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Analysis of spatiotemporal data to predict traffic conditions aiming at a smart navigation system for sustainable urban mobility

Kyriakou Kalliopi, Lakakis Konstantinos, Savvaidis Paraskevas, Basbas Socrates

Archives of Transport

2019

Vol. 52, iss. 4

27--46

Urban traffic congestion created by unsustainable transport systems and considered as a crucial problem for the urbanised areas provoking air pollution, heavy economic losses due to the time and fuel wasted and social inequity. The mitigation of this problem can improve efficiency, connectivity, accessibility, safety and quality of life, which are crucial parameters of sustainable urban mobility. Encouraging sustainable urban mobility through smart solutions is essential to make the cities more liveable, sustainable and smarter. In this context, this research aims to use spatiotemporal data that taxi vehicles adequately provide, to develop an intelligent system able to predict traffic conditions and provide navigation based on these predictions. GPS (Global Positioning System) data from taxi are analysed for the case of Thessaloniki city. Trough data mining and map-matching process, the most appropriate data are selected for travel time calculations and predictions. Several algorithms are investigated to find the optimum for traffic states prediction for the specific case study concluding that ANN (Artificial Neural Networks) outperforms. Then, a new road network map is created by producing spatiotemporal models for every road segment under investigation through a linear regression implementation. Moreover, the possibility to predict vehicle emissions from travel times is investigated. Finally, an application with a graphical user interface is developed, that navigates the users with the criteria of the shortest path in terms of trip length, travel time shortest path and “eco” path. The outcome of this research is an essential tool for drivers to avoid congestion spots saving time and fuel, for stakeholders to reveal the problematic of the road network that needs amendments and for emergency vehicles to arrive at the emergency spot faster. Besides that, according to an indicator-based qualitative assessment of the proposed navigation system, it is concluded that it contributes significantly to environmental protection and economy enhancing sustainable urban mobility.

Some aspects of Intelligent Transport System auditing

Bazan M., Bożek M., Ciskowski P., Halawa K., Janiczek T., Kozaczewski P., Rusiecki A.

Archives of Transport System Telematics

2015

Vol. 8, iss. 3

3--8

Nowadays, in urbanized areas one of the most important matters is to determine a priori the time of driving from one zone of the city to another at various times of the day. The problem of travel time prediction is crucial in Intelligent Transportation Systems. The solution to this problem is a foundation of any route guidance system that will redirect drivers to their target destination via routes that have a lighter traffic load and thus higher travel velocity. In this paper is present a concept of a statistical methodology, developed by the ArsNumerica Group, that enables a quantity audit a travel time prediction algorithm. The methodology assumes that we are given database records of vehicles recognized by their unique identifier as well as duration times for which the messages with the predicted travel time are displayed VMS. the second aspect of ITS auditing considered in this paper is a placement of video cameras to measure vehicle stream velocity. Inappropriate camera location results in the fact that the stream velocity measured by them has a low usefulness for travel time prediction.

Community Traffic: a technology for the next generation car navigation

Dembczyński K., Gaweł P., Jaszkiewicz A., Kotłowski W., Kubiak M., Susmaga R., Wesołek P., Wojciechowski A., Zielniewicz P.

Control and Cybernetics

2012

Vol. 41, no. 4

867--883

The paper presents the NaviExpert’s Community Traffic technology, an interactive, community–based car navigation system. Using data collected from its users, Community Traffic offers services unattainable to earlier systems. On the one hand, the current traffic data are used to recommend the best routes in the navigation phase, during which many potentially unpredictable traffic-delaying and traffic-jamming events, like unexpected roadworks, road accidents, or diversions, can be taken into account and thereby successfully avoided. On the other hand, a number of istinctive features, like immediate location of various traffic dangers, are offered. Using exclusively real-life data, provided by NaviExpert, the paper presents two illustrative case studies concerned with experimental evaluation of solutions to computational problems related to the community-based services offered by the system.