed approach can also achieve up to 5.72 times cost efficiency compared to the prior approaches.

The one-tambon-one-product (OTOP) project in Thailand has been successful according to statistical data from the Interior Ministry's Department of Community Development. However, there is a concern that dispatchers in the supporting trucking industry carry out both tasks of truckload assignment and driver scheduling. It is necessary to investigate more efficient and reliable alternatives to handle the growing and fluctuating quantities of inbound and outbound goods in each service area. This paper focuses on determining alternative ways for enhancing the efficiency of the transportation management system based on multi-zone dispatching (MZD) with the objective of minimising total load imbalances. This problem is considered to be a special case of the MZD but it is complex when compared to the conventional MZD, especially since planning horizons are considered and modified as a dynamic MZD (dMZD). In this paper an intelligent water drop optimisation (IWD) was used to solve the problem. In order to improve the solution quality, the novel two-phase approximated algorithm (2-PAA) with two additional processes from the variable neighbourhood and dynamic programming algorithms, were proposed. Experimental results with preferable settings showed the 2-PAA was computationally effective and feasible to generate dispatching alternatives for both static and dynamic problems. Using a set of real data, the simulation suggested a 3-period dMZD configuration for driver scheduling with preferable levels of tour length and imbalance statistics.

Intelligent Transportation System (ITS) is a comprehensive transportation management system based on advanced information, communication, sensing, control and computer technology. Stemming from and first applied to road transportation, this system has played an important role in strengthening the road traffic capacity and service, improving environment quality and enhancing energy efficiency. It has also spread to such transportation means as aviation, water transport and railway. International cargo transportation usually involves a number of different transportation means, which gives rise to the need for comprehensive monitoring, management and optimization of multimodal transport, but study on that area isn’t common yet. Concerning road, waterway, aviation and railway involved in multimodal transportation, this paper proposes the Multimodal Monitoring technology that is able to manage multiple vehicles holistically to realise full-time monitoring, dispatching and optimization. The paper also puts forth the models of technical system, service functions and network layers of Multimodal Monitoring, and provides the technical solution to establish the Generic Target Monitoring System (GTMS).

A successful information systems application should not only be determined by whether the functions of the system are running properly. Most of the times, the development of an information systems application judged only from the technical side, while there are actually many more non-technical factors like the human resources readiness, organizational support, and so on that could affects the level of user’s acceptance towards an information systems application. Therefore, this research will uncover the gaps in Transportation Management System (TRM), a car-rental application of XYZ Transportation Solution’s at PT. XYZ, by using Fit & Gap Analysis for further improvement in the future. Furthermore, this research will also uncover the factors that affect the degree of user acceptance towards that system by using Technology Acceptance Model (TAM) methodology. The result of this research show there are several gaps on the TRM. The factors of Perceived Ease of Use significantly influenced the factor intention to use.

With the rapid development of big cities, the pressure of traffic congestion is increasing, and intelligent transportation is the fundamental way to solve traffic problems. Intelligent transportation system is a transportation management system that effectively integrates information technology, data communication technology, sensor technology, control technology and data processing technology. Large-scale traffic data management, integration and mining are key technologies. This paper briefly introduces the current situation of intelligent transportation in various countries, analyses the causes of urban traffic congestion in detail, and discusses the concept, composition and relationship between various subsystems of urban intelligent transportation system. Using the Internet of Things technology to collect traffic big data, based on the Big Data analysis technology to construct a traffic prediction model for traffic situation prediction, combined with real-time monitoring and analysis, the combination of remote traffic indicator control and optimization strategy control for traffic collaborative management. The causes of traffic congestion are analyzed in detail, specific solutions are proposed, and suggestions for transportation infrastructure planning are proposed.

Transportation Management System is very needed in optimize efficiency and effectiveness on logistics company. One of the most important part of transportation management system is determining delivery route from a depot to each customer. A lot of studies have been done about determining the best route in a shipping ritation with various algorithms. In the previous research, determination of delivery route is done without any implementation in the application. During the route determination is still done manually it will make a flow process in the company is not maximize. Model development aims to make an implementation application that serves to determine delivery route based on the problem of vehicle routing problem using the nearest neighbour method which is restricted to heavy loads in the transportation. Implementation of route determination an application will be done based on business process in transportation company, named PT. X, so it is necessary to observe the company. Compared to previous research, this research will determine delivery route in application based on the problem of vehicle routing problem using the nearest neighbour method.

As one of the most commonly meat products, the quality of pork is directly related to people's health. Pig transportation is the key link between breeding farms and slaughterhouses. However, starvation and plague often occur in reality, which not only brings economic losses to farms and slaughterhouses but also brings some food safety risks. This paper analyzed the problems in the transportation process of pigs and puts forward a pig transportation management system model based on RFID and sensor technology under the background of the rapid development of information technology. The system model was analyzed and elaborated in detail from four key business models of slaughter, loading, transportation and receiving. At the same time, the article evaluates the system model from three aspects: problems resolution, economic benefit, and social benefit, which provide some references for the practical application of the system model.

This chapter has highlighted the major areas where IT has a significant effect on SC. Brief descriptions were provided as follows

As empresas estão investindo em Tecnologia da Informação (TI) e aproveitando ao máximo, as vantagens que ela proporciona, para mudar os processos de negócio e melhorar o desempenho das operações. Dentro deste contexto, pretende-se aqui investigar o uso de um Sistema de Gestão de Transporte (TMS). O objetivo geral deste estudo é avaliar o impacto do uso do TMS e sua relação com o desempenho das operações de transporte das empresas e com os prestadores de serviços de transporte na percepção dos usuários. O método utilizado foi a pesquisa survey e um modelo conceitual foi elaborado e validado utilizando modelagem de equações estruturais (PLS-SEM). O resultado indica que os benefícios de um TMS têm impacto positivo no desempenho das operações de transporte, que, por sua vez, têm impacto positivo na relação com fornecedores de serviço de transporte. Companies are investing in Information Technology (IT) and making the most of the advantages it provides to change the negotiation process in both the internal and external market. Considering this context, we aim to investigate the use of a Transportation Management System (TMS). The general objective of this study is to assess the impact of the use of the TMS and its relationship with the operational transportation performance of companies and with the transport service providers from the users’ perception. To do so, a survey was conducted, which provided data to form a conceptual model that was validated using structural equation modeling (PLS- -SEM). The result indicates that the use of a TMS has a positive impact on the operational transportation performance that, in turn, has a positive impact on the relationship with transport service providers.

This study proposed a generic work zone evaluation tool driven by a dynamic macroscopic traffic model. The tool can investigate the effects of a work zone on freeway conditions. The classical freeway traffic network model METANET was extended to better capture typical work zone characteristics in terms of speed and capacity reductions through modifying its speed-density relationship functions. The tool can present profiles of traffic state variables, system-level performance indices, and annotated traffic conditions maps. It was used to develop a prototype computer-aided decision support system and applied in two simulation-based case studies. Case studies show that it is effective and useful for understanding complicated work zone impacts and can aid in better design and assessment of work zone plans. It can be integrated in a dynamic transportation management system for better communication between road operator and travellers.

Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems, gives the possible system and methodology of the total transportation management, and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

Based on the increasing demands of transportation development, the concept of an Intelligent Transportation System (ITS) has received increasing attention in both academic and industry arenas. It integrates information, communications, computers and other technologies, and applies them in the field of transportation to build an integrated system of people, roads and vehicles by utilizing advanced data communication technologies. It can establish a large, fully functioning, real-time, accurate and efficient transportation management system. Intelligent transportation systems shift the focus from road managers to road users. In order to achieve this purpose, intelligent transportation systems use advanced technology to provide drivers with convenient information to help reduce traffic congestion and to increase available road capacity. This special issue is dedicated to exploring the most recent advances in intelligent transportation systems and big data based on intelligent technology.

Freeway work zone with lane closure can lead to disruption to local traffic and cause significant impacts on mobility, safety and environmental sustainability. To mitigate traffic congestion near work zone area, many variable speed limits (VSL) control approaches have been developed. However, VSL control system, as a critical transportation management system, is prone to the occurrence of traffic sensor faults. Faulty sensors can cause great deviations of traffic measurements and system degradation. Therefore, this study aims to develop a fault-tolerant VSL control strategy for freeway work zone with the consideration of the mainline sensor fault and ramp sensor fault. To analyze the traffic dynamics near work zone area, a traffic flow model has been built first. Then a sliding mode controller in the previous study has been utilized for VSL control. In addition to the traffic states estimated by a Kalman filter, two observers have been developed to provide analytical redundancy of traffic states estimation. By comparing the logarithm of the likelihood estimations from the Kalman filter and two observers, a fault diagnosis scheme has been designed to detect and identify the faults of mainline sensors and ramp sensors. Then the VSL controller can be reconfigured accordingly in case of sensor faults. The proposed system is implemented and evaluated under a realistic freeway work zone environment using traffic simulator SUMO. The results demonstrate that the developed system can accurately detect and identify the sensor faults in real time. Consistent improvements of mobility, safety and sustainability are also achieved under fault-free and sensor faults scenarios.

Cooperative and situation-aware public transportation systems can save time and effort, increase level of security and reliability, reduce fossil consumption and decrease the high pollution resultant from emission rates like CO2. Given the population increase, many problems appeared, such as parking difficulties, traffic congestion, increasing number of road accidents and energy consumption. Recently, The Internet of Things (IOT) brought significant opportunities to transportation systems. Researches expect IOT to contribute in decreasing or even eliminating such chronic problems. This paper integrates public IOT device's real-time feedback with the public transportation management systems to optimize vehicle size and route presenting a cost and environment friendly transportation management system. Further, the system uses the available information to avoid congestion, accident detection, and driver behavior evaluation. Results showed that integrating such information into transportation management systems presents a valuable asset with huge impact on the induced level of emission and the overall cost of transportation.

Traffic congestion is one of common urban problems. One of the main factors of congestion is the significant increase in the number motor vehicle growth that is not proportional with the growth of roads. To reduce traffic congestion, the government of Surabaya has made various efforts, one of which is the plan to encourage more people to use the public transportation system. To support the system a reliable transportation management system namely Surabaya Intelligent Transport Systems (SITS) is under development.As a part of SITS, this paper presents an advanced traveler information system which helps visitors planning their itinerary using public transport during their visit in the city. The problem is modeled as an orienteering problem (OP) and solved using genetic algorithm (GA). The model was developed upon six routes of ‘Angkot’ which has been chosen. Travel time data is obtained through Google Maps.The contributions of this paper are two folds. First, we provide a public two datasets that is the dataset of travel time and score where each form of matrix. Upon the public datasets, future research, especially in the area of orienteering and optimisation problems could be encouraged. Second, initial algorithm based on the Genetic Algorithm to solve the problem. The computational experiments showed that the number of generations, the number of populations, the crossover probability, and the mutations probability play important role in the performance of the proposed algorithm. It is expected that this research can help the development of Surabaya Intelligent Transport System and the implementation of public transport revitalization in Surabaya.

Purpose Urban logistics is a subject that interests both city planners and researchers. Although many works are found in non-food distribution, food-based logistics is less studied in an urban context, and sustainability issues of urban food systems remain little analysed, but it is a crucial element for local public authorities. This paper proposes a methodology for assessing scenarios of advanced urban food logistics; more precisely to answer the question of how school canteen distribution scenarios can be assessed in an economic and environmental viewpoint. Methods To deal with those issues, the paper proposes to develop a scenario assessment framework combining a demand estimation model, a transport supply estimation method and a set of evaluation indicators (for both economic and environmental impacts). The demand estimation model generates first a number of meals per canteen then transforms it into a food quantity, based on carrier’s information and other practice’s feedbacks; the transport estimation is made with a combination of route construction algorithms that reproduce the logic of current Transportation Management System (TMS). The economic evaluation is made via direct operational costs estimation (both fixed and variable) and the environmental evaluation using COPERT V for direct emission’s assessment. Results Three scenarios are assessed: the current situation with the operational warehouse, the use of an alternative platform in another location and the combination of both. To address the application issues, the proposed framework is compared to the use of disconnected commercial tools on the three scenarios. We observe that the first two scenarios are close in terms of travelled distances, times and costs. The third scenario, which involves two platforms, results on higher distances and times, and consequently on higher costs and higher for some of the environmental emissions. That scenario does not allow, with the considered demand, economies of scale (i.e. using less vehicles or in a more consolidated way). When comparing the two assessment methods, the proposed framework results on slightly higher travelled distances and times, but the same number of vehicles. However, results can be considered equivalent since the gap between the two assessment methods is in general around 5% for most indicators. Conclusion It can be observed that the location of the platform is a key element, not only related to travelled distances, but also to the infrastructures used and their accessibility to the delivery destinations. However, this first assessment shows the interests and opportunities of food city supply solutions and merits further developments. The proposed framework allows to assess food distribution scenarios representing current practices, and would be extended to represent different alternatives of food distribution. Urban logistics is a subject that interests both city planners and researchers. Although many works are found in non-food distribution, food-based logistics is less studied in an urban context, and sustainability issues of urban food systems remain little analysed, but are a crucial element for local public authorities. This paper proposes a methodology for assessing scenarios of advanced urban food logistics; more precisely to answer the question of how urban food logistics supply.

Transportation Management System is very needed in optimize efficiency and effectiveness on logistics company. One of the most important part of transportation management system is determining delivery route from a depot to each customer. A lot of studies have been done about determining the best route in a shipping ritation with various algorithms. In the previous research, determination of delivery route is done without any implementation in the application. During the route determination is still done manually it will make a flow process in the company is not maximize. Model development aims to make an implementation application that serves to determine delivery route based on the problem of vehicle routing problem using the nearest neighbour method which is restricted to heavy loads in the transportation. Implementation of route determination an application will be done based on business process in transportation company, named PT. X, so it is necessary to observe the company. Compared to previous research, this research will determine delivery route in application based on the problem of vehicle routing problem using the nearest neighbour method.

With technology and globalization, supply chain expanded and therefore supporting software is required in order to ensure prompt decisions are appropriately made. The emphasis of this paper is on the transportation management system (TPS) that supports the decision making process within the supply chain management framework. Our hypothesis focuses on the routing and mapping module within TPS. To support the hypothesis, questionnaires were distributed to the managers of small scale businesses in Al-Shuwaikh commercial district in Kuwait and the results obtained helped rank the importance of four carefully selected modules embraced by transportation management systems. A methodical regression analysis approach was used to substantiate the findings as elucidated in the study presented here.

Since the main type of regular trips of children and teenagers are the trips to school, ensuring school routes’ safety is the main direction of the children’s safety improving. While constructing the routes to school it is necessary to choose the safest possible one. The categorization of the route can be performed according to the presence of different factors, complicating traffic conditions. Selection of the best route relates to multicriteria optimization problems. To solve this problem it is necessary to use simulation methods as well as modern information technologies. The algorithm of the rational route selection as well as the algorithm of its safety assessment is offered in the article. Mathematical model of the school routes’ planning and the conceptual scheme of the Schoolchildren’s Transportation Management System are also presented in the article. Proposed system is aimed at reducing the risks of dangerous situations while pupils travelling to school.

4th World Conference on Educational Technology Researches (WCETR) -- NOV 28-29, 2014 -- -- Univ Barcelona, Barcelona, SPAIN, WOS: 000380397600109, People counting systems aim at automatically estimating the number of people indoor and outdoor places. They are widely used in retail environment; determining conversion ratio, advertising and promotional evaluations. And they can be used for transportation management system and video surveillance. This paper presents a model of estimating the number of people in several scenarios at campuses based on face detection. Several considerations for counting people at campuses are illustrated in this paper, and experimental results of the developed method are explained and shown preliminary results. Moreover, it is investigated the possible importance of people counting in the scenarios where potential learners are watching a bulletin board and are captured via a single camera. (C) 2015 The Authors. Published by Elsevier Ltd.

This paper suggests a proactive approach in the selection of transportation management systems (TMS). The idea is to perform a two directional benchmark analysis, where the company shall in the first step benchmark its business requirements to the market solutions. In the second step it shall assess its maturity level of its transportation management organization and business processes to determine the priorities of its optimization plans. This essential step will help the company to target precisely the requirements of its future TMS. This approach was applied to a major European airline company, from starting with the essential and necessary steps to get from the identification of the need for a TMS to the pre-selection of vendors and recommendation on how to proceed on the project to enhance maximal collaboration in transportation management.

In rail-based intermodal freight operations, full containers are moved by truck from shipper locations to rail ramps, transported via train to destination ramps, and then moved again by truck to consignee locations. We commonly refer to the roadway portions of this activity as dray operations. In a large metropolitan hub area, such as Chicago or Los Angeles, this drayage activity may involve several hundred drivers and up to 500 daily container moves to or from several distinct rail ramps. Both cost and environmental considerations drive the need to maximize driver productivity and minimize the time and miles not directly associated with moving loaded containers to or from rail ramps. In this paper, we describe a solution to this problem using a set-partitioning formulation and column-generation heuristic and report on a large-scale implementation. We focus on real-world implementation details that include (1) fast solve times to support near-real-time re-solving in the face of constantly changing data, (2) adjustments to account for traffic congestion and other operational considerations, and (3) integration with a commercial transportation management system to provide real-time data to the optimizer and to send solution recommendations to a driver-assignment process.

This paper covers issues related to the reliability of information transportations process in industrial enterprises. Under virtually all-round computerization, the transition to "smart" technological solutions in industrial field, including the transportation management, in our country is associated with certain difficulties. To a great extent, this is due to the lack of mutual understanding between members of scientific-research community, IT-specialists and production workers. For instance, Russian enterprises at present have a poor understanding of how information may create value for business, and strengthen their competitive capabilities. The study of reliability of transportation process, considered in our work, has a direct influence on the production quality and the increase of efficiency on the whole. As a result, we have developed an "Autobase-Expert" analytical application, wherein the included models make it possible to evaluate the efficiency of automobile transport operations of industrial enterprises. The approbation of the application has revealed ways of further development of management decision support systems based on the architectural approach.

Traffic congestion has been an important problem all over the world. Advanced transportation management system (ATMS) that provides information for traffic control and management addresses this problem. McMaster Algorithm, one of the most classical congestion detection algorithms, has been widely used in practice. However, it still has some limitations such as difficulty of determining its parameters which are lower bound of uncongested data (LUD), critical occupancy (Ocrit) and critical volume (Vcrit). This paper transforms this problem to be an optimization problem and presents a new method that integrates gradient descent algorithm and particle swarm optimization algorithm (GPSO), with which parameters of McMaster Algorithm are extracted from the occupancy and volume of the detector. Thus it can determine the parameters of McMaster Algorithm more quickly and precisely. The parameters of McMaster algorithm are validated with real data from Yuwu Expressway in Chongqing. The results show that GPSO can help operators find the best parameters more effectively and make McMaster Algorithm yield a higher accuracy rate and recall rate.

Although companies have to concentrate on effort to save cost to survive competition, transportation costs are taking more portions in total sales costs due to globalization in the modern times. It is the reason why Transportation Management System TMS is getting more important. However, it is practically difficult for the system to fit to all of transportation environments and actually many problems are taking place with the system. Therefore, a new idea is necessary to solve such problems fundamentally rather than temporarily or superficially. To do so, if TRIZ, which is a theory that systematically establishes creative ideas on the basis of patent analysis, is employed, it can bring forth a guideline with some creative principles that can be applicable in drawing out innovative ideas based on conceptual shift. The present study aims to propose a methodology of how TRIZ should be applied to solve the problems that happen in TMS as a whole. Particularly, the study focuses on how to design customized TMS system that can be employed for each of different situations in a creative way.

Subject Area Total quality management, logistic, entrepreneurship, management and small business management. Study level/applicability The case is relevant for undergraduates specializing in business. The case incorporates courses such as entrepreneurship and small business management. Case overview This case study outlines the factors behind the success of the Royal Transportation Management Systems Company. This company was established in 2007 and has become one of the most successful valet parking companies in Abu Dhabi. The case study will also highlight the company's core concept which is quality as baseline towards success of the business and how this has influenced the success of the company. Expected learning outcomes The case can be used to understand management concepts in entrepreneurship, small business management, and total quality management concepts in the provision of high-quality services, and help students to understand marketing strategies for developing a successful small business. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Missing and suspicious traffic data are inevitable due to detector and communication malfunctions, which adversely affect the transportation management system (TMS). In this paper, a tensor pattern which is an extension of matrix is introduced into modeling the traffic data for the first time, which can give full play to traffic spatial–temporal information and preserve the multi-way nature of traffic data. To estimate the missing value, a tensor decomposition based Imputation method has been developed. This approach not only inherits the advantages of imputation methods based on matrix pattern for estimating missing points, but also well mines the multi-dimensional inherent correlation of traffic data. Experiments demonstrate that the proposed method achieves a better imputation performance than the state-of-the-art imputation approach even when the missing ratio is up to 90%. Furthermore, the experimental results show that the proposed method can address the extreme case where the data of one or several days are completely missing, and additionally it can be employed to recover the missing traffic data in adverse weather as well.

This research suggested a real-time traffic signal control algorithm using individual vehicle travel times on an isolated signal intersection. To collect IDs and passing times from individual vehicles, space-based surveillance systems such as DSRC were adopted. This research developed models to estimate arrival flow rates, delays, and the change rate in delay, by using individual vehicle`s travel time data. This real-time signal control algorithm could determine optimal traffic signal timings that minimize intersection delay, based on a linear programming. A micro simulation analysis using CORSIM and RUN TIME EXTENSION verified saturated intersection conditions, and determined the optimal traffic signal timings that minimize intersection delay. In addition, the performance of algorithm varying according to market penetration was examined. In spite of limited results from a specific scenario, this algorithm turned out to be effective as long as the probe rate exceeds 40 percent. Recently, space-based traffic surveillance systems are being installed by various projects, such as Hi-pass, Advanced Transportation Management System (ATMS) and Urban Transportation Information System (UTIS) in Korea. This research has an important significance in that the propose algorithm is a new methodology that accepts the space-based traffic surveillance system in real-time signal operations.

In coal road transportation scheduling there are some problems such as the efficiency, loss, safety and so on, in order to solve these problems, this paper analyze the actual situation and the actual demand of coal mine, study the coal highway transportation integrated management system which is based on C/S+B/S architecture, integrate IC card technology, Web technology, GPS technology and other new technology, put forward that coal transportation integrated management system is composed of many systems, including vehicle franchise management system, parking management system, transport coal business management system, speech and SMS query system, GPS tracking system and freight settlement statistical system. The platform's practice effect in many counties showed, it can increase the coal tax of the regional local governments by 50%, increase the local transport business tax over ten million (annual coal output over five million tons), increase local employment of 100 people, make the coal transport vehicles overload decreased by 80%, improve the transportation efficiency by 30%, also bring considerable benefits to the coal logistics franchise company. The coal transportation management system realizes the high efficiency and convenient management of highway transportation vehicles, and has good social and economic benefits.

Intelligent Transportation System is to effectively integrate advanced information technology, communication technology, sensor technology, control technology and computer technology applied to the entire transportation management system, build a all-round role in a wide range of, real-time, accurate and efficient integrated transportation management system. The decision tree algorithm is an approximation of the discrete function values. It is a typical classification method, the data processing, the use of rules and decision tree induction algorithm to generate readable, and it is essentially a set of rules for data classification process. The paper presents to using Decision Tree Algorithm to construct Intelligent Traffic Management System. Experiment results show that the C4.5 algorithm can better than ID3 algorithm in constructing ITS.