Data collection for the provision of real time traveller information services is a key issue, both for the travellers as well as for traffic managers. This paper presents a methodology for estimating travel times in dense urban road networks using point-to-point detectors. The aim is to fill in the gap of existing travel time estimation methodologies, which are based on point-to-point detection devices. Bluetooth (BT) is considered as one of the less expensive technologies for estimating travel times. Data filtering and data correction require rigorous methodologies, which if not correctly applied may result in inaccurate results as compared to other methods. The main difficulty of data processing is to identify the correct set of Media Access Control (MAC) addresses for estimating travel times, especially in dense urban road networks, where three main error sources exist: the co-existence of various transport modes (private vehicles, buses, pedestrians, bicycles etc.), the existence of more than one possible paths between two BT detectors and the existence of stops or trips ending between two BT detectors. These error sources create outliers that need to be identified and taken into account. The results of the proposed methodology confirm that outliers are eliminated, as shown by a case study with 10 BT detectors installed at major intersections of Thessaloniki's Central Business District (CBD).
Extreme precipitation as a result of the ongoing change of climatic conditions is attributed to significantly hindering vehicle circulation in urban transportation networks. During 2014, Thessaloniki, Greece, experienced significant anomalies with respect to the expected amount and intensity of rainfalls, which severely influenced traffic flow and safety conditions. According to the National Meteorological Service, a 360% deviation in the total rainfall accumulation (compared with the 1959–1997 mean) was observed in the city for July 2014. Based on the increased intensity and occurrence of such events, this research studied the impacts of heavy rainfalls on road operations, with the road network of Thessaloniki, Greece, serving as a case study. Data for these analyses were obtained from floating car data of a 1,200-taxi fleet that contained GPS coordinates and speed information. Findings reveal that vehicle speed drops increase with precipitation intensity yet they differ depending on the examined road type. This paper highlights the importance of developing a sound quantitative understanding of the effects of such events in urban areas and considers the context of the changing climate and the concomitant increasing frequency of extreme events.
The sensor location problem is of particular importance when planning the allocation of limited field equipment intended to be used for advanced traffic management systems and traveller information services. The locations within a network that satisfy specific goals need to be carefully selected, based on predefined goals related to the effective collection of data and the subsequent estimation of traffic related information. The detection of traffic volumes is mainly associated with two purposes, the travel time and the Origin–Destination (O–D) trip matrix estimation. In this context, this paper presents a quadratic programing model, able to determine the optimal location of tracking sensors. The model is implemented in the urban road network of the city of Thessaloniki (Greece) in which specific number of sensors is installed and utilized for real-time travel time information provision. The proposed methodology models the sensor location problem under the general framework of a set covering problem, which is one of the most popular optimization problems and has been applied in many industrial problems. The results of the case study in Thessaloniki reveal that the proposed model defines the optimal location of the limited number of sensors in such a way that the network, which is created having all sensors as origin or destination of all possible paths, represents to great extent (87% of the traffic flow along the major paths) the traffic volumes of the whole road network of the city.
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