volume-03-Issue 1 (2021)
Latest Articles
The convergence of Internet of Things, Blockchain and Connected Vehicles: Conceptual Advantages and Disadvantages of a new Cooperative Intelligent Transportation System
JTTM, volume-03, Issue 1 (2021) , PP 33 - 41
Published: 24 Jan 2021
DOI: 10.5383/JTTM.03.01.004
by Vittorio Astarita, Vincenzo Pasquale Giofrè, Giuseppe Guido, Alessandro Vitale from Department of Civil Engineering, University of Calabria, Arcavacata di Rende (CS) 87036, Italy
Abstract: This paper intends to explore the convergence of some technological innovations that could lead to new cooperative Intelligent Transportation Systems (ITS). The technologies that might soon converge and lead to some new developments are: the Blockchain Technology (BT) concept, Internet of Things (IoT) and Connected and Automated Vehicles (CAV). Advantages and disadvantages of the new concepts founding a new ITS system are discussed in this conceptual paper. Blockchain technology has been recently introduced and many research ideas have been presented for application in the transportation sector. In this paper, we discuss a system that is based on a dedicated blockchain, able to involve both drivers and city administrations in the adoption of promising and innovative technologies that will create cooperation among connected vehicles. The proposed blockchain-based system can allow city administrators to reward drivers when they are willing to share travel data. The system manages in a special way the creation of rewards which are assigned to drivers and institutions participating actively in the system. Moreover, the system allows keeping a complete track of all transactions and interactions between drivers and city management on a completely open and shared platform. The main idea is to combine connected vehicles with BT to promote Cooperative ITS use, a better use of infrastructures and a more sustainable eco-system of cryptocurrencies. A short description of BT is introduced to evidence energy problems of sustainability in the implementation of Proof of Work (PoW) that is adopted by many blockchains. read more... read less...
Keywords: Intelligent Transportation Systems (ITS), Floating Car Data (FCD), Blockchain Technology (BT), traffic management, connected and autonomous vehicles.
Modelling of a Stochastic Spatiotemporal Variable in Transport Domain
JTTM, volume-03, Issue 1 (2021) , PP 25 - 31
Published: 19 Jan 2021
DOI: 10.5383/JTTM.03.01.004
by Peter Krammer, Marcel Kvassay, Ladislav Hluchý from Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia, 845 07
Abstract: In this article, building on our previous work, we engage in spatiotemporal modelling of transport demand in the Montreal metropolitan area over the period of six years. We employ classical machine learning and regression models, which predict bike-sharing demand in the form of daily cumulative sums of bike trips for each considered docking station. Hourly estimates of demand are then determined by considering the statistical distribution of demand across individual hours of an average day. In order to capture seasonal and other regular variation of demand, longer-term distribution characteristics of bike trips, such as their average number falling on each day of the week, month of the year, etc., were also used as input attributes. We initially conjectured that weather would be an important source of irregular variation in bike-sharing demand, and subsequently included several available meteorological variables in our models. We validated our models by Hold-Out and 10-Fold Cross-Validation, with encouraging results. read more... read less...
Keywords: Machine learning, Data mining, Regression, Data distribution, Spatiotemporal data, Modelling, Regression tree
Machine Learning and statistic predictive modeling for road traffic flow
JTTM, volume-03, Issue 1 (2021) , PP 17 - 24
Published: 12 Jan 2021
DOI: 10.5383/JTTM.03.01.003
by Nadia Slimani, Ilham Slimani, Nawal Sbiti, Mustapha Amghar from Computer Systems and Productivity Team, EMI, Mohammed V University, Rabat, Morocco. Laboratory SmartICT, ENSAO, Mohammed I University, Oujda, Morocco.
Abstract: Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart cities. This work is in line with this perspective and aims to solve this problem. The proposed methodology plans to forecast day-by-day traffic stream using three different models: the Multilayer Perceptron of Artificial Neural Networks (ANN), the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Support Machine Regression (SMOreg). Using those three models, the forecast is realized based on a history of real traffic data recorded on a road section over 42 months. Besides, a recognized traffic manager in Morocco provides this dataset; the performance is then tested based on predefined criteria. From the experiment results, it is clear that the proposed ANN model achieves highest prediction accuracy with the lowest absolute relative error of 0.57%. read more... read less...
Keywords: Road traffic forecasting, artificial neural networks, MLP, statistical forecasting, SMOreg, SARIMA.
Predicting Severity of Accidents in Malaysia By Ordinal Logistic Regression Models
JTTM, volume-03, Issue 1 (2021) , PP 11 - 16
Published: 08 Jan 2021
DOI: 10.5383/JTTM.03.01.002
by Uneb Gazder, Ashar Ahmed, Umaira Shahid from Department of Civil Engineering, University of Bahrain, Isa Town, Bahrain, 32038 , Department of Urban and Infrastructure Engineering, NED University of Engineering and Technology, Karachi, Pakistan, 75270
Abstract: This study was aimed at determining the relationships of accident severity using road environment and traveller characteristics. Ordinal logistic regression models were used in this study. The accident data was provided by Malaysian Research Institute of Road Safety (MIROS) for all accidents which occurred in Penang state during 2006-2011. It was observed that motorbikes were predominantly involved in these accidents, hence, it was decided to develop three separate models; one for the overall data, and others for accidents with and without motorbikes. Logistic regression models showed that commercial land use, road width and experience of driver are important factors that may increase severity of accidents. Shoulder width was found to decrease the severity of motorbike accidents. Commercial land use, road width and driver experience have more impact on motorbike accidents as compared to accidents of other vehicles. read more... read less...
Keywords: Traffic Accidents, Severity, Ordinal Logistic Regression, Motorbikes, Malaysia.
On the use of active mobile and stationary devices for detailed traffic data collection: A simulation-based evaluation
JTTM, volume-03, Issue 1 (2021) , PP 01 - 09
Published: 05 Jan 2021
DOI: 10.5383/JTTM.03.01.001
by Johan Holmgren, Henrik Fredriksson, Mattias Dahl from Department of Computer Science and Media Technology, Malmö, Sweden, 20506, Department of Mathematics and Natural Sciences, Blekinge Institute of Technology, Karlskrona, Sweden, 37179
Abstract: The process of collecting traffic data is a key component to evaluate the current state of a transportation network and to analyze movements of vehicles. In this paper, we argue that both active stationary and mobile measurement devices should be taken into account for high-quality traffic data with sufficient geographic coverage. Stationary devices are able to collect data over time at certain locations in the network and mobile devices are able to gather data over large geographic regions. Hence, the two types of measurement devices have complementary properties and should be used in conjunction with each other in the data collection process. To evaluate the complementary characteristics of stationary and mobile devices for traffic data collection, we present a traffic simulation model, which we use to study the share of successfully identified vehicles when using both types of devices with varying identification rate. The results from our simulation study, using freight transport in southern Sweden, shows that the share of successfully identified vehicles can be significantly improved by using both stationary and mobile measurement devices. read more... read less...
Keywords: Traffic data collection, stationary devices, mobile devices, traffic simulation