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Time geography is a powerful framework for analyzing human activities under various space–time constraints. At the core of time geography is the concept of the space–time prism, which delimits an individual's potential activity locations in space and time. The classical space–time prism, however, admits only deterministic travel speeds and ignores the stochastic nature of travel environments. In this article, the classical space–time prism model is extended to congested road networks with travel time uncertainty. A reliable space–time prism is proposed to consider explicitly an individual's on-time arrival probability concerns in the face of travel time uncertainty. The reliable space–time prism is defined as the set of space–time locations where an individual can participate in an activity and return to his or her destination with a given on-time arrival probability. To construct such a reliable space–time prism in a road network, a solution algorithm is developed. A case study using real-world traffic information is carried out to demonstrate the applicability of the proposed prism model. The results of the case study indicate that the proposed prism model can represent well individuals’ space–time taking into account various on-time arrival probability concerns. [ABSTRACT FROM PUBLISHER]

The bearing capacity of the road pavement is one of the most important indices that reflect the road condition. To collect such data, various deflectometers were developed in the past three decades. Note that the newly developed Traffic-Speed Deflectometer (TSD) declares to perform a nondestructive measuring at traffic speeds. However, TSD is limited as it needs more than 4 h to calibrate the system before a measuring task. This paper introduces a Laser Dynamic Deflectometer (LDD) developed at the Transportation Research Center of Wuhan University. LDD applies four laser Doppler sensors mounted on a measuring beam to capture the deflection velocity of the pavement surface. Unlike TSD using a servo to keep the beam static, LDD utilizes a gyroscope to measure and compensate the vibration of the beam. Moreover, in the procedure of calibration, LDD applies an efficient relative-motion method to calculate the relative angles between each two Doppler lasers, which reduces the time of system calibration to about 2 h. [ABSTRACT FROM PUBLISHER]

Floating car data (FCD) is an important source of traffic data for the design and management of an intelligent transportation system, and the efficiency and accuracy of FCD map-matching influences the application of FCD. Most map-matching algorithms are very time-consuming because many links need to be evaluated as to whether they are the correct link for the travelling floating car, especially in urban areas where most FCD are collected. In fact, the highest degree of map-matching a link for an FCD is not always the correct link, so the use of candidate links is proposed here. In this study, candidate links are defined as those that fall within the FCD error. We propose a novel algorithm for quickly identifying accurate candidate links on the basis of a vector to raster road map conversion approach. First, a road buffer is constructed based on the size of the confidence region by taking into account the position error of the FCD and the width of the link. Second, all link buffers are converted to a raster map with resolution in metres, each pixel position of the raster map corresponding to a geographical coordinate. A pixel can have one or more values, which represent the link ID whether the link buffer covers the pixel; so a spatial index map between geographical coordinate and link ID is built in this way. Then, candidate links are calculated rapidly in the next three steps: (1) Selecting a link ID from the spatial index map that, according to the FCD positioning fix coordinate, must fall within the confidence region of the FCD. The number is very small and this step is very fast because it does not include a traverse process. (2) Getting data for selected link from the road networks vector map by the link ID. This step uses a binary search and is very fast. (3) Calculating the degree of map-matching of these links to identify candidate links of the FCD. This algorithm is far less time-consuming than earlier approaches. The algorithm was tested using more than 13 million real-world FCD collected in Wuhan City, China, and the performance was shown to be satisfactory. [ABSTRACT FROM AUTHOR]

Abstract: Critical transportation infrastructure has been studied extensively in recent years. This paper presents a spatiotemporal analysis of critical transportation links based on time geographic concepts. With widespread adoption of information and communication technologies (ICT) and location-aware mobile devices, large tracking datasets have become readily available. This study uses a tracking dataset of approximately 12,000 taxis in Wuhan, China over 1week to analyze spatiotemporal origin–destination (O–D) patterns of trips that use three critical bridges connecting the three districts of Wuchang, Hankou, and Hanyang separated by the Yangtze River and the Han River. We use the space–time prism concept to identify alternative space–time paths passing through different bridges that observe the spatial and temporal constraints between each O–D pair derived from the taxi trajectory data. This case study illustrates the feasibility and benefits of using the proposed time geographic approach to analyze spatiotemporal patterns of travel demands on the critical links and their alternative paths in a transportation system. [Copyright &y& Elsevier]

The debates about patients' decisions regarding which hospital to visit have been shown in many health research literature for the past few years. Researchers have developed various methods to understand hospital bypass behaviour of patients; however, previous studies ignore the impact of spatial heterogeneity and awareness of patients about surrounding hospitals such as travel distance and hospital distributions in the process of bypass behaviour definitions and evaluations. To address these limitations, this study puts forward a Hospital Bypass Index (HBI) to understand urban hospital bypass behaviour by using big trace data collected by urban taxis. To evaluate the bypass behaviour, we defines three evaluation indicators for HBI including a potential bypass rate, an overall distance decay parameter, and a diurnal variation in distance decay parameter by mining large-scale patient-hospital trips from a spatiotemporal perspective. Experiments are conducted with 30 general hospitals and 13 specialty hospitals in Wuhan city, China, by using one month of taxi traces. The results of bypass behaviour evaluation and comparisons indicate that the proposed method is effective and feasible, which is promising for health departments to optimize the medical services and rationally allocate the medical facilities. • This study introduces a new method to understand urban hospital bypass behaviour based on taxi trace data; • The new method addresses the absence of spatial heterogeneity and patient awareness in previous studies; • The results indicate that the diurnal variation in distance decay parameter can improve the accuracy of bypass analysis; • Taking Wuhan, China as an example, this study analyses the urban hospital bypass phenomenon with real taxi traces. [ABSTRACT FROM AUTHOR]