Leo Tisljaric

• Doctor of Engineering
• Research Associate at University of Zagreb

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban...

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic p...

Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones...

Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification even...

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other model...

Current transport infrastructure and traffic management systems are overburdened due to the increasing demand for road capacity, which often leads to congestion. Building more infrastructure is not always a practical strategy to increase road capacity. Therefore, services from Intelligent Transportation Systems (ITSs) are commonly applied to increa...

Mixed traffic flows are opening up new areas for research and are seen as key drivers in the field of data and services that will make roads safer and more environmentally friendly. Understanding the effects of Connected Vehicles (CVs) and Connected Autonomous Vehicles (CAVs), as one of the vehicle components of mixed traffic flows, will make it ea...

The influence of autonomous vehicles (AVs) development can be undoubtedly noticed in the various traffic scenarios. Motorway networks are especially convenient for AV development and testing due to the low amount of traffic signs and with no influence of other types of traffic modes. In this paper, the research goal is to determine the influence of...

By coupling data-mining techniques with historical cellular and vehicular data, it is possible to find a certain spatiotemporal logic in the observed data. The primary motivation for combining multiple data sources derives from the fact that origin-destination matrices, extracted from cellular data sets, represent only the route's start and end-poi...

Traffic emissions are one of the main causes of air pollution in developed urban areas. Estimating emissions patterns presents an ongoing challenge for the research and decision-making communities to detect and propose solutions for system stakeholders contributing to the pollution the most. This paper proposes a data-driven methodology for estimat...

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that suppor...

Feature extraction is a crucial part of data preparation when using machine learning algorithms, especially for emerging datasets. The speed transition matrix (STM) emerged only recently as a traffic data modeling technique. In this paper, key features from STMs are extracted and proposed for the purpose of traffic state estimation. This step simpl...

Identifying distribution of users’ mobility is an essential part of transport planning and traffic demand estimation. With the increase in the usage of mobile devices, they have become a valuable source of traffic mobility data. Raw data contain only specific traffic information, such as position. To extract additional information such as transport...

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing...

Motorway bottlenecks are commonly occurring in developed countries with increased commercial activities. As opposed to the traffic jam, the source of the bottleneck is often a result of a specific traffic situation like traffic accidents, sudden breaks, or bad road design. They could be described as a sudden capacity drop that creates congestion on...

Data availability in recent years has grown exponentially, allowing researchers in the transport sector to harness valuable information regarding traffic flows. In that sense, cellular network data represents valuable traffic information when dealing with spatially large areas due to its property of collecting route data using distant mobile base s...

The increased development of the urban areas consequently results in a larger number of vehicles on the road network, which inevitably leads to traffic congestion, especially in the rush hours. Intelligent transport systems solutions present applications that can be useful in detecting and dealing with the problems that are related to congestion. T...

Traffic congestion occurs when traffic demand is greater than the available network capacity. It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and longer vehicular queueing. Congestion can also impose a negative impact on the society by decreasing the quality of life with increased pollution, especially in...

The increased development of the urban areas consequently results in a larger number of vehicles on the road network, leading to traffic congestion, especially in the rush hours. Intelligent Transport Systems (ITS) solutions present the applications that can be useful in detecting and dealing with the problems that are related to congestion. This p...

Road traffic anomaly detection is an essential research topic within the Intelligent Transport System (ITS) context. Urban road anomaly detection systems are a crucial part of the ITS regarding the trip planning, road security, and congestion estimation applications. In this paper, the method for traffic anomaly detection using Speed Transition Mat...

Determining the traffic state is one of the most attractive problems for experts in the field of Intelligent Transport Systems (ITS). In this paper, a deep learning model for determining the traffic state is presented. Model is based on Convolutional Neural Networks (CNN) and uses weekly speed profiles as input data. The proposed model consists of...

Congestion is one of the key problems faced by traffic engineers and authorities. The application of Intelligent Transport Systems (ITS) is one of the main approaches to solve this constant problem. The ITS is particularly significant in cases of post-disaster occurrences such as flood, fire, or earthquake. The new pandemic caused by SARS-CoV-2, na...

Tensor-based models emerged only recently in the field of traffic data analysis. They out-perform other data models because they can simultaneously capture both spatial and temporal components of the observed traffic data. In this paper, the Non-negative Tensor Decomposition (NTD) method is used to extract traffic patterns in the form of Speed Tran...

The recent development of geo-location technologies has made it easier to collect large spatio-temporal datasets. The spatio-temporal dataset represents a set of data that contains space and time components and parameter under consideration. Anomaly detection can be defined as a problem of finding unexpected behavior of some observations in the dat...

The invention relates to a method for processing a telephone emergency call, wherein a plurality of predetermined signal patterns (25) are detected by an identification device (12) in an audio signal (16 ') of the emergency telephone call and by an analysis device (14 ) of the combination (27) of the currently detected signal patterns (20) one of a...

Nowadays, due to the new laws and policies related to the greenhouse gas emissions, and the rise of social and ecological awareness of transport sustainability, logistic companies started to incorporate green technologies in their distribution activities. Here, electric vehicles, as a cleaner mode of transport than conventional vehicles come to the...

Nowadays, due to the new laws and policies related to the greenhouse gas emissions, and the rise of social and ecological awareness of transport sustainability, logistic companies started to incorporate green technologies in their distribution activities. Here, electric vehicles, as a cleaner mode of transport than conventional vehicles come to the...

Traffic congestions appear mostly in urban areas, at intersections. Therefore, it is important to have a measure to quantify intersection performance, especially during rush hours. Usually, intersection's Level of Service (LoS) is used as a performance measure in project design, today. In this paper, we present a method to estimate queue length, co...

Traffic data is main starting point for estimating performance or proposing improvements for traffic system. Vehicle speed and control delay are main parameters in Level of service ase-sment for roads and intersections. In this thesis methods for collecting, storing and processing GPS data in order to estimate traffic parameters is proposed. Method...

Artificial intelligence (AI) is the branch of computer science which aims to incorporate human intelligence into computer programs. Natural language processing (NLP) is a branch of AI regarding to morphological, syntactic, semantic analysis and discourse processing of language. This paper presents technique of using NLP algorithm in marketing of In...