Investigating the Effects of Introducing Automated Straddle Carriers in Port Operations with a System Dynamics Model

Port automation has been in the forefront of maritime innovation in the last decade. On that front, Automated Straddle Carriers (ASCs) are increasingly used to move containers efficiently. However, the introduction of ASCs in port operations can be disruptive if not handled properly, especially since the field can face many uncertainties such as increased container trade. The purpose of the paper is to investigate whether the introduction of Automated Straddle Carriers in port operations can improve the overall efficiency. To achieve the objective, a System Dynamics model was developed and tested under different scenarios. The results indicate that the introduction of ASCs is accompanied by an increase in productivity of the vehicles which results in more TEUs serviced. One of the most interesting results of the various scenarios is that for all rates of incoming TEUs, berth productivity is superior when operations are performed with 5 ASCs than with 10 manned vehicles. Finally, another issue that port authorities should always have in mind is the need for coordination among the various sub-processes and optimization of the necessary vehicles in order to avoid under-utilization of resources

Agent-based simulation framework for the taxi sector modeling

Taxi services account for a significant part of the daily trips in most cities around the world. These services are regulated by a central authority, which usually monitors the performance of the taxi services provision and defines the policies applied to the taxi sector. In order to support policy makers, fleet managers and individual taxi drivers, there is a need for developing models to understand the behavior of these markets. Most of the models developed for analyzing the taxi market are based on econometric measurements and do not account for the spatial distribution of both taxi demand and supply. Only few simulation models are able to better understand the operational characteristics of the taxi market. This paper presents a framework for the development of agent based taxi simulation models. It is aimed at assessing policy makers, taxi fleet managers and individual drivers in the definition of the optimum operation mode and the number of vehicles.Peer ReviewedPostprint (published version

Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki

Εκτίμηση και πρόβλεψη της κυκλοφορίας σε πραγματικό χρόνο για προηγμένες υπηρεσίες πληροφόρησης μετακινούμενων στη Θεσσαλονίκη

This paper presents the methodology for the estimation of traffic conditions in real time for the provision of Advanced Traveler Information Services in the city of Thessaloniki. The required data is collected by traffic models, flow measurements and measurements of travel time on major routes in the city. The proposed methodology includes the flow estimation based on travel time on fixed routes, short term forecasting of traffic volumes and spatio-temporal expansion of the traffic flows. All procedures have been developed for real-time applications in large urban road networks. The methodology presented is applied since May 2012 under the Intelligent Management System of Urban Mobility in Thessaloniki

Calculating Optimal School Bus Routing and Its Impact on Safety and the Environment

Traveling to school is a complex undertaking that refers to students’ daily trips from their residences to their schools and vice versa. The school bus routing problem differs from a conventional vehicle routing problem because it involves a procedure of receiving and delivering transported vulnerable objects (students). In the Greek school transportation system, this procedure is executed in complex transport networks, following a series of routes formulated with an empirical approach; not a mathematical model. Many schools design these routes by using a manual process, taking into account primarily the parents’ requirements. However, the complexities of school bus routing problems, such as local conditions, operating costs, and customer needs, make the whole procedure extremely challenging and render the adoption of a software solution a necessity. Considering this framework, this paper presents a seven-step method developed for optimizing the school bus routes of a private school in Thessaloniki, Greece. The method is based on cluster analysis and genetic algorithms while taking into account the geographic characteristics of the road network as well as the distribution of the student’s travel behavior and requirements. The results derived from the pilot testing verify initial considerations: reducing the distance and travel time by optimizing school bus routing lessens the possibility for students to be involved in road accidents and enhances the air quality through a reduction in fuel emissions

Calculating Optimal School Bus Routing and Its Impact on Safety and the Environment

Traveling to school is a complex undertaking that refers to students’ daily trips from their residences to their schools and vice versa. The school bus routing problem differs from a conventional vehicle routing problem because it involves a procedure of receiving and delivering transported vulnerable objects (students). In the Greek school transportation system, this procedure is executed in complex transport networks, following a series of routes formulated with an empirical approach; not a mathematical model. Many schools design these routes by using a manual process, taking into account primarily the parents’ requirements. However, the complexities of school bus routing problems, such as local conditions, operating costs, and customer needs, make the whole procedure extremely challenging and render the adoption of a software solution a necessity. Considering this framework, this paper presents a seven-step method developed for optimizing the school bus routes of a private school in Thessaloniki, Greece. The method is based on cluster analysis and genetic algorithms while taking into account the geographic characteristics of the road network as well as the distribution of the student’s travel behavior and requirements. The results derived from the pilot testing verify initial considerations: reducing the distance and travel time by optimizing school bus routing lessens the possibility for students to be involved in road accidents and enhances the air quality through a reduction in fuel emissions

Management of autonomous straddle carrier fleet

The continuous development of maritime trade over the last decades has led to a significant growth of needs in"br" every section of maritime transport and port operations. During the next 5 years the annual volume at global"br" container terminals will rise significantly, making terminal utilization rates higher than the ones where the"br" systems operate optimally. There is a need for finding more efficient, quick and economic ways of transporting,"br" handling and storing goods as well as seeking more productive strategies for yard management and terminal"br" operation. Inspired by markets’ growing demands and the possibilities of transforming conventional vehicles"br" into automatic ones, an algorithm for smart job allocation and routing of automated vehicles (Straddle Carriers)"br" in terminals is presented in this paper. A management strategy for handling a fleet of autonomous straddle"br" carriers in port yard areas aiming at minimizing the energy consumption while maintaining the performance of"br" the port operations is developed. The strategy is based on a three-layer approach, with job assignment and"br" individual routing at the first two levels and conflict resolution at the last layer, aiming at providing collisionfree"br" trajectories and speed profiles. The algorithm is integrated into the terminal operating system of the port and"br" constitutes a complete solution for small-medium sized ports

Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki

Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki

Informed versus non-informed taxi drivers: agent-based simulation framework for assessing their performance

Data driven research is becoming a standard in Transport. Recent advances in the Artificial Intelligence and Machine Learning related areas enable the possibility of automatically generating highly-accurate predictive analytics frameworks, under any context. Such frameworks can potentially provide unprecedented levels of information to all mobility actors regarding not only the current but also the future status of network variables – such as Origin-Destination flows. This fact elevates the decision support to a new standard, where operations can be optimized in real-time and in near-autonomous fashion. However, such advances also bring new questions: How much can a transport operator benefit from this? Is there a limit for the amount of information that all actors should have? This paper aims to answer such questions by introducing an agent based model able to simulate the behavior of individual taxi drivers on their passenger-finding strategies. Multiple strategies are proposed and compared through exhaustive computer-aided simulations. The goal is to find how different drivers will benefit from the availability of accurate information about the future spatiotemporal demand distribution. The experiments were conducted using real-world operational data collected from a large scale taxi fleet operating in Thessaloniki, Greece. The obtained results illustrate different perspectives of the cost-benefit tradeoff on disseminating future demand-related information at different scales and ratios.Postprint (author's final draft